INFORMATION TO USERS
This manuscript has been reproduced from the microfilm master. UMI
films the text directly from the original or copy submitted. Thus, some
thesis and dissertation copies are in typewriter face, while others may
be from any type of computer printer.
The quality of this reproduction is dependent upon the quality of the
copy subm itted. Broken or indistinct print, colored or poor quality
illustrations and photographs, print bleedthrough, substandard margins,
and improper alignment can adversely affect reproduction.
In the unlikely event that the author did not send U M I a complete
manuscript and there are missing pages, these will be noted. Also, if
unauthorized copyright material had to be removed, a note will indicate
the deletion.
Oversize m aterials (e.g., maps, drawings, charts) are reproduced by
sectioning the original, beginning at the upper left-hand corner and
continuing from left to right in equal sections with small overlaps. Each
original is also photographed in one exposure and is included in
reduced form at the back of the book.
Photographs included in the original manuscript have been reproduced
xerographically in this copy. Higher quality 6" x 9" black and white
photographic prints are available for any photographs or illustrations
appearing in this copy for an additional charge. Contact UMI directly
to order.

University Microfilms International
A Beil & Howell Information C o m p a n y
3 0 0 North Z e e b R o a d . Ann Arbor. Ml 4 8 1 0 6 - 1 3 4 6 U S A
31 3 /7 6 1 -4 7 0 0

800/521-0600

O rder N u m b er 9233950

R egional developm ent and econom ic instability: C ontribution
o f the forest products sectors to diversification of the M ichigan
economy
Vega Segura, Carlos Guillermo, Ph.D.
Michigan State University, 1992

UMI

300 N. Zeeb Rd.
Ann Arbor, MI 48106

REGIONAL DEVELOPMENT AND ECONOMIC INSTABILITY:
CONTRIBUTION OF THE FOREST PRODUCTS SECTORS TO
DIVERSIFICATION OF THE MICHIGAN ECONOMY

BY

Carlos G. Vega Segura

A DISSERTATION

Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of

DOCTOR OF PHILOSOPHY

Department of Resource Development

1992

ABSTRACT
REGIONAL DEVELOPMENT AND ECONOMIC INSTABILITY :
CONTRIBUTION OF THE FOREST PRODUCTS SECTORS TO
DIVERSIFICATION OF THE MICHIGAN ECONOMY.
by
Carlos G. Vega Segura

Michigan's

economy

has

been

heavily

dependent

automobile manufacturing for many decades.

on

The automotive

industry, although employing a large number of workers and
providing an important source of income, is very sensitive to
cyclical changes that occur mainly due to external shocks
produced

by

the

international

economy.

This

work

has

hypothesized that to achieve a more stable economy Michigan
needs to develop a more diversified economic structure.

In order to evaluate the current situation of the state
economy a regionalization was made by using the Q-technique of
factor analysis.

once the

regionalization was achieved,

regional indicators of diversification and instability were
calculated.

Kort's indexes of diversification were compared

with two other measures: the ogive and percentage of durable
indexes.
establish

After that OLS and WLS regression models were run to
the

relationship

between

DIV

and

REI

indexes.

Several statistical tests were performed to corroborate the
hypotheses that were posed in the objectives.

This study found that better sources of data are required
to obtain more precise results in absolute terms at this level
of

analysis.

Nevertheless,

important

conclusions

attained when indicators in relative terms were used.
found that the economic structure of Michigan
significantly during the period 1982-1988.
negative

relationship

between

the

.05.

has changed

Kort

The ogive

It was

A significant

diversification and instability was found, at
significance of alpha =

were

index

of

the level of

index showed a

significant relationship while the percentage of durable goods
did not.

The analysis corroborated the positive relationship

between diversification and regional population size.

The assessment of the forest products sectors showed that
most of them have become more important in terms of basic
activities when the years 1982 and 1988 were compared.

Six of

the seven sectors that make up the industry showed positive
growth.

Finally, some regional policy recommendations are

made

order

in

structure.

to

improve

the

current

regional

economic

These policies are oriented to add productive

capacity in forest products sectors.

Dedicated
To my wife, Marcia, and my children,
Carlos, Jessina, and Joel for their
confidence and support.

ACKNOWLEDGMENTS

I wish to express my sincere appreciation to my major
professor,

Dr.

Daniel

Chappelle,

for

his

guidance,

encouragement, and constructive criticism through my entire
doctoral program.
My appreciation and gratitude is also extended to others
members of my guidance committee, Drs. Larry Leefers, Paul
Nickel, and Scott Witter, for their contribution during my
doctoral studies, and the development of this research.
Special thanks to David Mendez for his comments, and
recommendations on the computational procedures used during
the preparation of this work.
Finally, my gratitude to my sponsors through my studies
at Michigan State University : The Fulbright- LASPAU program
for Latin America,

The Mclntire-

Stennis

funding of the

Agricultural Experiment Station at MSU, which has provided the
funds for this research through an research assistanship, The
University of Panama,

and The Ministry of Finance of the

Republic of Panama.

ii

TABLE 07 CONTENTS
LIST OF TABLES....................................... v
LIST OF FIGURES.................................... vi
Chapter

Page

I . Introduction.................................... 1
1.1 Problem setting......................... 2
1.2 Concepts and Definitions................ 7
1.3 Study Objectives and Hypotheses........ 11
1.3.1 Research Objectives................ 15
1.3.2 Hypotheses......................... 15
1.4 Scope and Limitations of theStudy...... 17
II.

Literature Review.............................. 19
2.1 Progress Attained During the Last Decade
Regarding the Economic Contribution of
Michigan Forests Products Indudtry..... 20
2.2 A Brief Survey of Industrial Diversification
Measures............................... 27
2.2.1
Measures Based on NormalProportion.28
2.2.2
Durable goods Measures............. 34
2.2.3
Portfolio Variance Measure......... 35
2.2.4
Entropy Measures................... 38
2.3
Review of Some Important Studies of
Attl

u x VC1914.1WQblUil

K c i d UCU

LU

Instability............................ 41
III. Research Methods.............................. 47
3.1
Data Collection and Sources............ 47
3.2
Sectorization by Economic Activity..... 50
3.3
Regionalization........................ 54
3.3.1 Delimitation of Regions............ 55
3.3.2 Use of Factor Analysis to allocate
Counties into Regions.............. 55
3.4
Measures of Diversification and
Instability............................ 59
3.3.1
Relationship between diversification
and instability.....................59

in

IV. An Economic Regionalization for Michigan...... 63
4.1 Factor Analysis Results................ 63
V. Regional Economic Base of Michigan............ 69
5.1 Michigan's Basic Activities............ 69
5.2 County Economic Structure.............. 73
5.3 Regional Economic Basic Structure...... 86
5.3.1 Delimitation of Planning Regions....90
5.4 Sectoral Employment by Region.......... 91
5.5 Development of the Basic Forest Products
Industries: Comparison of years 1982
and 1988............................... 93
5.6 Comparison of Recent Studies........... 95
5.7 Share of the Forest Products Sectors
Employment in Michigan................ 97
VI. Results....................................... 98
6.1 Diversification (DIV) and Instability
(REI) Indexes Results.................. 99
6.2 Results of Hypotheses Tests........... 101
VII.

Conclusions and Recommendations............ 109
7.1 Conclusions........................... 110
7.1.1 Data Requirements................. 110
7.1.2 The need for Better Information....110
7.1.3 Basic Activities.................. Ill
7.1.4 Michigan Diversification.......... 112
7.1.5 Michigan Economic Structure....... 113
7.1.6 Diversification and Instability....113
7.1.7 Diversification and Population.... 114
7.2 Recommendations and Future Research...114

Appendices
A. List of Michigan Counties.................... 117
B. Factor analysis Results. Year 1988........... 118
C. Factor Analysis Results. Year 1952....... ...126
D. Forest Products Basic Industries by County
Years 1982 and 1988......................... 138
List of References................................. 152

iv

LIST OF TABLES
Table
3.2
4.1
5.1
5.2
5.3
5.4
5.5
5.6
5.7
6.1
6.2
6.3
7.1

Page

Sectors of the Michigan Economy and their standard
Industrial Classification (SIC) Codes.......
52
EconomicRegionalization of Michigan.Year 1988....66
Michigan Location Quotients. 1988................ 71
Percentage of Total Nongovernamental and
nonagricultural employment Involved in Economic
Basic activities. Michigan 1988................... 73
Regional Economic Basic Structure. 1988............. 87
Sectoral Employment by Region. 1988................ 92
Percentage Change in Forest Products Basic
Activities : Periods 1982-1988..................... 93
Michigan Forest Products Industry Employment:
Comparison of Two Recent research Studies.......... 96
Share of the Forest ProductsActivities in
Michigan Employment: 1982 and 1988................. 97
Population, Diversification, and Instability
Measures by Region. 1988......................... 100
WLS and CL5 Regression of REI and Alternatives
Measures of Industrial Diversification............ 104
Kolmogoroff Smirnoff One sample Test Using
Standard NormalDistribution....................... 105
Kort's Diversification Indexes : 1982 and 1988.... 112

v

LIST OF FIGURES

Figure
4.1
1.D
2.D
3.D
4.D
5.D
6.D
7.D
8.D
9.D
10.D
11.D

page

MichiganRegionalization. 1988.................... 67
Sector 6: Logging contractors. Basic industry
county share. 1982.............................. 138
Sector 6: Logging contractors. Basic industry
county share. 1988.............................. 139
Sector7: Sawmills and planing mills.
Basic
industry county share. 1982......................140
Sector7: sawmills and planing mills.
Basic
industry county share. 1988......................141
Sector 8: Millwork, flooring, structural members.
Basic industry county share. 1982................ 142
Sector 8: Millwork, flooring, structural menbers.
Basic industry county share. 1988................ 143
Sector9: Wood furniture and fixtures.
Basic
industry county share. 1982......................144
Sector9: Wood furniture and fixtures.
Basic
industry county share. 1988......................145
Sector 10 : Wood pallets and skids. Basic
industry county share. 1982......................146
Sector10: Wood pallets and skids. Basic
industry county share. 1988......................147
Sector11: Venner and plywood, other lumber and
wood products. Basic industry county share.
4 A A A

4 4A

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
..
.
.
.
.
.
.
.
.
.
.
.
..
.
.
..
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
..
.

12.D
13.D
14.D

l*tO

Sector12: Venner and plywood, other lumber and
wood products. Basic industry county share.
1988............................................. 149
Sector12: Paper and allied products. Basic
industry county share. 1982..................... 150
Sector12: Paper and allied products. Basic
industry county share. 1988..............
151

vi

Chapter I

Introduction

The economy of Michigan has been characterized by a
series of fluctuations that have impeded balanced state
economic growth.

Various groups in Michigan have argued

that these swings of the economy have occurred because of
heavy dependence of the State's economy on durable
manufactures whose prices have shown an unstable trend
during the last years.

Nowadays, Michigan's main industry (automobile
manufacturing) is facing a contraction in its sales due to
foreign competition (Japan), restrictions to foreign
products in others markets, innovations, etc.

This

situation has created an unstable situation in Michigan that
can be translated in lower levels of production, income, and
employment.

Hence, Michigan's economy needs to be

diversified into a structure that guarantees a more stable
situation in the future.

This study attempts to analyze the effect of these
economic fluctuations, and to measure their magnitudes.

The

Michigan industrial structure is assessed through indicators
of economic diversity and instability.

Once the recent

situation of the state economic structure has been assessed,
1

new avenues will be open for those who have the task of
promoting and recommending programs and projects that might
help to ameliorate the current situation of the state's
economy.

It is thought that if in the future Michigan achieves a
more diversified economy, then a healthy

regional economy

could be attained along with a stable economic environment.
This chapter emphasizes the problem, provides a conceptual
framework, and establishes the objectives of this research.

1.1

Problem Setting.
For many decades Michigan's economy has been led by the

automobile manufacturing industry.

This fact has created a

fragile economic structure based on this industry and the
other industries linked directly to it.

Measuring

specialization in a particular region is never easy because
of the complex linkages between a particular sector and the
rest of sectors sharing in the process of regional
development.

In the particular case of Michigan, dependence of the
state's economy on the automobile industry can not be
measured based on the contribution of this industry alone.
However we are also interested in specialization in other
sectors within the state.

Information about output,

3
employment,income, value added, etc. attributable to other
sectors linked with the automobile industry has to be taken
into account.

Therefore, the analysis needs to consider all

sectors of the regional economy having transactions with
sectors that transact with the auto industry and so on.
Total impact would be the summation of the various
transactions in the economy that occur as a consequence of
the initial transaction (direct effect).

For many years economic leaders in Michigan have agreed
that in order to strengthen the regional economy
diversification seems to be a necessary step. So, most of
them have advocated that private - public sector cooperation
be enlisted to promote diversification of the State's
economy.

The most recent achievements of this type were

reached during the beginning of the last decade (1983), when
Governor James G. Blanchard assumed office.

That year

Michigan initiated a target industry program.

Initially,

the program identified three industrial sectors that showed
good perspectives for net growth.

The sector or target

industries identified in this first stage were:
(a)

The forest products industry.

(b)

The food processing industry (Michigan grows large
quantities of diverse agricultural commodities,
much of which are processed in other parts of the
country).

4
(c)

The robotics industry (here considering certain
especially high value parts of the automobile
industry, with strong ties to existing skills of
segments of Michigan's labor force).

(d)

Later, a new initiative in resource-based economic
development was added, the travel and tourism
industry.

According to this program, three of the four target
industries are directly linked to natural resources.

Based

on this fact one could expect that diversification of the
State's economy through these sectors will have a strong
positive impact on Michigan's rural economy.

In his "Michigan Renewable Resource Development
Initiative" Governor Blanchard (1987), addressed the
policies and benefits conferred to these target industries:
- Each one shows a high contribution to the existing
economic base and to its diversity in terms of value
added and employment.
-

Each one of them exhibits a suitable growth
potential, along with the creation of new jobs.

-

The rural and semirural economies likely will
benefit from development of these types of
industries.

In this study the roles and importance of the
forest products sectors are emphasized.

So far the forest

product industry program has involved several specific
efforts in the following areas:
(a) improving the business environment for forest
products sectors;
(b)

assuring a stable and expanding supply of
increasingly valuable timber; and

(c) promoting a stronger coordination of public and
private forestry activities.

In order to achieve a suitable increase in economic
diversification, it is necessary to foster the effective
cooperation of both public agencies and private firms.
Also, private firm decisions would have to be implemented
wisely since many potential projects that are necessary to
fuel increased growth and economic diversification will be
undertaken by this sector.

On the other hand, public

enterprises must be involved in activities related to
timber production, the provision of most outdoor recreation,
and research dealing with forest and park resources
management, in order to complement private effort.

It can

be noted that successful projects and programs will require
the mutual cooperation of the public and private sectors.

6
Michigan's economy includes several types of regional
economies, each one of them showing a particular economic
structure and level of diversification.

It is our

assumption that the most diversified the regional economy
will be, the most improvement in stability of employment and
income.

This issue has been constantly debated by authors

in the field ( e.g., Richarson,1969; Hoover, 1963).
Initially, it was necessary to assess the existent sources
of information providing a disaggregation level that makes
feasible the type of regionalization desirable for our
analysis.

Once needs have been identified, it is necessary to
develop

a set of programs and projects related to

target

industries at the regional level within the state to achieve
an environment of economic stability.

It is expected that

development of these target industries could lead to a more
favorable situation for the state.

Improvement in the

employment situation, new sources of income, and a situation
of better well being are some expected results of an
appropriate regional diversification policy.

However,

diversification of regional economies within the state does
not guarantee the State's diversification.

Regions within

Michigan usually show different rates of growth
corresponding to different mix of economic activities whose
share in terms of employment and income could lead to an

unbalanced situation for the State's economy.

So, it is

important to diversify through enterprises that behave
stably during the business cycle or expand basic industries
that show inelasticity in employment and income when exports
are involved.

1.2

Concepts and Definitions t Regional Development,
Diversification, and Stability
Regional development is defined according to the

objective or goals pursued.

Hoover (1984, p. 355) said that

development of a region needs to be seen in terms of its
size, income level, and structure.

External conditions of

two types could affect the desirable growth of the region -a
demand for the region's outputs and the supply of inputs to
the region's productive activities.

The goal of regional

economic development would be to attain a healthy growth of
the region and to promote individuals' well-being in terms
of opportunity, equity, and social harmony.

Some regional

economists approach regional development as a "balanced
growth".

They say that regions need to grow in such a way

that inequalities in income and employment are reduced.

In

general, a regional development goal is achieved when the
region's residents improve their levels of well being.

A

better educational system, an economic structure which
provides more and better job opportunities, and enhanced
personal income along with a suitable social services system

are some of the requirements to fulfill this goal.

When the

State lacks a diversified economy, significant changes in
the major economic and social variables can occur.

Economic

fluctuations can affect the well being of the people
positively or negatively depending on the orientation of the
fluctuation.

When this happens we conclude that the State's

economy is highly sensitive to business cycles.

"Business cycle" is the economic term used to represent
regular oscillations in the level of business activities
over a period of years.

A concept that is related to

business cycle is that of cyclical stability which implies a
situation in which an economic variable remains steady
through the business cycle.

Regional economic stability in our case will refer to
the joint effect of the region's industrial stability on the
fluctuations in the total regional employment. That is, in
the presence of unexpected economic fluctuations regional
employment will remain stable.

Regional economic stability

could be measured in terms of other economic variables such
as regional income, value added, wages etc as well.

It

could be pointed out that tecnolological change is an
important variable to consider when employment stability is
one of regional policy objectives.

Technological

9
development might have a negative effect on employment
growth.
Another concept that needs to be addressed is that of
diversification. Diversification is the opposite of
specialization.

Diversification involves the presence of

contrasting types of economic activities in the same region.
According to Hoover (1963, p. 283) "... the terms
specialization and diversification pose a problem of
definition, since there is no agreed measure of how similar
or how different any two industries or occupations are".
But as we will see in the next chapter diversification has
been measured using several indicators.

Rodgers (1956) says that diversification has been
defined in several ways.

In a broad sense diversification

has been identified with an area having a great number of
different types of industries.

Others talk of it as a

"balanced" industrial structure, but according to this
author this definition faces the problem of an appropriate
definition of "balance".

The term "absolute

diversification" has been used in the literature to
represent a situation of equal employment in all major
industrial groups. This is usually not a desirable situation
since productivities vary by type of economic activity.

10
An issue that is necessary to address when conducting a
study of diversification is that of the industrial (or
sectoral) composition in an area.

Some industries,

specially those classified as producing durable goods, tend
to be more sensitive to seasonal and cyclical fluctuations
of employment than nondurable goods industries.

Richardson (1969, p.276) is another author who
addressed the issue of the difficulties in defining
"diversification".

Diversification, according to

Richardson, could mean a balance between nondurable or
stable industries and durable or unstable industries or an
industrial structure near to that of the national, or
nearest approximation to a uniform share in all industries.

The general concensus is that it is healthy that the
regional economy include a large and varied number of
industrial groups as an important part of its economic base.
Industrial diversity provides a shield against external
changes that could affect the average level of income and
employment among different economic activities that comprise
the regional economy.

Industrial diversification has also been approached in
terms of balanced employment across industry classes or
activities, or in terms of inducing the expansion of a few

11
stable industries (Conroy,1974).

This has been in general

regarded as a positive goal in terms of regional economic
development.

Our study is expected to provide indicators of

these differences between industries.

Finally, it is necessary to define industry.

In this

study, industry represents a group of firms that produces a
similar output or service or employs people devoted to
similar economic activities. Industry is identified here
with manufacturing, so the Standard Industrial
Classification (Standard Industrial Classification, 1987) of
the federal government is used to identify economic
activities. A group of economic activities with similar
characteristics represent a sector in our study.

1.3

Study Objectives and Hypotheses.
Regional fluctuations have been seen as matter of great

concern among regional economists for several years.

These

can be classified according to its periodicity in seasonal
or short term fluctuations, business cycle or medium term
fluctuations, and growth trend or long term shift (Thompson,
1965 p. 133).

The usual interests of researchers have been

centered in the origin and causes of the business cycle and
measures to prevent the likely negative effects brought
about by a period of slump in regional economic activity.
As it is known, when this occurs regional income and

12
employment are affected because of reduction in the demand
of certain goods produced in the region and the derived
demands that these final demands cause.

In order to measure effects of these fluctuations on
the region, the starting point of analysis should be focused
on the industrial composition of the region under analysis.
Authors such as Isard (1960) and Richardson (1969) assert
that a large part of the cyclical responsiveness of a
specific region depends on the industrial composition of the
region.

Regions whose structure is more diversified could

respond much better to cyclical changes.

Under this view regional cycles are considered local
manifestations of cyclical changes in national industries.
According to Richarson (1969, p. 275) this type of analysis
"... imputes to each regional industry the national average
cyclical change in activity in that industry.

Any regional

cyclical experience not explained by its industry mix can be
regarded as a residual".

The validity of the analysis will

depend on the relative size of this residual

whose

importance could be large or small depending on the
magnitude of the industrial structure of the region.

In

this arena it is important to evaluate the patterns of
certain indicators that could guide policy decision making.
The percentage of activities in durable industries, its

13
degree of diversity and balance, and the rates of growth of
each particular industry have been seen as factors that in
great part could explain the causes and origins of the
business cycle both at the regional and national level
(Richardson, 1969, p. 276).

This work focuses mainly in developing solutions for
medium and long term oscillations.

It is assumed that if a

suitable level of stability is achieved in the medium term,
then policy makers might implement an strategy to hold this
situation for the future (growth trend stability).

A more

diversified economy could help to attain this objective.
Most regional economists assume that as a region's
industrial structure becomes more diversified, its economy
becomes less vulnerable to cyclical changes.

That is,

diversification could lead a region toward a more stable
situation in terms of income and employment.

Nevertheless,

different arguments have been posed against the
effectiveness of this strategy.

It has been said that as

the region becomes more diversified

its propensity to

import declines since the region is less dependent of other
regions and the external world.

On the export side, it is

said that given an industrial composition of the region as
contrasted with the rest of the nation, exports could or
could not represent an unstabilizing factor.

If exports

represent a large share, and most export industries are

14
unstable, then more sensitive will a given region be to
declining national demand (Engerman 1968, p. 296). Low
export share is linked then to a lower level of instability
and sensitivity to region's external shocks.

However, diversification should be seen as an
appropriate solution

in many cases.

Diversification

provides additional alternatives in terms of employment and
income distribution.

For those regions which rely

upon a

single or small groups of industries and whose products
exhibit a steep

decline in demand when a slump in the

national economic activity occurs, diversification
represents the best stabilizing option.

What diversification does is to dilute the risk brought
about by unstable industries which face reductions in demand
during the business cycle and creates a kind

of self

sustenance to the regional economy.

In the case of Michigan, the industrial composition
needs to be reinforced.

That is, additional industries that

allow a more suitable distribution of regional income, and
employment need to be promoted in the mid and long term to
cope with the swings of the business cycles.

A set of fast

growth "stable" industries need to be identified in order to
initiate the process of diversification in a broader scale.

15

1.3.1

Research Objectives.
This study attempts to meet the following objectives :

(a)

To

analytically investigate diversification that

has taken place in Michigan's economy, and
quantify it to the extent feasible.

(b)

To investigate if any consistent relationship
exists between size of the regional economy and
level of diversification.

(c)

To relate diversification with economic
instability of regional economies within Michigan.

1.3.2

Hypotheses.
In order to meet our objectives the following

hypotheses have been formulated :

(a) A significant change in the regional industrial
structure of the State has occurred during the
period of analysis.

We expect that because of frequent fluctuations of
Michigan's economy the industrial structure in terms of
employment has changed.

In this case the economic structure

16
of Michigan for years 1982 and 1988 will be assessed.

(b) There exists a negative relationship between
regional diversification and regional instability.

We expect that the more diversified the State's economy
is, the more stability could be achieved within the State.

(c) There exists a positive relationship between regional
diversification and the size of the region in terms of
population.

In this case a comparison between diversification and
population will be made. It is expected that the higher the
regional population, the more diversified the region will
be.

So, diversification will be a function of the

population size as Kort (1981) and Thompson (1965) have
asserted.

These hypotheses will be tested in order to provide a
clearer picture of the Michigan economy's behavior during
the last ten years. Study findings are expected to increase
knowledge regarding stability of the state's economy, and
form the basis for recommendations regarding efforts needed
in the future to achieve this goal.

In order to fulfill our

objectives, results of this research are combined with

17
previous works in the field.

Such results will provide

guidelines to decision makers, both public and private,
concerned with investment related to the forest sector
directly and indirectly.

It is expected that the study will indicate activities
likely to provide improved economic bases for regional
economies within Michigan thereby reducing negative effects
of instability

caused by the excessive reliance on the

automotive industry and other industries characterized by
unstable export markets.

From our results and previous investigations we
identify potential economic activities that are likely to
provide for higher economic growth rates and lead to a more
productive use of human resources and capital.

1.4

Scope and Limitations of the Study.
This study is limited to Michigan. Economic regions

consisting of counties with a similar mix of economic
activities are identified.

That is, aggregation of counties

is based in their homogeneity in terms of mix of sectors.
The research methods chapter will explain how this
allocation was carried out.

18
The study faces certain data limitations.

Annual data

were used for measuring the indexes of economic diversity
and instability .

Annual data were used because in this

study we are concerned with mid-and long-term movements
(i.e., business cycles and long term movements based on the
changes of the growth rates of income and employment among
industrial activities).

However it is important to

recognize that the year is made up of seasons, each with
certain characteristics which can lead to seasonal economic
activities (e.g., recreation).

Nevertheless, seasonal

movements are not going to be considered here since as it
has been pointed out this research is concerned mainly with
business cycle effects.

Another point to address is that this study only used
secondary data. So, our results rely on the quality of this
information.

Finally, it is important to point out that

some methods applied in the study are in process of
improvement, specifically the indexes of diversification and
instability.

There exist several ways to measure

diversification and instability.

Strengths and weaknesses

of these measures will be examined in the next chapter.

CHAPTER II

LITERATURE REVIEW
This chapter examines three fundamental aspects of this
study.
in

First, the progress attained during the past decade

Michigan regarding contributions of forest products to

the state's economy.

A set of studies that provide evidence

of the importance of the forest products sectors in the
development of the regional economy are examined focusing
essentially in their relationship with the economic
diversification and instability aspects.

Second, a survey whose goal was to explore the main
measures of diversification and instability was carried out.
Each measure has been evaluated to provide further insights
about their range of accuracy.

Finally, diversification and instability measures have
been used for measuring changes in the industrial structure.
Several cases concerned with the United States and other
countries are exhibited

to show some results that could be

expected.

19

20
2.1 Prograss Attained During the Last Decade Regarding the
Economic Contribution of Michigan's Forests Products
Industry.
The first effort in this decade was made by James et
al. in 1982.

The objective of this study was to document

the status of the forest products industry as of 1980.

The

information was developed by a survey of establishments and
includes regional location, quantity of timber, employment,
raw timber products values and value added by manufacturing.
This work showed that employment in the forest products
industry appeared to be higher than Census Bureau estimates.
This work found 24.6% more employees than the 1977 U.S.
Census of Manufacturers and 17.7% more employees as compared
to the 1979 County Business patterns.

The difference was

explained by the fact that this new survey collected
information from smaller establishments that were missed by
the Census Bureau.

The next step was to carry out research that provided a
directory of Michigan forest product industrial
establishments (Heinen and Ramm, 1983).

The directory

constituted the sample frame for the survey of forest
products.

The information obtained from the survey facilitated
the development of input-output accounts of several forest

21
product sectors (Chappelle et al., 1986).

This information

was combined with secondary data for the rest of the State's
economy.

This demand-driven 1980 input-output model of

Michigan economy included 37 sectors, ten of which were
forest product sectors.

The I/O table allows us to obtain

information about direct product coefficients and
interdependency coefficients for the forest product sectors.
Type I (direct and indirect effects) and Type II (direct,
indirect and induced effects) multipliers were calculated
for the forest industry sectors,

these multipliers were

based on information about output, employment and income
obtained from the survey.

The authors' multiplier analysis

indicated that the sector having the greatest impact varied
depending on the goal being pursued.

Therefore, results

appeared to indicate that if the state's goal was to achieve
a sales maximization, then the sawmills and planing mill
sector should have priority.

On the other hand, if income

maximization is the target, then wood pallets and skids
sector should have priority.

If employment maximization is

the goal, the integrated pulp and paper or paperboard mill
sector should be given priority.

Regional and local

conditions should also be taken into account to select the
most appropriate sectors to be expanded.

A forecast of final demand for each forest sector was
derived to obtain estimates of future production.

Data

22
Resources Inc. (DRI) was contracted to do this job.

They

forecasted final demand for 39 forest industry sectors (four
digit SIC codes) for the years 1984, 1985, 1990, 1995 and
2000 in terms of 1972 dollars.

This study for Michigan

showed that the annual growth rate in final demand was
expected to be the highest through the year 2000 for the
sawmills and planning mills and the second highest for the
wood pallets and skids sector (Data Resources Inc.,1985).

These forecasts allowed sectors to be identified that
should be considered in any industrial targeting activity by
state government.

Final demand forecasts are important

since they indicate the likely path of regional economic
growth and the movement of the export sector.

The objective of the next study was to forecast total
output required to meet forecasted final demands (Chappelle,
1986). We should note that a demand driven I/O model assumes
that demands are known. This study indicated sectors that
should be targeted for expansion by the State government.

The I/O model described before was driven by 1990 final
demand estimates developed in the DRI study for the forest
industry sectors and the Regional Economic Models, Inc.
(REMI) data base for the remaining sectors of the Michigan
economy, with a few exceptions that required forecasting by

23
the author based on complementary information (e.g.,
agriculture). Also, results were calculated using forecasts
for forest product sectors within the REMI system.

The study showed that forecasted total output varied
greatly with respect to the demand forecasts developed by
DRI and REMI.

Final results indicated that only the wood

furniture and fixture sector was expected to grow at or
above the expected inflation rate for both series of
estimates of final demand.

Therefore, it could be concluded

that final demands for forest product sectors in the state
of Michigan are quite uncertain (Chappelle and Webster,
1987, p.21).

Different studies have shown divergent signals
regarding specific forest industry sectors that should be
considered for future expansion, given regional, national
and international markets.

Since the two main sources of

information in the future final demand (i.e. DRI and REMI)
did not provide similar results, there exists a problem of
consistency.

According to Chappelle and Webster it is

expected that differences in final demand can
be explained by differences in export forecasts (Chappelle
and Webster, 1987, p.22).

24

Estimation of the different production levels led to
determination of the economic feasibility of locating
additional capacity of various types of forest products
establishment in the state.

This approach will require an

additional set of data, namely capacity levels of industrial
plants in Michigan.

Since information about the pulp and paper sector and
the composite wood panel sector was available at that time,
only the wood pallet sector was studied (Obiya, 1986).

This

particular sector showed the highest income multiplier in
the I/O study.

The automobile manufacturers were, according

to the findings, the major customers of this sector.

Nevertheless, this study of effects of measures
constraining expansion of the wood pallet industry in lower
Michigan indicated that this particular industry was
operating at undercapacity and hence did not appear to be a
good choice for expansion, given the current technologies
that are utilized and demand levels.

A new lower cost

technology or increases in demand could change this
conclusion and provide new options.

25
The next study considered the economic importance of
the Upper Lake State forest resources (Pedersen and
Chappelle, 1988; Pedersen, Chappelle and Lothner, 1989;
Pedersen and Chappelle, 1990).

In this study the IMPLAN

input/output modeling system of the Forest Service, USDA was
used to measure impacts of forest products and recreation
sectors on the regional economy.

This study provided an

analytical framework for a Regional Governors' Conference on
Forestry, held in April 1987 in Minnesota.

The major

finding of this study can be summarized as follows:
(Pedersen and Chappelle, 1990)
"The forest products of industry of Michigan, Minnesota and
Wisconsin account for about 8% of the region's manufacturing
sales, employment and income. In real terms, sales of
forest products are forecasted to grow from $15 billions in
1982 to over $22 billions by 1995. Sales related to wood
energy and outdoor recreation in forest areas of the region
account for another $2 billion. Adding the multiplier
effect, economic activity attributable to these three uses
of the forest resource is projected to grow from over 30
billions in 1985 to over $40 billions by 1995."

Pedersen (1990) completed a study that focuses on
estimation of economic impact of recreation in the threestate region.

He concluded that in order to get reliable

estimates of economic impacts of forest-based recreation,
the IMPLAN data system must be improved, specifically
estimates of regional production as a proportion of regional
purchases.

26
Chappelle and Webster<1990) analyzed possible linkages
between unemployment rates and economic bases of multicounty
regions within the lake states.

The study concludes that it

appears that relative magnitudes of regional unemployment is
related to regional characteristics, including economic
base.

Patterns of employment exhibited by the predominant

industry is reflected in the regional unemployment
patterns. In each of the three states the forest industry
development centers and tourism/recreation development
centers have lower rate of unemployment than do relatively
undeveloped areas.

The study found that within the regional

patterns mentioned before, Michigan contrasts with Wisconsin
and Minnesota.

The unemployment rates are appreciably

higher in Michigan compared to both Wisconsin and Minnesota.

The most recent study by Chappelle and Pedersen(1991),
examines the economic contribution of Michigan forest
products during the eighties. They found that although the
decade was characterized by an economic pattern of recession
in its first years, the rate of growth of employment in
forests products firms at State and National level was
higher than that of

manufacturing firms as a whole. They

estimated that the total impact of the sector (direct and
indirect) in the employment
1987.

reached

around 134,000 jobs in

The total impact in the value added attributable to

the sector was estimated in more than $6 billion for the

27
same year.

One important finding of this research was that

only a half of Michigan's timber consumption came from
Michigan timber harvest.

So, they conclude that there are

good prospects for developing new projects in the sector and
to expand the existent industries.

Production to export to

other regions of the country could be feasible as well.

2.2

A Brief Survey

of Industrial Diversification Measures

Through the years, researchers have developed several
indicators that attempt to measure industrial
diversification.
complexity.

These measures usually differ in

Most of them fulfill the basic requirements of

the researchers. The complexity of a measure and its
validity generally depend on assumptions behind the
indicator used and the objectives pursued.

A professionally

accepted standard criterion for measuring diversification
does not exist so far.

However,improvements in measurement

have been achieved in recent years.

This work classifies regional diversification measures
into four broad categories : measures based on normal
proportions; durable goods measures; portfolio analysis
approach; and entropy indexes.

This classification is based

on the type of indicator or index used to measure the

28
changes in the industrial structure of the region or
geographic area of inquiry.

The works of Bahl, Firestine, and Phares (1971); Conroy
(1975); and Jackson (1984) are used here to provide a
detailed survey of diversification measures.

2.2.1

Measures Based on Normal Proportion
These methods are based on deviation from a normal

proportion of employment for each industry.

Normal

proportion in this study represents an average or expected
distribution of employment.

Alternative measures of normal

proportion have been examined in the literature by Bahl,
Firestine, and Phares (1971).

These authors considered

three groups of measures which fall in this categories
(i.e., characterized by a specific definition of "normal"
employment).

These measures are : (1) equal percentage or

ogive; (2) minimum requirements; and (3) national average.

The most frequent of these proportion measures is the
equal percentage.

This measure assumes that each industrial

sector would exhibit an equal percentage of employment if
the economy is fully diversified. For instance, if a region
comprised of

25 SIC activities, it is expected under this

29
approach that share of 4 percent should be achieved for each
sector to represent total diversification.

The usual

formula for this measure is :

Where, N = number of individuals sector in the region; and
e{ = the employment in industry i.
et = the total employment in the region.

The first to use of this type of index was McLauglin
(1930), followed by the works of Tress (1938), and Rodgers
(1957) who modified the measure developed by Tress.

Rodgers

constructed a Lorenz curve based on the distribution of
total

employment in sector by manufacturing group for each

industrial area 1 and compared this with the average

distribution for all industrial areas analyzed (an average
or uniform distribution is represented by the main diagonal
in a Gini's concentration index model).

The term "ogive" is

used in the literature to refer to this type of measure.
Keinath (1985) used this approach with a slight
modification.

He computed absolute deviations from the

equal percentage value, instead of using the sum of squares
1 The term "industrial areas" is used in Rodgers's paper
in a descriptive sense to indicate a manufacturing area.

30
deviations as the basis of computation.

Both the Rodgers

approach or Keinath approach lead to similar results.

This measure has gained wide acceptation because of its
easy computation and interpretation.

Here based on the

assumption of equal proportion for each industrial sector,
it is expected that the index will weight heavily the
absence of employment in a specific sector without any
consideration of the overall employment distribution.

In

larger urban areas where there is employment in almost all
economic manufacturing activities, diversification will be
greater.

The major criticism of this approach arises from the
weakness of the equal proportion assumption.

This measure

poses that in order to obtain an optimal diversification it
is necessary to attain a uniform distribution of employment
in the region or area analyzed, which occurs rarely.

More

important, this situation is usually not desirable.
Technologies vary from one activity to another. So, the
magnitudes of employment and income is different for
different economic activities.

It is very difficult to get equal proportions in the
real world.

Other aspects concerned with supply and demand

of inputs and final goods are not taken into account using

31
this approach.

Institutional and legal regulations in the

industry are ignored as well.

The minimum requirements method was developed by Ullman
and Dacey (1960) and by Alexandersson (1956).
employment is classified into

Here

total

basic and non basic sectors.

The minimum requirement employment is defined as the
percentage needed to maintain the internal needs of the
region (non-basic sector employment). To obtain the
diversity indicator a least squares analysis should be
carried out plotting the minimum percent for an activity
against the population associated with this activity.

The regression provides expected minimum requirements
for each activity.
final index.

These values are used to compute the

According to Ullman and Dacey (1960), the

employment percentage

in excess of the minimum requirement

(non-basic) represent the export or basic employment.

The

least squares regression takes the form :
Afj - aj + Pjlog(Population)

Here, i= 1

n SIC codes.

M, = minimum percent employed for each industry and
population.

32
Based on the result of this regression a diversity index (D)
is calculated, as follows :

E ? (Pj

-Ml)2 / Ml

(ZR
i pi - E " i

>3 / E

Mi

Here, P, = percent of employment in the i-th industry
class.

It has been pointed out that this procedure does not
give results independent of the population size class.
Therefore, there exists a positive correlation between basic
employment and population size class.

Bahl, Firestine, and

Phares (1971) assert that the regression process used to
estimate the minimum requirement percentages should take
into account the city size differential since population is
a variable in the regression model exhibited above.

The

diversification index is corrected for city size by dividing
the initial index (unadjusted) by the ratio of squared basic
employment percentage and the total minimum requirements
employment percentage.

33
The weakness of this approach is that the minimum
requirements percentage is biased with respect to the
population size.

The index can be corrected for city size

by using only the numerator of the current ratio which is
based on the results of the regression. The national average
approach uses the national average employment as the norm.
The national measure refers to the sum of the regional
deviations from the national percentages in industrial
categories (Jackson,1984).

It is established that the

greater is the sum of these deviations, the lower will be
the degree of industrial diversity.

These groups of

measures also are found in the works of Borts (1961) and
Florence (1948).

Under this approach the national economy

is assumed to be diversified and that industrially
diversified region's employment percentages should replicate
the national economic structure. This last proposition is
very difficult to accomplish.

This measure can be

represented as follows :

m

v

-

v* I-2i
Z -i

Where,

et

- A |
Ee

NAV = National Average Measure.

N = number of industrial sectors in region i.
e,

= the employment in industry i.

et

= total employment in theregion.

Ej

= national employment in industry i.

34
Et = total national employment.

These measures are extremely sensitive to differences
in technology and production capabilities between regions,
accessibility to resources and other economic variables that
could affect each sector's employment share.

Weaknesses of

this measure are quite similar to those of the ogive
approach.

2.2.2

Durable Goods Measures.
Analysts have attempted to explain regional cyclical

variations in industry based on an diversification measure
made up of the proportion of durable goods in a region.
Siegel (1966) and Cutler and Hansz (1971) used this type of
indicator to measure industrial diversification.

The

general form of this measure is :
pnrn?

„ ~

1 C

/ o \ * inn

,

~

C /

•

------

Here,
e<t =

employment in durable goods industry i and
region t.

et

=

total employment in region t.

The proportion of a region's employment in durable
goods production has been usually considered one of the most
sensitive to the business cycle.

Also, it has been used as

an indicator of a region's reliance on export income.

35
Jackson (1984) says that because durable goods are
characterized by a high income elasticity of demand during a
period of downturn, one could expect that consumers restrain
their purchases of such goods.

Then, lower levels of

production could occur along with likely labor layoffs in
the specific industry and those linked in some way to it.
One can conclude that use of this type of measure could
bring about serious problems due to its sensitivity to
business cycle, and its limitation to diversification
analysis since only durable goods activities are taken into
account.

An important bias against larger urban areas could

result as well since most durable goods activities
usually concentrated there.

are

As was the ogive, this measure

has been considered overly simplistic and naive.

2.2.3

Portfolio Variance Measure.
The source of this approach can be found in the pioneer

work of Markowitz (1959).

In this work Markowitz developed

a portfolio analysis method whose purpose was to measure
risk associated with returns to various portfolios of
financial assets and the effect of diversification in
reducing the intrinsic risk associated to each portfolio.

In Markowitz's model, financial risk is represented as
the historical variability of the return to a dollar, when
it is invested in an specific financial asset.

Then a

36
covariance matrix is calculated whose elements represent the
similarities in risk fluctuations between the different
assets that are part of the portfolio.

The portfolio index

consist of a weighted average of all values in the
covariance matrix.

Conroy (1975) used Markowitz's portfolio variance
approach in the analysis of the regional industrial
structure.

He considered the returns, in terms of

employment level, that a region could derive from its
economic activity, under a risk of the instability in such
returns.

Note that under this approach the variability in

the employment level for an activity is considered the risk.

This variability in employment is measured through the
covariance of the residuals obtained from the time series
employment information.

The portfolio variance (ap) is

defined in Conroy's work as :

% - EiEi wi"Vi,
Where,

w,, Wj

represent the proportion of regional

resources (or other relative weights) allocated to i and j.
a fj denotes the covariance of predetermined returns
criterion over time for industries i and j .

37
Jackson (1984) points out that the portfolio variance
measure reflects the structural composition of the region's
economy in terms of the employment covariation in the sector
and between sectors. The measure is dependent upon both the
observed variance within categories of industries and the
average covariance of each sector with every other sector".
Under Conroy's approach a region's portfolio variance
measure represents the weighted sum of the time- sectoral
variance and covariances. The lower the measure of risk, the
more cyclically stable and diversified industrial structure.

Despite the fact that Conroy's approach represents a
great advance in measuring industrial diversification (it
was the first time that risk is embodied in the economic
diversification analysis), some weaknesses have been found
in his approach. The first is related to his assumption that
the matrix of covariance of employment in alternative
industries is identical for all regions.
unlikely to occur.

This situation is

Second, a large amount of data is needed

to calculate the full portfolio of activities, so the task
of compiling reliable information in terms of periodicity
and desegregation is difficult.

Brown and Pheasant (1985),

found additional difficulties with Conroy's approach.
Conroy's work excludes non-manufacturing industries, leaving
out important sectors of the economic activity.

The scope

of his study was limited to metropolitan areas, which could

38
create a serious bias when economic diversification is
measured since one could be omitting important contributions
in terms of employment originated from the non-manufacturing
industries, such industries usually are not located in urban
regions or they may be linked to manufacturing industries
not located in urban regions.

2.2.4

Entropy Measures.

Another technique that has been widely used to measure
industrial diversification relies on the concept of entropy.
Entropy is a term borrowed from the physics.

An entropy

index attempts to measure the deviation of a given
distribution from complete concentration (minimum entropy)
or complete dispersion (maximum entropy).

In measuring

industrial diversity the idea of the index is to compare the
diversity of a industrial structure against a uniform
distribution of the employment among all industry sectors
(Wasylenko and Erickson, 1978).

It is measured in general

terms as :
D (Pi ,p2 ,

Here,

P n) -

C J 2 " ~ Pi lo9 Pi

n = number of industry classes.
p, = the relative proportion of each industry class.

39
C = arbitrary constant which determines the scale
of measurement.

An application of this sort of measure can be found in
Garrison and Paulson (1973), who used this measure to
evaluate a geographic concentration of industry in the
Tennessee Valley region.

Hackbart and Anderson (1975), were

the first to apply entropy as a measure of economic
diversification.

Wasylenko and Erickson (1978),

demonstrated that entropy measures yield diversity ranking
for regional economies similar to that of ogive index.

The most important recent contribution using this type
of index is that of Kort (1981).

In his work Kort used the

diversification index to predict a measure of regional
instability.

The relationship used by Kort in his

regression model is quite similar to that used by Conroy
(1975) and Siegel (1966).

Kort (1981, p. 597) suggest that

"... the relationship between industrial diversification and
economic instability varies with the city size, and that any
model explaining variations in REI must accordingly
incorporate the relationship among all three variables."
So, he incorporates city size to the analysis of regional
instability.

Kort's relationship between instability and

diversification was given by :

40
R E IS JP5PJ - a yfPGPl + b (DIi JP5FJ) + Ej /P5P7
Here,

POP, = population of regional economy i.
REI, * instability index of region i.
DI,

= diversification index of region i.

REI and DI formulas will be developed in the research
methods chapter.

One important point to note when this approach is used
is that a relationship between Regional Economic Instability
(REI) and diversification can lead to a likely patterns of
heteroscedasticity when ordinary least squares (OLS) is
used.

A weighted least squares (WLS) could be used to

correct the problem.

The weights to use can be the

population size of each region at that period.

Brewer and

noomaw (1986) made a small correction on the weights used in
Kort's formula.

This new reformulation corrects the

regression to a homoscedastic pattern. Therefore, the best
linear unbiased estimators2 (BLUE)

could be maintained.

2 Classical assumptions of linear regression model provide
the best linear unbiased estimators and equal variances
among the different groups regarded.

41
2.3

Review of Some Important Studies of Industrial
Diversification Related to Instability
Several of the most important empirical works are

summarized below.

Experiences in the United States and

Canada will serve to show the usefulness of these measures
of diversification and instability in developing guidelines
for regional economic decision making.

Siegel (1966) was the first to measure regional
economic instability.

His index was based on proportions of

employment for durable goods sectors.

The idea was to

compute the standard error of the estimate of a trend line
obtained through a time series of the regional employment.
The residual of the observed data against the adjusted
information was the basis of the regional economic
instability index.
knowing

Siegel (1966, p. 44) was interested in

"... whether or not regions differ from each other

in cyclical performance for reasons other than industry
mix."

The research was conducted over the United States,

using data from 1949 to 1962.

Standard

Metropolitan

Statistical Areas (SMSAs) were the basis for compilation of
information at the two digit level in the industrial
classification.

He found that SMSAs displayed similar

average cyclical amplitudes for durable goods and different
average amplitude for non-durable goods.

However,

his

results were not conclusive enough to answer his initial
question.

42
Bahl, Firestine, and Phares (1971) analyzed Industrial
diversity in urban areas of the United States.

They

compared several "normal" proportions measures such as
minimum requirements, ogive, and national average.

They

were interested in knowing which of these measures was the
best.
for

They used information from the 1960 Industrial Census
212 SMSAs.

They found that the minimum requirements

approach yielded the best results.

The other measures of

diversification produce different and ambiguous results when
population is considered in the relationship.

Therefore,

they argue (p. 419) that "the minimum requirements measure
links industry norms to population size".

This is coherent

with the body of literature that suggests a relationship
between employment requirements and population size.

The

main finding of this study was that population size is
associated in both absolute and relative terms to increases
in tertiary employment (services).

The study concludes (p.

421) that blue-collar work "... will increasingly be located
not just in suburban areas but beyond the SMSA", this fact
creates strong implications in public policy issues.

Some

additional evidence has shown that this in fact happened in
the US.

Conroy (1975) carried out one of the most important
studies in diversification and instability in recent years.
He noted that the earlier literature in the field does not

43
clearly exhibit the actual relationship between regional
economic instability and diversification.

Conroy's

contribution was a new diversification measure that was not
used before in the regional economic field

-portfolio

variance analysis.

The nature of this measure has been explained in
section 2.3, above.

In his work he tested several measures

of industrial diversification for the year 1963 against
instability measures, including the new one.
series of the research was for 1958-67.

The time

He included fifty

two SMSAs based upon 118 industrial sectors.

From his results he concluded that the portfolio
variance measure was the one most highly correlated
regional employment instability.

to

Nevertheless, he notes

that one should be careful in using this method because of
the strong assumption that interindustry covariances are
uniform nationwide.

Since Conroy's work several researchers have tried
improve the portfolio variance method
results in empirical inquiry.

to

to obtain better

St. Louis (1980), developed a

model to measure regional industrial diversification
following a Markowitz-Conroy portfolio context.

He used

annual employment data for nine Canadian provinces and

44
Canada as a whole.

The Standard Industrial Classification

(SIC) at the one digit level was used.

The idea of this

study was to measure industrial diversification using the
concept of the regional efficient frontier, which was
developed using a quadratic programming technique.

The

study concludes that this new technique could be used to
improve

results obtained using Conroy's model. Kurre and

Weller (1988) investigated regional cyclical instability
based on twenty five years of monthly data for the period
1958-1983 for Erie, Pennsylvania which is a Metropolitan
Statistical Area (MSA) consisting of one county of the same
name.

Information was collected at the two

digit SIC

level.

This study attempted to measure cyclical fluctuations
not only with respect to employment but also to wages and
hours worked.

The study found that in the case of the wages

and hours worked, Erie manufacturers tend to do their
adjustment by hiring and firing workers, and to a lesser
extent, by adjusting hours worked per week.

Conroy's

portfolio variance analysis, was used to analyze employment
in order to complement this study.

Recently, Board and Sutcliffe (1991) carried out and
study using a portfolio model in six regions of Spain.
developed single, aggregate, and multiregional portfolio

They

45
model whose objective was to generate frontiers of
risk/income efficient industrial mixture for a regional
economy.

The models were applied to the tourist industry

which is responsible for 15% of the Spanish employment.
Their results showed that portfolio theory can be an useful
tool to analyze the level and stability of regional
employment (or income), to produce good estimates of income/
risk efficient frontiers.

So, the policymakers' task is to

choose the appropriate point on such a frontier to meet
policy objectives.

Kort (1981), is the most notable recent work in this
field.

He demonstrated the importance of incorporating the

size of population in models that attempt to explain

cross

sectional variations between regional economic
diversification and instability.

Using information for 106

SMSAs for the period 1967-1975, Kort applied an entropy
index of diversification.

Average annual data at the two

digit SIC level were obtained from the Bureau of Labor
Statistics (BLS).

A model that accounts for differences in

stability between large and small cities was developed in
this study.

One of the most important findings of this study was
that the variance of the error term decreased with increased
regional size.

Kort's major finding was that the

46
relationship between regions economic instability (REI) and
industrial diversification varies by city size.

He points

out that diversification is not the only factor accounting
for variations in REI.

More meaningful conclusive results

are obtained when the REI/ diversification model is
corrected for city size variation (heteroscedasticity), and
at least diversification is one factor accounting for REI
differences.

Chapter III
Research Methods

3.1

Data Collection and sources
The

information

used

in this

research was

entirely

compiled from secondary sources. This information was used in
the delineation of economic regions, which served as our units
of analysis.
define

our

Initially a sectorization was made in order to
set

of

regionalization

economic

would

be

activities

based.

Once

upon

which

the

the

appropriate

sectorization was chosen the information compiled was used to
determine

economic

structure

of each county.

This

last

aggregation was the base for our regionalization.

During this stage Michigan's statistics were gathered and
assessed.

Several sources of data were considered.

The best

option to choose was the one that could provide suitable
information

at

the

level

of

disaggregation

required

to

calculate diversification and instability indexes.

Four potential sources were evaluated to isolate the one
that

would

best

provide

disaggregation likely to

a

desirable

level

lead to rational

of

sectoral

measurement

of

impacts of the business cycle on Michigan's economy in the
medium term.

The following data sources

47

were explored :

48
(a)

County and City Data Book.

It was found that this

source provides some information at the level of
county and city employment and population,
but not by economic activity.

This information

did not fullfill the requirements of
disaggregation pursued in this research.

(b)

The Michigan Employment Security Commission (MESC)
Statistics.
and annually.

This information is compiled monthly
The number of employees, average

weekly hours worked, average hourly and weekly
earnings at four digit code can be obtained
through this source, based on MSAs boundaries3.

(c)

County Business Patterns.

Data about employment,

population, wages, etc. can be obtained from this
source at the three SIC digit level and in many
cases information is available at the four SIC
digit level as well.

This information is

collected annually.

(d)

Information compiled through the Center for
Redevelopment of Industrialized states (CRIS)
at Michigan State University.

The Center collects

3 MSA represents the Metropolitan Statistical area
classification and its boundaries, as defined on June
30, 1983.

49
much information on many economic and social
variables.

This source can provide annual

employment and wages data at the level of three
digit SIC code for the period 1982-1987.

County

population data can also be obtained from this
source for the period 1982-1990.

CRIS

information is a combination of information from
County Business Patterns and other Michigan Market
Statistics.

An annual time series of employment based on CRIS'
database and County Business Patterns was gathered for the
period 2976-1989.

CRIS information covered the 1982-1987

period and was complemented with information gathered from
the 1988 CBP annual report.

This information was collected

at the three digit SIC level for most of the economic
activities

and the four digit SIC level for the forest

products industry.
from 1976 to 1987

Employment data by economic activity
based on the 1982 Standard Industrial

Classification was adjusted to the 1987 Standard Industrial
Classification.

It was expected that this information

fulfills the requirements of data necessary to meet the
study objectives.

50
3.2

Seetorisation by Economic Activity
Sectorization was made with a focus on three important

research objectives of :

(a)

It was necessary to use a sectorization
that reflected the current economic
structure of Michigan.

(b)

To obtain suitable indicators of diversification
and instability.

(c)

To measure the importance of the forest products
industry in Michigan's economy.

To meet these objectives, it was necessary to use a
sectorization that was as disaggregated as possible.

A

significant level of sectoral disaggregation is an important
ingredient to achieve better knowledge of the effect of
diversification on the region as a whole.

The criterion to choose a specific sector for
delineation was its importance on the local economy.

This

means that only those sectors whose economic contributions
were important for the local economy
potential candidates.

were considered as

The number of firms in the sector,

sales, and number of employees were variables taken into
account for the final decision.

Under this criterion a

51
sectorization that

is a slight modification of that used by

Chappelle et al. (1986, p. 6) was considered.

Chappelle's

sectorization provided a suitable framework for the type of
sectorization required in this research.
been made.

Few changes have

Economic activities were aggregated at the three

SIC digit level for most economic activities

and the four

digit SIC level for the forest product activities.
cases the two digit SIC code was used.
the sectorization used.

In a few

Table 3.2, shows

52
Table 3.2.

Sectors of the Michigan Economy and their

Standard Industrial Classification (SIC) Codes.

Sector

Description

1

Fishing, Hunting, and

SIC Codes
Agr.

071-078, 091-097

Services
2

Metals Mineral, Crude Petr.

101-109, 122-124

3

and Natural Gas
Construction

131-138, 141-149
part 138, 152-179

4

Food and Kindred Products

201-209

5

Textiles and apparel

221-229, 231-239

6

Logging contractors

2411

7

Sawmills and planning mills

2421

8

Millwork, flooring, Struct,

2426, 2431, 2439

members
9

Wood furniture and Fixtures

2434, 2511, 2512,
2517, 2521, 2531,
2541

10

Wood pallets and Skids

2448

11

Veneer and Plywood, other

2429, 2435, 2436,

Lumber and Wood Products

2441,- 2449, 2451,2452, 2491, 2492,
2499

12

Paper and Allied Products

2611, 2621, 2631,
2661, 265, 267

13
14

Printing and Publishing
Chemicals and Petroleum Prod.

271-279
281-289, 291-299

15

Rubber and Leather Products

301-308, 311-319

16

Stone, Clay, Glass, and

321-329

Concrete Products
17

Primary Metals Industries

331-339

53
Table 3.2 (cont'd.)."
Region
18

Activity

SIC Code

Fabricated Metal Products,

341-349

Excp. Machinery and Transp.
Equipment
19

Machinery

and Equipment

35(except 355,
356, 358, 359)
361-369, 37

20

Transportation and Public
Utilities

40-42, 44-49

21

Misc. Manufacturing

381-399, and all
24,25,26 excluded

22

Wholesale and Retail trade

above.
501-599

23

Finance, Insurance, and

601-679

Real Estate (F.I.R.E.)
24

Other Services

08, 355,356,
358,359,
701-899 (excl.
88)

Twenty four sectors were selected to represent the
industrial economic structure

of the State.

These sectors

comprise SIC categories which range from 01 to 89.
Sectorization of the Michigan economy was a required step
before delineating regions needed in the diversification
analysis.

54
3.3 Regionalisation
The delimitation of regions has been seen as a problem
whose nature depends mainly on specific research
objectives.

Czamanski (1973, p. 3) pointed out that it is

necessary to make the distinction between the concepts of a
region and area or zone.
two dimensional space.

An area represents any part of a
A zone could be considered as a

special area which has some "characteristics or
characteristic in contradiction to the remaining part of a
given space".

Region is a term that implies an area within

the national economy (in our case Michigan) that is
sufficiently comprehensive in structure that it can function
independently and that is linked with the rest of the
economy.
Usually three approaches are considered when regions
need to be defined - uniform or homogeneous regions, nodal
or polarized regions, and programming or planning regions.
Homogeneous regions are defined by Richardson (1969, p. 19)
in terms of "...unifying characteristics, and where internal
differences and intra-regional interactions are considered
unimportant.”

Here, certain areas cohere together to define

a region when they are homogeneous from the view of sharing
an specific characteristic. For instance, like occupational
distribution of the manpower or production structures quite
similar, or similar per capita income etc.
Nodal regions are based on the functional integration
principle, which emphasizes the relationships and linkages
of the different spacial and economic units within the
region. According to Richardson (1969, p. 227) " Nodal
regions are composed of heterogeneous units, but these are
closely interrelated with each other functionally ".

55
Planning regions are defined based on the coherence and
unity of economic decision making. So, regions are defined
as political jurisdictions whose sizes and levels will
depend on the policy objective that the government pursues.
In general, once the main criterion is chosen, it is
possible to integrate elements of the other criteria into
the regionalization.
3.3.1 Delimitation of Regions
Regions were defined based principally on the criterion
of homogeneity in this study.

The delineation of the

regional boundaries was based on economic base and regional
characteristics. Counties were used as building blocks of
regionalization.
An economic region was constructed in such away that
its internal structure reflected not only some degree of
homogeneity among its most important economic activities given by the sectorization -but also exhibited the existing
degree of diversification among the set of activities
characteristic of such a region.

Regions, under our

classification, reflect significant differences in terms of
types and mixes of economic activities.

Summing up, a

region is defined here as a group of counties that share
similar economic structures in terms of sectoral employment.
3.3.2

Use of Factor Analysis to Allocate Counties into
Regions
Factor analysis was used to define economic regions.

Information from Table 3.1 provided the sectorization used
in the model.

Data base information of employment by

economic activities at the appropriate SIC code was then
used to feed the factor analysis model.

56
Factor analysis (Kim,1978 p. 9) refers to "...a variety
of statistical techniques whose common objective is to
represent a set of variables in term of a smaller number of
hypothetical variables or factors"(factors).

Factor

analysis is a multivariant statistical technique that is
based on partial correlations.
Factor analysis starts by considering a square simple
correlation matrix.

This matrix is factored generating the

principal factor solution.

Following that, a rotation of

the principal factor is made in order to maintain principal
factors orthogonal.

This rotation does not affect the

proportion of the total variance explained by the factors.
Nevertheless, it redistributes their explanatory function
among corresponding number of new factors (Harman, 1967).
Each variable loads on each factor, and the loading can be
considered equivalent to a simple correlation between a
variable an a factor.

The value of a factor loading varies

from -1 to +1 with the sign indicating the direction of the
correlation between the variable and the factor.

Our model

was designed to maximally reproduce correlations, the
general form is:
Z. = a t1 F1 + ai2 F2 -t-

aim

+ af U f

Where,
i = l,2,....n variables (or type of employment in the
economic activity i).
m = 1,2,

m counties.

ai|B is the factor loading for the ith variable on
factor m, with F, being common factors4 and a, and uf being
4 Common factors are those that are involved in the creation
of more than one observed variable while those that are
used in creating only one observed variable are called
unique factors. Likewise, factor loadings are equivalent

57
the unique factor loading and unique factor respectively.
Z{ represents the standardized variable Zi =( Xj - X) / &,
where X, is the employment in sector i, X is the average
employment, and s is the respective standard deviation.
is distributed N (0,1).

Z,

Factor loadings were determined by

fitting this model (i.e., the aiB s).
The af(| is the proportion of variance
explained by factor m.

of variable i

The proportion of total variance

explained by a factor is :
VAR^ =

E f a2i(l / (trace of the factor matrix)

The communality of a variable is defined as the portion of
a variable's total variance that is accounted for by the
common factors.
h2 =

Communality is determined as follows :

a2„ + a2j2 + ...... a2,.

Only loading of the common factors are utilized in
computing communality. Two types of factors were introduced:
(a)

Common factors which involve more than one

variable.
-

General factor (almost all variables load
highly on one factor).

-

Group factor (more than one variable is taken
into account, but not all variables loaded on
the factor).

(b)

Unique factor which involves a single variable.

to correlations between factors and variables where only a
single common factor is involved.

58
It is important to note that common factors account for
variable interrelations (Harman, 1967)

whereas unique

factors represent that part of a variable not accounted for
by its correlations with other variables.
Factor analysis can be performed mainly through two
types of techniques; the R-Technique and the Q- technique.
Most studies in the field use R- technique, which provide
the usual covariance matrix. That is, given an entity mode
(rows) which represent objects or cases, the resultant
covariance matrix is for the relationship among the
components of the variable mode (columns).

Meaning that the

R- mode produces a correlation matrix of the characteristics
inherent to our objects or cases.
The Q-mode is the transposed factor technique and was
used in this work.

This variant of the general factor

analysis produced groups of

counties sharing similar

economic activities in terms of employment participation.
The common rotated factors obtained represent our regions.
In summary, at this stage our goal was to create
economically uniform regions made up of a group of counties
(m) characterized by certain variables (n economic
activities) which were reduced into a small number of
factors (regions).

These regions are characterized by a

high degree of uniformity in terms of economic structure.
The main characteristic used was a maximum variance between
groups and minimum within groups variance.
Finally, in order to define the regionalization to be
used in this research, the Q-mode factor analysis procedure
was applied to two nonsuccessive years (1982 and 1988).
After that, having the two

regional cross-sections, it was

feasible to evaluate if our regionalization changed

59
meaningfully or not.

Based on this information a

regionalization year was chosen.

Results of this analysis

are discussed in chapter four.
3.4 Measures of Diversification and Instability
A second set of methods are required for measurement
of diversification and instability. These indexes are
developed to provide information about the degree of
vulnerability of the Michigan's economy to external shocks.
At this stage a model quite similar to that used by Kort
(1981) was applied to evaluate diversification and
instability.
Results of this analysis were compared with
and ogive index, and a percentage of durable goods index5.
3.4.1 Relationship between Economio Diversification and
Instability
Until a few years ago, experts in the field doubted the
real existence of a relationship between regional economic
instability (REI) and regional diversification (DI).

Hoover

(1985, p.371) for example, remarked that "...Actually this
is neither true nor logical, as was showed quite a long time
ago by Glenn E. McLaughling.

Diversification is roughly

neutral in its effects on cyclical instability".

This

general thought remained until recently when two important
works made important contributions to this area.
The works of Conroy (1974, 1975) and Kort (1981) were
pioneering in demonstrating existence of an important
relationship between REI and DI.
For our purpose Kort's methodology was used.

Some

reasons for this decision were:

5 The description and mathematical representation of the
last two indexes are found in Chapter 3.

(a)

Kort's indexes of diversification and instability
can be easily calculated from quite disaggregated
industrial data.

(b)

Kort's model can take into account the scale
effect measured

by

the size of the region.

method provided weights

The

that allow for estimation

of economic instability as related to the economic
structure of the State, particularly in terms of
employment.
(c)

The method facilitates measuring the extent to
which diversification of Michigan's economy
through expansion of forest product sectors may
contribute to economic stability.

3.4.1.1

Measurement of Regional Economic Instability (REI)
Regional Economic Instability was measured
based on the following relationship :

REI,

=

{

E,

( (eft - e,t)/ejt )2 / T-2

>1'2

where,

i = 1,2,
ejt =

regions.

total yearly employment for region i at time
t.

ejt =

linear approximation of the long run growth
trend in employment in region i at time t.

T

=

Number of time periods.

Higher values of the REI indicate greater
relative economic instability.

61
3.4.1.2

Measurement of Industrial Diversification (DIf)
An index of industrial diversification (DI) was
calculated for each region based on the following
formulation :

which is equivalent to

Where,
e,j

=

employment in region i, industry j.

e,

=

total employment in region i.

In

=

natural log.

A higher DI, value indicates greater relative
diversification, while a lower value indicates less relative
diversification, or alternatively greater relative
specialization.

After calculating this measure for each

region the next step was to measure the relationship between
regional economic instability (REI) and regional industrial
diversification (DI).
Previous experiences have shown that use of ordinary
least (OLS) to measure this relationship leads to
inappropriate results since the assumption of constant
variance between regions does not hold.

REI models as we

know are characterized by heteroscedastic error variances
and the variances likely decrease with increased region size
(scale effect).

Therefore, our model was build taking into

62
account this fact.

A weighted least squares model (WLS)

which corrects the problem of unequal variances was then
applied.

The model included the correction in weights

suggested by Brewer and Moomaw (1986).
REI, * POP,1/4 = a * POP,1/4 + b(DI, * POP,174 ) + Ei * POP,174
Where,
POP,

=

population in region i.

a,b are estimated parameters.
E,

=

disturbance error in region i.

If the index of diversification (DI) increases in value
as the level of industrial diversification increases, and if
the REI index increases in value as the level of economic
instability increases, then the sign of the coefficient b
will be negative

(b< 0).

If the level of diversification

decreases with higher diversification values, then b> 0
(Kort, 1981).
These indexes of diversification and instability

were

compared with those obtained from the ogive and percentage
of durable goods indexes.
chapter five.

Results are shown and analyzed in

Chapter IV
An Economic Regionalization for Kichigan

The objective of creating an economic regionalization
for Michigan is to provide a tool that allows one to measure
changes in employment in most of the economic activities
within regions (in this case, groups of counties not
necessarily contiguous).

The regionalization

delineated

groups of counties that share similar economic structure in
terms of employment.

4.1

Factor Analysis Results
Michigan's economic regionalization was carried out

through a Q-mode factor analysis approach. This technique
was applied initially on economic information for the year
1988.

Economic activities at the level of three and four

digit SIC codes were grouped into economic sectors.

Q-mode factor analysis provides for aggregation of
counties.

Initially, a correlation matrix among counties is

obtained.

This correlation matrix served as the basis of

the factor analysis. Correlations between two counties
indicates the extend to which the two counties analyzed
resemble each other with regard to employment patterns.

63

64

A point that needs to be addressed is that in this
particular case the correlation matrix may show clusters or
groups of counties that are alike but which have no
particular similarity to those in some other groups.
According to Cattell (1952, p. 92) "...Q-technique is an
ideal method for finding types if such types actually exist
with respect to the variables in question.

The individual

who shows the highest mean intercorrelation with all others
in the cluster is the most perfect representative of the
#
type".

In order to apply the Q-mode technique, the matrix of
information was standardized before starting the correlation
of the counties.

The standardization process transformed

the information into a Normal distribution with mean zero
and variance one.

That is, one was assuming that counties

had the same means and the same variances.

After that,

factor analysis was carried out for the year 1988.

A varimax rotation was done in order to preserve
orthogonal factors.

A number of six rotated factors

(Regions) were considered appropriate to initiate the
allocation of counties for both years.

Allocation of

counties into each factor was based on the highest rotated
loading for the county.

Absolute values of the loadings

were taken into account (see appendix B).

For the year 1988

65
the six rotated factors selected explained more than 95
percent of the variance.

In factor analysis it is important to distinguish the
difference between positive and negative factor loadings.
Factors are usually named according to the majority of
variables having the same direction in relation to the
component.

Some variables could be negatively correlated

with the factor.

So, bi-polar factors will be those that

load positively on some variables and negatively on others.
Factor analysis showed a large concentration of counties in
rotated factor 1 (Region 1).

In fact, sixty one (61)

counties were included in this region. Region 2 included
thirteen counties (13), followed by

Region 3 with four

counties (4); Region 4, three counties, and Regions 5 and 6,
which comprised single counties.

The constraint of contiguity was not considered in this
regionalization due the fact that it was an objective to
group counties with similar economic structure. This type of
grouping does not necessarily lead to contiguous regions,
but rather homogeneous regions.
show our results.

Table 4.1 and Figure 4.1

66
Table 4.1.

Economic Regionalization of Michigan. Year 1988.

Region

Counties**

Region 1

1,2,3,4,5,6,7,8,9,11,12,
14,15,16,17,18,20,21,23,
24,25,26,27,28,29,30,31,
32,33,35,36,37,38,39,41,
43,45,46,49,50,52,53,54,
55,56,57,58,59,60,61,63,
65,68,69,73,74,78,79,81,
82,83

Region 2

10,13,19,34,44,51,62,
64,67,70,75,76,80.

Region 3

40,66,71,77.

Region 4

22,42,47.

Region 5

72

Region 6

48

See

Appendix

A

for

the

key

to

the

counties.

67

rMAIOUCTTK

I OCLTA

C M * * C t V O i* L

I O SCO OA

j

A lC O N A

T(Uy,'

‘ukmmi^ttron jMfllAUKgCl "Q«COM- | QBgMAW I l®*CO
Regi on 1

LA K E

o tc m * »cunt

akiuic

Regi on 2
'o C E k N A ^ W A T K O . ' b E C O JT A | l

J m O l AND
''t u . c o i a

Regi on 3
lMJIKE. 1 -

Regi on 4

KENT

Regi on 5
Regi on 6

,C L I I « T < J l . | * ' ' ' * » *

, OAKLAND
A L L C IA N

, lA R A r

VAN IU N C IA , K A l A U A Z . '

CASS

Figure 1.

VATIC

C A LH O U N * f -M C H O N

HUNCH

Michigan Regionalization.

M IL L tO A L C

L (M A W C (

1988.

|

I

68
Factor analysis was also applied to year 1982 to see if
regional structure for both years (1982 and 1988) were
comparable.

The results showed some differences in the

composition of the regions.

Differences in raw data such as

number of activities involved each year, non available
information etc., made the comparison fairly weak.

So,

results for year 1982 were considered unsuitable for
comparing the two years.
analysis results.

Appendix C shows the 1982 factor

Therefore, year 1988 was chosen as the

base year for our regionalization.

This year provided a

suitable regional framework for the development of indexes
of diversification and instability.

Having identified the

regions, the next step was to assess the economic bases of
these regions.

Chapter V

Regional Economic Base of Michigan

In order to assess the economic base of each region the
economic composition of employment at county and sector
level was analyzed for the year 1988.

Several tables were

analyzed to obtain a clear picture of the economic
composition of each region.

Results of this analysis are

important in order to determine the economic composition of
each region in terms of regional employment.

5.1

Michigan's Basic Activities.
The development of basic activities is an important

component of regional economic development.

Basic

activities in our case are those that export to the outside
world or other states generating significant increases in
value added, services, taxes, residences, etc. for the local
economy.

In this work it is important to consider basic

employment, e.g., the employment engaged in basic
activities.

A location quotient technique6 was used to determine
6 Location quotients were calculated, considering
i = 1,...24 activities (sectors)
j = 1,...6 regions
then,
69

70
basic activities.

Location quotients shows the degree of

specialization of a sector in a region belonging to a system
of regions.

Basic activities are those whose location

quotients were greater than one.

Table 5.1 shows our

results.
A location quotients greater than one indicates that
the activity is providing export jobs.

That is, employment

engaged in exporting activities to the rest of the country
or to the outside world.

Economic base activities in region 1 are located in
sectors 1, 3, 7, 10, 15, 19, 23, and 24 . The reader should
see Table 3.2 (pp. 51-52) in order to identify sector's
names.

Most basic activities here are miscellaneous

industries that usually are linked to some kind of service.

where,
LQ,j = location quotient for activity i and region j.
E-j
= employment in activity i and region j .
Ej
= employment in activity i for theentire State.
E= employment in region j .
E
= employment in the entire State.

71
Table
Sector

5.1

Regionl

.

Michigan L o c a t i o n Q u o t i e n t s .

Region2

1988.

region3

Region4

0.00

0.22

0.00

0.00

0.21

0.00

0.00

Region5

Re g i o n o

sectl

1.11

0.38

sect2

0.84

0.42

sect3

1.01

0.51

0.86

2.02

0.00

9.71

sect4

0.76

3.55

0.00

0.08

0.00

0.00

sect5

0.97

0.51

0.77

2.93

0.00

0.00

sect6

0.86

0.00

1.92

0.0

sect7

1.09

0.45

0.00

0.33

0.00

0.00

sect8

0.90

2.34

0.00

0.00

0.00

0.00

sect9

0.99

1.37

0.00

0.41

0.00

0.00

sectlO

1.03

1.18

0.00

0.00

0.00

0.00

sectll

0.62

4.75

0.00

0.27

0.00

0.00

sectl2

0.65

4.52

0.00

0.00

0.00

0.00

sectl3

0.94

0.56

0.92

3.32

0.00

0.00

sectl4

0.90

2.22

0.00

0.31

0.00

0.00

sectl5

1.06

0.75

1.93

0.26

0.00

0.00

sectl6

0.95

0.68

0.00

2.91

0.00

0.00

~ — — *. 7 *7
O G l r C l /

n
—» -i
\J • / A

4
i
H . U 1

0.00

u

. 12

0.00

u .00

sectl8

0.96

0.48

0.87

3.18

0.00

0.00

sectl9

1.01

1.13

0.91

0.35

0.00

0.00

sect20

0.94

1.08

0 . 57

2.22

0.00

0.00

sect21

0.89

1.68

0.45

1.70

0.00

0.00

sect22

0.94

1.46

0.31

1.29

0.00

0.00

sect23

1.09

0.49

0.61

0.19

1 0 . 47

0.00

sect24

1.09

0.55

0.06

0.21

0.71

0.00

199.74*

110.10*

1088.34*

* Note: These are labor intensive activities such as mining,
crude petroleum production, and timber production
located in low populated areas (Kalkaska, Ontonagon,
Luce, Presque Isle, and Sc hoolcraft).

72
Region 2 is the more diversified in terms of basic
activities because there are more sectors that are basic.
Sectors 4, 8, 9, 10, 11, 12, 14, 17, 19, 21, and 22 are
considered basic.

Region 3, basic sectors are linked to

activities identified as labor intensive (sectors 2, 6, 15).
Region 4 basic activities are located in sectors 3, 5, 6,
13, 16, 18, 20, 21, and 22.

Most of these activities have

been labeled as conventional or traditional.

Examples of

this type of activities are: construction; textile
production; timber production; clay and concrete products
etc.

On the other hand, what has been called new

industries are those characterized by technologies that are
changing periodically (food production processes, chemical
processes and so on).

Regions 5 and 6 are not diversified.

These regions are engage in very few activities.

It is important to note that some service activities
have qualified as basic in the analysis due to the link that
those activities have with the manufacturing sector, some
services are part of the chain that make an activity basic.

An important point to address is the location of the
automotive industry.

It is located

in several sectors of

regions 1 and 2.i.e., sectors 5, 11, 14, 15, 19, 20, and 21
are related to different stages of automobile manufacturing.
One could increase the number of sectors if the

73
distributional component is considered (wholesale and other
services).

Finally, it is necessary to point out that the location
quotient technique is not a quite exact technique in the
sense that it should be applied under very special
conditions.

The location quotient technique assumes that

for a specific region :
-

Locals residents face the same demand schedule that
prevail at the state level.

-

They face the same level of productivity in terms
of output per employee through the system of
regions.
Nevertheless, location quotient technique is an

accepted rough way of delineating basic sectors.

5.2

County Economic structure.
Once regional basic activities were specified, the next

step was to identify basic activities within counties in
order to identify the economic potential of each of them.
Table 5.2, depicts the share of the employment in each
county by sector in terms of basic activities for the year
1988.

The table identifies counties strongly involved in a

specific economic activity or sector.

74

Table 5.2

Percentage of Total Nongovernaental and Nonagricultural
Employment Involved In Economic Base Activities.
Michigan 1988.

SECTOR

ALCONA

ALGER

ALLEGAN

ALPENA

ANTRIN

ARENAC

BARAGA

secti

0.0

0.0

0.0

0.0

0.0

0.0

0.0

sect2

0.0

0.0

0.0

0.0

0.0

0.0

0.0

sect3

0.0

0.0

6.1

13.9

9.8

0.0

0.0

sect4

0.0

0.0

0.0

0.0

0.0

0.0

0.0

sect5

0.0

0.0

0.0

0.0

0.0

0.0

0.0

sect6

0.0

0.0

0.0

0.0

0.0

0.0

0.0

8ect7

0.0

0.0

1.0

0.0

0.0

0.0

0.0

sect8

0.0

0.0

0.0

0.0

0.0

0.0

0.0

sect9

0.0

0.0

0.0

0.0

0.0

0.0

0.0

sect10

50.0

0.0

0.0

3.4

0.0

0.0

0.0

secti1

0.0

0.0

0.0

0.0

0.0

0.0

0.0

8ect12

0.0

0.0

0.0

0.0

0.0

0.0

0.0

sect13

0.0

0.0

0.0

0.0

0.0

0.0

0.0

sect14

0.0

0.0

0.0

0.0

0.0

0.0

0.0

sect15

0.0

0.0

4.6

0.0

0.0

0.0

0.0

sect16

0.0

0.0

0.0

0.0

0.0

0.0

0.0

8ect17

0.0

0.0

0.0

0.0

0.0

0.0

0.0

•.me*
• « T W i Itt

e* m
VeV

** n
W.U

u •u

**

A
u.u

•»

A
u.u

A •»
u.u

Ok A.
u.u

sect19

0.0

0.0

6.3

0.0

9.8

0.0

0.0

sect20

0.0

0.0

0.0

0.0

0.0

0.0

0.0

sect21

0.0

0.0

0.0

0.0

0.0

0.0

0.0

sect22

0.0

0.0

0.0

0.0

0.0

0.0

0.0

sect23

0.0

0.0

8.5

21.4

9.8

5.8

0.0

sect24

50.0

100.0

73.6

61.3

70.7

94.2

100.0

For. Prod.

50.0

0.0

1.0

3.4

0.0

0.0

0.0

Industry

Note:

Sector names are defined in Table 3.2.

75

Table 5.2 ( c o n t'd .) .
SECTOR

BARRY

BAY

BENZIE

BERRIEN

BRANCH

CALHOUN

CASS

secti

0.0

0.0

0.0

0.4

0.0

0.6

0.0

8ect2

0.0

0.0

0.0

0.0

0.0

0.0

0.0

aectS

5.9

6.4

0.0

7.1

0.0

4.8

0.0

sect4

0.0

0.0

8.1

0.0

0.0

0.0

0.0

sect5

0.0

0.0

0.0

0.0

0.0

0.0

0.0

sect6

0.0

0.0

0.0

0.0

0.0

0.0

0.0

sect7

3.0

1.3

0.0

1.7

0.0

0.6

0.0

sect8

0.0

0.0

8.1

0.0

0.0

0.0

0.0

sectR

0.0

0.0

0.0

0.0

0.0

0.0

0.0

sectIO

0.0

0.0

0.0

0.0

0.0

0.0

0.0

secti1

0.0

0.0

0.0

0.0

0.0

0.0

0.0

8ect12

0.0

0.0

8.1

0.0

0.0

0.0

0.0

sect13

0.0

0.0

0.0

0.0

0.0

0.0

0.0

sect14

0.0

0.0

8.1

0.0

0.0

0.0

0.0

sect15

0.0

2.9

0.0

2.5

0.0

1.0

0.0

sect16

5.9

0.0

0.0

0.0

0.0

0.0

0.0

sect17

0.0

0.0

8.1

0.0

0.0

0.0

0.0

sect18

0.0

0.0

0.0

0.0

0.0

0.0

0.0

ssctl?

n a
w•
v

r/
0.0

u.u

6.4

u.u

2.5

0.0

sect20

8.8

0.0

8.1

0.0

0.0

0.0

0.0

sect21

0.0

0.0

8.1

0.0

0.0

0.0

0.0

8ect22

0.0

0.0

24.3

0.0

0.0

0.0

0.0

sect23

11.8

11.2

0.0

12.9

16.4

13.7

13.2

sect24

64.5

71.6

19.0

67.0

83.6

76.8

86.8

3.0

1.3

16.2

1.7

0.0

0.6

0.0

For. Prod.
Industry

76

Table 5.2 (c o n t'd .) .
SECTOR

CHARLEVOIX

CHEVOYAM

CHIPPEUA

CLARE

CLINTON

CRAWFORD

DELTA

secti

0.0

0.0

0.0

0.0

0.0

0.0

0.0

8ect2

0.0

0.0

0.0

0.0

0.0

0.0

0.0

sect3

27.4

16.3

5.3

0.0

0.0

0.0

2.7

sect4

0.0

0.0

0.0

0.0

4.8

0.0

0.0

sect5

0.0

0.0

0.0

0.0

0.0

0.0

0.0

sect6

0.0

0.0

0.0

0.0

0.0

0.0

0.0

sect7

0.0

4.9

0.0

0.0

0.0

0.0

0.0

sectB

0.0

0.0

0.0

0.0

4.8

0.0

0.0

sect?

0.0

0.0

0.0

0.0

4.1

0.0

0.0

sectIO

0.0

0.0

0.0

0.0

0.0

0.0

0.0

sect1
1

0.0

0.0

0.0

0.0

0.0

0.0

0.0

sect12

0.0

0.0

0.0

0.0

0.0

0.0

0.0

sectlS

0.0

0.0

0.0

0.0

0.0

0.0

0.0

sectH

0.0

0.0

0.0

0.0

9.2

0.0

0.0

sect15

5.2

3.5

0.0

0.0

0.0

0.0

0.0

Bect16

0.0

0.0

0.0

0.0

0.0

0.0

0.0

sect17

0.0

0.0

0.0

0.0

4.8

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

A

M

sect18
<*

»

A

A

«

a

A

A

secti
v

?.£

0.3

u.u

u.u

«
t
.
U

UeU

UeU

sect20

0.0

0.0

0.0

0.0

18.6

0.0

0.0

sect21

0.0

0.0

0.0

0.0

15.6

0.0

0.0

sect22

0.0

0.0

0.0

0.0

34.0

0.0

0.0

sect23

0.0

11.4

15.8

17.2

0.0

9.4

15.9

sect24

62.1

55.4

78.9

82.8

0.0

90.6

814
0

For. Prod.
Industry

0.0

4.9

0.0

0.0

8.9

0.0

0.0

77

Table 5.2 ( c o n t'd .) .
SECTOR

DICKINSON

EATON

EMMET

GENESEE

GLADUIN

GOGEBIC

GRAND TRA­
VERSE

secti

0.0

0.7

0.0

0.7

0.0

0.0

0.0

8ect2

0.0

0.0

0.0

0.0

0.0

0.0

0.0

8ect3

15.9

7.7

6.8

8.4

11.2

0.0

11.2

sect4

0.0

0.0

0.0

0.0

0.0

0.0

0.0

8ect5

8.7

0.0

0.0

0.0

0.0

0.0

0.0

sect6

1
.1

0.0

0.0

0.0

0.0

0.0

0.0

sect7

0.0

2.3

1.3

0.1

11.2

0.0

1.4

sect8

0.0

0.0

0.0

0.0

0.0

0.0

0.0

sect9

0.0

0.0

0.0

0.0

0.0

0.0

0.0

sect10

0.0

0.0

0.0

0.0

0.0

0.0

0.0

secti1

0.0

0.0

0.0

0.0

0.0

0.0

0.0

8ect12

0.0

0.0

0.0

0.0

0.0

0.0

0.0

sect13

9.5

0.0

0.0

0.0

0.0

0.0

0.0

sect14

0.0

0.0

0.0

0.0

0.0

0.0

0.0

sect15

0.0

1.3

0.0

2.8

0.0

0.0

1.5

sect16

8.7

0.0

0.0

0.0

0.0

0.0

0.0

sect17

0.0

0.0

0.0

0.0

0.0

0.0

0.0

sect18

8.7

0.0

0.0

0.0

0.0

0.0

0.0

1 *
>

*

«*rHO

0.0

£.0

sect20

18.2

0.0

0.0

sect21

9.5

0.0

sect22

19.7

8ect23

A

w«w

44

A

A

A

«1
>
«
.

v«v

4.o

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

30.1

7.2

11.2

11.2

14.8

10.5

sect24

0.0

51.9

81.4

70.8

55.1

85.2

70.5

For. prod.

1
.
1

2.3

1.3

0.1

11.2

0.0

1.4

Industry

78

Table 5.2 (C o n t'd .).
SECTOR

GRATIOT

HILLDALE

HOUGHTON

HURON

INGHAH

IONIA

IOSCO

secti

0.0

0.0

0.0

0.0

0.9

0.0

0.0

sect2

0.0

0.0

0.0

0.0

0.0

0.0

0.0

sect3

3.8

2.8

10.7

6.1

8.0

0.0

8.7

sect4

0.0

0.0

0.0

0.0

0.0

8.5

0.0

8ect5

0.0

0.0

0.0

0.0

0.0

0.0

0.0

sect6

0.0

0.0

0.0

0.0

0.0

0.0

0.0

sect7

0.0

0.0

0.0

0.0

0.6

0.0

0.0

sect8

0.0

0.0

0.0

0.0

0.0

0.0

0.0

sect9

0.0

0.0

0.0

0.0

0.0

5.5

0.0

sectIO

0.0

0.0

0.0

0.0

0.0

0.0

0.0

secti1

0.0

0.0

0.0

0.0

0.0

4.2

0.0

8ect12

0.0

0.0

0.0

0.0

0.0

4.2

0.0

sect13

0.0

0.0

0.0

0.0

0.0

0.0

0.0

sectH

0.0

0.0

0.0

0.0

0.0

5.7

0.0

sect15

1.7

0.0

0.0

6.1

1.5

0.0

0.0

sect16

0.0

0.0

0.0

0.0

0.0

0.0

0.0

sect17

0.0

0.0

0.0

0.0

0.0

4.2

0.0

sect18

0.0

0.0

0.0

0.0

0.0

0.0

0.0

4
1
«(

rs

VtV

u.u

S.6

5.1

11.2

u.G

sect20

0.0

0.0

0.0

0.0

0.0

12.5

0.0

8ect21

0.0

0.0

0.0

0.0

0.0

11.5

0.0

sect22

0.0

0.0

0.0

0.0

0.0

32.4

0.0

sect23

12.9

11.6

21.7

28.3

16.0

0.0

34.1

sect24

79.8

85.6

67.6

50.9

69.9

0.0

57.2

0.0

0.0

0.0

0.0

0.6

13.9

0.0

Fop. Prod.
Industry

79

Table 5 . 2 (Cdnt’d . ) .
JACKSON

KALKA.

KENT

KEUEENAW

IRON

sect)

0.0

0.0

0.6

0.6

0.0

0.7

0.0

sect2

0.0

0.0

0.0

0.0

90.2

0.0

0.0

sect3

0.0

8.3

8.7

7-7

0.0

11.1

10.0

sect4

0.0

0.0

0.0

0.0

0.0

0.0

0.0

sects

0.0

0.0

0.0

0.0

0.0

0.0

10.0

sect6

0.0

0.0

0.0

0.0

9.8

0.0

0.0

sect7

0.0

0.0

1.2

1.0

0.0

1.2

0.0

sect8

0.0

0.0

0.0

0.0

0.0

0.0

0.0

sect9

0.0

0.0

0.0

0.0

0.0

0.0

0.0

sect 10

0.0

0.0

0.0

0.0

0.0

0.1

0.0

sect 11

0.0

0.0

0.0

0.0

0.0

0.0

0.0

sect 12

0.0

0.0

0.0

0.0

0.0

0.0

0.0

sect 13

0.0

0.0

0.0

0.0

0.0

0.0

10.0

secti4

0.0

0.0

0.0

0.0

0.0

0.0

0.0

sect 15

0.0

1.4

1.6

2.0

0.0

2.7

0.0

sect 16

0.0

0.0

0.0

0.0

0.0

0.0

10.0

sect 17

0.0

0.0

0.0

0.0

0.0

0.0

0.0

sect)8

0.0

0.0

0.0

0.0

0.0

0.0

10.0

sectl9

0.0

1.4

3-7

3.4

0.0

5.4

0.0

sect20

0.0

0.0

0.0

0.0

0.0

0.0

20.0

sect2l

0.0

0.0

0.0

0.0

0.0

0.0

10.0

sect22

0.0

0.0

0.0

0.0

0.0

0.0

20.0

sect23

33.3

14.5

12.6

11.2

0.0

12.3

0.0

sect24

66.7

74.5

71.6

74.1

0.0

66.4

0.0

0.0

0.0

1.2

1.0

9.8

1.3

0.0

For. Prod.
Industry

ISABELLA

KALAMAZOO

SECTOR

80

Table 5.2 (C o n t'd .).
LAKE

secti

0.0

0.0

0.0

0.6

0.0

0.0

0.0

sect2

0.0

0.0

0.0

0.0

0.0

0.0

0.0

sect3

0.0

0.0

19.9

4.8

10.5

46.5

23.8

sect4

0.0

5.1

0.0

0.0

0.0

0.0

0.0

sect5

0.0

0.0

0.0

0.0

9.8

0.0

0.0

sect6

0.0

0.0

0.0

0.0

0.1

53.5

0.0

sect7

0.0

0.0

0.0

0.8

0.0

0.0

0.0

sect8

0.0

0.0

0.0

0.0

0.0

0.0

0.0

sect9

0.0

9.3

0.0

0.0

0.0

0.0

0.0

sectIO

0.0

0.0

0.0

0.0

0.0

0.0

0.0

secti1

0.0

0.0

0.0

0.0

0.0

0.0

0.0

8ect12

0.0

5.1

0.0

0.0

0.0

0.0

0.0

sect13

0.0

0.0

0.0

0.0

9.2

0.0

0.0

sect14

0.0

6.7

0.0

0.0

0.0

0.0

0.0

sect15

0.0

0.0

0.0

1.2

0.0

0.0

0.0

sect16

0.0

0.0

0.0

0.0

9.9

0.0

0.0

8ect17

0.0

5.1

0.0

0.0

0.0

0.0

0.0

sect18

0.0

0.0

0.0

0.0

9.4

0.0

0.0

sect1
9

u.O

7.7

u.u

2.0

U.U

u.u

u.u

sect20

0.0

20.4

0.0

0.0

19.9

0.0

0.0

sect21

0.0

8.5

0.0

0.0

9.7

0.0

0.0

sect22

0.0

32.1

0.0

0.0

21.5

0.0

0.0

sect23

0.0

0.0

0.0

12.7

0.0

0.0

16.8

sect24

100.0

0.0

80.1

78.0

0.0

0.0

59.4

0.0

14.4

0.0

0.8

0.1

53.5

0.0

For. Prod.
Industry

LAPEER

LEELANAU

LENAUEE

LIVINGSTON

LUCE

MACKINAC

SECTOR

81

Table 5.2 (C o n t'd .).
SECTOR

MACOMB

MANISTEE

MARQUETTE

NASON

MECOSTA

MENOMINEE

MIDLAND

secti

1
.
1

0.0

0.0

3.8

0.0

0.0

0.0

sect2

0.0

0.0

0.0

0.0

0.0

0.0

0.0

sect3

12.7

0.0

6.1

9.4

5.1

12.9

18.2

sect4

0.0

9.1

0.0

0.0

0.0

0.0

0.0

sect5

0.0

0.0

0.0

0.0

0.0

0.0

0.0

sect6

0.0

0.0

0.0

0.0

0.0

0.0

0.0

sect7

1.7

0.0

0.8

0.0

0.0

0.0

3.0

sectS

0.0

0.0

0.0

0.0

0.0

0.0

0.0

sect9

0.0

9.1

0.0

0.0

0.0

0.0

0.0

sectIO

0.2

0.0

0.0

0.0

0.0

5.9

0.0

secti1

0.0

0.0

0.0

0.0

0.0

0.0

0.0

sect12

0.0

9.1

0.0

0.0

0.0

0.0

0.0

sect13

0.0

0.0

0.0

0.0

0.0

0.0

0.0

sect14

0.0

9.1

0.0

0.0

0.0

0.0

0.0

sect1
5

2.5

0.0

1.7

0.0

0.0

0.0

3.5

sect16

0.0

0.0

0.0

0.0

0.0

0.0

0.0

sect17

0.0

9.1

0.0

0.0

0.0

0.0

0.0

sect18

0.0

0.0

0.0

0.0

0.0

0.0

0.0

ocv. i 1 7

6.2

r.T

2.5

5.8

u.u

u.u

7.2

sect20

0.0

9.1

0.0

0.0

0.0

0.0

0.0

sect21

0.0

9.1

0.0

0.0

0.0

0.0

0.0

sect22

0.0

27.3

0.0

0.0

0.0

0.0

0.0

sect23

10.3

0.0

12.6

22.1

14.4

23.5

6.4

sect24

65.5

0.0

76.3

61.0

80.5

57.7

61.8

1.8

18.2

0.8

0.0

0.0

5.9

3.0

For. Prod.
Industry

82

Table 5.2 (C o n t'd .).
SECTOR

MISSAUKEE

MONROE

MONTCALM

MONTMORE.

MUSKEGON

NEUAYGO

OAKLAND

secti

0.0

0.8

0.0

0.0

0.0

0.0

0.9

sect2

0.0

0.0

0.0

0.0

0.0

0.0

0.0

sect3

0.0

9.3

2.6

17.5

11.4

0.0

9.3

sect4

0.0

0.0

0.0

0.0

0.0

9.1

0.0

sect5

0.0

0.0

0.0

0.0

0.0

0.0

0.0

sect6

0.0

0.0

0.0

0.0

0.0

0.0

0.0

sect7

0.0

1.3

0.0

0.0

1.0

0.0

1
.
1

sect8

0.0

0.0

0.0

0.0

0.0

0.0

0.0

sect9

0.0

0.0

0.0

0.0

0.0

9.1

0.0

sectIO

0.0

0.0

2.5

0.0

0.0

0.0

0.0

secti1

0.0

0.0

0.0

0.0

0.0

0.0

0.0

sect12

0.0

0.0

0.0

0.0

0.0

9.1

0.0

sect1
3

0.0

0.0

0.0

0.0

0.0

0.0

0.0

sect14

0.0

0.0

0.0

0.0

0.0

9.1

0.0

sect15

0.0

3.4

0.0

0.0

4.0

0.0

1.7

sect16

0.0

0.0

0.0

0.0

0.0

0.0

0.0

8ect17

0.0

0.0

0.0

0.0

0.0

9.1

0.0

sectlS

0.0

0.0

0.0

0.0

0.0

0.0

0.0

sectIV

u.u

4.6

2.5

u.u

5.6

y.i

5.6

sect20

0.0

0.0

0.0

0.0

0.0

9.1

0.0

8ect21

0.0

0.0

0.0

0.0

0.0

9.1

0.0

sect22

0.0

0.0

0.0

0.0

0.0

27.3

0.0

sect23

10.5

10.5

12.1

0.0

10.0

0.0

17.0

sect24

89.5

70.1

80.4

82.5

67.7

0.0

66.4

0.0

1.3

2.5

0.0

1.0

18.2

1
.
1

For. Prod.
Industry

83

Table 5 . 2 (Cont’d . ) .
SECTOR

PRESQUE ISLE

R0SC0MM

SAGINAW

ST CLAIR

ST JOSEPH

SANILAC

SCH00LCR

secti

0.0

0.0

0.8

0.0

0.0

0.0

0.0

sect2

74.5

0.0

0.0

0.0

0.0

0.0

67-3

sect 3

0.0

0.0

8.7

9.6

0.0

0.0

0.0

sect4

0.0

0.0

0.0

0.0

8.1

7-9

0.0

sect5

0.0

0.0

0.0

0.0

0.0

0.0

0.0

sect6

0.0

0.0

0.0

0.0

0.0

0.0

32.7

sect7

0.0

0.0

2.1

l.l

0.0

0.0

0.0

sect8

0.0

0.0

0.0

0.0

8.1

0.0

0.0

sect9

0.0

0.0

0.0

0.0

0.8

5.9

0.0

sect 10

0.0

0.0

0.2

0.0

0.0

0.0

0.0

secti1

0.0

0.0

0.0

0.0

1.9

2.0

0.0

sect 12

0.0

0.0

0.0

0.0

0.0

7.9

0.0

sect 13

0.0

0.0

0.0

0.0

0.0

0.0

0.0

sect 14

0.0

0.0

0.0

0.0

8.1

7.9

0.0

sect 15

25.5

0.0

3.2

1.2

0.0

0.0

0.0

sect 16

0.0

0.0

0.0

0.0

0.0

0.0

0.0

sect 17

0.0

0.0

0.0

0.0

8.1

7.9

0.0

sectl8

0.0

0.0

0.0

0.0

0.0

0.0

0.0

sect 19

0.0

0.0

6.6

3.8

2.6

7.9

0.0

sect20

0.0

0.0

0.0

0.0

11.5

12.2

0.0

sect2l

0.0

0.0

0.0

0.0

18.1

10.1

0.0

sect22

0.0

0.0

0.0

0.0

32.7

30.4

0.0

sect23

0.0

100.0

12.0

14.0

0.0

0.0

0.0

sect24

0.0

0.0

66.4

70.2

0.0

0.0

0.0

For. Prod.
Industry

0.0

0.0

2.3

l.l

10.8

15.8

32.7

84

Table 5.2 (C o n t'd .).
SECTOR

SHIAUASSEE

TUSCOLA

VAN BUREN

UASHTEM

UAYNE

WEXFORD

secti

0.0

0.0

0.0

1.3

0.4

0.0

aect2

0.0

0.0

0.0

0.0

0.0

0.0

sect3

6.8

7.3

0.0

7.8

6.3

0.0

sect4

0.0

0.0

8.6

0.0

0.0

0.0

sect5

0.0

0.0

0.0

0.0

0.0

0.0

sect6

0.0

0.0

0.0

0.0

0.0

0.0

sect7

0.0

3.6

0.0

0.6

1.3

0.0

sect8

0.0

0.0

0.0

0.0

0.0

0.0

sect9

0.0

0.0

7.0

0.0

0.0

0.0

sectIO

0.0

0.0

0.0

0.0

0.0

6.2

secti1

0.0

0.0

2.9

0.0

0.0

0.0

sect12

0.0

0.0

5.7

0.0

0.0

0.0

sect13

0.0

0.0

0.0

0.0

0.0

0.0

sectH

0.0

0.0

6.1

0.0

0.0

0.0

sect1
5

1.9

0.0

0.0

1.7

2.4

0.0

sect16

0.0

0.0

0.0

0.0

0.0

0.0

sect17

0.0

0.0

8.6

0.0

0.0

0.0

sect18

0.0

0.0

0.0

0.0

0.0

0.0

0

fs

1.9

t d.
•s • w

4A
1

sect20

0.0

0.0

11.7

sect21

0.0

0.0

sect22

0.0

0.0

sect23

1
6.1

sect24

73.3

For. Prod.
Industry

0.0

V

» ^

$

f%

tit

VeV

0.0

0.0

0.0

9.4

0.0

0.0

0.0

29.7

0.0

0.0

0.0

16.0

0.0

9.4

14.1

8.6

69.5

0.0

76.1

70.9

85.2

15.6

0.6

1.4

6.2

3.6

85
For instance, sector 2 which is related to non­
renewable resources such as metals, minerals, crude
petroleum etc. is heavily represented in Schoolcraft,
Presque Isle, Kalkaska, and Ontonagon counties.

That is,

these counties have a higher proportion of employment in
these activities compared with other counties in Michigan.
Activities related to food and kindred products (sector 4),
have important relative contribution in employment in Ionia,
Manistee, Newaygo, Oceana, Osceola, and Van Buren counties.

Rubber and leather product industries are important
components of employment in Presque Isle, Huron , and
Charlevoix.

Stone and concrete products industries have

an important employment share in Livingston and Dickinson
counties.

In the same way, activities related to forest products
such as veneer and plywood production are important
components of Ionia and Van Buren counties' economic
activity.

Forest Products Activities (sectors 6 to 12) represent
an important component of the basic economy

of the

following counties : Benzie (16.2 %), Luce (53.5 %),
Manistee (18.2 %), Neywaygo (18.2 %), Oceana (18.2 %), and
Schoolcraft (32.7 %).

The forest products sectors

86
contribute 2% of the state basic activities in terms of
employment.

This figure should be taken as a minimum given

the constraints in the data used.

Tertiary activities such as transportation and public
utilities, wholesale and retail trade, finance, insurance,
and real estate are the most important components in most
counties.

Oakland and Wayne counties are the most important

centers that concentrate most of the heavy, medium, and
basic industries.

5.3

Regional Economic Basic Structure.
In order to accumulate more evidence on Michigan's

economic structure, Table 5.3

was built.

This table

attempts to provide further insights on Michigan's economic
structure in terms of employment distribution.

The share of

each sector in each of the six regions determined in the
factor analysis is evaluated in terms of basic economic
activities for year 1988.

Most sectors are strongly

represented in Regions 1 and 2, while the other regions show
a lower share in the economic employment distribution.

The regionalization provided by the factor analysis
provides a kind of hierarchical classification in terms of
specialization in employment.

Region 1 shows a great

concentration of tertiary activities (F.I.R.E. and other

87

Table 5.3.
Sector

Basic Sectors Share by Region.

Michigan 1988.

Regionl

Region2

0.0
0.0

0.0
96.0

0.0
0.0

0.0

sect2
sect3

0.6
0.0

0.0

0.0
0.0

8.5

0.0

0.0

11.0

0.0

100.0

sect4

0.0

8.2

0.0

0.0

0.0

0.0

sect5

0.0

0.0

0.0

9.7

0.0

0.0

sect6

0.0

0.0

0.0

0.0

0.0

sect7

1.2

0.0

0.0

0.1
0.0

0.0

0.0

sect8

0.0

2.9

0.0

0.0

0.0

0.0

sects

0.0

2.9

0.0

0.0

0.0

0.0

sectlO

0.0

0.0

0.0

0.0

0.0

sectll

0.0
0.0

0.0

0.0

0.0

0.0

0.0

0.0
0.0

0.0

sectl2

3.5
5.4

sectl3

0.0

0.0

0.0

9.2

0.0

0.0

sectl4
sect15

0.0

8.8

0.0

0.0

0.0

0.0

2.2

0.0

3.5

0.0

0.0

0.0

sectl6
sectl7

0.0

0.0

0.0

9.8

0.0

0.0

0.0

6.8

0.0

0 =0

0 =0

0.0
9.4

0.0

sectlS

0.0
0.0

0.0

0.0

sectl9

4.4

4.2

0.0

0.0

0.0

0.0

sect20

0.0

12.7

0.0

19.7

0.0

0.0

sect21

12.7

0.0

9.7

0.0

sect22

0.0
0.0

32.0

0.0

21.3

0.0

0.0
0.0

sect23

13.7

0.0

0.0

0.0

100.0

0.0

sect24
TOTAL

69.2
100.0

0.0

0.0
100.0 100.0
14.7
0.0

0.0
100.0

0.0

0.0
100.0

secti

F . Prod.
Note:

1.3

Region3

Region4

0.1

Regions Region6

100.0
0.0

0.0

0.0

Figures represent percentages of nonagricultural and
nongovernmental employment engaged in basic activities.
Sector
name can be found in Table 3.2.

88
services).

Most populated and developed counties are

represented in this region.

Region 2 includes mostly a group of counties usually less
developed in terms tertiary activities than those in the
first region.

Food and kindred products production, forest

products sectors (except those associated with sawmills and
planning mills) are located in this region.

Chemical and

petroleum products, primary metals industries, manufactures
in general, and machinery and transportation are part of the
region as well.

Wholesale and retail sales are the only

significant component of the service sector in this region.

It can be noted that this region includes counties
whose activities are related to the use of natural and
agricultural resources.

Also, it can be remarked that some

of these types of industries involve production processes
characterized as potentially harmful to the natural
environment.

Likely this region along with region one will

need to maintain suitable environmental regulations that
will guarantee the well being of the people and species
living in the region.

Region 3 is strongly linked to a set of counties
involved with primary activities such as metals, minerals,
crude oil, and natural gas production.

89
Region 4 includes a group of counties engaged in some
basic economic activities.

Construction, textiles and

apparels, printing and publishing, fabricated metal
products, and stone,clay etc. are some of the activities
developed in this region.

Activities of transportation,

some manufactures, and wholesale and retail sales are also
important

components of the economic structure of the

region.

Regions 5 and 6 represent single counties regions with
few industrial economic activities, and are likely engaged
in agricultural activities.
Our characterization of this economic regionalization
for Michigan is :

Region 1 : Services and miscellaneous industries.
Region 2 : Food production, new industries7 (forest
products,chemicals, machinery and
transportation, other manufactures), and
distribution (wholesale and retail sale).
Region 3 : Extractive industries.
Region 4 : Conventional or basic industries and
distribution.
Region 5 : Basic agriculture and forestry.
7 New industries here represent those industries whose
technology is changing periodically. They do not represent
new entering industries in the market.

90
Region 6 : Other basic industries.

This classification could not be taken as entirely
exact, but as an attempt to associate a specific label to
each delineated region.

5.3.1

Delimitation of Planning Regions.

Our economic regions can become functional regions for
policy objectives.

Within each economic region (mainly

regions 1 and 2) counties can be grouped according to
certain characteristics (contiguity, population size or
similarities in the development of certain economic and
activities).

For instance, within a specific economic

region incentive policies for expansion of manufacturing
capacities could be applied to certain counties and not
necessarily to the whole group in the region.

The configuration

or type of planning region defined

within our economic regions is going to depend on the type
of policy

the policy-makers have in mind.

In other words,

given a set of policies to be performed and knowing the
economic regionalization of Michigan, planning regions could
be delimited based on objectives to be pursued by the
policy.

91
5.4

Sectoral Employment by Region.
The distribution of the employment was assessed for

year 1988 and compared with that of 1982.

Table 5.4

indicate the non-agricultural and nongovernmental employment
obtained from our regionalization.

These figures could be

low due to the problems of disclosure that one faces when
using this source of information.

Activities as was expected were concentrated in region
1.

Region 1 contains around 85 % of the State employment.

Region 2's share is 10% andthe rest is distributed among the
other four regions.

The automotive industry is located in

several sectors of regions 1 and 2.

92
Table 5.4.
Sector

Regionl

Sectoral Employment by Region.

Region2

Region3

Region4

Regions

1988.
Region6

TOTAL

sectl

7382

285

0

67

0

0

7734

sect2

5208

295

1650

60

0

0

7213

sect3

97509

5522

110

8907

0

60

112108

sect4

23502

12316

0

120

0

0

35938

sects

56675

3317

60

7826

0

0

67878

sect6

1639

0

145

87

0

69

1940

sect7

2056

649

0

195

0

0

2900

sect8

478

3000

0

0

0

0

3478

sect9

8000

1366

0

534

0

0

9900

sectlO

1634

106

0

0

0

0

1740

sectll

3000

613

0

120

0

0

3733

sectl2

6500

14000

0

0

0

0

20500

sectl3

46421

3104

60

7438

0

0

57023

sectl4

47649

13178

0

753

0

0

61580

sectlS

24830

1958

60

274

0

0

27122

sectl6

56648

4543

0

7917

0

0

69108

sectl7

16059

10216

0

120

0

0

26395

sectl8

49931

2808

60

7545

0

0

60284

sectl9

100601

11692

60

800

0

0

113153

sect20

147421

19091

120

15914

0

0

182546

sect21

90660

19080

60

7852

0

0

117652

sect22

274956

48073

120

17194

0

0

340343

sect23

157163

7799

116

1239

175

0

166492

sect24

791949

45124

60

7063

60

0

844256

TOTAL

2017871

228135

2681

92025

235

129

2341016

Note:

Sector names can be found in Table 3.2. Figures represent only
nonagricultural and nongovernamental employment.

93
5.5

Development of the basic Forest Products Industries:
Comparison of years 1982 and 1988.
The contributions of the forest products sectors

(sectors six to twelve) were analyzed from an economic base
perspective.

Years 1982 and 1988 were analyzed using the

1988 regionalization scheme.

At this stage the 1987 SIC

classes were used to modify the 1982 data to reflect those
standards.

Relative figures were evaluated in order to

avoid likely undercounting in sectorial employment due to
the way our

information was collected and aggregated.

The impact of basic forest products industries was
measured for each of the seven sectors that made up the
industry as a whole.

Table 5.5 depicts the percentage of

change in each sector for the period 1982-1988.

Table 5.5.

Percentage Change in Forest Products Basic
Activities : periods 1982 -1988.

Sector

6
7
8
9
10
11
12

Activity

Percentage
Change
in Employment

Logging contractors
Sawmills and planning mills
Millwork, flooring, structuralmembers
Wood furniture and fixtures
Wood pallets and Skids
Veneer and plywood, other lumber
and wood products
Paper and allied products

86.7
840.6
2503.4
25.7
-19.2
183.6
194.3

94
Most sectors showed a positive increase in
but sector
decrease.

employment,

10 (wood pallets and Skids) had a 19.2 %
Sectors 7 and 8 showed the highest increase while

sectors 6 and 12 were characterized by a moderate increase.
Sector 9 showed a small increase during the period of
analysis.

Summing up, basic forest products employment as a

whole grew overall 2.5 times the employment existent in
1982.

Therefore, forest products activities have made an

important contribution to Michigan's economic
diversification.

Forest product basic activities were analyzed at the
level of county for each one of the six sectors involved for
years 1982 and 1988.

Appendix D, shows maps for each sector

for both years.

Sector 6 activities have improved a little bit in 1988
as compared with 1982.

In this case the possibility of

undercount should be considered (see for example, Chappelle
et al., 1996).

Counties in several regions are engaged in

these types of activities.

Sector 7 is highly related to counties in region 1.
This sector has shown significant increase in 1988
with 1982.

compared

The core of this industry is concentrated in

Michigan's central counties.

95
Sector 8 represents activities carried out by counties
in region 2.

Most counties increased their

shares in the

activity during the 1982-1988 period.

Sector 9 characterizes another activity of region 2
which has been growing during the period of analysis.
Sector 10 is an activity that was carried out in many
counties located in regions 1 and 2 in 1982.

The activity

now is concentrated in a few counties of region 1.

Sectors 11 and 12 represent activities that have been
increasing during the 1982-1988 period.

Region 2 counties

perform these type of activities.

5.6

Comparison of Recent Studies.
Forest products employment for the whole activity was

compared with a recent study developed by Chappelle and
Pedersen (1991). Percentage change between years 1988/1982
(our study) and 1987/1982 (Chappelle-Pedersen study) were
calculated for each sector.

This comparison allows us to

evaluate the precision of our figures since both of them
used Census information.

Table 5.6 shows both results.

Some classes have been adjusted to make the comparison
feasible.

96
Table 5.6. Michigan Forest Products Industry Employment:
Comparison of Two Recent Research Studies.
1982- Base Year
Sector

1991-Study*

1992-Study

% of Change
87/82
88/82
1991-Study* 1992-Study

6- LOGGING

1100

1062

55

73

7- SAWMILLS

2400

1920

8

51

8- MILLWORK

2700

1586

67

119

9- W.PALLETS

7825

9913

7

0.4

10- W.FURNIT.

1500

1199

33

45

11- VENNER..

2900

3489

38

7

12- PAPER...

20000

18482

3

11

F. PRODUCTS

38425

37651

14

17

*

Chappelle and Pedersen, 1991.

From the above table, it can be noted that the
differences

in total forest products is small for both

studies but when the analysis is done by sector in some
cases the differences appear important.

It is likely that

problems of disclosure of some figures and undercounting of
sectoral employment are factors that cause these
differences, along with the way in which the information was
aggregated for each sector.

Nevertheless, the

comparison

indicates a positive growth for all forest products sectors.

97
5.7

Share of rorast Products Ssotors Employment in
Michigan.
Forest products employment contribution to the whole

economy has increased when years 1982 and 1988 were
compared.

Only direct jobs are included here.

Table 5.7

show our figures.

Table 5.7.

Share of the Forest Products Activities in

Michigan Employment*: 1982 and 1988.
Year

State
Employment

1982

2,305,470

37,651

1.6

1988

2,341,016

44,141

1.9

*

Forest Products
Employment

Percentage

Nonagricultural and Nongovernamental
employment. Direct jobs only.

Forest products employment share increased from 1.6 %
in 1982 to 1.9% in 1988.

These figures indicate that the

activity as a whole has increased, although the difficulties
that have been affecting other sectors related to forest
products activities (e.g., automotive industry).

A very low

nongovernmental of economic growth during the last years
combined lately with long period of recession have reduced
the demand for certain goods that use forest products as
input.

Chapter VI
Results

Michigan's regionalization was the key step for
calculating indexes of diversification and instability.
These indexes are the result of several processes of
aggregation.

Starting from a specific sectorization,

counties were grouped by region based on the type of
economic activities they best performed.

Many of these

activities were defined at three and four digit SIC codes.
Hence, it is necessary to point out that our information
could suffer from lack of accuracy in the sense that part of
it could show some degree of undercounting that likely
arises from several sources.

As it is known, County Business Pattern information at
the county level are not published entirely for industries
at four and three digit SIC levels because of disclosure
problems.

This information is included in the total of the

next broader industry (i.e., the two digit SIC level).

This

undercounting required analysis primarily in relative terms.
Nevertheless, County Business Pattern information is
considered "...The only series that provide annual
subnational data by two-, three-, and four digit level of
SIC system.

The series is useful for analyzing the

98

99
industrial structure of regions..." (County Business
Patterns 1988, Michigan, p. 9).

In the case of the forest products sectors a problem of
undercounting of employment has been noted in the number of
establishments for most of the seven sectors that made up
the forest products industry when compared with other
sources of information (Michigan Directory of Forest
Products Manufacturers, 1983 and Primary Wood Using
Industries : Michigan Directory, 1987).

This fact, of

course, affects the number of employees counted in these
activities.

Therefore, one should be cautioned that the

absolute numbers in this study are likely to be low.
However, the rates of change (relative numbers) can still be
useful.

In light of these limitations, measures of

diversification and instability were calculated for each
region for year 1988 and compared with other diversification
measures such as the Percent of Durable and Ogive index.

6.1

Diversification (DIV) and Instability (REI) Indexes
Results.
In the case of Kort's diversification index a higher

value indicates greater relative diversification.

When the

Ogive index is considered it is thought that for a regional
economy to be diversified an equal percentage of regional
employment should be allocated in each industrial category :

100
the greater the index, the lower industrial diversity.

In

the case of the Percentage of Durable, the greater the
reliance on export income, the less diversified the economy
is considered.

Table 6.1 shows Kort's diversification indexes for year
1988 along with the other measures of diversification and
instability that were calculated.

Kort's index indicates

that region 2 is the most diversified.
shown above includes

Region 2, as was

most secondary activities such as food

processing and forest products production, machinery and
equipment, and most of the manufacturing industries.
Regions 4 and 1

follow in the ranking of diversification.

Region 4 represents another important group of
industries(traditional or basic industries) while region 1

Table 6.1.

Population, Diversification, and Instability
Measures by Region.

1990
Population
(thousands)

Region

1988.

Kort's
Index

Percent
of Dur.

Ogive

REI
Index*

1
2
3
4
5
6

8284.6
796.6
44.4
144.2
19.7
5.8

2.2353
2.5285
1.4396
2.3143
0.5681
0.3564

13.4066
23.5503
12.1223
18.7482
0
53.4837

1.0441
0.8963
1.1880
1.1551
1.8334
1.8334

0.0661
0.0690
0.1023
0.1031
0.1294
0.1683

Michigan

9295.3

2.3163

14.6613

1.0066

0.0665

Region
Region
Region
Region
Region
Region

101
*Based on a 1976-1989 employment time series,
includes most of the services.
activities

Region 3, where

are found, is next in importance.

extractive
Finally,

regions 5 and 6 that represent single county regions are the
least diversified, as was expected.
The other measures of diversification (percentage of
durable and ogive) showed a behavior quite similar to that
observed by previous investigations (Kort,1981; Brewer,
1985).

Table 6.1 shows regional indexes of instability

(RE1) that were calculated based on a time series for the
period 1976-1989.

The indexes seems to be strongly related

to diversification indexes. However,inversely related to
Kort's index; positively related to percentage durable and
ogive index.

So, more diversified regions depict lower

instability indexes.

Less diversified regions show greater

REI values in the case of the Kort indexes.

6.2

Results of Hypotheses Tests.
In chapter I a group of hypotheses were put forth as

necessary to meet in order to corroborate the situation of
the regional economic development of Michigan and its
perspectives.

Hypotheses related to change in regional

industrial structure; relationship between regional
diversification and instability; and between regional
diversification and size of region.

Several indicators have

been used to test the different hypotheses formulated.

102
Results of the different tests will serve to assess our
initial statements and to provide policy makers with
additional tools for their decisions.

Hypotheses 1 :

A significant change in the regional
economic structure of State has occurred
during the period of analysis.

Information for years 1982 and 1988 were utilized to
test this hypotheses.

Initially a matrix of covariances for

each year was calculated, since the idea was to compare the
economic structure of the State for those specific years.
Because of the symmetry of each matrix (24 sectors by 24
sectors) only the values below the main diagonal were
considered.

After that a process of vectorization was

carried out to obtain a unique vector for each year.
process permitted comparison of the two years.

This

A Wilcoxon

test for paired samples was performed to test for a
significant change in economic structure.

The test was

considered appropriate since it requires only ranks (nonparametric test).

The SYSTAT computer package was used to

obtain the following results :

H,, : Structures 2 = Structures 8
H1 : Stucture 82 = Structures8

103
Struct82

Struct88

Struct82

0

96

Struct88

204

0

Total

_

Here, the numbers represent cases.

A level of significance of alpha = .05 was considered
appropriate, since it is the usual level used in this type
of test.

Meaning that there is only a 5% probability that

one can make a mistake making the decision (Type I error).
A two tailed test was selected since our hypotheses is
nondirectional.

Here, because of the size of the sample
statistic was standardized (Z values).

the Wilcoxon

The value obtained

was Z = 5.6 which indicates the rejection of the
null hypotheses which means that the economic structure of
Michigan significantly changed over the period 1982 to 1988.

Hypotheses 2 :

There exists a negative relationship between
regional diversification and regional
instability.

The first step in this second case was to run OLS
regressions having REI as dependent variable and each
diversification index as the independent variable in order
to measure the existence of some kind of relationship

104
between then.

From previous works (Kort,1981; Brewer,1985

etc.) OLS estimates seems to produce weak results due to the
presence of heteroscedastic residuals and the recommendation
given has been to apply WLS (Weighted Least Squares) which
eliminates this problem.

Following this path, several WLS

regressions were estimated using as a weight the proportion
of the population recommended by Brewer and Moomaw (1986).
Ordinary Least Squares results did not show signs of a
severe pattern of heteroscedasticity.
Weighted Least Squares was used.

Despite this fact,

The use of WLS improved

meaningfully the models and the adjusted R-Squared changed
in some cases in a significant way.

Table 6.2 depict our

results.

Table 6.2. WLS and OLS Regression of REI and
Alternatives Measures of Industrial Diversification.
Diver.
Index

DIV.
coertic.
WLS OLS

Constant
Term
WLS OLS

Adjust.
R-Squared
WLS
OLS

Kort's -.039 -.037 0. 171 0.164
.986 .763
(-3.70)** (-4.13) (14.49) (10.27)
0.001 0.107 0.084
(1.19) (6.53) (3.53)

F
WLS OLS

174.2 17.1

P. Dur.

0.001
(2.34)

.973

.517

91.9

Ogive

0.084
0.087 0.001 -.008
.984
(3.44)**(4.53) (.026)
(-.32)

.796

155.4 20.5

Note : Number in parentheses are t values.
** Significant at .05 level.

1.4

105

In order to evaluate the degree of normality of our
variables (it is important to recall that we are working
with a small number of observations and the assumption of
normality can not be made) a Kolmogoroff-Smirnoff one sample
test for normality was performed.

At a level of

significance of 0.05 our variables behave normally.
Therefore, parametric tests can be carried out.

Table 6.3

depicts our results.

Table 6.3.

Kolmogoroff- Smirnoff One Sample Test Using
Standard Normal Distribution.

Variable

N of Cases

Max Dif

Prob (2-Tail)

Kort Index

6

0.259

0.730

P. Durable

6

0.261

0.725

O

A
U •£ 9 9

U•

REI

6

0.200

0.932

Population

6

0.423

0.173

DIV82

6

0.235

0.827

J. . .

r<1

Also, the finding that our variables behave normally
allowed one to perform a set of Pearsons' correlation tests.
Given the level of significance (.05), a theoretical value
of r = .729 was obtained from the Pearson's correlation
p
test table.

This value was used to decide if

a

106
relationship was significant.

Values below rp means

accepting of the null hypotesis.

Values above rp means

rejection of the null hypothesis.

The first of this group of tests attempted to measure
the direction of the relationship between Kort's index and
REI.

It is thought that the higher diversification is, the

lower the instability index will be.
our hypotheses would be

Therefore in this case

: H0 :

r =0

HI :

r < 0

A Pearson's correlation coefficient test at one tail
was carried out.

This type of test was used since in this

case one was trying to prove a directional hypotheses (the
existence of

a negative relationship between REIand

indexes).

value of r = -.83 wasobtained.

A

DIV

Thisresult

indicates the existence of a significant negative
relationship.

Hence, the null hypothesis was rejected at

alpha = .05.

By the same token,
the other two indicators.

hypothesis tests were performed for
In the case of the Ogive index,

the hypotheses was that a positive relationship exists
between this indicator and REI.
H0 : r = 0
H, : r > 0

So,

107
The null hypotheses was rejected for a value of r
=.915.

Therefore, in this case the positive relationship

was corroborated as well.

For the percentage of durable the hypotheses was based
on the existence of a positive relationship.

The greater

the percentage of durable the more specialized the economy
will be.

Here,

Ho : r = 0
H1 : r > 0

A value of r = .511 was obtained.

The positive

relationship is so weak that the null hypotheses can not be
rejected.

Hypotheses 3 :

There exists a positive relationship between
regional diversification and the size of
the region in terms of population.

It has been posed by Thompson (1965) and established by
Kort (1981), that a positive relationship exists between
diversification and population size.
of diversification

The 1988 Kort indexes

were compared with the population in

each region to confirm the above statement for the case of
Michigan.

The natural logarith of the population was the

108
used instead of the absolute population in order to avoid
scale problems when compared both sets of data.
Ho :

r ■ 0

Ht :

r > 0

Thus,

A directional hypotheses was postulated at alpha = .05,
finding that r = .788.
rejected.

So, the null hypotheses was

A significant relationship between

diversification and population can be established for
Michigan.

This chapter along with chapter 4 group the main
findings of this research.

Several behavioral hypotheses

for Michigan in terms of economic structure, diversity, and
degree of instability have been posed.

These results will

be the basis for our conclusions and recommendations.

Chapter VII
Conclusions and Recommendations

This work has hypothesized that to achieve a more
stable economy Michigan needs to develop a more diversified
economic structure.

The automotive industry, although

employing a large number of workers and providing an
important source of income, is very sensitive to cyclical
changes that occur mainly due to external shocks produced by
the international economy.

In order to avoid these "swings" of the domestic
economy, the promotion and development of economic sectors
that generate stable employment and income seems to be a
good policy.

Several empirical studies have detected

economic areas that offer potential possibilities for the
State's economy.

The food processing, recreational and tourist,
robotics, and forest products industries were selected as
target industries during Governor Blanchard's
administration.

This study has emphasized

the last area:

The forest products industry.

Several important conclusions were obtained from the
data analysis, indicators, and other types of information
109

110
that were used and calculated throughout the research.

Some

policy recommendations and ideas for future research are
encouraged.

7.1

Conclusions.

7.1.1

Data Requirements.
Despite a large amount of information used to obtain

results, these findings should be viewed in relative terms.
County Business Pattern information, although provided

at

the level of disaggregation required in this research, lacks
accuracy when information at three and four digit SIC level
is used.

This fact restricted development of some results in
absolute terms.

Nevertheless, by using this information

carefully important results were obtained.

Most findings

obtained were based on information at three and four digit
levels.

For that reason indicators were calculated in

relative terms to prevent misleading conclusions.

7.1.2

The Need for Better Information.
Current economic information at county level do not

offer the correct level of disaggregation necessary to
attain good results in absolute terms.

In order to achieve

a more suitable set of conclusions it will be necessary

Ill
that information at county level be more disaggregated when
economic industrial structure needs to be evaluated.

Some

economic variables that do not jeopardize the financial
structure of specific enterprises could be published at more
disaggregated levels than currently.

Complementary surveys

could be carried out periodically for those sectors that
offer potential possibilities of stable growth.

7.1.3

Basie Activities.
The number of employees engaged in basic activities

increased significantly between the two years that were
analyzed (1982 and 1988) for most sectors.

More

specifically, for forest products sectors the increase was
positive, except for the Wood Pallets and Skids sector which
showed decline.

Forest product employment as a whole (basic

and non basic) showed a positive trend when years 1982 and
1988 were compared.

According to our figures it seems that in recent years
governmental and private initiatives have been growing in
such a way that some counties have improved their economic
base structure.

Forest products employment haa benefited

in part by this initiative.

An example of this initiative

is the target industry program initiated during Governor
Blanchard's administration.

112
7.1.4

Michigan's Diversification.
Results for 1982 and 1988 based on Kort's

diversification indexes indicate that the State economy was
more diversified in 1988 compared with 1982.

However, not

all regions in the state increased in diversification.
Regions 1,2 and 4 increased in diversification, while
regions 3,5 and 6 decreased.
Table 7.1.
Region

Kort's Diversification Indexes : 1982 and 1988.
DIV88

DIV82

1
2
3
4
5
6

2.2353
2.5285
1.4396
2.3143
0.5681
0.3564

1.9156
2.1812
1.8443
1.9564
1.4264
1.5408

0.3197
0.3473
- 0.4047
0.3579
- 0.8583
- 1.1844

Michigan

2.3163

1.9449

0.3714

Region
Region
Region
Region
Region
Region

DIFFERENCE
(DIV88-DIV82)

Region 3, which includes oil and minerals industrials
was less diversified in 1988 than 1982.

Differences in

diversification among those regions that showed positive
change fluctuated between 0.32 and 0.36.

From the table,

one can infer that those regions with higher populations
show positive differences in diversification while the less
populated regions were less diversified.
could be playing a key role.

Worker migration

People have not been

encouraged to stay in the region because of the lack of
employment opportunities.

113
7.1.5

Michigan Economic Structure.
The economic structure of Michigan has become more

diversified during the period of analysis.

One explanation

could be that because of extreme dependence of the state
economy on the automotive industry (which has been facing a
recessionist period during recent years), many workers have
searched for alternative employment in other sectors that
are more stable or show positive growth.

A second cause could be that the economy per se is
changing, incorporating new technologies or new activities
that result in a more stable and profitable structure.
Government incentive policies and programs, and private
initiatives could be playing a key role in this case.

For

instance, the Governor's target industry program could have
been an important influence.

7.1.6

Diversification and Instability.
Our findings have shown a significant negative

relationship between diversification and instability, at
alpha = .05 when a Pearson's correlation test was carried
out using the Kort indexes.

This result is supported by our

Weighted Least Squares results which showed significant
increases in adjusted R-Squares.

114
The comparison between the Ogive diversification index
and REI index verified the existence of a positive
relationship.

However, in the case of the durable goods

index, the positive relationship could not be corroborated.
Weighted Least Squares correlation coefficients were
significant for both cases.

7.1.7

Diversification and Population.
A positive relationship between population and

diversification was found, verifying Kort's and Thompson's
premises.

The higher population in the region, the more

highly diversified the region will be.

So, as population

grows, the regional economy becomes more diversified in
order to meet consumption requirements for the different
segments of the local economy and for exporting to other
regions.

7.2

Recommendations and Future Research.
It has been fruitful to work with the information on

the structure of the Michigan economy.

New avenues have

been opened that facilitate planning of programs and
projects with a lower level of uncertainty.

It is

recommended that results of this study be considered when
economic policies and projects are planned.

115
Government and private initiatives should continue in
order to reach a combination of stability and
diversification appropriate for Michigan.

Based on our

results, planning sub-regions within our economic regions
could be delimited.

These planning regions could be made up

of groups of counties that share some characteristics
inherent to the type of policy to be performed.

Planning

regions could be part of a specific target program since
this research has identified the distribution of activities
by employment within each of the our economic regions.

Some counties that have initiated the development of
stable activities in the field of forest products production
should be supported in order to make their economies more
stable. It is recommended that some forest product
activities that have shown stable growth during the last
years be supported.
expansion,

Low interest rates for capacity

tax credit, some market facilities etc. are some

of the potential incentives that government could apply.
Sawmills and millwork activities should be supported.

In the future it would be appropriate to combine this
type of study with an input-output study in order to develop
an analytical framework that allows a more complete analysis
of the Michigan economy.

Regional employment multipliers

that allow the measurement of indirect effects in the

116
delineated regions would be useful in decision making.
Similarly, regional productivities could be measured for the
economic
sectors engaged in the process of diversification (target
sectors).

Variables in addition to employment, such as

income or value added could be useful.

Periodical evaluations of Michigan's economic structure
should be completed. This could support evaluation of the
performance of programs and projects in which the private
sector, government, and other institutions are involved.

Research needed to improve the accuracy of the new
diversification indexes should continue.

By the same token,

instability indexes need to be improved in

the future.

It

will be necessary to assess current indicators and the
information they require.

A future research study that analyzes the stability
of the forest products industry market in Michigan and its
growth perspectives would be the next step.

For that

research, it will be necessary to explore work force,
salary structure, hours worked per week, availability of
technology and resources,

the research could be carried out

at the level of county or MSAs.

APPENDIX A
List of Michigan Counties

APPENDIX A
List of Michigan Counties
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.
30.
31.
32.
33.
34.
35.
36.
37.
38.
39.
40.
41.
42.
43.
44.
45.
46.
47.

Alcona
Alger
Allegan
alpena
Antrim
Arenac
Baraga
Barry
Bay
Benzie
Berrien
Branch
Calhoun
Cass
Charlevoix
Cheboyan
Chippewa
Clare
Clinton
Crawford
Delta
Dickinson
Eaton
Emmet
Genesee
Gladwin
Gogebic
Grand Traverse
Gratiot
Hilldale
Houghton
Huron
Ingham
Ionia
Iosco
Iron
Isabella
Jackson
Kalamazoo
Kalkaska
Kent
Keweenaw
Lake
Lapeer
Leelanau
Lenawee
Livingston

48.
49.
50.
51.
52.
53.
54.
55.
56.
57.
58.
59.
60.
61.
62.
63.
64.
65.
66.
67.
68.
69.
70.
71.
72.
73.
74.
75.
76.
77.
78.
79.
80.
81.
82.
83.

117

Luce
Mackinac
Macomb
Manistee
Marquette
Mason
Mecosta
Menominee
Midland
Missakee
Monroe
Montcalm
Montmorency
Muskegon
Newaygo
Oakland
Oceana
Ogemaw
Ontonagon
Osceola
Oscoda
Otsego
Ottawa
Presque Isle
Roscommon
Saginaw
St. Clair
St. Joseph
Sanilac
Schoolcraft
Shiawassee
Tuscola
Van Buren
Washtenaw
Wayne
Wexford

APPENDIX C
Factor Analysis Results.

1982.

APPENDIX C

LATENT ROOTS (EIGENVALUES)
1

2

3

4

5

3.548

1.882

1.468

1. 058

6

7

8

9

0.837

0.772

0.614

0.567

69.384

11

12

0.315
16

0.291
17

0.083
21

0.059
22

0.024
26

0.010
27

0.000
31

0.000
32

0.000
36

0.000
37

0.000
41

0.000
42

0.000
46

0.000
47

0.000
51

0.000
52

0.000

0.000

13
0.240
18
0.052
23
0.005
28

0.000
33

0.000
38

0.000
43

0.000
48

0.000
53

0.000

14
0.203
19
0.046
24
0.000
29

0.000
34

0.000
39

0.000
44

0.000
49

0.000
54

0.000

10
0, 398
15
0. 112
20
0. 033
25
0. 000
30

0,000
35

0,oon
40

0.000
45

0,000
50

0,000
55

-0.000

56

57

58

59

60

-0.000

-0.000

-0.000

-0.000

-0.000

118

119

61

62

63

64

65

-0.000

-0.000

-0.000

-0.000

-0.000

66

67

68

69

70

-0.000

-0.000

-0.000

-0.000

-0.000

71

72

73

74

75

-0.000

-0.000

-0.000

-0.000

-0.000

76

77

78

79

80

-0.000

-0.000

-0.000

-0.000

-0.000

81

82

-0.000

-0.000

o
o

3

»
d
o
3
£$<$$<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<

3

N H O ^ » sJWU1^UMHO^®>JO\UI*UNHO«)0)'IO\UI^WMHOWO)nJ^U1^WMH

>
3
o
w

H

OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO H
CDU)V0VOO3^)VO<£)OOH^O,
,
4lOU)VOU)U>VOU)lOU)(0U)ti)U>U)U)UIU)U)tOU)VO<OU1UIVOU><OOVOtf)(O(DCDVOOW>U)*>J(O
'>)H>JA>)V0U)MU)UHU)Nj'4U1(7)O\^«>]a)<SNa)U)(D0>U)U)UI>IUIHUIO(VQ)U(O>JU)U)NjU(BtOCD(BlkfrNU(O
MUiC'CJOJ^owuWHOCDOOC'UIHMUI^OJm^-'J*. Ul^W'JOUMn^HUvJ'JOOfOOUWlflUJUOHOVO
*

*

*

*

*

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

«

«

•

•

»

«

•

•

•

•

•

•

120

J> •

1

ii

i

i

iii

i i iiitiiiii i

i

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
*

M

*

»

*

-

*

*

*

O

•

O

M

•

O

•

O

•

O

•

•

O

L

O

•

n

•

O

•

K

)

•

•

O

O

•

O

•

O

•

O

O

•

•

O

O

•

O

•

O

•

O

O

•

•

h

-

•

•

•

•

•

•

•

•

«

»

■

•

•

, O O M O O » - » l - » O O O I - , O

•

O

•

O

•

O

O

•

O

•

•

•

•

•

O

M

O

O

t

•

O

•

O

•

O

•

W

O

O\U)O)«JO\HUHO^OH^^frO'O^^UOUUIMOOtOAHOHNNOO^U^UK)^UUN}'J0tHOt‘OU)O
tO^(^Nj^o\tgvJH(a^tOU^a\U1vJUU^MUMOUUO)^M(a'JU10\UlA(SO}UO\HAO)HHUNJ'^OHO^U

J

JL 1 •
i i
o o o1 o
o o o o o o o1 o1 o o o o1 o o o o o oi o
o o oi io o oi o o oi to o o o oi o o oi o oi oi oi oi oi oi oi oi o oi oi ut
W^yiOWHOOH«)Ul^HHUUHWHMCDOUOHHHUyiOO»'JU'400»W^NW(OHUUlDinUO\OU)WO
9tvOKJ^U)UUl^HU^UlNJUlN]^0000)OPU>^ODOttOOU»U1(OOOlH^O]CtU(OM(OUIK)UIMVOU1UllJHtO>J
*

*

M

O

*

O

*

O

*

O

•

O

O

»

O

•

•

O

O

•

U

*

l

•

O

•

H

•

»

O

•

«

O

O

•

O

•

O

•

O

•

O

•

O

O

•

•

O

O

•

O

•

O

•

O

•

O

•

M

O

•

•

O

M

•

O

•

O

•

O

•

O

•

>

O

•

•

I

-

•

*

•

O

•

O

•

I

-

•

•

•

•

•

•

•

•

•

•

•

l O O O f - * H O I - l O O K > l - 1

IKJNWN}HHHHHHMHHI-'«)(DNJ(MnftUNH
IIIIS S IIIIS IIS IIIS I55S
UNH0U)0*JI^U1^UNP 0 s
-

<

I

I

I

I

I

I

I

O

O

*

I

1

t

o
o
o •o •o ■o •o •o «o •o •o •o •o •o •o • o • o • o • o • o # o • o • o
•
•
•
O

O

O

O

O

O

U

I

O

M

O

O

O

O

O

O

O

5 ISSS SS ISS SSIIS 5 S S IIS S IS 5 5 I I S 5

O
OCDOO'sJ'sJ'Kl'sJ'sJ'sJ'sl'J'sJ'sJOiatOlOlOtatOa'iCnOtUlUtUIUlUlUlUl
NPOVOOOv)OtU)^UIOHOU)0)Nj^U1j)>UN}HO<i}CDvlChUl^U

— ww

O

O

M

O

I

a

ll

ll

•

•

•

•

l i i i

•

•

•

•

•

•

•

•

•

■

•

i i i

•

•

•

•

II

•

•

•

•

#

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

*

*

I

I I

I I I

I

I

I

I

I I I

O H M O M O A M H O O H H H W O H O H P H W U t O P W U H U O
« H I O U I O O O H M > I U I O l N H » 9 l < l « U W H O a i O I M > H M a W

O O H » O O O N > H O O O K > O O O H I O O O O H I - * I - *

III

»

o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o

a

0)09(OOAtOI->OUlUl090U^O^HUUI^NOl
t-tOJ^UiatWOttOOiVOOtMfeOOUtCDOUIIoaouiHtO

I

•

DOlvlMO)»»«J>lieUO^O'10I>lftO»»OUHOOIOID'Jffl

I I I

O O O O O O O O O O O O O O O O O O O O O O O
• • • • • • • • • • • • • • • • • • • • • a *
O O P O O O O M H O O O V - , O t - * O O O O I - * O l - * U > ©

N)>JUnAMIOHPNO9)HHOUOtUAUl^0lNl
OU}IUVOVO^^M0^09^(JIUlHO)^CnO\UNl>JO

I I
O
O

O
O

O
M

I I
O
O

O
O

O
O

I
O

O

O
M

I I
O

O

O
O

O
O

I I
O

O

O
O

O
O

I
O

O

O
O

|

O
O

O
O

P

O
O

O
O

O
O

O
M

O
O

OU^HO)0>JUH^U(OinUH<»«MkN(0(SO)

^sJOUyiHCDOHlDWHHH*Jl
t»WsJUl1
(k(DMfvJ

u>

I

I I I

I I

I

O
o

O

O

O

O

O

O

O

O

O

O

O

•

•

•

•

•

•

•

•

•

•

•

•

I
O
•

I I I

O
•

O
•

O

O

•

I I

O

•

O

•

O

•

O

a

O

*

*

O
*

O
*

O
*

O
*

O
*

O
*

O
*

O
*

O O O O t O O O O O O O O O O O U U I O O O O O O I O O O O W O O
C I O O U I U I M > > I U I O O 9 l l > U I O M M O f r O « 0 t U < J K I I M I I U H H

OXJIOONODUU^OlUDUH^HUHOIODU^^kOlUaP

III
O
•
C

I

O

O

O

O

O

O

O

O

O

O

O

O

•

•

•

•

•

•

•

•

•

•

•

•

O

O

H

O

O

M

O

O

O

O

O

O

I
O
•

O

I I

O

O

•

O

O

•

M

C

I

O

•

O

•

J

O

•

O

M

I

O

•

O

•

O

•

O

O
•

O

O
»

O

N

I

O
•

O
•

H

O
•

O

O
•

O

O
•

O

O
•

O

O

M^OHUIOtK)NAUIftOHO^HOO(OK)<»M(DAHUlV>ftOO)

sIUINMHK)UIOPO)UUUIOD(0(O<^IOOOMO)^N*JU)>1OU1MV>

121

•

•

uiou)aH'J>iftaa)atauftiouuato^o)oO'JH'Jvia>'i»

o o o o o o o o o o o o o o o o o o o o o o o
•

•

*0tOIOtOO)U)(BU)U)U)U)U)OU)O)\l(O(OU)(OU)vO*>l,
>
4vOtO<
>J<OU>M)
<ncD(nCT1~joifoi-*oiOJ'jvimcD>o<»uia)o\a>oiu'OUitnw<£ioii-*~)

O-J-JOOlHUmWUlOUU-JWUIIMIIMUlOPM

•

o o o o o o o o o o o o o o o o o o o o o o o o o o o o a o
•

O

cowNior*to*fO®iNOncot^**ino>H<*HOmmr-iotfoinofviCM«^r®Hinconc4C\ocia**or*.c'*inioin«-iooiovoif>HOi
ffl^ooonfsONN^^ooONMnojoooiononvJ^^C'O^h^tninonNHnacoHinHrjH^NntDconhcD
O

O

O

O

O

O

O

O

O

O

O

O

O

O

O

O

O

O

r

H

f

H

O

O

O

O

O

O

O

O

O

r

-

I

O

O

O

O

O

H

O

O

O

O

O

O

O

^

r

O

O

O

O

O

O

O

O

O

O

O

O

o o o o o o o o o o o o o o o o o o o o o o o o o o o o o c o o o o o o o o o o o o o o o o o o o o o o o o o o
I

I I I

I

I

I

I I

I I

I

I

I I I

I I

I

I I I

I I

c trv iO if M
r >or n
g oori' *if on jnH®i n ® o ® a t < N H ® v o o r - i o t r H o \ v o c o a t r j c > j a t r o c H r - i v o r ^ c H m ® o c \ i n i n c t ® v o < N C N j o < \ i o t
M
O ^Mvnooc \oi ^i onvoo ^

r^ooiooin<n<H0tHnoimon®<MS0in®in<MU>'?o4r,
)rtMU<
)®oinu)oi®r'Oinn<-<

( H O O f H O O O O O O O O O O O O O O O O O O O O O

l

O

O

O

O

O

O

r

H

O

O

H

O

O

O

O

O

O

O

O

O

O

O

O

O

O

H

O

i

H

O

O

O

o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o

122

I

III

I

I

I I

I

I

I I I

II

I

I

I II I I

OHr)<OdMiiAr)O)DOOhOionHoi«hH(O^niOhn<
<i(OHVoi(DinOHO(D®QHNNHOriH«o^r)o^v0(fla
C N O O O O O O O O O O O O O O O O O n O O O O O O O O O O O O O O O O O O O O O O O O M O O O O O O f H C N O r H O O
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 O O O O

III

I I II

II

II

IIIIII I I

CM ® o H ® in H o (N VO n at r* o at CM m in in H o rl O at CN VO ® in
*3*o ® o
oCO CO n o N o
CO n o H CO t
o <N a
\r* N t
o CO N in O
O o o o O o o o H o ©
H o o o o o O O o O O o o o o o O o
® ® • • • • • • • • • • • • * • • • • • • • • • • • • • • •
O o o o 0 o O 0 0 o 0 o o o 0 o o o 0 o o o o o o o 0 o o o
1
1
1
1
1 1
1
1

I

II

icon(j>^,ocjmrs
-'Ocjrjinnr,
'ifiOnn»foj
in^'tor*NnH(n^H\orxNOhc^(j»^,a
>
>OHOOOOO«HOOOOOOOO«HOOO

^in®r*®ovoHf'in^*tf>®r»®cnoHCM®<fin®r^®o»OHMr)^in®r^®o>o»-iM®^in®pt
e®'3rorTMln?irriorvm*Sr
njNtNWN<Mnnnnnnnnnn^«f'«f^<f^'»^^»riftinininininiftinifl(n«5vo»o»o«o«)«)W(o»oK^Msr»rvrs|s.fs.rs.
1

w w > w w w < w w % - * « — - w * w —» s - * > w w w

r w

< » > w

w

w w

< > w w

w

w

* w

w ^ w

i « * s - . w

w

w

w w

w

w

w » « . w . w \ - . ^ > - . « « . w

- w

w

w

L . w

w

L * L «

123

VAR(80)
VAR(81)
V A R (82)

-0.001
-0.026
-0.029

-0.043
-0.019
0.090

0.056
0.008
0.034

0.011
0.046
-0.119

VARIANCE EXPLAINED BY COMPONENTS
1

2

3

4

5

3.548

1.882

1.468

1.058

6

7

8

9

0.837

0.772

0.614

0.567

2

3

4

5

4.326

2.296

1.790

1.290

6

7

8

9

1.020

0.942

0.749

0.691

69.384

PERCENT OF TOTAL VARIANCE EXPLAINED
1
84.614

2212!SlS£SSiS52^22!2SSSEJS5I;ti,
''5'^0,,,00'0,0'®0'‘0r<,,r''0,,in,»or>®o,off'^»<or>uip»r»
0000000000*HrHOOrHOOOOOrH(Mr-lf-««HOOIr-l(NOr-ir>OrHr-IOO«HOOr-*^OOr-<<M<NOOOOO
o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o

Aciriioiniriininin<n<ooo>-icoin'«>-ioitor'n(ooa>i'>r-i<o<oinr>«i-iuto^<o<-)0€OK>(»r<notTro>HcDr40<o
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
r^O
O
H
rlO
i-IO
i-tO
rtriO
nf-IC
M
rtf-laH
nO
M
O
clrlrtO
O
M
O
nr^r)

■»

n

°V ?V ?I ?I ?V ?I ?I ?I ?I ?I ?I ?V ° I 9I 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
|
|

f)^nHHHr4H<0IAhhHNC0O(DV>O^nrl<fl0Hr4Q^^'r>M*O<0n^<ClAr4nnnH(0v0in(04l
H4,{DH^
•^Mr4ff^«^«-4r4r4H*-l«-««HH«HOOOr40(MCIOOOC>HOr>'OOMOOOOOOHCs>O^OOHOOHr-liH(np-i
.
010 0I0 0 0II0 0 0I0I0I0I0 0 0 0III
0 0 0 0I0I0I0I0 0•
O

O

O

O

O

O

O

O

O

O

O

O

O

O

O

O

O

O

O

O

O

O

O

O

O

-

O

I

O

O

O

O

O

O

O

O

O

O

O

O

O

O

O

O

O

O

O

O

t

O

t

O

O

O

O

O

O

O

H

O

O

O

O

O

O

O

O

O

O

O

O

O

O

O

O

O

O

O

O

O

n

O

O

t

O

t

O

O

O

O

O

O

O

O

O

O

O

O

O

O

O

O

O

O

O

O

n

h^^otoioiq)ANcoo^N«eooyot(hr>nHHvotai h .conOQ<finininNMri(oo(OinHoo>oo(OcD\a«A
222 222 222 222 222 222 2n2 2 : 2!2 2S2 H 5 " ,H!jl0O0>0'™*l0r'™'®'''*°***2XS3
124

o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o

«22£££I5£22222222223!23t220'®w,®,or*H ® ® ot',',owo'»Nr<<»iincon«>«i«.1Hr,HfM
£2£22S225£2£22fcC:tSJ21S2!2S2222r,2!^0 0 0 ® ® ® ® ® r"<,'0“>,f*'*,»r>Nr*',*^o«»>

9O tO Vw O tO^vtwinCnOtOlOtOlOt0IOtOlOlOI0tOIOtOtO)0tflflOlO)Ol0tOtO)<OCOOC0Q)O(D(OCO(D8O(DCDCD(B(Dnfliis
• • • • • • • » • • • •

.

o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o

CO

o

2
H

—

3
Q

mmmmmtmmmmimmUmimmmU

2
t
a
H
<
H
o
«

o

t** x ^ ®

M

x.’fl* n h H O t O t O O I A ' f t O O k ^ H t ^ t t t s H A

o

Ok ( 4 t O t t l N n i f l

_

q

a

_j

" 2 . ^ 2 . " 2 . 2 . d i d ” ” »®*"W"«nSSSo.£S5>?SS3HS!SP«SS;!

■0

■0

■0

I

I

I

I

( O n H i n ^ H ( O H O r l H < n M H < C
C O « f H m ( M O H O ( M M H O r l d n

o o o o o o o o o o o o o o o
•
• « • • • •
•
•
•
•
•
•
•
•
o o o o o o o o o o o o o o o

0
0

0

CO CO in CO in CD H CM CO co o rH 10
O CM CM *T»H
o CM in CM o d
o O o o O o o O O O o o o o
O O
O o
o
o o o
II

rl co co co V0 co o\n co CM CO
CO CM 10 H t**n h O co CM H o
rH CM rH CM rH
CM H O O rH H
• •
o o O o O O O O o O O3 O0

•

•

I

•

•

•

•

I

•

•

•

•

I

•

•

•

•

•

•

a

*

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

r^r4co9totr^^o\oin<HininH^inook^i^in(Doar^r)^ocDr^Ofo
inokOttotoHot^otcftconcocicott^ftoioiocoiftotHtoetoim
O

O
•

125

O

O
•

O

O

O

•

O

•

O

O

O

f

H

•

*

•

•

O

O

O

O

O

O

*

O

O

•

O

O

H

•

O

O

•

O

H

•

•

O

O

O
•

O

H
•

O

O

i

H

O

•

•

•

•

•

•

•

•

•

•

•

•

•

•

O

O

O

O

O

O

O

O

O

O

O

O

O

O

H

O

O

n

O

i

n

O

O

O

O

^^hHinMftQO«IA«OU>OriVOOkHHinH«OOthU>OhOO(0^
(n»coHn«hno»»hffl(*)NOho>«nHN^oo»rtOH^on
ocMTrroco^rroocNto^j'Otn.H^r-vo^t^inin'Or^r^invovo^rmvo
•

O

•

O

•

O

•

O

•

O

•

O

•

O

•

O

•

O

•

O

•

O

•

O

•

O

•

O

•

O

•

O

•

O

•

O

•

O

•

•

«

•

•

•

•

•

•

•

•

O

O

O

O

O

O

O

O

O

O

O

rtOhH'OMflnh.yj^Hto^heonh^incocoin
MOnHnvOOOCDHtOCONNincOHOOOhHO
OOOOOOOOOOOOOOOOOOOOOOO

O O O O O O O N O r t r l H r l N H O O H N O O r l H

^nvowHttpjinwhNinmhHomHconHrno
r-U)HOfMtor^t^oroHHOioO(Njoco^r^r-tnr^
O V O i H i H O O O O r H O r H O i H O O m O O O O ^ r O O

o o o o o o o o o o o o o o o o o o o o o o o

^ CD r^ov^rtr^nHinor^n^^Trf-i^o^rr^o^oo
H

n WoinHri'Of'jcoHfiHHffl’t H r j o ^ h O n

O

w . ., . . . .
N O H O

O

O

C M O H O C
• .......

O

•

CM rH m o CO 10 10 10 in CM CM
O
o\co CO rH 10 CM r> in CO
r- Ol CO M* CO V CM CO
SO 00 10
•
• • • • • • • »•• » •• •>»•• »•• » •'
o o o o o o O o o3 o0 o0 O0 0

»»
•

’I
•

'I
•

V
•

I '
•

W
•

%~
•

» ' ■ ( ■ > I I I ' » I w w w
w
•
•
•
•
•
•
•
•
•
»

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

O

I

O

O

O

M r
O

H O
O

O

till

O

O c

O

O

O O
O

O

O O
O

O

t *O

O O
O

II

O O
O

O

O

O r
O

H O
O

(

O

III

OrH^coor-co'DcMCM^TfonrMr-co^yor'-co.-Hr'-r-'
a«oH<oHnofMOdcio^Hn«rj(ou>Mn(rno

O• O •O O
O O r H O r H O O C M O r H C M O C O O H O O r H# O#
• • • • • • • • • • • • • • • a * * .
o o o o o o o o o o o o o o o o o o o o o o o

n ^i no he o o » OHNn5 f i nv o he o f t OHP) n^m« h( o o » OHN

tr>tnmminininovx)voK)vo«)vDoo\or'fs*r*r>*rs*r*r*r«*r^r**®coa)

o ^^^
HNn^invhOAdddHddddHHMrjNN

l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l

I llp |l|s s s ls ls is g la a ls

1I1IIIIIIIIIIIIII1IIIIIIII1I11II1IIIIIIII1IIIIIIIII11I11
> f l « ' J O \ U l A U M H O y ) © ' 0 ( ^ U l ^ U M H O ' O t O > J < f t O l i » U K J M O t f O ) s l » W * » W M M O » 0 © > 4 ( f t U I ^ U | O H O y ) t t ’slO»W^

■0

o o o o o o o o o o c>o o o o o o o o o o

o

0
0

oo ooo
o
oo•
oo•
o o •o •o o
o
• •
• • • • • • • • • • • • • o
• • • • • • •
1 • o*
• •
O O H O O t O H O U H o H c»o o o o o o o o o o H o o l-» o H o o o o o Mo o o o o o o H
4
k
A Q X O f f i a i O f t H O l O u ch vo o* to OS U
i 014kin01*
0\ CD U
1OS in 0s 00 00 VO w'O1000 VO H OV 09 M
in 4*''JiouvovocDoin fO 00 h ' H ^ W
09 09 M0S VO 00 to to VO h* 01Mo 00 0V inin o o 09into in V
O

W H H M O l O » m i

It

I I

I

I

I I I

1 1

II

o o o o
• • • •
oooo
*-»OV 00 o
4k H in H

1
1
1
1
o o o o o o o © o o o o o o
• • • • • • • • • • • • • •
oooooooooooooo
o 0V 0V o in M in M OS o 4» u H
A A OS M
09 CD lO in -o VO in u

1 1
1
o o o o
• • • •
ooou
in H in VO
to VO o 00

oooooooooooooooooooooooooooooooooooooooooooooooooooooooo
O

O

M

C

o

o

o

o

J

O

M

M

O

O

O

O

O

O

O

O

O

o

o

O

O

o

o

O

O

O

o

o

o

M

O

O

M

O

O

O

f

O

O

O

O

O

O

O

O

O

O

O

M

J

O

O

O

O

O

O

O

O

O

O

O

O

O

O

O

O

MUUH^^»0y^^0)(JHHNlU>J0sU>UlU1OUlM^0aHNlU>l>l>IQ9O0^^Ol0tUlK)O«J\0^UI^>)^00Nl>|sJ0t09>lH
U10^'4-slOO\03(OtOlSJ(M£)ON)^«0«OQ)UIHIOMMW^UI(BO)M»^HU<gOUslO)U\0(DH«JO)^(OOU)00^0)UlOH

o

o

0

M

09H*
o

o

o

»-» o w o o
4k 4k u H
< VO in h

to09o

u

o

ooooooooooo oooooooooooo
o o o o o o »
-»o o Ho o o o o
o o o to o o M M
os in 4kos VO 00 4k M kj 4k u o o OS o os H
w OS in to u ino in vj o
inin U> 0S os OS o 4k o os VO to M1000 00 o os o 00 VO in u VO VO 00 o
o

o

o o o
VO V0
u

0

o

0

o

0

o

0

o

0

o

to to o o to to O H» o
s o o
© H*
o
4k to
u> vj H 00 00 OS u •O 4k 00 u> 4k in
in vl in
•o u> 10 09 to 4k 00 w
VO 00

ooooo
»-»o o o M
10
o 4k 00 in
o in OS 4k VO

126

o
o •o •o •o ■o •o» o• o• o• o• o* o* o• o• o• o
o
o
o
o «o •o •o *o •o• o♦ o o o o o o o
•
•
•
*
•
•
•
• ■ • • • •
O O O O M H O O O O O O O O H O O O O O O O O O O O H O
oooooo
U I H O U l f l v H O U O t n f f i ^ M O O U I M O W U l U I H ^ O H H W O
0V 4k 10in o> u
N j f O U ) O U U l ( O ^ W C D s J O « W U ) s J O > C D ^ I ^ M U C \ U C D < ^ W < k *o to 4k to 4k H

127

VAR(80)
VAR(Sl)
V A R (82)

0.083
0.140
0.094

-0.033
0.024
0.023

0.077
0.069
0.069

0.062
0.058
0.252

3

4

5
1.135

VARIANCE EXPLAINED BY ROTATED COMPONENTS
1

2

39.679

26.706

2.604

4.565

6

7

8

9

1.668

0.754

1.444

1.574

PERCENT OF TOTAL VARIANCE EXPLAINED
1
48.389

2

3

4

5
1.384

32.569

3.175

5.567

6

7

8

9

2.035

0.919

1.761

1.920

APPENDIX B
Factor Analysis Results.

1988.

appendix b

LATENT ROOTS (EIGENVALUES)

2

3

9.803

3.868

6

7

8

1.078

0.651

0.245

1
62.176

11

12
0.042

0.065
16

17

0.007

21

0.003
22

0.000
26

0.000
27

0.000
31

0.000
32

0.000
36

0.000
37

0.000
41

0.000
42

0.000
46

0.000
47

0.000

0.000
51

52

0.000

0.000
56

57

-0 .0 0 0

-

0.000

128

13
0.030
18

0.002
23

0.000
28

0.000
33

w.OCG
38

0.000
43

0.000
48

0.000
53
0.000

4
2.931
9
0.190
14
0.019
19

0.001
24

0.000
29

0.000
34

0.000
39

0.000
44

0.000
49

0.000
54
0.000

5
1.782
10
0.098
15
0.009
20

0.000
25

0.000
30

0.000
35

0.000
40

0.000
45

0.000
50

0.000
55
-0.000

58

59

60

-0.000

-0.000

-0.000

129

61

62

63

64

65

-0.000

-0.000

-0.000

-0.000

-0 000

66

67

68

69

70

-0.000

-0.000

-0.000

-0.000

-0 000

71

72

73

74

75

-0.000

-0.000

-0.000

-0.000

-0 000

76

77

78

79

80

-0.000

-0.000

-0.000

-0.000

-0 000

81

82

83

-0.000

-0.000

-0.000

O
o
2
o

55
M

§O

H
55

C
%
Cf
l

? P ? P P P ? P ? ? ? ? ? ? 0 0 0 0 0 0 0 0 0 0 ® 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

}3222222222!£!£225!££55£}0'I>,0'D'0,0'0,0''0'0'0®'0®®®'D,<>'D®®®®®'0® ® ® ,0®'PU

???P?.0 ? ? ? P ? 0 0 0 0 0 ° 0 0 ° 0 ° 0 0 0 ° i o o o o o o o o o o o o o o o o o o o o o o o o o

u S 2 m 2 2 2 2 2 2 2 2 S 2 K ,^25?|]i!®®0*m '
nIUHi;|,Un,® o%j® ® ® u,® u,o^®^|^ oo,n,l,® ^ m

I I I I

|

|

I

I

I I I

I

???P????. 0 .
0?000000000000000l30000000000000000000000000
1
^
-_
* * * * * eevw • • • • • • • • • • • •
•#•••••••••••••#••#••••••
o 2 u 2 S 2 2 2 2 5 S S 2 2 C
! ° P 9 0 0 0 0 0 <?H0()0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2 K S S 2 S S S S i f ^ 2 5 ^ ' ,M', 9|M
'J* 0 » * 1
ll«>
'1
>HM
OUIU*UOkM
UHUUONHOPNU«0»

? ? ? ? ? ? ? ? ? ? ? 0 0 0 0 0 ®0 0 0 0 0 0 ®®°t , 0® 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
^
»
«
»
*
*
*
t
» > « « « « «
t
I
> «
t
»
t
>
t
I
«
I
I
I
«
I
♦
t
t
I
H
O
O
O
O
O
O
O
O
Q
Q
Q
Q
Q
Q
Q
M
Q
Q
Q
Q
f)noW
tJ^iLJl_*t-»^ OOOOM
OOOOOOOOOOOOOOOO
M
M
M
K
)H
H
M
H
H
U
M
K
)ftU
U
A
U
llkM
N
|U
N
U
O
O
U
^>H
U
U
IAU«JUK)UtO>iUNOtM0MtOUIAOOOl»O
U
^H
O
BO
BflBC
O
^^^flJW
O
lW
goW
U
^W
sJft^H
llU
IIO
M
O
l OUIO«OO»^MHN^^O«»OtUIOOOO<0

1

■kii■
>
«m ia •
*

.
«

I.r
«z

r v’— •
— — v»«i«t*iw«k*»"wiviivw(uwh*wuwr,
*u/UHU!0'40WHNHUWU0

U U M l O I O l O m i f l s l H M l O O ' l U U U U O M l O S l M W W H O A U v l O H U K - H I O O l U H I M J I O O M O m U l U U O l U l

130

Li2i!SSSSSSi,i!l,!l!J- W W H U U N M M O W W H i ' * O H O M H H H H O H H O H O O O H O O O O O O <*''

nr^ior^inn'OMMH^fonMinoconoroinH^rt^nnoin^m
n«nOhMNaoo«N*«HriniAnnriHaooHriinaHin
r

l
•

H
•

O
•

O
«

H
•

N
•

H
•

O
•

n
•

N
•

O
•

O
•

H

O

•

•

O
•

O

O

•

•

O
•

O
•

O
»

O
•

O
•

O
•

O
•

O
•

N

n

O

O

H

a

•

•

■

•

•

•

o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o
III

I I

II

I I

I

I

I I I I

«»«Nio«oninioaoahhnn«Non«iMihnnoini»r'

HnnHnaaonnrivmnvnHMinnonvnririinrii'i*
HriHOHOnOONNnOHHHONIINn«NnnNNNn«H
•

•

•

•

•

■

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

a

*

010I0 0I0I0 0I0I0 0 0 0 0t0 i
0 0l0 0
0 0 0 0 0 0I0
0 0 0 0I0
0
l
I
I

hHn«NIA«HlflOthOIAA4r4(OnHOfl«rlA<tOt9tlA«f)0
mmnoon^f-tointtoi^or'f^r^tttnOfHtHid^pinHr-tinrvkir^

OOOOOOOOHHOOOOOOMOHOOOOHHOlOtaaHO
•

•

•

•

•

•

*

*

•

•

•

«

•

•

•

*

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOo

131

I

I I II

II

III

II

III I III I I

oihHnoiAiAconotfvionHoi^o^^HhnoohhonMri
a>coM>«OMnsor^a>*-«r^r>ir<«f-<f-iinoeDinr^^oiin^onn<o«oc^
NW«wnnH^ntff««vD®^ioinheohhh«rirtnnc n
•

•

•

•

•

*

•

»

•

•

•

•

•

•

•

•

*

•

•

•

•

•

•

•

•

•

•

•

•

«

<

OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO
II

II

I II I I I I

I II I III I

I

Hnwnino<onoiHisnHHOiOMs««NH

«S222!Ji3inNOnOHp'N ®oo'ino
O

O

O

O

O

O

O

O

O

O

O

O

O

r

H

O

O

O

O

O

O

O

O

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1I

II

III

III

w
wO
w OwOu Ou O
uO
u oO oOoOo Oo O
O OOOOOOOOO O
OO
OOOO
o o o o o o o o o o o o o o o o o o o

I

W

W

W

W

W

W

W

W

S3S5333SS555SSj25S32235SSS5S55&S3

I

o o o

I

eofomo>moi^SHSa)SP?“ S 2 S S ^ 2 2

W

*

W

W

^

W

W

W

W

III B l M

n

^

~

IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII
w

W

^

H

^

U CD Ul V) " J ^

o

U O Ot

o o p p o o o p o o o o o o p o p p o o o o p o o o c j p o o o o o o o o o o o o o o o o o o o o o o o o o o o o
W N U I W O W O H M f t O O ^ » O ^ D D O W U U > l H U N « ) D O l M g s | g ^ % J U | M O I H » > I M O I | k H S ! o H W « I j l u o S o O

132

22S«SS£J2SSf:t;Kf;r0 0 0 ^ ^ 0 0 ^ M 0 0 » O O O O O O O O O O O O O O O O O O O O O M O O H O O o b

133

VAR(40)
VAR(77)
VAR(71)
VAR(48)
VAR(72)

0.033
0.069
0.045
-0.751
0.105

-0.015
-0.035
0.123
0.027
-0.076

VARIANCE EXPLAINED BY COMPONENTS

1
62.176

2

3

4

5

9.803

3.868

2.931

1.782

3

4

5

4.660

3.531

2.147

7

6
1.078

0.691

PERCENT OF TOTAL VARIANCE EXPLAINED
1
74.911
6
1.299

2
11.811
7
0.784

p p o o o o o o o o p o o o o p o p o p p p p p p o o p o o p o o o o o o o o o o o o o o o o o o o o o

«ooMSNMSSSssAA"SaSSSoooMiSiSiSS!S»uiSi>skkisisiskis'o'‘>'o'o'p'o'o'o'D'D'D^>
M M U H.>iwo>w <»w «Hw*.oMo»s,^o«.SMMlS5!££SS221323oS{SgS5SS25"S®5S3"

134

p p p o o o o o o o p o o o o o o o o o p o o o o p o o p p o p o o o o o o o o o o o o o o o o o o o o

o o o o
2
US HSH° H 2 l 5 I I H s S § § g § S S S S S g ^ g 2 g g g g S S g 3 § g g 3 S : 3 g g ° « 3 g ° o o o o o
H

o p p p o p o o o o o o o p p o p o p o o p o p p o c o o o o o o o o o o i i o i i A i i i A i i i i i

WoS»Mo2 52ggggg2S!2S5222gSS:;:£:gS2t;iigS22Sg2gggCK5K§S5S§S
o o o o
• •
OOOOOOOOOOOOOOOOOOOOppppopOOOOOOOOOOOOOOOOOOOOOO
o o o o
vj u m Ul
CD (4 tflro

SS2§fr!ooSSuo2Sfc23*HSwoiIS!!o!i!2o*Mi!uS222SSO O H ^ 0 ° 0 0 0 0 0 0 0 0
WU,W M»UIU|wHID01^*fc0ooOM>»»lOU.MUvJflUJo.«JJoS5 3«S!i!!S“ oIoSSSS22Si52

it

iii

O

O

O

O

O

O

O

O

O

O

O

O

O

O

O

O

O

O

O

O

O

O

M

O

O

O

O

M

M

O

O

O

O

O

O

O

O

O

O

O

O

O

O

O

•••••••••(*••«•«•••••»

U O ) O ^ I U I U I M U « W H M M M O O O O O M M I - >

II
O
*

O
*

O
•

O
»

O
>

O O O O O O O O O O O O O O O
• • • « * • » • • • » • • • ■

O
H
I

O O O O O O O O O O O O O O O O O O O O O
M M O O N M M M H I O M M M M M U M N M M M
D I I I k K ^ H H O M U O O O O H H H H H H M M

■
III
I
H
9U^UHOHH^JOO)OIIO«O«O>IOtO<4O^AUIOft0t^U*»
U 9 O ) H U Q » O » O O H U I A ^ I * 0 k a « > < f t O N l M H O A O M O M U H P
O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O
•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

*

«

•

«

•

•

•

•

•

•

•

•

UJMOOOWUWttMOl^OlOOH^OMC DOH^M^Ol^UK)^

1 III
o«
»«
o o
o
• •
o oo oo
o Li ui m a
•

11 1
oooooooo
•••••••
At O I O i O O O O O
uuo>a^uiuH
OtOOOkOtUO^O
•

1 1 1i 1 1
o
oo o
•o
• •o o
• • • •
ooo oJ
obu
oH
uim m hi
H
09 Nj * *b o o

1I•
O O
o o
•
•
• •o •
»
o
o
o
o
Jb (*>H M w
* u A 09H

111
1
ooo o o
o
• • • • • •
o o©ooo
90 Ulo 90 o M
90 H -4* <
71U

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O
• • ■ • • • • • • • • • • • • • • • • • • • • • • • • • • • A

NC
tBO\DOlOOO»UOHMOOMIiHHOUHNONNH»H»
UOiAtfHOOAOttfOaftHUUUUIkUlOUI^UIHHUlVCBftU
«OOOUIUIMs|pOO«HAOlOOkHOflBM0MUMNOMUl^^NA
II

II

1

1

1

o o o o ©
•
•
o o o o H
u
* o o CO
* b K ) vl > 1 u

1

1

o o o o o o o o o o o o o
o * o
o 90 M o o o H o o
u 09 O H 0 9 09 O o u hi 1 0 o o
0 1 to 4 b hi to H
o hi u o Ui 4
4b

135

O
•

I

O
*

OOOOOOOOOOOOOOOOOOOOOOOOOoinAjlIJLJLJL 1 * 1 I '
I I I III
oobbbbbb'oHOMoeoo
S S l j i j o S S K i i ^ ^ a ^ ^ H w o S S u S M S o M S S S S S S S poooooo
t ----------^OH*4HOHHHOOMO^MOUIOAOMH*
'j^uwaHffl^iootHui^owwuoMwumuosooSSu 0HUAKIOt»«UI»HOtt^OnAU)WUlQBHO
o S S S S S S 2 ° ° ° ° ° ° ° ° ° ° O M O O O U O o o o Q Q n o L ‘ ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? *o o

136

137

V A R (42)
VAR(47)
V A R (22)
VAR(72)
VAR(48)

0.037
0.024
0.033
0.003
-0.001

-0.016
-0.029
-0.199
0.028
-0.960

VARIANCE EXPLAINED BY ROTATED COMPONENTS
1

2

51.680

16.548

6

7

1.443

0.715

3

4

5

3.979

5.426

2.498

3

4

5

4.795

6.537

3.010

PERCENT O F TOTAL VARIANCE EXPLAINED
1
62.265

1.738

2
19.937

0.861

APPENDIX D
Forest Products Basic Industries by County.

1982 and 1988.

138

u u

n u u

IMIT |

>l a m * " K W I

0.3
Region 1
Region 2
Region 3

’j

Region 5

'UMTIIMI

*(ni
iTT
jmttVMU•(««-*<
Note :

Figures represent percentages of non agricultural
and non governmental employment.

Figure ID.

Sector 6: Logging contractors.
county share.
1982.

Basic industry

139

,iau N i a i c

•IITA

l
O
t
e
o
o
A,A
k
C
o
a
*
.
M
H
i
i
r

yglouw.'tuaiv■*•*»
• t e i t u t cc*nt

Region 1
Region 2

«<*t»

>< n « n i I U M U < l w u n

Region 3

u*.

Region 4
Region 5

M U M

Note

:

J w lllM U

IM M tC

,

Figures represent percentages of non agricultural
and non governmental employment.

Figure 2D. -Sector 6: Logging contractors.
county share.
1988.

Basic industry

140

1 uicc

0.5
25 • 6«*<M*oi.ciA?r

L

_

2.2.

’• S i ^ U | u ^ r o - i(i.e«Q. , «.co.«

i«m»
«
1

•ICCtUAi

Region 1
Region 2

L mmw,lOUH.'ouiiv i»
«
*
e
*

'tl A V V M ‘ * « ( ■ ! £

'KUIUtV«H ^ICMTI^e^Wui
v

Region 3

I
/tiko»

’i

k
w
a
i
c
.*

0.4

Region 4
Region 5
Region 6

IIHIU ,
«
•
»
*
•
*

Note

:

Figures represent percentages of non agricultural
and non governmental employment.

Figure 3D. . Sector 7:
Sawmills and planning mills.
industry county share.
1982.

Basic

141

inner i

14 .9 jom.0 'ZeZZ>T,««><

III'

uuuu)

,
0
>
e
»
« ,UCM1

Hi.im

■ r n * l " ' “"

[_J

fnuwai
I ■
I I

'^ M a i u . r r . IO IC S N . ' g | | | | |

I IIW

M C U U I all(

Region 1
Region 2
•C U M , « » « • •

^7,

l m C » |T l ” | l^ * * I L > > l* i « L * « i "1

1 3.0

Region 3
M ffltC . '

Region A
Itr.eui*

H

Region 5

rm

‘

Reelon 6

(■AUNT

3.0 | 2.3 • 0.6

••■anal

*”
Note

:

_______ ,__________j _ _

Ili^MHilwV^.lUIIII

Figures represent percentages of non agricultural
and non governmental employment.

Figure 4D. Sector 7: Sawmills and Planning mills.
industry county share.
1988.

Basic

1.1

142

l t m w tu r 9 m

Region 1
|

Note

|

Region 2

j^j

Region 3

^

Region 4

^Ej

Region 5

rm

R#<Hnn 6

:

i

i

,
1

•
i

Figures represent percentages of non agricultural
and non governmental employment.

Figure 5D.

.Sector 8: Millwork, flooring, structural menbers.
Basic industry county share.
1982.

143

jOTWMiMONnaeaJ

j^]

Region 1
Region 2

j^j

Region 3

IMUa|«*HW 1KfC91T1
i

!

•

!

Region 4

EE]

Note

:

Region S

Figures represent percentages of non agricultural
and non governmental employment.

Figure 6D. . Sector 8: Millwork, flooring, structural members.
Basic industry county share.
1988.

144

f
U
C
i
i
a
M
r

jOTMM 'm h t M A j

|j

Region 1

j

Region 2

|

Re8i°n 3
|^j

Region 4

|

Region 5

|

r m

Note

:

i

i

i

•

*i

•

Region 6

Figures represent percentages of non agricultural
and non governmental employment.

Figure 7D.

Sector 9: Wood furniture and fixtures.
industry county share.
1982.

Basic

145

IIAIaaa

^oieooA ,aicoaa

iirn>d’o«
L
A
I
C

Region 1
Region 2

mk*«T•quma*i■
<

oicioui m u

I
" •t
I
ocum

"
t
"••

•— ■
T I C C O I A ! ® * ML4C .

Region 3

,C5e9 *

i
H
U
I
K
C
.'

***

Region 4
Region 5

rm

Rafflon

6

j.ffllA ,ALtNTM)1

'5;5--'|4.lii
I.,

|CARRT ,

I

Jrail

i

1UUII ,
»
•
*
*
•
«

Note

:

Figures represent percentages of non agricultural
and non governmental employment.

Figure 8D.

.Sector 9: Wood furniture and fixtures.
industry county share.
1988.

Basic

146

\AIM* j
[0.6 |KH»Ot.C««»T
' " • * “^
•CITA

- * U • 1 r* — —
—
IL.._j
®
1
*
*
*
0a
i
t
e
a
r
a
o
A
t
I
A
a
r
a
i
a■
1
,_ _ /

_l :_!

u u w n leuarm,cinM , u r n

pntMoqioumT'ouw**!i*Ha
riCM»«lT4Mki

Region 1
Region 2
Region 3

/tetut,e*iTw'1we*mr1**•**-*■*

J

0 ._5

0-;7>}l:'6v--.'0.3 !

^MtKiTtl.

hvoi'rcAta

u t n a n **•■■*»

Region 4
r.i

Region 5

*

i

0, 1

i w •

*

”T
c
i
i
i
i
t
o
V
"
1
*
*
*
-

- ,
1 I
■o.jjl__:___ ;
__
u u

t u

I

ml,

j

HIM

J

K i m

< « « «

HHO Wu
Note

:

“ *»

r« n T i w i * * T"«

LU»ft

Figures represent percentages of non agricultural
and non governmental employment.

Figure 9D.

-Sector 10:
Wood pallets and skids.
industry county share.
1982.

Basic

'

147

I««•*«*

lill

lOICOVA,AUM*
'

!50.0

■IKW.l|UUI >

tmuu

«ieouia l u

Region 1
Region 2
■ m tm

' • ( c a n < i I M ( | u < li M * k i n

Region 3
Region 4
C tllT W

Region 5

rm

oi-3'] o.i !

!

;

fs

uuua

,imr J«
*
»
■
• J>
■
■
■
*
»

M C llO f t

C lll

Note

:

'■ U N T I U V |

RlAftCN #H *iH

Figures represent percentages of non agricultural
and non governmental employment.

Figure 1 0 D . . Sector 10:
Wood pallets and skids.
industry county share.
1988.

Basic

148

luu

|

j

j

Region 1

\F]

Region 2

j^ )

Region 3

i

!

1

i

t

Region 4
|

|

IIII

Region 5

Ruairm ft

c*L*mm (vac**o» Vuim***j

Note

:

Figures represent percentages of non agricultural
and non governmental employment.

Figure 11D.-

Sector 11:
Venner and plywood, other lumber and
wood products.
Basic industry county share.
1982.

149

OKTMi

1 lU C I li

Iff
^ ““Iciwnw'
i i r n n i , M > Jm

Region 1

1411

u m zd

• o sc m . ' n u n

, K U “ I C l* « l

I i* i m

'« I M » I »

Region 2
Region 3
•■trim **•!•*■

Region 4
UMJtf
C lIN T M

Region 5

rm

0.2

, mum*

•hum

C H I

l l t J H I M I I .O C N

I*.

‘I

•L.-9i
Note

:

i

;
, t „ T :I**

'W i l l

1

i

Figures represent percentages of non agricultural
and non governmental employment.

Figure 12D..

Sector 11:
Venner and plywood, other lumber and
wood products.
Basic industry county share.
1988.

150

^C
V
H
I
M
r

pTMMjwHTHOtLj

L. . !

'
r
i
i
a
i
r
r
i
i
»
*
*
»
•
•
*L

T'o.et.ur,

Region 1
p~~T|

Region 2

^

Region 3

*

*

I

Region 4

Note

g

Region 5

ITTl

Region

:

AtfUl,

6

Figures represent percentages of non agricultural
and non governmental employment.

Figure 13D. . Sector 12:
Paper and allied products.
industry county share.
1982.

Basic

151

I

IILTA

CulUmiL

•O W M . ' | U U I I "

Region 1
Region 2
««'« l
n
c
c
n
r
<
i
lu
*
i
u
4
l
H
i
L
M
i
' n u i i i | ,i",Llc

Region 3
[■•arum

'•■“

Region 5

;..»rur,

..cuaroa

5; 1 '

1.4
Region 6

i laaar

,

CAlAM**.’ U k l

j rat»i«

Uii»;

5.7

Note

:

Figures represent percentages of non agricultural
and non governmental employment.

Figure 14D. - Sector 12:
Paper and allied products.
industry county share.
1988.

Basic

LIST OF REFERENCES

References

Alexandersson, G. 1956. The Industrial Structure of American
Cities. Lincoln. University of Nebraska Press.
Bahl, R., Firestine, R., and Phares, D. 1971. Industrial
Diversity in
Urban Areas : Alternative Measure and
Intermetropolitan
Comparatione. Economic Geography,
47:414-425.
Blanchard, J. G. 1987. Michigan Renewable Resources
Development Initiative. State of Michigan.
Board, J. and Sutcliffe, C., 1991. Risk and Income Tradeoff
in Regional Policy : A Portfolio Theoretic Approach.
Journal of Regional Science, 31(2) : 191-210.
Borts, G.H. 1961. Regional Cycles of Manufacturing
Employment in the United States. National Bureau of
Economic Research. Occasional Paper Number 75.
Brewer, H.L., and R.L. Moomaw. 1986 Regional Economic
Instability and Industrial Diversification in the U.S.
Comment. Land Economics, 62(4):412-415
Brewer, H. L. 1984. Regional Economic Stabilization : An
Efficient Diversification Approach. Journal of Regional
Science, 25(3): 463-470.
------------- 1985. Measures of Diversification Predictors
of Regional Economic Instability. Review of Regional
Studies, 14(1): 8-21.
Brown, D. J. and Pheasant, J. 1985. A Sharpe Portfolio
Approach to Regional Economic Analysis. Journal of
Regional Science, 25(1): 51-63
Cattell, R. B., 1952. Factor Analysis : An Introduction and
Manual for the Psychologist and Social Scientist.
Harper & Brothers. New York.
Chappelle, D.E., S.E. Heinen, L.M. James, K.M. Kittleson and
D.D. Olson. 1986. Economic Impacts of the Michigan
Forest Product Industry: A Partially Survey-based
Input-Output Study. Research Report 472, Agricultural
Experiment Station, Michigan State University, E.
Lansing, 17pp.
Chappelle, D.E. 1986. Predicting Michigan Production
levels to Meet Forecasted Demands for Certain Forest
152

153
Products.
Manufacturing Services Bureau, Michigan
Dept, of Commerce, Lansing, Michigan. 43pp.
Chappelle, D.E, and H.H. Webster. 1987. Regional Economic
Impact analysis and Target Industry Identification in
state Economic Development: The Case of Michigan.
Review draft. A summary of this paper was presented at
the annual meeting of the Western Forest Economists on
May 5, 1987 at W el c h e s ,Oregon. 38pp.
Chappelle, D.E., and H.H. Webster.
1988.
Forest Resources
as a Regional Economic Asset in the Lake States.
In
W.E. Shands (Ed.).
The Lake States Forests.
A
Resource Renaissance.
Report and Procedures of the
Great Lakes Governors' Conference on Forestry.
April
9-10, 1987, Minneapolis, MN.
The Conservation
Foundation, Washington, DC.,pp. 36046.

----------. 1990. Effect of Economic Structures and Resources
on Regional Unemployment within the Lakes States. Paper
presented at the annual meeting of the midwest Forest
Economists, Madison, WI, August 22, 1990. 33pp.
Chappelle, D. E. and Pedersen L. D. 1991. Economic
Contributions of Michigan's Forest: Progress During
the 1980s and Future Prospects. Research Report 514.
Michigan State University. Agricultural Experiment
Station. East Lansing. 17pp.
Conroy, M.E.
1975.
Regional Economic Diversification.
Praeger Publishers, Inc., New York, 163pp.
------------ :— 1974. Alternative Strategies for Regional
Industrial Diversification. Journal of Regional
Sc i e n c e .14(1):31-45.
Cutler, A.T., and Hansz, J.E. 1971. Sensitivity of Cities to
Economic
Fluctuations. Growth and Change 2(1):23-28.
Czamanski, S. 1973.
Regional and Interregional Social
Accounting.
D.C.
Heath and Co.,
Lexinton, Mass.
Data Resources, Inc.
1985.
Secondary wood/processing
Remanufacturing Demand Analysis and Forecast.
Final
Report submitted to Office of Business and Community
Development, Michigan Department of Commerce, 167pp
(Appendix, 105pp)
(June).
Engerman, S., 1968. Regional Aspects of Stabilization Policy
(chapter 9). In Needleman.
Regional Economics.

154
Penguin Modern Economic series.

Baltimore,

pp.

277-

334.
Executive Office of the President.
Office of Management and
Budget, 1972. Standard Industrial Classification.
Washington D. C.
Executive Office of the President.
Office of Management and
Budget, 1987.
Standard Industrial Classification.
Washington D. C.
Ewing, D. P. et al., 1989.
Corporation. Indiana.

Using 123 :

Release 2.2.

Fisher, W.D.
1969.
Clustering and Aggregation in
Economics. The Johns Hopkins Press.
Baltimore,

QUE

195pp.

Florence, P. S. 1948. Investment Location and Size of Plant.
Oxford University Press.
Cambridge.
Garrison, C.B. and Paulson, A.S. 1973. An Entropy Measure of
the Geographic Concentration of the Economic Activity.
Economic Geography 4(4):319-324.
Hackbart, M.M. and D.A. Anderson.
1975.
On Measuring
Economic Diversification.
Land E co no mic s:51(4):374378.
Harman, H.H.
1967.
Chicago Press.

Modern Factor Analysis.

University of

Harris, R.I.D., 1986.
The role of Manufacturing
Growth. Regional Studies 71(4): 301-312,

in Regional

Hedderson, J., 1991.
SPSS/PC+ Made Simple.
Wadsworth
Publishing Company.
San Diego, California.
Heinen, S.E. and K.S. Ramm.
1983.
Michigan Directory of
Forest Product Manufacturers.
Michigan State
University (Cooperative Extension Service and Dept, of
Forestry) and Michigan Dept, of Natural Resources (Div.
of Forestry), 136pp.
Hoover, H. 1963. The Location of the Economic Activity.
Knopf. New York.
Hoover, E. and Giarratani, F., 1985.
An Introduction to
Regional
Economics.
Third Edition.
Knopf.
New York.
Isard, W . , 1975.
Introduction to Regional Science.
Prentice- Hall.
Englewood Cliffs, NJ. 505 pp.

155
Jackson, R.W. 1984. An Evaluation of Alternative Measures of
Regional Diversification. Journal of Regional Studies
18 (2): 103-111.
James, L.M., S.E. Heinen, D.D. Olson and D.E. Chappelle.
1982. Timber Products Economy of Michigan. Research
Report 446. Michigan Agric. Exp. Sta., E. Lansing,
24pp.
Joreskog, K.G.
1979. Advances in Factor Analysis and
Structural Equation Model. University of Upsala Press,
Sweden, pp242.
Keinath, W.F. 1985. The Spatial Component of the PostIndustrial Society. Economic Geography, 61: 223-240.
Kim,

J. 0., 1978. Introduction to Factor Analysis : What
is and How to Do it. Sage Publications, Inc.
San Francisco, California.

Kmenta, Jan. 1981. Elements of Econometrics.
Inc., New York.

Macmillan

Kort, J.R. 1981. Regional Economic Instability and
Industrial Diversification in the U.S. Land
Economics 57(4):596-608.
Kurre, J.A. and Weller, B.R. 1988. Regional Cyclical
Instability: An Empirical Examination of Wages, Hours
and Employment Adjustment, and an Application of the
Portfolio Variance Technique. Journal of Regional
Studies 23(4): 315-329.
Markowitz, H.M. 1959. Portfolio Selection. John
Sons. Inc. New York.

Wiley &

McLaugling, G.E. 1930. Industrial Diversification in
American Cities. Quarterly Journal of Economics.
45:131-149.
Michigan Department of Natural Resources, 1983. Michigan
Directory of Forest Product Manufacturers. East
Lansing.
-------1987. Primary Wood Using Industries : Michigan
Directory. East Lansing.
Moriarty, Barry M. 1986. Regional Industrial Change,
Industrial Restructuring and U.S. Industrial Policy.
The Review of Regional Studies 16(3):1-9.

it

156
Nourse, H.O., 1968.
York 247 pp.

Regional Economics.

McGraw- Hill.

New

Obiya, A. 1986. Effects of Resource Constraints on the
Expansion of the Pallet Industry in Lower Michigan.
Ph.D. Dissertation, Michigan State University, East
Lansing.
Pedersen, L. and D.E. Chappelle. 1988. The Economic
Importance of Upper Lake States Forest Resources:
Modelling Lake States Forest Products and Services.
Iq W.E. Shands (Ed.). The Lake States Forests. A
Resource Renaissance. Report and Procedures of the
Great Lakes Governors' Conference on Forestry. April
9-10, 1987, Minneapolis, MN. The Conservation
Foundation, Washington, DC., pp. 167-194.
Pedersen, L., D.E. Chappelle and D.C. Lother. 1985. The
Economic Impact of Lake States Forestry. An InputOutput Study. Gen. Tech. Rep. NC-136, North
Central Forest Experiment Station, Forest Service,
USDA, ST. Paul, MN, 32 pp.
Pedersen, L. and D.E. Chappelle. 1990. Economic
Contributions of the Lake States' Forest Resources,
1982-1985. Northern Journal of Applied Forestry
7(8):10-13.
Pedersen, L. 1990. Use of IMPLAN to Estimate Economic
Impacts Stemming from Outdoor Recreation Expenditures
in the Upper Lake States. Ph.D. Dissertation, Michigan
State University, E. Lansing, 247 pp.
Perkinson, L.B. 1971. Determination of Relationships
between Local Government Services and Socioeconomic
Structures in Michigan. Ph.D. Dissertation, Michigan
State University, E. Lansing, 164 pp.
Richardson, H.W., 1969. Regional Economics.
Publishers, Inc. New York, 457 pp.

Praeger

Rodgers, A. 1957. Some Aspects of Industrial Diversification
in the United States. Economic Geography 33:16-30.
Roepke, H., Adams, D., and Wiseman, R., 1974. A New
Approach to the Identification of Industrial Complexes
Using Input/Output Data. Journal of Regional Science
14(1): 15-29.
Siegel, R.A. 1966. Do Regional Business Cycles Exist?.
Western Economic Journal (now Economic Inquiry ) 5:
44-57.

157
St. Louis, L.V. 1980. A Measure of Regional Diversification
and Efficiency. Annals of Regional Science 14:21-30.
Thompson, W.R., 1965. A Preface to Urban economics.
Johns Hopkins Press.
Baltimore.

The

Tress, R.C. 1938. Unemployment and Diversification of
Industry. The Manchester School 9:140-152.
Ullman, E.L. and Dacey, M.C. 1960. The Minimum
Requirements
Approach to the Urban Economic Base.
Papers and Proceeding of the Regional Science
Association 6:175-194.
University of Michigan.
1985.
The REMI Resource Package
for Michigan: Capabilities, Products, and Information
Base.
The University of Michigan, Ann Arbor, 21 pp
(s a m e ).
Wasylenko, M.J. and R.A. Erickson. 1978.
Economic
Diversification: Comment.
54(1):106-111.

On Measuring
Land Economics

Webster, H.H. and D.E. Chappelle.
1989.
Community and
Regional Economic Growth and Development.
In
Ellefson
(Ed.)
Forest Resource Economic and Policy Research:
Strategic Directions for the Future.
Westview Press,
Boulder, CO, pp. 263-275.
Wilkinson, L., 1989.
the PC. SYSTAT,

SYSTAT : The System for Statistics for
Inc.
Evanston. IL.