THE DELMARVA PENINSULA:
PLANNING FOR INEVITABLE CHANGE
by
KENNETH GUY COOPER
Submitted in Partial Fulfillment
of the Requirements for the
Degree of Bachelor of Science
at the
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
June, 1972
Signature of Author.-
Department of Urban Studies and Plannin,, May 12, 1972
Certified by.............................. .
...
)
Thpsis Sup;/yisor
Accepted by
Chai
an,
epP
ntal Committee on Theses
Rotch
JUS2NT4. 9
JUL 24 1972
kJBRARIE
2
ABSTRACT
The Delmarva Peninsula has, until recently, remained a relatively isolated
and rurally-oriented region.
It must now face problems of high unemploy-
ment, a large unskilled labor force, and the transition into an era of
increasing exposure to, and the influence of, the more highly developed
areas that surround it.
The wide range of problems and issues that must be examined in the study
of a regional system such as Delmarva necessitates the use of a methodology-a framework for analysis--that is both comprehensive and flexible.
The
concept of system dynamics is implemented in building a computer simulation model of the dynamic interdependencies within this system.
model is
centered around population migration,
The
employment patterns,
and
social policy development.
It
is
concluded that a general policy of steady growth and "balanced devel-
opment" should be followed for Delmarva.
Specific policy alternatives ex-
amined for their relative effects over time are:
active drives to periodi-
cally attract large employers to the Peninsula; job training programs for
unskilled, low-income workers; provision of low-cost housing; and combinations of these programs.
Thesis Supervisor:
John R.
Harris, Associate Professor of Economics
3
ACKNOWLEDGENENTS
My sincere appreciation goes to those people on Delmarva who have contributed both directly and indirectly to the body of information upon which
this work is based.
Special thanks is extended to Mr. Hartley F. Hutchins
and the rest of the Delmarva Advisory Council.
Any mistakes in the inter-
pretation of the information provided are my own.
Through their constructive criticism, many - especially John Harris,
Tony Yezer, Leonard Buckle, and Ralph Gakenheimer - have assisted my
efforts.
The typing of my wife, Melissa,
produced this report, but it
is
for her
unwavering moral support and patience that I thank her most deeply.
4
CONTENTS
Introduction .
. .
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. . . . . .
The Methodology. .
. . . .
. . . . . . .
The Delmarva Model . . . . .
An Overview . . . . . . .
The Population Sector . .
The Employment/Industrial
The Social Policy Sector.
Further Development . . .
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Sector.
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Analysis of Alternative Policies . . . .
Conclusions. .
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Footnotes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
74
Appendix (Delmarva Model Equations).
75
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Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . .
85
5
ILLUSTRATIONS
Feedback Loop .
. . .
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. .. .
. .
11
Causal Diagram .. . . .
- - - . - .
.
...
13
System Flow Diagram . . .
-.
. . --.-.
15
Delmarva Model Causal Diagram.
High-Income Population. . . .
. .
.
........
. . . . .
Middle- and Low-Income Population . .
. .
.. .
. .
. . .19
.
.
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21
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22
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24
Migration Due to Higher Education Facilities.
Migration Due to Basic Education Facilities .
25
Migration Due to High-Income Unemployment . . .
.. . .
26
Migration Due to Property Tax Rates . . .
.. .
.
27
.. . .
28
. . .
Migration Due to Environmental Quality. . .
. . . . .
. . .
Migration Due to Level of Recreational-Cultural Facilities.
Migration Due to Middle-Income Unemployment
Migration Due to Low-Income Unemployment. . .
.
29
. . . .
30
.
.
31
.. . .
32
Migration Due to Level of Low-Cost Housing. . .
.
.
Migration Due to Level of Public Facilities
Determinants of Industrial "Migration"..
33
. . . .
.
.
.
.
Industrial "Migration" Due to Level of Business Services.
Industrial "Migration" Due to Availability of Labor . . .
36
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.
37
.
38
.
40
.. . .
41
.
Industrial "Migration" Blocked by Social Resistance . . .
Low-Cost Housing. . ... . . . . . . . .
Job Training. .
. . . . . . . . . . . . .
Basic and Higher Education Facilities .
Basic Simulation . . . . . . . . . . . .
Basic:
. .
High-Income Sector. . . . . . .
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35
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46
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48
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6
ILLUSTRATIONS (cont.)
Basic:
Middle-Income Sector. . . .
50
Basic:
Low-Income Sector . . . . .
51
Basic:
Industry. .
52
. . .
.. .
-.
Business Cycles . .
. . . .
. . . .
53
Manufacturing
.
.
55
Drive
Job Training. . . .
.
.
. . .
.
.
.
.
. . . . .
Job Training:
Low-Income Sector.
Job Training:
Middle-Income Sector
Job Training With Business Cycles
. .
57
.. .
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58
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59
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60
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.. . . . . .
Low-Income Sector
.
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.
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Lo w-Income Sector . . .
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.
.
.
Job Training With Housing Program
Job Training With Housing Program:
No Tax Factor . .
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.
Low-Cost Housing Program. .
Housing Program:
. .
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.
61
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62
64
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65
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67
No Tax Factor:
High-Income Sector.
68
No Tax Factor:
Middle-Income Sector.
No Tax Factor:
Low-Income Sector . .
.
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69
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70
7
INTRODUCTION
The Delmarva Peninsula has been an isolated land throughout its history.
It is surrounded now by the megalopolis, and, in a time of rapid communication and transportation, cannot expect to remain isolated for much
longer.
Bounded on the east by the Atlantic, on the north by the Chesapeake and
Delaware Canal, and on the west and south by the Chesapeake Bay, both the
land and the air have remained virtually unspoiled, and the people who
live there like it that way.
However, Delmarva lies at the center of a
ring formed by Norfolk, Washington, Baltimore,
and Wilmington,
and the
inevitability of change for Delmarva is apparent.
Delmarva is
a contraction of the names of the three states represented in
the region:
Delaware (most of the state), Maryland's Eastern Shore, and
Virginia's Eastern Shore.
A 1960 population of 382,000 lived on the 5,300
square miles of the Peninsula.
Sixty percent of that area is farmland,
worked by fifteen percent of the labor force.
1
The average level of education and skills has consistently remained below
the national and regional average.
The deficiency in general community
facilities, the rurally-oriented way of life, and the sometimes unseen,
but existent, resistance to change has generated the aura of "backwardness" that is perceived by the urban dwellers surrounding Delmarva.
The pace for change has, however, been set.
A primary force has been the
greatly expanded travel between the Peninsula and the more urbanized
"Western Shores" of Maryland and Virginia.
Within just the past two dec-
ades, the construction of the Chesapeake Bay Bridge (connecting the two
8
portions of Maryland)
and the 17 -mile Chesapeake Bay Bridge-Tunnel
(con-
necting the two eastern counties of Virginia with the rest of the state)
has catalyzed a greater exchange of people and ideas.
2
The Overall Economic Development Program for the Peninsula states that
Delmarva
...
is
a land where people yearn for development... and dread
it." 3 In response to the inevitable change, there is a continuing call
for comprehensive planning, irrespective of the state and local boundaries.
Part of this planning must be a comprehensive examination of social policies for Delmarva.
that effort.
The work presented here is dedicated to assisting
The concept of "system dynamics" is used to develop a simu-
lation model of population characteristics, employment patterns, and
social policy alternatives.
By examining the simulated relative impacts
of various social policies on the development of the area over a period
time, the evaluation of those policies can be greatly aided.
THE METHODOLOGY
System dynamics has developed over the past two decades as a framework
for the analysis of a wide variety of systems.
The first applications
were within industrial and managerial systems:
inventory control, man-
power policies, quality control, and the coordination of policies such
as those governing marketing and production.
For this reason, the con-
cept was known, and still is to many, as industrial dynamics.
It was in this form that the methodology entered the curricula of a number of universities throughout the country.
The initial work by Jay W.
Forrester, and much of the subsequent development, has, however, been at
the Massachusetts Institute of Technology.
This development has been in
the form of a much wider range of applications than the name "industrial
dynamics" implies.
Concurrent with the development of system dynamics has been the development of the computer language DYNAMO.
This has facilitated the study
process which involves the "construction" of a computer "simulation model".
The concept (system dynamics) should not, however, be confused with the
tool (DYNAMO).
It is system dynamics that provides the framework within
which a problem can be structured and examined, while DYNAMO merely makes
the mechanics of writing equations a simpler process.
The processes involved in conducting a system dynamics analysis are, to
a point, similar to those of any systematic study.
One must decide,
within a general problem area, the specific questions that are to be addressed.
Given this direction, the boundaries of the system must be
defined.
This entails two specific tasks:
10
(1) Determining the time horizon for the problem; and
(2) Determining the level of aggregation at which the system is to
be studied.
These two tasks are particularly important in this type of study.
A
model is to be built to simulate the operation of the system over time.
If one were studying seasonal fluctuations in unemployment, the appropriate time horizon is neither a matter of days nor one of decades, but
rather a matter of months or a few years.
Concerning the level of aggre-
gation, one would not consider separately the annual production of apples
and that of oranges in studying the economic growth of Japan.
Rather,
the analyst would aggregate them, or "lump them together" with other foods
in
a category such as "annual agricultural output".
At this point the system dynamics methodology departs from others.5
representing a system, the basic building block is
In
the "feedback loop".
The process of information feedback is illustrated in Figure 1.
Decisions
are made on the basis of information about some part or parts of a system.
These decisions, translated into action, effect changes on the state
of the system from which the original information was obtained.
in
This,
turn, changes the information about that (or those) part(s) of the
system, which then guides future decisions.
loop.
This is a complete feedback
Any complex system has, by definition, a number of feedback loops,
many of which are interconnected with each other.
Information about one
part of a system may be used in more than one decision, and a particular
decision may affect more than one part of the system.
For example, the
level of unemployment in an area may be considered in a decision on the
part of policy-makers to implement job-training programs and in a decision
on the part of workers to move into or out of the area; and the worker's
11
S TA T E
OF
INFORMATION
SYSTEM
SYSTEM
ABOUT
policy
action
DECISIONS
TO
CHANGE
Figure 1
Feedback Loop
decision to move affects not only the unemployment rate,
but,
for example,
the demand for housing, as well.
In studying such a system,
the consideration of time is
important in
two
different respects:
(1) There exist time delays between the actual state of the system
and the information that one has about it (e.g.,
the national
population and the decennial national census), and time delays between policy decisions and the resultant actions that change the
state of the system; and
(2) The state of the different parts of the system change over time,
i.e.,
they are dynamic.
It is the dynamic characteristics of a system that are overlooked in most
methods of study, but which are focused upon in a system dynamics analysis.
In working toward a simulation model in such an analysis, it is useful to
represent relationships between different parts of the system (or "variables",
since their values vary over time)
in
a "causal diagram".
a diagram shows which variables cause changes in other variables.
Such
Fig-
ure 2 shows a sample diagram of the part of a system previously hypothesized.
The next tangible step toward the simulation model is the development of
a more specific diagram, detailing the flow of information (and goods and
people, etc.), the time delays, and the different variables to be used in
the model.
There are two basic types of variables in the simulation model.
These are known as "levels" and "rates".
the state of the system at any given time.
The levels of a system describe
Thus, there may be a level of
population and a level of employment opportunities.
(In a less aggregated
HOUSING
DEMAND
WORKER
OK
MIGRATION
'WORKERS
PERCEIVED
UNEMPLOYMENT
>UNEMPLOYMENT
RATE
JOB
TRAINING
Figure 2
Causal Diagram
14
form, these may be levels of adults and children, and levels of highpaying jobs and low-paying jobs.
It should be remembered that the de-
gree of aggregation depends upon the purpose of the study.)
Variables referred to as "rates" are just that:
levels change.
the rates at which the
These rates may be controlled by natural processes
the aging rate of children into adulthood),
(as in
or by policy decisions (the
rate at which employment opportunities enter or leave an area depends
upon businessmen's decisions on cutbacks,
hiring,
plant shutdowns,
An example of a more specific flow diagram is shown in Figure 3.
levels are drawn as boxes,
in a tank.
representing, by analogy,
etc.).
The
the level of water
The rates are drawn with valve-shaped symbols, as in the
valves which can be opened to let water flow into or out of the pool
(thereby increasing or decreasing the level),
or closed to stop the flow.
Solid lines indicate the flow of tangible objects, with arrows to show the
direction of the flow.
Dashed lines indicate the flow of information,
i.e., information concerning which levels affect which rates.
The simulation model simply consists of a number of equations, each of
which describes the specific manner in which one variable affects another
within one interval of time.6 For example, the level of adults is determined by the following equation:
A (this time period) = A (last time period) + AR (over the time interval)
DR (over the time interval) + MR (over the time
interval)
in which
A
AR
DR
MR
= Adults
= Aging Rate
= Death Rate
= Migration Rate
-
15
rate
rate
WORKERS
UNE
migration
PLOYMENT
elay
-.
PERCEIVED
UNEMPLOYMENT
Figure 3
System Flow Diagram
16
Given a set of initial values for the levels, a computer can determine
(as could a person, in considerably more time) from the equations supplied
the values of the other variables for the initial time period.
From this
set of values, those for the following time period can be computed, and
so on, stepping through time, providing, when the values are printed out,
the simulated behavior of the variables over time.
Using this tool of simulation, the analyst can test the relative effects
of different policies on the behavior of the system over time.
Given the
alternative behaviors resulting from two different policies, a policymaker can use the simulations to make judgements about which course to
follow.
There is
nothing in
the simulation that will say which is
"best".
One may, for example, learn that a policy yields lower unemployment at
the cost of a decreased supply of low-income housing over some time interval.
No cost/benefit analysis or other technique can generate a
single "index of goodness" to measure the desirability of alternative
policies.
The decision must be made given the knowledge that one has
about the system and the anticipated effects of different policies.
The
policy-maker decides between alternative programs or policies by a comparison of the anticipated outcomes, and a value judgement as to which
outcome is more desirable.
is
This in.itself constitutes a "model", as it
an abstraction of reality.
A "formal"
simulation model aids the de-
cision process by stating specifically the assumptions and information
one has about the operation of a system over time.
If there exist.dis-
agreements over these assumptions or perceptions of the system, different
values may be tested in
the model,
and the relative effects of different
policies under each set of assumptions can be examined.
is
If the behavior
significantly different for the two (or more) different assumptions,
17
further investigative study is indicated.
If the relative effect of a
specific policy (under the varying assumptions)
is
the same,
the decision
on policy can be made with the assistance of the simi-ation model,
without undue concern over the specific value in
This is
and
question.
the purpose of the dynamic simulation model to be described: - to
provide a framework for the analysis of the relative impacts of different
policy alternatives on the regional development and growth of the Delmarva
Peninsula.
18
THE DELMARVA MODEL
An Overview
The first point of discussion of the Delmarva Model is the justification
for treating the peninsula as a "system".
One might argue that the analy-
sis could have been more appropriately conducted at the county
or state-
wide level.
The analysis was performed at the given level for a number
of reasons.
The foremost of these is the fact that the area is considered
an entity by those living in and around it.
Decisions regarding migration
or employment (business) relocation are made with respect to leaving, or
moving to, the "Shore".
The economy of parts of the region
cably interwoven with that of the other parts.
is
inextri-
The peninsula is isolated
from the surrounding areas not only geographically, but, to a large extent, with respect to the attitudes of the people who live there, and to
the activities, economic and otherwise, that take place there.
Surround-
ing the region are the more "advanced", or more developed, centers of
trade and learning.
In short, because people's decisions are made pri-
marily with regard to Delmarva, rather than to its parts or to the area
of which it is a part, the peninsula itself constitutes the system in
this analysis.
With respect to social policy considerations, the implementation of the
same program or policy throughout all Delmarva may, in reality, be hindered
by state borders.
tion. in
Policies examined here are assumed to have implementa-
all three states on the peninsula.
No attempt has been made to
analyze the effects of geographically disaggregating Delmarva for the purpose of this analysis.
19
PUBLIC SERVICES
POPULATION
RATE
TAX
RATE
Figure 4
Delmarva Model Causal Diagram
For the purpose of description, the model may be considered as consisting
of three sectors:
the population sector, the employment/industrial sector,
and the social policy sector.
As can be seen from the causal diagram of
Figure 4, these sectors are actually interdependent.
The specific relationships used in
the model, which will be discussed later,
have been formulated as a result of conferences with planners, businessmen,
politicians, and other people on the peninsula.
The time and resources
available did not permit gathering extensive empirical data to support
these assumed relationships.
The behavior generated by the model and
the resultant analyses are dependent upon, and dictated by, these assumptions (e.g., the factors governing migration of working-age, middle income
people, vis-a-vis other age and income groups).
The Population Sector
The population of the area is disaggregated in two ways.
First, it is
divided into three groups according to income, and roughly corresponding
to skill level:
reasons.
high, middle, and low.
This was done for a number of
Different types of industries, for example, require different
proportions of high-, medium-, and low-skill labor.
different impacts on the three groups.
Social policies have
Many policies have the goal of
redistribution, as in the programs for job-training of unskilled labor and
the provision of low-cost housing.
force participation rates.
The three groups have different labor
Low-income households tend to participate more
often with a second worker in the family in order to supplement family income.
Finally, the migration of different income groups depends upon dif-
ferent factors.
21
19-221
-income
COLLEGE
UPGRADING
F
".',
--. \
MI
MI
Higher
Education
Facilities
Figure 5
High-Income Population
High Inc. Unemployment Tax Rates
Rec.-Cult.
Environmental Quality
Facilities
Tax Rates
Public Facilities
Basic Educ. Facilities
Recreation-Cultural Facilities
22
Middle-income
7,4
19-22
M
MR
M
100
---
Unemp.
Higher Educ
Fac.
Basic Educ. Fac.
Unemployment
Tax Rates
Envir. Quality
Rec. -Cult. Fac.
Public Facilities
SCHOOLING
UPGRADING
Lowv-Income
T
MR
MR
Unemp. loyment
Figure 6
Middle-
and Low-Income Population
Unemployment
Low-Cost Housing
Tax Rates
23
The second manner in which population is disaggregated is by age group.
As shown in Figure 5, each income group is divided into four age groups:
0 to 18 years, 19 to 22 years, 23 to 65 years, and over 65 years of age.
There are three main reasons for this division.
First, the migration of
different age groups (as in different income groups) depends upon different factors.
Social planners on Delmarva are particularly concerned
over the exodus of many within the second age group.
Second, the needs of
the population vary according to age (as in educational facilities required).
Third, this division permits measurement of the level of the
working-age population.
Figure 5 illustrates the dependence of the size of this group (through
the factors assumed as determinants of migration) on other parts of the
system.
The middle- and low-income/skill level groups have the same struc-
ture, though different factors influence migration, as can be seen in
Figure 6.
The specific manner in which the different factors influence the rates of
migration in the model can be seen in the "tables" (their DYNAMO term)
illustrated in Figures 7 - 16.
These tables are used in the model as an
extremely convenient manner of representing non-linear relationships.
Any curve that can be drawn can be tested in the model as a relationship
between two factors (and by doing so, one may test the sensitivity of the
model system's behavior to changes in specific values).
Accompanying
each table is the applicable portion of an information flow chart.
From the computed value of the variable shown on the table's horizontal
axis is read the corresponding value of the percentage of the population
which migrates as a result of changes in that factor (a negative percentage
24
CAPACITY
OF LOCAL
HIGHER
EDUCATION
FACILITIES
POTENTIAL
COLLEGE
STUDENTS
/
/
/
(CHEF/
MIGRATION
DUE
TO
HIGHER
EDUCATION
PCS)
TABLE
10
5
% Migration
Due to
Higher Educ.
Facilities
0
1
1.1.
-5
-10
-15
Ratio of Higher Education Capacity
to Potential College Students
Figure 7
Migration Due to Higher Education Facilities
25
. .HOOLS'
SCHOOL-AGE)
CHILDREN
CAPACITY
(capacity/ children)
ION
TABLE
4
2
% Migration
Due to
Basic Educ.
Facilities
0
1
1.2
-2
Ratio of Children to Capacity
Figure 8
Migration Due to Basic Education Facilities
26
H-1
LABOR
H-i
LABOR
FORCE
DEMAND
H-1
UNEMPLOYED
TABLE
% Migration
Due to
High Income
Unemployment
-l
-1
-3
High Income Unemployment %
Figure 9
Migration Due to High Income Unemployment
27
RATIO
OF PRESENT
TO 1960
POPULAT10
TAX
TAX
DMV
AVG.
RATE
OUTSIDE
DMV
RATE
(DMV/
OUTSIDE)
I
J
TABLE
3
1
% Migration
Due to
Property Tax
.5
.7
.9
-1
Tax Ratio
Figure 10
Migration Due to Property Tax Rates
1960
PRESENT
--
ENVIRONMENTAL
ENV.
--
QUALITY
LAW
QUALITY
\
OPULATION
/
\
/I
ENV.
QUALITY
(1960 pollution level / present)
INDEX
TABLE
MIGRATION
% Migration
Due to
Environmental
Quality
.4
.6
.8
Index
NDUSTRY
1
Figure 11
Migration Due to Environmental Quality
29
EMPLOYME NT
DESIRED
R-C
RECREATIONALCULTURAL
ACILITIES
FACILITIES
(R- C/
DESIRED)
-TABLE
2
% Migration
0
Due to
Recreation-Cul tural
Facilities
-2
.5
.7
.9
1.1
Ratio
Figure 12
Migration Due to Level of Recreational-Cultural Facilities
30
M-1
M-I
LABOR
LABOR
DEMAND
M-I
UNEMPLOYED
-
IABLE
% Migration
Due to
Middle Income
Unemployment
Middle Income Unemployment %
Figure 13
Migration Due to Middle Income Unemployment
31
L-1
L-IO
LAB OR
L ABOR
DEMAND
1%
/
7
T ABLE
4
% Migration
Due to
Low Income
Unemployment
2
0
-2
-4
Low Income Unemployment %
Figure 14
Migration Due to Low Income Unemployment
32
OCCUPIED
TOTAL
LOW - COST
ILAPIDATED
HOUSING
UNITS
(ODU/
--
TOTAL)
yT
AB LE
3
% Migration
Due to
Low Cost
Housing
1
.2
.4
-1
Ratio
Figure 15
Migration Due to Level of Low Cost Housing
3
PUBLIC
PUBLIC
FACILITIES
FACILITIES
DESIRED
/
/
(PF/
DESIRED)
T ABLE
2
% Migration
Due to
Public Facilities
0
1
8
1.2
Ratio
-2
Figure 16
Migration Due to Level of Public Facilities
34
implies outward migration).
The cumulative effect of all the factors
which affect migration of a given income/age group over one time interval
is obtained by addition of the appropriate percentages, and multiplication
of that sum by the number in the income/age group.
The Employment/Industrial Sector
The levels of the different kinds of "industry" are measured in
the number of jobs provided.
terms of
This data is readily available, and the
level of employment and unemployment is important in guiding social policy.
There are seven categories of industries, or employment opportunities:
agriculture, agricultural manufacturing, other manufacturing, tourism,
business-serving,
household-serving,
and government employment.
The
rate at which each industry enters or leaves the area (or, expands or contracts the number of jobs available within it) is determined for the simulation by a set of factors unique for that industry.
of industrial migration are shown in Figure 17.
These determinants
The model equations
are set up so that, should there be disagreement over the causes of migration (of people or industries), the relative effects of "switching off"
these factors, or adding new ones, can be easily tested.
Some of the factors which were described as affecting migration of the
population also affect industrial migration.
The tables of the specific
effects of those factors have already been provided.
Those tables of
factors that are exclusively determinants of the expansion or contraction
of employment opportunities are presented in Figures 18 - 20.
The appro-
priate information structure is pictured with each table.
These tables are set up in the same manner as were those for population
migration.
Each table provides the percentage of an industry that would
35
Agriculture
Househo'd-Serving
Agricultural Manufacturing
Total Employment
Agricultural Manufacturing
Other Manufacturing
Agriculture
Property Taxes
Labor Availability
Business-Serving Industry
Public Facilities
External Economic Conditions
Property Taxes
Labor Availability
Business-Serving Industry
Public Facilities
External Economic Conditions
Social Resistance to Change
Tourism
Government
Regional Population
Public Facilities
Environmental Quality
External Economic Conditions
Agricultural Manufacturing
Other Manufacturing
Tourism
Total Population
Business-Serving
Agricultural Manufacturing
Other Manufacturing
Household-Serving
Figure 17
Determinants of Industrial "Migration"
36
DEMAND
FOR
BUSINESS
SERVICES
BUSINESS
SERVICES
(SE RVICES/
-r'
DEMAND)
T ABLE
1
% Migration
Due to
Business-Serving
1
1.2
-1
Ratio
-3
Figure 18
Industrial "Migration" Due to Level of Business Services
37
L-1
M-1
UNEMPLOYED
UNEMPLOYED
AVG.
UNEMP.
T ABLE
5
4
% Migration
Due to
Labor Availability
3
2
1
0
2
4
6
8
10
Average Unemployment %
Figure 19
Industrial "Migration" Due to Availability of Labor
38
ADULT
MIGRATION
POPULATION
SINCE
1960
/
(A M / A P)
CHANGE
TABLE
/
BLOCKED
50
40
% of Change
Blocked by
Social Resistance
to Change
30
20
10
0
.1
.2
.3
.4
Ratio
Figure 20
Industrial "Migration" Blocked by Social Resistance
39
migrate in response to changes in just the factor on the horizontal
axis.
The Social Policy Sector
The public policies considered in this model are the provision of:
low-
cost housing; job training for the unskilled; basic (elementary and high
school) educational facilities; and higher (college) educational facilities.
In addition, the effects of varying property tax rates and environ-
mental regulations on industry are examined.
The low-cost housing policies are formulated in the model so that programs
can respond (after a delay) to a perceived pressing need for such housing,
or so that a constant level of housing construction (at any specified
leVel) can be used as input.
the feedback structure.
In the first case, the policies are part of
In the second, they are not.
Figure 21 illus-
trates the structure used in the model.
The implementation of job training programs is set up in the model in a
manner similar to that of low-cost housing.
The provision of this hous-
ing can be made to respond to a perceived need, or can be set at a given
level.
The information structure for this is
shown in Figure 22.
The
levels of basic and higher education facilities are determined by nearly
identical information structures:
delayed response to a perceived need.
These structures are both diagrammed in Figure 23.
These sections have presented the primary relationships between variables
in the Delmarva Model.
A complete listing of the DYNAMO equations used
for the basic set of simulations is provided in the appendix.
40
M-1
SOND
deterioration
OCCUPIED
LOW -COST
DILAPIDATED
HOUSING
190USING
filte
rate
ddition
7
DIFFERENCE
delay
TOTAL
aoandenment
L-I
POPULATION
--.DEMAND
DIFFERENCE
Figure 21
Low-Cost Housing
41
L-I '
19-22
TRAINEES
M-I
23 -65
UNEMPLOYMENT
GOAL
delay
UN E M P.
Figure 22
Job Training
42
SCHOOL- AGE
CHILDREN
BASIC
EDUCATION
rate of
change
CAPACITY
POTENTIA
COLLEGE
STUDENTS
HIGHER
EDUCATION
rate of
change
CAPACITY
Figure 23
Basic and Higher Education Facilities
43
Further Development
The impact on the results of this study of geographic disaggregation has
not been examined.
model.
Delmarva has been treated as a single system in this
Differential impacts of policies on parts of the peninsula could
be examined by a study considering these parts as separate "subsystems".
Sensitivity of the model to changes in the individual values of the factors described previously has been tested, but testing the effects of
combinations of these changes has yet to be done.
Finally, should there develop disagreement over, or questions about, the
existence of the relationships used in the model, further work should be
performed in
testing the alternative effects and in
affirm or disclaim the model structure postulated.
gathering data to
44
ANALYSIS OF ALTERNATIVE POLICIES
The exact value of many of the parameters used in
be subject to some debate.
the Delmarva Model may
is
however,
The crucial point to consider,
whether different values would change the type of behavior exhibited by
the variables over time.
range of parameter values,
Many simulations were performed with a wide
and in no case did the behavior differ signi-
ficantly from the behavior of the "basic" model.
Different lengths of
time delays or different "table" values slightly alter,
for example,
the
specific point at which unemployment peaks or "bottoms out", but the
shape of the graphs over time are identical.
It is important to remember
that this model does not attempt to make point-level predictions,
rather,
but,
to provide insight as to what directions can be expected to be
taken by different variables.
After gaining an understanding of the
reasons for the observed behavior, the relative effects of policies on
key variables can be examined.
Graphs of the basic simulation model behavior are shown in Figures 24 to
28.
The basic model consists of the formulations for all three sectors
as described in
the previous section.
and all other runs,
In addition, this computer "run"
contain a "scarce allocation factor" in
tation of social programs.
This factor,
the implemen-
which can be set at any level,
allows for the usual condition that not all the resources
(funds,
etc.)
that are needed to achieve the goals of the social programs are available.
The first graph of the basic simulation plots the behavior of three variables--total population,
over time
total employment,
(on the vertical axis).
used to obtain the "initial
and percentage unemployed--
Information from the year 1960 was
conditions" for the system.
Therefore,
45
Time "0" is 1960.
Each time interval on the graph represents one year.
Since all the plotted output begins at Time 10, the simulations start
with 1970, and illustrate the kind of behavior to be expected over the
next 40 years, or to Time 50.
The reading of the graphs is quite simple.
For each graph, a specific
letter is assigned to each variable plotted.
To find the vertical scale
for that variable, find the letter in the upper right corner of the graph.
The scale for the variable it represents is aligned with the letter.
More than one variable may use the same scale.
On some of these scales
(across the top), the letter "T" or "A" is located after the numbers, as
in 350.T or 55.A.
These letters are scaling factors, standing for, respecThus, 350.T means 350,000, and 55.A
tively, thousands and thousandths.
means .055 (or, in the case of unemployment, 54 percent).
In the first graph, it can be seen that the model has generated no growth
of population between 1960 and 1970.
The 1960 "initial condition" was
382,000, which is the level of the 1970 population as well.
From shortly
after that point, however, the population exhibits a steady and slightly
increasing rate of growth.
of employment.
A similarly steady growth is seen in the level
The percentage unemployed falls over the first 10 - 15 years
from over 7 percent to about 5 percent.
industry
This is due to the fact that more
(or employment opportunities) has appeared in the area as a re-
sult of the combinations of the factors governing its migration, as described in the previous section.
One of the primary causes of this is
the large availability of labor, as reflected in the unemployment rate.
Because the migration of people does not respond immediately to the additional employment available, unemployment drops below what the model
treats as "normal" unemployment (between 5 and 5
percent) for the general
46
PAGE 10
DELMARVA MOCEL
5/06/72
BASIC4
TOTPOP=P,TOTEMP=E ,AVGU=U
390.T
140.T
45.A
350.T
120.T
35.A
10.-
.P
.
.
U
P
Op
P
-
.
0
E
E
0
0
U
P
U
EF
U E
U
0
0
0
-
E
0
0
E
0
0
F
P
0
F
P
0
F
fl
0
F
II~.1
U
F
P
U
0
0
0
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0.
U
0
0
0
E
P
U
0
E
P
U
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U P
0
0
U
.
U
0
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0
E
p
P
P
20.
65.A
E
*
.
470.T P
200.1T E
75.A U
440.T
410.T
160. T180.T
55.A
UP
0
30.-
-
.
U -P-
U
U,
0
-
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E
P
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-
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E
P
a
0
.
.
.
S
.
0
.
0
U
P
U
P
U
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U
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.
0
0
0
.
0
0
0
0
0
0
0
0
0
.
.
.
.
-
0
50
Total Population = P
Total Employment = E
Unemployment = U
Figure 24
Basic Simulation
.
pE
E.
P
E
P
E
P
.E
-E
-.
.0P
.0P
P -E
S
40.-
U
U
U
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U
U
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U
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E
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E
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P E
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PE
0
P
P
EP
E P
.
.
.
-
-
-
-
-
- -
- -
- -
- -E-P -
-
-
47
economy.
At around 5 percent, unemployment begins to increase again.
Workers are migrating in, in response to the additional jobs, but the
entrance of further industry has been cut off by a scarcity of labor.
The unemployment rate shows signs of levelling off near the end, as it
approaches the 5
percent mark.
The next four graphs illustrate the
components of this behavior.
Figure 25 graphs elements of the high-income portion of the population:
unemployment; growth of the 19 - 22 and 23 - 65 age groups; and migration
levels of those age groups.
Unemployment drops from a high (for this group) level of over 5 percent,
fluctuates somewhat below 5 percent for about a decade.
The fluctuations
are caused by the fluctuations in the migration of the working age population.
The reason for these is less obvious.
As higher educational
facilities are built in -response to the 19 - 22 age group, more of this
group remains on the peninsula (or their place is taken by others moving
in).
As more stay (notice the youths' net migration increases, but re-
mains negative throughout), more age into the working age group.
This
aging rate becomes the primary determinant of the level of the working
age group, and as more are entering the group in the 1980's, unemployment increases, migration of the working age group decreases, and, in
turn, the unemployment rate begins to turn down.
Unemployment continues
to fall to a more reasonable rate, below 4 percent.
This generates a
final increase in migration of the working age group, but not enough to
reverse the direction of unemployment, as the level of industry is high
enough to accommodate additional manpower.
MCCEL
DELMARVA
PAGE 11
5/06/72
48
BASIC4
HP0P3=3, HU=U,MIGH3=0,HPOP2=2 ,MIGH2=Y
16.T
40.A
95.
1Y00.
-90.
14.T
35.A
85.
1600.
-120.
10.-
-
-- --
--
- 2Y- Y
2
- -0-
- 3 - -3
3
3
Y.
2
2
Y
.
2
2
3
Y
2-
.
.
3
-Y-
-
U
U
3
-
Y
2
2
2
2
3
.3
Y 3
.Y 3
Y
3
03
.
.
.
---.2
2
30.-
O0
2
.
O0
U.
U.
Y
U
0
C
50.-
-----------------------
-Y-
-2-
,
Y
Y
Y
Y
Y
.
.
.
.
.
.
.
C
0
U----------------
Y
-Y -
Young Adults = 2
Adults 23 - 65 = 3
Migration, Young Adults = Y
Migration, Adults = 0
Unemployment = U
Figure 25
High-Income
Sector
-3-
Y
0.
.0
-
UY
.
-
2.3
.2 3
.
.
.
Basic:
-
C2
U3
.
.u.0
.
.0
.3
U
3
2Y
3.
Y2
Y 2
3.
Y
Y
U.
.
UC
U.
OY
3
U
Y3.U
-Y -3-- --UY 3
U.
Y 3
U.
Y
3
U.
2
Y
3U
-
U
- 0
U
0
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U
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2
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40.-
U
0
-
c
2
.
.
O
U
0.
Y
2
0
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3u
0
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.
0
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2
.
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U
U .
.
.
.
--
U
.0
3
2
0
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.0
3
Y
0
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3
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0
2
Y
U
.
Y
2
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.
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2
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22.T
55.A
125.
2000.
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- -u
01
c
Y
20.s-
- - - -
20.T
50.A
115.
1900.
-30.
- - - - -
3
Y
2
0
18.T
45.A
105.
1800.
-60.
- - - - - - - -
-
23
32
3
32
32
S
3
2
3
-
-
-
-
-
-
2
3
- 33-
2
-
2--
49
The corresponding variables are plotted in Figures 26 and 27 for the
middle- and low-income groups, respectively.
The middle-income group
displays behavior quite similar to the high-income group.
This should
be expected, as much the same factors are formulated as influencing the
behavior (i.e., migration) of the two groups.
The low-income group displays quite different behavior.
decreasing unemployment rate (explained in
In spite of a
the analysis of the first
graph), migration is not compensating for it.
This is due to two factors.
First, this group does not have as much mobility as the other groups, and,
secondly, the unavailability of low-cost housing limits the migration.
As more housing becomes available through filtering and construction, this
limit is less constraining.
In the meantime, the unemployment rate in
the area falls to a low (for this group) 4 percent.
After this, the lag-
ging migration of low-income owrkers increases, driving the unemployment
rate back up to around 62 percent.
Migration of the 19 - 22 age group,
which is considered to be more mobile, or less tied down to their present
residence, decreases in response to the higher unemployment rate, while
the 23 - 65 age group migration remains fairly stable, with the availability of housing.
Figure 28 illustrates the steady growth of the different "industries",
which is as should be expected.
The effects of a number of revisions (including policies) in the model
were simulated.
Figures 29 to 42 illustrate the results of the changes
in the basic model.
The first change is the addition of a business cycle of eight years
(which is approximately the length of the average business cycle in this
PAGE 12
5/06/72
DELPARVA MCCEL
50
BASIC4
MPOP2=2,MPUP3=3, MU=U ,MIGfv3=0 ,MI GN2=Y
12.5T
120.T
50.A
80.
-6C0.
1I.5T
110.T
40.A
700.
-800.
10.-
---
3
------
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3
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3
0
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2
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Y
Y
U
U--- ------ -----
.
-
Young Adults = 2
Adults 23 - 65 = 3
Migration, Young Adults = Y
Migration, Adults = 0
Unemployment = U
Figure 26
Basic:
U-
c
2
U
*
50.-
-- - - U ~U
-
2
-
0
-------
2
2
Y 20 I
2Y
3
0
3
O
2
Y
U
3
2
Y .
ou
3
2
Y.
U
.YU
3 2
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32
UY
2
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2 3.
U
Y
23.
2.3
Ut
Y
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U
Y
.2
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Y
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Y
20.-
-
-
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3
U
Y
0
30.-
15.5T
150.T
80.A
1100.
.0
14.5T
140,T
70.A
1000.
-200.
13.5T
130.T
60.A
900.
-400.
2------
Middle-Income Sector
3
2
0
3
2
Y
Y -----
-
2
0
3 0
30
2--3-
---30
DELMARVA
PAG E 1.3
BASIC4
5/06/72
MCCEL
51
LPOP2=2,LPP3=3,LU=UMIL3=0,MIGL2=Y,LHH,0DH=X
55.T
4C.A
.0
10.
20.T
30.A
-200.
.0
.0
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SOH- - Y H.,O0
10.-
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H
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3 3
3
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0
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H
HU 2
200-
- ---------
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X
0
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-- - - - - - - -X
U
U
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X
X
2
.
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X
V
YV
YV
X
.0
0
------
400
- -XX
C
X
X
C
X
.
Y
.
YV
Y
Y
0
------
2
.2
.2
.
Y
X
Y
X
X
Young Adults = 2
Adults, 23 - 65 = 3
Migration, Young Adults = Y
Migration, Adults = 0
Unemployment = U
Total Low-Cost Housing Units = H
Occupied Dilapidated Housing = X
Figure 27
Basic:
Low-Income Sector
0
0
0
c
.H
0
.H
.H
0
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Y
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0
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3.
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PAGE 14
52
BASIC4
5/06/72
DELMARVA MCCEL
AG=AAM=FJM=OTM=TG=G, BS=BHS=H
24.T
15.1T
21.T
5.T
23.T
27.T
35.T
- - - - - - -GOH.
GOH
22.T
13. T
19.T1
3.T
21.T
25.T
30.T
10.-B- - -8
B
-A
- -A
G
A
A
B
.
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G
G
A.
B
A
A
B.
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-
30.------
40.-
------
-
0
50.-
HO
HO
G
G
C
H
HO
F.
.F
.F
T.
T.T
-A---
Figure 28
Industry
A
F
0
T
G
B
H
0
F
.T
. T
O- - -T-
.
OH
.
OH
.
.
AG
OH
AG
OH
.
OF
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OH
F
F
F
-- -- ---------F
B
GA
C.
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B
.
F
B
GA.
T
F
0
B
G.A
F
0
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P .CA
0
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F
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F
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- T - -F--G A -HO-BF .
T
B G A
HO
.
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F.
B G A HO
B GA
F
T
HO
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T
B G A HO
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T
B G A H O
F
C T
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.0
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F
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BG AH
F
0T
BG A
-OT-F-- - - - - - F
GA. Oc
F
TO
GA
F
TO
.GBA
F
T 0
. HGBA
HGA
T
OF
OF
HGA T
OF
H GABT
GAT
H
F
A
Agriculture =
FO
ATB
H
=
F
Manufacturing
Agricultural
A
B
-F-0Other Manufacturing = 0
0
Tourism = T
Government = G
Business-Serving = B
Household-Serving = H
Basic:
9.T
27.T
31.T
45.T
30.T
21.T
27.1
11.T
29.T
33.T
50.1
- -
F
T
T
A G
AG
-B- ---
20.-
T F
T
T
OH
G
B
-
28.T
19.T
25.T
26.T
17.T
23.T
7.T
25.T
29. T
40.T
TF------
.
OH
. OH~
.
OH
OH
-GHA. OTGBH
.
GBH
. GH
.
.
AB
AB
.
.FO,TB
AG
-
ATG
DELMARVA
PAGE 17
MOCEL
5/06/72
4-CYCLES
53
TOTPOP=PTOTEMP=EAVGU=U
35.A
10.-
P-
-
E.
E
P
P
P
E
U
E
E
.
U E .
E
u
U
p
a
F
F--E---- -- -.
E
.
E
E
E
P
PU
U P
P
U
U
U
U
-0-
0
PU
0
0
0
-EE
P
PU
PU
-
-
0
U P
E
.
E.
P
P
P
0
U
PE.
P
U
P
U
U
40.- -
P
E.
E.
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P .E
.
.
0
0
0
.
.
0
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S
.
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,0
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a
0
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-
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U
. U
0
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a
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.
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.
0
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---
0
a
E
P .
P
. P
. U
P
a
0
0
U
.
P
U P
0
0
U
E
---
a
U
P
P
U
a
a
'JO
P
0
75.A
65.A
------
P
30.-
470.1 P
200.T E
-E-
.P
.
.
.
.
440.T
180.T
410.T
160.T
55.A
380.T
140.T
45.A
350.T
120.T
50.Total Population = P
Total Employment = E
Unemployment = U
Figure 29
Business Cycles
U
U
.OP
.
U
.0
U
.
U
U
.a
.
E
P
E
P E
PE
P
PE
PE
P
EP
U
U
EP
-E-PU------------------------
-
-
54
country).
The effect of this cycle is to alternately increase (during
general prosperity) and decrease (during recessions) the absolute value
of the "migration" of manufacturing and tourism.
pared to Figure 24.
Figure 29 should be com-
While one might expect the effects of such a cycle to
significantly change the system behavior, such a change does not occur.
Unemployment fluctuates, but exhibits the same type of long-term behavior,
eventually oscillating stably around a value just over 5% percent (as in
the basic simulation).
Another change tested was the effect of an active drive to bring more
manufacturing into the peninsula.
As can be seen in Figure 30, the ef-
fect of adding major manufacturing facilities (500 employees) every ten
years does not significantly help the unemployment problem, except perhaps for a year or so per decade.
After unemployment bottoms out, it still
reaches the -same level it would have achieved without the drive.
cause is not so obscure.
The
The additional employment decreases the availa-
bility of labor (i.e., lowers unemployment), thereby decreasing what
would normally have been the subsequent inward migration of industry, and
increasing the migration of labor into the area (due to the lower unemployment percentage).
Combined, these factors result in the same percentage
of unemployment as a policy of non-interference with the natural migration.
This does not mean that industries should not be informed about
Delmarva.
The "natural migration" depends on their having that informa-
tion, and officials of Delmarva should be eager to disseminate it.
The basic model incorporated a small amount of job training in response
to a critical need (i.e., a very high level of low-income unemployment).
That assumed a program which had complete flexibility in its capacity.
DELPARVA MCCEL
PAGE 19
5/06/72
4-O4 DRIVE
55
TOT P&P=P,*TOT EMP=EAVGU=U
350.T
120.T
35.A
380.T
14a0.T
45.A
10.-
.- P-E
p
0 pE
.
.
.
P
.
.
.
E
P
F
P
E
P
P
P
.
U
P
-
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U
.
*U.u
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470.T P
200.T E
75.A U
4 40 . T
11
80 .T
65 .A
410.T
160.T
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U
.
E .U
U E
E
U
0
0
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.
0
0
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E
P.E
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30.0
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0
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0
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F
F
U
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P .
S
P.
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.P
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U
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E
P
U .
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.
P
E
U
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P
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E
P
U.
uP
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.
U
P
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P .E
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E
.
--u
-E----.p
U.
E
U.
.
P E
U
P E
PE
0
P
--
----
0
0
S
----
50.-
Total Population = P
Total Employment = E
Unemployment = U
Figure 30
Manufacturing Drive
U -------
-------
P
.
-
E P
E P
-
.
.
P
EP
.U
.
U
U
.
U
.
P
.
0
0
.
-E-P-
56
In testing a program of training at a fixed capacity, with a constant number of trainees per year, some interesting behavior resulted, as seen in
Figures 31 through 33.
Compared to Figure 24, the graph in Figure 31 dis-
plays identical rates of growth in the population and level of employment.
However, the percentage unemployed stabilizes at a noticeably lower level.
The components of this change are shown in Figures 32 and 33, graphs of
the behavior of the low- and middle-income groups, respectively.
The
low-income unemployment is driven significantly lower than in the basic
simulation.
Migration increases, but is limited again by the availability
of housing and low-income mobility.
So unemployment rises, but this time
toward 5 percent rather than to 7 percent.
The exact levels should not
be relied upon as predictions, as mentioned, but the difference between
them is significant.
The effect of business cycles on the results of such a job training program is shown in Figure 34.
The oscillations are present, again, but
the general behavior is unaltered.
Figure 35 demonstrates the effects of a program providing a constant level
of additional low-cost housing, rather than the flexible-capacity program
used in the basic model.
The impact on unemployment is strikingly similar
to that of constant job training, but for a different reason, as can be
seen in Figure 36.
The migration of the -low-income group into the area
is to a large extent limited by housing availability, since the program
does not respond proportionally to need.
So, while the level of occupied
dilapidated housing initially decreases, the resulting increased migration soon brings this into balance, and the migration itself balances at
a lower level, resulting in a lower unemployment percentage.
Unlike the
job training, there is virtually no impact on the middle-income population.
PAGE
21
5/06/72
DELMARVA MOCEL
4-JTEX
57
TOTPUP=P ,TOTEMP=EAVGU=U
400.T
140.T
45.A
380.T
120.T
35.A
-
10.- -P- -
-
-
-
-
-
420.T
440. T
160.T
55.A
180.T
65.A
460.7 P
200.T E
75.A U
- -
F
P
U
P
.
EF
E
p
.
P
U.
U
E
.
E .
U E .
E
P
.
U
E
P
*
P
P
U
P
*
20.----------- ----P
*
U
u -- - -
--
.
-
-
-
-
-
U
P.
0P
U
U
PU
PU
30,.0
0
S
a
-
E
.
.
.
.
.
0
-E----E
E
.
E
E
-
E
EF
E
PU
0
E
.0
P
U
PU
E
UP
.
-- --- UP- - -U P
U P .
30.-------------
S
-
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E
.PI
UI.
U.
U.
U
P
.
.0
.
E
P
.
EF
P
.
0
P
U
0
.0
0
.
UP
---
--
--
--
..
P
U
.
EF.
P
.
-
P
40.--- ----------------
-
-----
P-----E----- --- -- -
-U- - ---------P
.E
P. E
. PE
P E
U
U
U
u
0
U
U--- ------
0
50*-
-
Total Population = P
Total Employment = E
Unemployment = U
Figure 31
Job Training
.0
.0
.0
.
-----
--------
P
PE
E P
EP
P
P
E
E---
P-
4-JTEX
5/06/72
DELMARVA MOCEL
PAGE 22
58
LPP2=2,LPP3=3,LU=U,MIGL3=0,MICL2=Y,LH=8,0DH=X
7500.
60.T
20.A
7000.
50.T
.0
-300.
40. A
300.
20.
40. T
3 -X-
.0
.0
.0
10.
20.T
-------Y H. O
10.------
H
3
0
Y
0
H
H
0
--
20.- -
X
. U
.U
X
.U X
xU
X x .U
----
-
- U-
x
.
x
0
U
Y
.
x
x
x
x
x
x
x
.2
2
Job Training:
Low-Income Sector
.
.
2UX
2X
V
y
0
.
.
0
Y
V.
Vy V.
0.
Y.
Y.
V.
0.
0
Y
.H 03
y
.8H03
2
Y
3
.H
2
-3- - Y------2---HY
3
2
.H0
3Y
2.8HC
3
.2 HO
20
U
U
-U------------0
.
.
3H
3H
3H
3Y
2H
Y3
H02
Y 3
HO
2 Y
3
0
2Y
3
08
Y2
3
2
3
08
Y
H -Y- 2 -3--
3H
30
YV
H83
29
U
U
U
.U
.U
.U
Figure 32
.
23
2Y
2X
.0
3
Y.
.0
- - 3H- - -0- - - - - - - - - - Y
0y
0
3H
y
0
3.
Y
0
H3.
Y
H3 0
y
H3 0
2
.
Young Adults = 2
Adults = 3
Migration, Young Adults = Y
Migration, Adults = 0
Unemployment = U
Total Low-Cost Housing = H
Occupied Dilapidated Housing = X
50.-
.
.
3
U
-U.
.
.
.
.
.
.
.
cY
-
2
X
.
Y
3
x
x
40.-
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U
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x
CH
3X
Y
.
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2
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x
-
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x
-
2
3
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c
Y
HX
U
-
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2-
2
3 0
3
3
H 3
H 3
2
--
2
03-
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U
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-Xx
H--
H
u
x
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2
2
2
x
30.-------
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U
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--
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X3
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2
y
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0
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3
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2XC U.
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3
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9000.
90.1
80.A
900.
40.
80.1
8500,
80.T
60.A
600.
30.
60.T
c00.
70.T
OH
.
.
-
DELPARVA
PAGE 23
59
4-JTEX
5/06/72
MODEL
MPUP2=2,MPOP3=3,NU=U,MIGN3=0,MIG2=Y
13.5T
120.T
65.A
650.
-600.
3-23
3.
.3
. 3
13..T
110.T
60.A
550.
-800.
10.-
2
2
2
2
2
Y
2
2
2---2
Y
.0
3 U
Y
3
0
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3
U
3
U
- 3 --3
Y.
Y.
UV-
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----
C.
.
0
2
2---- -- --2
2
2
2
O
U
U.
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3 Y
.
3Y
- -
0
U
Y
3
- -3-
.
0
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a
YU
.
y
3
. 3
U
2 0
2
.0
Y
.
Y.
Y.
2
Y.
0
.0
U
0
U
U
a
.
--
Young Adults = 2
Adults = 3
Migration, Young Adults = Y
Migration, Adults = 0
Unemployment = U
Figure 33
Job Training:
2
0
Middle-Income Sector
a
3.
2
.U
50.- - - --
Y
U
U
U
U - --
3
y
U
a
3Y
U
U
20
2 .
02
- -U!)
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Y 3
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20
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3.
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0
y
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30.-
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2
2
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---
2
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Y
0
--
0
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2
3
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C
y
U
.
.0
Y
.
.
.
2
20.- - - --
-U
3
.
2
15.T
150.T
80.A
950.
.0
---
14.5T
140.T
75.A
850.
-200.
14. T
130.T
70.A
750.
-400.
.Y
2Y
20
3
3------ -- -- -3
3
3
3
3
3
3
S.Y2
2
.
Y
C
C
.
Y
--C--------Y---
3
2
- -2- --
3
-3-
PAGE 25
5/06/72
DELMARVA MOCEL
4/JTEX/CYCLES
60
TOT POP=P ,TUTEMP=E ,AVGU=U
400.T
140.T
45.A
380.T
120.T
35.A
.
-
-
10.- -P.
P
.
p
-
-
-
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-- - - E
U
E
P
U
P
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----
U
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E
U
P
U
.
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SP
P
U
.
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P.P
.
.
U
p.
U
k
U
P
U
E
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.0
P
U
U
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.
.
P
P
U P
E
E
.
E.
- - - - - - -E------
-
PU-
30.-
- - -
- - - -
-
E
.0
P
- -
- -
E
.0
U
-
--
PP
.
.
U.
E
--
PU
9
U P.
0
u
4
E.
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.P
P *p
0
U
U
U
U
P
.
U
P
.P
.
.
-
40.-
-
.
E
P
F
P
P
U
P
- -U-
-
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F
EP
E
E
-
- -P-
-
E
.
.
- E -
uP
0
-
E
0
p
*
*
*
20.------*
460.T P
200.T E
75.A U
-
-
p
.0
440.T
180.T
65.A
420.T
160. T
55.A
U
0
U
0
0
E
PP.
.
E
. P
.
U
E
PE
EP
E
0
U
50
0
.
U
U.
U---------------------------------------
0
0
---
-
-----
Total Population = P
Total Employment = E
Unemployment = U
Figure 34
Job Training With Business Cycles
P
P
E
U
0
.
.
E
-
P
E
---- E---
P
5/06/72
MODEL
DELMARVA
PAGiE 27
61
4-LHEX
TOT POP=PTOTEMP=E, AVCU=U
4 CC.T
140.T
45.A
380.T
120.T
35. A
--
--
10.- -P-----
--
---
-0-
U
E
U
E
U.
E
P
P
E
P
.0
P
P
.0
.
.
U
U
E
.
E .
U E
F
P
P
--
-
0
0
-
-
a
F
0
F
0
F
F
.
U
P U
P
0
F
0
U
P
0
0
-
.
U
U
U
U
U
.P
P
30.-
u0
U
.
P.
0
.
U
20.-
P
F
.
0
F
UJ
- - P
0
E
- -
-
-F
-
PU
0
UP
U
40.0
a
a
-
Total Population = P
Total Employment = E
Unemployment = U
Figure 35
Low-Cost Housing Program
F
P.
U . P
U.
U.
U.
U.
U.
U.
-UU.
U.
U.
U.
U.
U.
U.
U.
U.
U-
-0-
50.-
460.T P
200.T E
75.A U
440.T
180.T
65.A
420.T
160. T
55.A
F
0
E
P
P
PF.
P
P
P
r-
E .
EF.
E.
PP*E
P .
*P
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E
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0
P
0
.0
.
.
E P
E
PE
0
P
E
E
P
E
a
P
P
-
DELPARVA MODEL
PAGE 28
2a/06/72
4-LHEX
LPUP2=2,LPOP3=3,LU=U,MIGL3=K,MIGL2=Y,LH=H,0DH=X
.0
25.T
10.-
20.-
8600. 2
8300.
8000.
90.T 3
70.T
80.T
70.A U
60.A
50.A
600. C
400.
200.
40. Y
30.
20.
80.T H
40.T
60.T
45.1 X
35.T
40.T
-OHX
- - - - 3-------y H.
U
3
ox 2
Y 0
H
2U
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x
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YO
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C 2.
3
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2
0
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x
0 3YX
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H
2
Y
H
2
X 03
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.
30
H2
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.
. X
3
3- -Y---------2- H - ----- 30
U
x
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H
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30
Y
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2
3 0
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30
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2
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2
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30
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2
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30
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30
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3.
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2
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30
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3.
H YU
3
.0 20
2
. HY U
C 2
.0
.0
3
.
YH
YU
.0
0
2
Young Adults = 2
H
U
2
3
.
O
Adults = 3
2
30.
H
U
.
C
Migration, Young Adults = Y
- -- -23 -H-------- -- - ----7700.
60.T
40.A
.0
10.
20.T
30.T
7400.
50.T
30.A
-200.
.0
-
------
30.-
0
X
X
X
x
x
40.-
-X-
x
x
x
.
.
50.----- -----
x
x
x
x
x
x
x
Migration, Adults = 0
Unemployment = U
Total Low-Cost Housing = H
Occupied Dilapidated Housing
Figure 36
Housing Program:
Low-Income Sector
62
63
As might be expected,
the combination of a constant level of job training
and low-cost housing reduce unemployment to an even lower equilibrium
value, as can be seen in Figure 37.
As the job training reduces unemploy-
ment in the lower-income group, workers are more attracted to the area,
but the additional migration means a higher demand for housing,
being added at just a constant rate (see Figure 38).
which is
This cuts off migra-
tion at a much lower rate than would normally be implied with the job
training program and low unemployment.
climbs quite slowly after bottoming out.
The result is that unemployment
A policy of no supplemental
low-cost housing would, of course, keep migration and unemployment of the
low-income population at a low level, but such a policy disregards the
welfare of the present residents.
The impact of the combination of these programs on the middle-income population is exclusively a result of the job training program.
Middle-income
unemployment displays the same type of behavior over time as in the basic
simulation, but at a consistently higher level.
The impact on the high-
income population is, again, negligible.
As noted, the relationships used in the model are plausible assumptions.
Property tax rates, for example, may not be a factor in the decision to
migrate.
If this is the case, and the other relationships are valid, the
system behaves as in Figure 39.
The same general behavior is exhibited,
but the levels are quite different from those of the basic run.
Because
the lower tax rate over the time span simulated encourages migration,
when its effect is "switched off", both population and employment grow
much more slowly.
High- and middle-income unemployment (as shown in
Figures 40 and 41) are at lower levels than in the basic run.
This is
due to the lower levels of migration (caused by the absence of the tax
DELMARVA
PAGE 30
5/06/72
MCCEL
64
4/JTEX/LHEX
TOTPOPP,TOTEMP=EAVGU=U
380.T
110.T
35.A
10.- -P-
400.T
130.T
4.A
----------------
420.T
150.T
55.A
-----E
-
P
P
.
.
P
P
P
0
U
U
P.O
20.-
-
U
-U----
-
U
U
-
-
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.
E
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-
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--
--
E-
---
-
-
E.
E.
0
-
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U
P
.
U
-
-
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U
U
.0
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U
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.
.
.
.
.
.
P .
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P
P
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U
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.
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*
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40.- ------------------------- -- -----
0
-------
E
E
EF
.
P.
E
E
E
.P
.
.
.
.
.
Total Population = P
Total Employment = E
Unemployment = U
-- ---
E
.
.
U---------
Job Training With Housing Program
-
E
-------P
Figure 37
.
E
E
P
U
U*..
U*.
E
P
U-
U
u
U
U
S
E
P
U
*
-
-
E
S
P
30.-
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S
0
U
.
S
U
E
UE
. PU
P
U
-
E
0
S
U
.
U
U
S
U
p.
P
.P
U
50.-------------
S
S
.P
.
- - -
E
-- -- ---
U
- -
S
UU
P
- -
- - -
E
.
.
*
P
- -
-
460.T P
190.T E
75.A U
--
440.T
170.T
65.A
- - -
E
P
P
E
P
P
P
-
------
E
EF
E .
PAGE
5/06/72
DELMARVA MODEL
31
4/JTEX/LHEX
65
LPDP2=2,LPOP3=3,LU=U,MIGL3=0,MICL2=Y,LH=HCDH=X
7600.
50.T
.0
-300.
.0
.0
25.T
10.- -
- Y----- -- --YV
YV
Y
8200,
70.T
40. A
300,
40.
40.T
35.T
7900.
60.T
20.A
.0
20.
20.T
30.T
-0- - H. 0
H
.H
Y
. H
0
2
02
3
.3 2
3
S3
8500.
80.T
60.A
600.
60.
60.T
40.T
X--- ---------U
2
8800. 2
90.T 3
80.A U
900. c
80. y
80.T F
45.T X
--OH
U .
U
u
X
.
.
X
.
U3
X
0 U.
3 X
2.
YV
X 3
2
.
C.
U
H Y
3
X 0
H U Y
2
-I Y-X- -----2----- ---U- H-.-0-3- 20.- - 2
3
H X
Y
U
V.
2
30
X H
YV.
H
2
U.
3 0
X
2
UX.
3
0
H
.
X2
U
H
3
0
X
2U.
3
0
H
.
V
H
3
.
X
V
2.
.
.
Y
H
X
U2
3
0
3
0.
X
U 2
.
H
YV
- - - - -3- -0- - - - - - - - - - - -- - -U2 - 30.- - X---- -- --30.
U
2
HF.
y
.
X
30 .
yYV
.
H .
U
2
3 0.
.
H.
2
U
3.
1.
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2
03
U
2
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Y
U
03
2 .H YV
0 3
.2 Y
.X
U
C£3
U
. H 2
x
0
3
U
.
Y
2
-Y-U- -------0
-3
-2------40.X
X
YH
2
0
3
U
x
2
0
3
X
YH
U
3
U
.
Y H
X
2
0
3
U
.
H
20
X
3
0 2
U
.
Y
H
3
0
2
U
.
Y
H
2
3
0
H
U.
Y
23
Y
H
C
Young Adults = 2
32
Y
H
Adults = 3
-------H
- 2VY0------------3
50
Migration, Young Adults = Y
Y
H
.Y2
2
H
.3
C
U
O
Migration, Adults = 0
Unemployment = U
Total Low-Cost Housing = H
Occupied Dilapidated Housing = X
Figure 38
Job Training With Housing Program:
. Ux
Low-Income Sector
YH
30
2U
30
2H
2Y
YH
Y14
66
factor).
Low-income unemployment is, however, significantly higher
(Figure 42), raising the average unemployment.
Taxes were not formu-
lated as a factor in this group's migration, so that it continued at
almost the same rate.
Industrial migration was, however, affected by
the tax factor, and is, therefore, lower for this set of runs.
This
combination results in the higher low-income unemployment.
Switching off the tax factor is
also equivalent to either a policy of
equalization of property taxes throughout the area surrounding Delmarva,
or that of restructuring taxes to avoid local dependence on the property
tax.
PAGE
5/06/72
DELMARVA MCCEL
33
NO TRF
67
TUTPOP=P,TJTEMP=EAVGU=U
360.T
145.T
50.A
340.T
135.T
40.A
10.-
-
-
420.T P
175.T E
80.A U
4C,00.T
1 65.T
70. A
-FE
.
0
380.T
155.T
60.A
.
P
E
E
.
P
P
E
U
P
EP
E
U
U
0
P
PE
U
-0
U
E
P PE-
U
0
U
U.
U.
.
P
U
U
0
E.
E
P
Uu
U
PU
E
P. PF
U
.
0
P . E
P.
0
PE
-
I
-U------------U----P--E------ - - --
200-
.
0
U
. P
U
U
E
P
.
E
30.-
U .
U
0
P
.
E
E
P
.
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0
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.U
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0
.
P
P
U
U
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-
0
P
P
U
U
-U-
.
U
.
U
E.
E
P
EF
PE.F
P
U
0
0
.
0
.
.
0
500Total Population = P
Total Employment = E
Unemployment = U
Figure 39
No Tax Factor
.U
U
II
U
U
U
U
U --------------------
E
E
Op
.
E
p
P
.0
.
E
r-
P
E
P
.
.
.
E
P
EF
P
--
------
E
P-FE-
.
.
--
5/06/72
DELMARV A MODEL
PAGE 34
NO TRF
68
HPUP3=3,HU=U, MI GH3=0,HPCP2=2 ,MIG2=Y
16.T
26.A
45.
1500.
-90.
15.T
24. A
35.
1400.
-120.
10.- -
- --
-
- -
- 3 -
-
-O-
- -
- -
.2
.2C
U 2.
2.
2.
2.
2 .
2.
Y 2.
C
3Y
U
3 Y
3
a
YU
U3
Y
U
U
20*-
3
Y
3
Y
3
U
0
-- U
-
--
3-
-
- 2
U3
C
- - -
-
- -
--
0
U
0
0
C
2
.3
C.
2Y
U
2Y
3
.
CU
3
0
2Y
.
3
0
C.
3
2
.
3
2
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-
0
0
-------
--
0------0
.
.0
0
ci
.
0.
.0O
U
0
UO
50.- -
-UYoung Adults = 2
Adults = 3
Migration, Young Adults = Y
Migration, Adults = 0
Unemployment = U
Figure 40
No Tax Factor:
High-Income Sector
U.
.U
.U
.U
- - - - .pU
2
2Y
2Y
2Y
- -
- U.0 2Y
U.
Y2
U
3
Y2
U.
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3
U.
3
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U.
3
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U.
3 Y 2
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2
3 Y
U
2
3Y
- 2 - -U- - - - -.Y3
2U
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2
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3
2
2
3
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3
2
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U
3
2
.Y
U
3
2
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U
3
2
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2
Y
3
.
0
40.--------
2Y
2Y
2Y
2Y
3
O
O
.
C
0
U 2
0
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.0
3Y
-
O
.
O
3
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0
2
Y
. 3Y
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3
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-
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3.
- -
------U
- -
3u.
30.-- -
-
2---
3.
.3
19.T
32.A
75.
1800.
.0
18.T
30.A
65.
1700.
-30.
17.T
28.A
55.
1600.
-60.
-
-
-
-
-
-
-
-
-
-
-
Y
----
-3-2-
.
.
--3Y-
DELPARV A MODEL
PAGE 35
5/06/72
NO TRF
69
MPOP2=2,MPO)P3=3,9MU=U,9MI G 3=C ,M IGP2=Y
13 .T
.T
115 1'
45 .A
40 0.
-40
- 3- 0.
12.5T
12.T
95.T
25.A
.0
-800.
10.
105.'T
3 5 .A
200.
-600.
-Y-
0 - Y
0
--- 2 - - -
3 .2
2 .3 0
Y
. 3
Y
2
2
Y
.
2
.U
U.
0
OU
3
U
.
2
2
U
2
U
U
U
U
U
2
2
2
.
2
30.-
--
-
-
-
-
-
-
--
.
U----
---
U
6
U
0
U
U
50.-
--
-
- - -
.U
U
U
U.
U
- U
Young Adults = 2
Adults = 3
Migration, Young Adults = Y
Migration, Adults = 0
Unemployment = U
Figure 41
No Tax Factor:
Middle-Income Sector
- - --3-
-
- - 3U
0.
-c-
-3-
3
2
2
2
Y -
.
.
.
2.
.2
.
.
-
-
- -
-
-
-
-
-
y
Y
Y
Y
Y
3
03
30
30
30
Y
Y
03
Y
y
03
-
-- -
2--
cV
0
30
30
.3
.3
.
.
.
.
U2
2
U
2
U
-
- -
YO.
0.
3
OY
3
0.Y
3
0 Y
3
U
2
U
2
U
2
2U
0
40,-
3
U
U
.2
.2
0
y
3
-U---
- --
2
3Y
u
3 Y
U
C
0
3YU
-0-- - - -UY3U
Y
C
0.
3
Y
0.
Y
U3
2
2
U.
0
3
Y3
12
2
2
30
U
2
20.-
U
2
3
Y
14.T 2
135. Tr3
65.A
800.
.0 ly
13.5T
125.T
55.A
600.
-200.
- - - - -
-03 - -Y - - Y
03
Y
03
Y
03
0 3 Y
0 3Y
3Y
0
2
3Y
0
2
2
0
- - -
3Y
Y3
2 0
2 - Y 3--
20
70
5/06/72
DELMARVA MCCEL
PAGE 36
NO TRF
LPOP2=2.LPOP3=3,LU=U,MIGL3=O,MIGL2=Y,LH=HgDH=X
-400.
-10.
.0
10.- -
3 Y3
Y------
.0
40.T
-0H.
H
3
3
.H
3. H
H
3
-H-
O
Y
U
2
U
2
U
U
2
2
.
.
.
2
-
40.-
S Ye
.
.
X
H
U----U
.
.
H
U
Y
Y
.
---
y
y
YV
2Y
2
2
Y
Young Adults = 2
Adults = 3
Migration, Young Adults = Y
Migration, Adults = 0
Unemployment = U
Total Low-Income Housing = H
Occupied Dilapidated Housing = X
-
-
-
Y
Y
C
0
Y
3
3
3
U
U - .U
.U
.U
,U
.U
.U
U
U
U
2
2-----U-
F
H
3
---
C
C
O
C
0
.
.
.
H
H
H
-
O
.
H
H
Y
Y.U
Y
YV
Y
.
Y
H
Y 3
.
U.
U.Y
.
.
Y H
3
U
Y
Y
0
.
H
H
.3
. 3
.3
U
2
Low-Income Sector
y.
V.
,
.0O
.0O
3.
-H-
2
Figure 42
,
H
3
3
Ye
Y 3X
H
3
23
0.
H
3
.2
.
.
Y
0
3
2
Y*
*Y
O
3
Y
No Tax Factor:
Y
0
C
X
X
X
X
50.-
Y-*
0
X
--
x
2
O0
H.
-
X
2
UJ
Y
3
.
X
2
.X
2U
2
U
X
2
XU.
U
2
X
X
-2-,
2X
X2
X 2
.
2
X
2 .
X
X
2 .
2
X
X
. 2
. 2
-------
2
X.
XH
X--H
3 --3
2
3
U
0
Y
FX
.2
U
H
U
30.-
OX .2
2 X 0
X
2
H
2
2
-
JuX.
U
.
U.
Y
0
HU
X----------------------------
.x
x
X
O
3
U
--------------------
C
.03
.U 3
20.-
--
----
8400.
85.
95.A
400.
IC.
80.T
-2-
8100.
80 .T
85.A
200,
5.
60.T
7800.
75.T
75.A
.0
7500.
70.T
65.A
-200.
- 5 .1
20.T
7200.
65. T
55.A
.0
3--H-
3
H
3F
3
H
F
H
H
H
--
---
.
0
3.
.3
*
.
.
--
3H
C
e
.
0
.
0
- - - - - - - 0
3
3
3
71
CONCLUSIONS
Decisions governing social policy cannot be made by a simulation model.
They can be made with the aid of the model.
will happen with statistical accuracy.
No model can predict what
However, by documenting the per-
ceptions of how different components of a system relate to one another,
the policy-maker can gain insight as to the comparative effects of policy
alternatives on the behavior of the system over time.
At the very least,
the model can provide a framework for discussion of which perceived relationships are valid, and which components of the system need further
study.
Full utilization of a simulation model of the type described in
these sections is achieved with its use as an aid to policy evaluation.
More specifically, choices and trade-offs must be made by the policymaker with respect to the results of the Delmarva Model.
room
There is
for further work in validating the model, but on the basis of its present
plausible structure, a number of observations can be made.
Only short-term impacts on employment can be achieved through active,
vigorous drives to bring specific industrial employers into the area.
This course must be weighed against the more restrained policies of
building a solid base with which to attract additional employment opportunities.
This base should be founded upon improvement of the most valuable asset
of Delmarva--the people.
Through carefully designed policies such as
job-training programs and the provision of low-cost housing, this can be
achieved.
72
Even within such programs, however, there is need for caution.
The area
should not respond to apparently immediately critical situations.
Rather,
policies of some degree of moderation (but not neglect) should be selected
(such as the constant job training and housing programs), which will provide a more desirable result in the long term behavior of the region's
economy.
Here, again, decisions must be made by the policy-maker.
Job-
training programs that raise the skill level of workers when appropriate
employment opportunities are not available on Delmarva will most surely
increase, in the short term, unemployment of the skilled labor force and
subsequent migration out of the area.
not be forthcoming,
Immediate benefits to Delmarva will
with the exception of somewhat lower unemployment
rates among the unskilled.
Such programs will, however, increase the at-
tractiveness of the area for new industry, which will respond slowly but
surely.
In the meantime, a number of people will have been assisted.
In the long run, it will be the Delmarva economy that benefits.
An assumption of the basic model is that Delmarva will respond if only
slowly, to the needs of its young people.
Without allowance for the pro-
vision of the educational facilities which so many seek, the large emigration of youths will continue as it has in the past.
While it may not be
necessary to reverse the phenomenon, to decrease it is desirable.
Providing higher education facilities will, however, provide only a shortterm influence on migration.
Youths attracted to the area (or who stay
in the area) because of these facilities will leave after a few years unless they are subsequently persuaded to remain by those factors affecting
working-age migration.
is obvious.
The need for coordinated and comprehensive planning
73
Elimination of the dependence on local property taxes has a mixed impact
on the system as modelled.
opposing such a strategy fo
The choice must be made between supporting and
the states of Delaware, Maryland, and Virginia.
The effects implied by implementation of such a policy are decreased net
migration of industry and high- and middle-income people (and lower unemployment in these sectors), and relatively unchanged migration of lowerincome people (and subsequent higher unemployment in this sector of the
population).
The inferences from this work do not suggest a course of rapid industrialization and growth.
There is much that is to be preserved on the Delmarva
Peninsula, and much to be improved.
Many planners on Delmarva have advo-
cated "a policy of balanced development... which recognizes both the desirability and the inevitability of growth."7
This course, which must
have support throughout Delmarva to succeed, appears to be a wise one.
74
FOOTNOTES
1.
Overall Economic Development Program--The Delmarv:
Delmarva Advisory Council, 1967
2.
The Recreation Potential of the Delmarva Peninsula,
M.I.T. , 1966
3.
Overall Economic Development Program-------------------------
4.
Industrial Dynamics Newsletter, May, 1969
5.
Peninsula,
D.
L.
Rubin,
For a more extensive explanation, see Industrial Dynamics, Forrester,
M.I.T. Press, 1961
6.
Dynamo II Users Manual, A. L. Pugh, M.I.T. Press, 1970
7.
Overall Economic Development Program-------------------------
75
APPENDIX
PAF
V P%%2 A-
1
&
DELMARVA
L.0
MOCEL
5/06/T2
DELMARVA MODEL
*
NOTE PCPULATICN SECTOR
NOTE
NOTE
NOTE
HIGF-INCCME
NOTE
NOTE AGE 0-18
R
FBR.KL=(BRH)(HPOP3.K)
C BRF=.025
L HPOP1.K=HPOP1.J+(DT)(HBR.JK-ARH1.JK+MIGH1.JK)
R MIGH1.KL=MIGH3.K/2
R ARH.KL=f 4 POPl.K/18
NOTE AGE 1S-22
L HPOP2.K=HPOP2.J+(DT)(ARH1.JK-ARH2.JK+MIGH2.JK)
R ARH2.KL=HPOP2.K/4
R MIGH2.KL=IMIGH2.K/MIGH2D
C MIGH2D=2
NOTE
AGE 23-65
L HPOP3.K=HPOP3.J+(DT)(ARH2.JK-ARH3.JK+MIGH3.J+AUH3.J)
R ARH3.KL=FPOP3.K/43
A MIGH3.K=IMIGH3.K/MIGH3D
A AUH3.K=CLIP(0,UH3.K,HU.K,0)
A UF3.K=MIN(UPH3.K,CUH3.K)
A
UPH3.K=(HE.K)(MPCP2.K/PCS.K)/12
A CUH3.K=-(IHU.K)(fHL .K)/UD
C UD=2
C MIGH3D=3
NOTE
OVER 65
L HPOP4.K=HPOP4.J+(CT)(ARH3.JK-HDR.JK+MIGH4.JK)
R
FDR.KL=HPCP4.K/DRH
C DRH=8
R MIGH4.KL=IMIGH4.K/MIGH4D
C MIGH4D=3
NOTE
NOTE MIOCLE-INCOME
NOTE
NOTE
0-18
R MBR.KL=(BRM)(MPCP3.K)
C BRM=.025
L MPOP1.K= FPO P1.J-+ (DT) (MBR.JK-ARM1.JK+MIG'1.JK)
R ARM1.KL=mPOP1.K/18
R MIGM1.KL=MIGM3.K/2
NOTE 19-22
L PPOP2.K=MPOP2.J+(DT)(ARM.JK-AR2.JK-UPH3.J+MIGM2.JK+UPM2.J)
R ARM2 .KL= (MPOP2.K/4)-UPH3.K
R MIGM2.KL=IMIGM2.K/MIGM2D
C MIGM2D=3
A UPM2.K=(LPOP1.K)(BEUPF.K)(ISRF.K)/18
A BEUPF.K=TABHL(BEUPTBER.K,.8,1.2,.1)
T BEUPT=0/.02/.05/.07/.1
NOTE 23-65
L MPOP3.K=MPOP3.J+(DT)(ARM2.JK-ARM3.JK+UPM3.J+MIGM3.J)
R ARM3.KL=OPCP3.K/43
A MIGM3.K=IMIGM3.K/MIGM3D
C YIGM3D=5
NOTE OVER 65
L MPOP4.K=MPOP4.J+(DT)(ARM3.JK-MDR.JK+MIGP4.JK)
R kCR.KL=MPCP4.K/CRM
76
PAGE 2
DELMARVA MODEL
I- a
0%
a -0
-
5/06/72
0RM=9
C
R MIGM4,KL=IMIGM4,K/MIGM4D
C MIGM440=5
NOTE
NOTE
LOW-INCOME
NOT E
NOTE
0-18
R LBR.KL=(BRL) (LprP3.K)
C BPL=.025
L LPOPl.I<=LPOPI.J+(DT) (LfR.JK-ARL1.JK-UPM2.J+MIGLL.JK)
R ARL1,KL=(LPOP1*K/18)-UPM2.K
RIIGLlKL=MIGL3.K/2
NOTE
19-22
L LPOP2.K=IPOP2.J4(DT)(ARL1.JK-ARI2.JK+MIGL2.JK-JPM3.J)
R ARI2,KL= (LPOP2,K/4)-UPM3.K
R MIGL2.KL=IMICL2.K/MIGL2O
C MJGL2D=5
NOTE 23-65
I LPOP3.K=LPOP3.J+(DT) (ARL2.JK-ARL3.JK4MICL3,J)
R ARI3.KL=LPOP3*K/43
A MIGI3.K=IMIGL3,K/IJ1 IGL3D
C ?'G13D=?
NOTE OVER 65
L IPOP4.K=IPOP4.J+(DT) (ARI3,JK-LDR.JK)
R LDR.KL=LpC-P4.K/DPL
C CRI=9
NOTE
TOTAL POP*
A TCTPOP .K=HPO-P. K+HPOP2. K+HPOP3 .K+HPOP4 .K+MPOPI..K+MPOP2.K+MPOP3 .f(4
X ?POP4K+POPIK4LPOP2.K+IPOP3.K+LPOP4.K
NOT E
NOT E
NOTE
EMPLCYMENT SECTOR
NOT E
NOTE HIGH-INCOME
A HU.K=(I-MDF.K )(PHUK)
A IPIK=(HPCP3.K) (HHPF)
C fHI-PF=*6
A PHU.*K= (IL.K-H4LD.K)/HI.K
A HMDF.K=TA8HI(HMDTRHU.KOt,04,,O1)
T HMDT=1/1#05/1*1/1.1f1,15
A HID. K=AGHO.K4AMHC.K+OMHDoK+THD.K+BSHD.K+HSHO .K+GHD.K
NOTE
MIDOIS-INCOME
A tUK=fMMCF*K )(PiU*K)
A ML.K=(MPCP3*K) (MFtPF)
C MIPF=.75
A MMDF.K=TAIHL(MMOTRMU.KO,.089,2)
A RMUK=(MLK-MIDtl/ML.K
A MID.K=AGMO.K+AMMO.K+OMMD.K+TMD.K+BSMD.K+HSMC.K+GMD.K
NOTE
LCW-INCOME
A IU.K=(IMOF.K)(RUK)
A LI.K=(IFCP2.K+LPCP3,K)(LHPF)
C LHPF=.8
A LMDF.K=TABHI(LMCTPIU.KOy,.1,.02)
T LMOT=1/1.2/1 25/1,25/1,3/1,35
A RLUK=(LK-IID.K)fII.K
A IID.K=AGLD.K+AMIO.K+OMID.K+TID.K+BSLD.K+HSIC.K+GID.K
NOTE
TCTAL EMP.
77
PAGE 3
CELMARVA MOCEL
5/06/72
78
A
TCTEMP.K=AHEMP.K+AMEMP.K+ALEMP.K
A
A
AFEMP.K=F-L.K-(HL.K)(AHU.K)
AMEMP.K=ML.K-(ML.K)(AMU.K)
A ALEMP.K=LL.iK-(LL.K)(ALU.K)
A AHU.K=MAX(HU.K,0)
A AMU.K=PAX(MU.K,0)
A ALU.K=MAX(LU.K,O)
NOTE
NOTE
NOTE
INDUSTRIAL SECTOR
NOTE
AGRICULTURE
NOTE
L AG.K=AG.J+(DT)(CAG.JK)
R CAG.KL=((AGMF.K-AG.K)/AAT)+AGEX.K
C AAT=2
A AGEX.K= STEP (AGSH ,AGST)
C AGSH=O
C
AGST=10
A AGMF.K=AM.K/AMPAC
A AGHD.K= (AGHDF) (AG.K)
C AGHDF=O
A AGMD.K=(AG.K) ( 1-AGHOF-AGLDF)
A AGLD.K=(AGLDF)(AC.K)
C AGLDF=.7
NOTE
AGRIC.-MANUFACTURING
L AF.K=AV.J+(DT)(CAM.JK)
R CAM.KL=((IAM.K-AM.K)/AMAT)+AMEX.K
C AMAT=4
A AMEX.K=PULSE(AMPHAMPTAMPI)
C AMPH=O
C AMPT=20
C AMPI=10
A
AMHD.K=(AMHDF)(AM.K)
C AMHDF=.05
A AMMO.K=(AP.K)(1-AMHDF-AMLDF)
A AMLD.K=(AM.K)(AMLDF)
C AMLDF=.5
NOTE OTHER MFG.
L OM.K=OM.J+(DT)(CCM.JK)
R CCM.KL=(ICOM.K/COMD)+OMEX.K
C CCMD=4
A CMEX .K=PULSE(OMPSHOMPSTOMPSI)
C CMPSH=O
C CMPST=20
C CMPSI=1c
A
CMHD.K=(CM.K)(Cv'-DF)
C
A
CMHDF=.05
OMMD.K=(CM.K )(1-CMHDF-OMLDF)
A
CMLD.K=(CM.K)(ON'LDF)
C CMLDF=.5
TCURISM
NOTE
L TM.K=TP.J+(DT)(CTM.JK)
R CTM.KL=((ITM.K-TM.K)/TMAT)+TMEX.K
C TMAT=4
A TMEX.K=PULSE(TMPHTMPTTMPI)
C TMPH=O
C TMPT=20
C TMPI=10
PAGE 4
DELMARVA MODEL
5/06/T2
A THD.K=(T!'.K)(TMHDF)
C TMHDF=.05
A TMD.K=(TN.K)(TMMCF)
C TMMDF=.55
A TLD.K=(TM.K)(TMLCF)
C
TMLDF=.4
NOTE BUSINESS-SERVING
L BS.K=BS.J+(CT)(CBS.JK)
R CBS.KL=(IBS.K-BS.K)/BSAT
C BSAT=4
A IBS.K=AM F.K+CMF.K+HSF.K
A
AMF.K=(A M.K) (BSAMF)
C
BSAMF=.5
A
CMF.K=(CN.K)(BSCMF)
C BSOMF=.5
A HSF.K=(FS.K)(BSHSF)
C
BSHSF=.2
A BSHD.K=(BS.K)(BSHDF)
C ESHDF=.1
A BSMD.K=(BS.K)(1-BSHDF-BSLOF)
A BSLD.K=(B-S.K)(BSLDF)
C BSLDF=.2
NOTE
HOUSEHOLD-SERVING
L HS.K=HS.J+(DT)(CHS.JK)
R
CHS.KL=(IHS.K-HS.K)/HSAT
C
HSAT=4
A
IFS.K=(FSEMPF)(TCTEMP.K)
C
HSEMPF=.25
A
FSHD.K=(fHS.K)(HSHDF)
C
HSHDF=.1
A
FSMD.K=(HS.K)(HSMDF)
C I-SMDF=.7
A HSLD.K=(HS.K)(HSLDF)
C HSLDF=.2
NOTE
GOVT. (PUBLIC)
L G.K=G.J+(DT)(CG.JK)
R CG.KL=(IG.K-G.K)(GSAF.K)/GAT
C CAT=8
A
IG.K=(AM.K+OtM.K+TM.K)(GIF)+(TOTPVP.K)(GPF)
C GPF=.04
C GIF=.2
A GHD.K=(G.K)(GHDF)
C
GFDF=.05
A GMD.K=(G.K)(CMDF)
C CMDF=.55
A GLD.K=(G.G)(GLDF)
C GLDF=.4
NOTE
NOTE
NOTE SCCIAL PCLICY SECTOR
NOTE
NOTE
TAX RATES (RATIOS)
A IPTR.K=(AM.K+CM.K-17000)/34000
A PPTR.K=(TCTPOP.K-190000)/380C00
NOTE JCP TRAINING
A PLU.K=SPMCDTH(LU.KPLUPT)
C PLUPT=5
C OKLU=.06
79
PAGE 5
- -
.
..
a
-
.
.
.
..
-
-
f- A
DELMARVA MODEL
r- RONp
& 1-9 -%
5/06/T2
A
R
A
A
C
C
A
CKLUSW.K=CLIP(1,0,PLU.K,CKLU)
RIJT.KL=IJT.K+JTEX.K
JTEX.K=(JTEXSW)(AJTEX.K)
AJTEX.K=STEP(JTHJTIME)
JTH=300
JTEXSW=O
IJT.K=((OKLUSW.K)(ISRF.K)(JTSAF.K)(JTINESW.K)(PLU.K-OKLU)(LL.K)/JTAT
X
)(1-JTEXSW)
A JTIMESW.K=CLIP(1,O,TIME.K,JTIME)
C JTAT=10
C JTIME=15
L LUJT.K=LUJT.J+(CT)(RIJT.JK-UPM3.J)
A UPM3.K=LLJT.K/JTT
C JTT=2
NOTE LOW-INCOME HCUSING
L LH.K=LH.J+(DT)(HFR.JK+LHCR.JK-LHOR.JK)
R
FFR.KL=M-.K/1FT
C
A
C
L
R
HFT=40
MH.K=(MFCPI.K+MPCP2.K+MPOP3.K+MPOP4.K)/MHSA
MHSA=3
CDH.K=CDH.J+(DT)(LHOR.JK-DHAR.JK)
CHAR.KL=CLIP(IDHAR.K,OEXTLH.K,O)
A
IDHAR.K=EXTLI-.K/LMT
C LMT=4
A EXTL.K=TCTLF.K-(DESLH.K)(LMOF.K)
A DESLH.K=(LPOP1.K+LPOP2.K+LPOP3.K+LPOP4.K)/LHSA
C LHSA=3
R LHDR.KL=LF.K/LHDT.K
A LHDT.K=(LHDTM.K)(NLHOT)
A L4R.K=TCTLH.K/DESLH.K
C NLHDT=20
A LHOTM.K=CLIP(1,LHR.K,TOTLH.KDESLH.K)
A TOTLF.K=LH.K40DH.K
A PCDH.K=SvCCTH(OCH.K,PODHPT)
C PODHPT=5
R LHCR.KL=LHC.K+LHEX.K
A
LHC.K=((ISRF.K)(PODH.K)(LHSAF.K)/LHCRAT)(L4TSW.K)(1-LHEXSW)
C LHCRAT=4
A LTSW.K=CLIP(1,OTIME.K,LHTIME)
C
LHTIME=15
A LHEX.K=(LHEXSW)(ALHEX.K)
A ALHEX.K=STEP(LFH,LHTIME)
C LHH=500
C LHEXSW=O
BASIC EDUCATION
NOTE
L BE.K=BE.J+(DT)(CEE.JK)
R CBE.KL=(IBE.K-BE.K)(BESAF.K)/8EAT
C BEAT=8
A
IBE.K=(LPOP1.K+MPOP1.K+HPOP1.K)(.67)
NOTE
L
HIGHER EDUCATION
HE.K=HE.J+(DT)(CHE.JK)
R CHE.KL=(IHE.K-HE.K)(HESAF.K)/HEAT
C
EAT=8
A IHE.K=(PCS.K)(HEP2)
C HEP2=.6
A PCS.K=MPCP2.K+HPCP2.K
NOTE
SCARCE ALLOCATION
A LHS SAF.K=1+STEP(STLHSTLT)
80
PAGE
6
DELMARVA MCCEL
5/06/T2
C STLH=-.5
STLT=O
C
A HESAF.K=1+STEP(STHH,STHT)
C STIH=-.7
C STHT=O
A LRSAF.K=1+STEP(STLRH,STLRT)
C STLRH=-.3
C
STLRT=0
A JTSAF.K=(LRSAF.K)(JTP.K)
A JTP.K=CLIP(1,AJTPLRSAF.K,1)
C AJTP=1
A BESAF.K=(LRSAF.K)(BEP.K)
A BEP.K=CLIP(1,ABEP,LRSAF.K,1)
C ABEP=1
A GSAF.K=(LRSAF.K)(GP.K)
A GP.K=CLIP(1,.AGPLRSAF.K,1)
C AGP=1
NOTE
NoTE
NOTE
ATTRACTIVENESS FACTORS
NOTE
NOTE
HIGHER EDUCATION
A HER.K=HE.K/IHE.K
A AFEDF.K=TABHL(AHEDT,HER.K,.5,1.1,.1)
T AHEDT=-.15/-.12/-.l/-.05/-.02/0/.02
A I-EDF.K=SMOOTH(AHEDF.KHEPD)
C HEPD=8
NOTE
BASIC EDUCATION
A BER.K=PE.K/IBE.K
A ABEDF.K=TABHL(A9EDT,BER.K,.8,1.2,.1)
T ABEDT=-.C2/0/0/0/.05
A BEDF.K=SMOOTH(ABEDF.KBEPD)
C BEPD=8
NOTE
HIGH-INC.UNEMP.
A
-UF.K=-(RHU.K-KHU)
C CKHU=.02
NOTE
TAXES -
INDIVIDUAL
A
TRF.K=TABHL(TRTPPTR.K,.5,1.1,.2)
T
TRT=.03/.02/.01/-.01
NOTE ENVIRONMENTAL QUALITY
A EQI.K=EQBASE/PEQ.K
A
PEO.K=TCTPOP.K+TM'.K+(OM.K+AM.K)(EREG.K)
C EQBASE=417000
A EREG.K=1+STEP(ERSHERST)
C ERSH=O
C ERST=15
A AENVQF.K=TABHL(AENVQTEQI.K,.4,1,.2)
T AENVCT=O/.01/.01/.02
A ENVQF. K=SMOCTH( AENVQF.K, EQPD)
C EQPD=10
NOTE
REC./CULTURAL
A RCR.K=HS.K/I FS.K
A ARCF.K=TABHL(ARCT,RCR.K,.5,1.1,.2)
T ARCT=-.02/-.01/-.01/.01
A RCF.K=SICCTH(ARCF.K,RCPD)
C RCPD=10
NOTE
MID.-INC. UNEMP.
A
MUF.K=-(RMU.K-KiMU)
.81
PAGE 7
5 /06/7 2
DELMARVA MCCEL
C OKMU=.05
NOTE
LCW-INC. UNEYP.
A LUF.K=-(RLU.K-OKLU)
OUSING
LOW-INC.
NOTE
A ALHF.K=TABHL(ALHT,OHR.K,.2,.8,.2)
T ALHT=.C3/.02/0/-.01
A Ct4R.K=CDH.K/TOTLH.K
A LHF.K=SMOOTH(ALHF.K,LHPD)
C LHPD=8
NOTE
NOTE AGRICULTURE
A
AGF.K=(AG.K)(AMPAG)
C
AMPAG=.7
NOTE
A
T
TAXES -
INDUSTRIAL
ITRF.K=TABHL(ITRTIPTR.K,.5,1.1,.2)
ITRT=.03/.02/.01/-.01
NOTE
BUSINESS -
SERV.
A BSR.K=BS.K/IBS.K
A ABSF.K=TABHL(ABSTBSR.K,.6,1.2,.2)
T ABST=-.03/-.C1/0/.01
A BSF.K=SMOTH(ABSF.K,BSPD)
C BSPD=4
NOTE
PUBLIC FACILITIES
A GR.K=G.K/IG.K
A RGF.K=TAEHL(AGTCR.K,.6,1.2,.2)
T
AGT=-.01/0/0 /.01
A GF.K=SMOCTH(RGF.K,GPD)
C GPD=4
NOTE REG. POP.
A REGP.K=REGPN+(RAMP(REGG,1))(REGGSW)
C REGPN=6E6
C REGGSW=1
C REGG=10E4
A AREGPF.K=(REGP.K)(TMPREG)
C
TMPREG=.001
A REGPF.K=SMOOTH(AREGPF.K,REGPD)
C REGPD=4
NOTE LABOR
A AVGU.K=(LU.K+MU.K)/2
A ALAF.K=TABHL(ALATAVGU.K,0,.1,.02)
T ALAT=0/0/0/.02/.04/.05
A LAF.K=SICCTH(ALAF.K,LAPD)
C LAPD=4
SOCIAL RESISTANCE
NOTE
L TAMIG.K=TAMIG.J+(DT)(CMIG.JK)
R CMIG.KL=MIGH3.K+fMIGM3.K+MIGL3.K
A AMIGR.K=TAMIC.K/(HPOP3.K+MPOP3.K+LPOP3.K)
A ASRF.K=TABHL(SRTAMIGR.K,0,.33,.11)
T SRT=-.5/-.4/-.15/0
A SRF.K=(SRSW)(ASRF.K)
C SRSW=1
A ISRF.K=1+SRF.K
NOTE EXTERNAL ECONCMIC CONDITICN
A EECM.K=SWITCH(1.EC!N.K,ECSW)
C ECSW=O
A ECIN.K=1+ECAMP*CCS(6.283*TIME.K/ECPER)
C ECPER=8
C ECAMP=.5
82
PAGE 8
DELMARVA MCDEL
5/C6/72
INDIC. MIG.
NOTE
A
IMIGH2.K=(HEDF.K)(HP0P2,K)
A
IMG-3K(P3K)(IEFK(A)HU.)A)+RFKCA)
X (ENVCF.K) (D.AS)+tRCFK)(EAS)+(GF.K)(FAS))
A IMIGF4,K=(HPCP4.K)((TRFK)(HAS)+(RCF.K)(IAS))
A lfIGM2K=(MPCP2.K)(NEDF.K) (JAS)+(MUF.K)(KAS))
A IMIGM3.K=fMPOP3.K)((BEDF.K) (LAS)+(MUF.K)(MAS)4(TRF.K)(NAS)+(ENVQF.K)
X
(OAS)+ (RCFK)(PAS)+( GF.K)(QAS))
A
pIGM4.K=(MPCP4.K) (TRF.K )( RAS)
A
IMIGL2,K=(LPCP2.K) (LUF.K)(ISRF.K)
A IMIGL3.K=(LPCP3.K)( (LUF.K)(TAS)+(LMF.K)(UAS))
A IAM.K=(AGF.K)(l+((ITRFK)(ASA)+(LAF.K)(ASB)+(BSF.,K)(ASC)+(GF.K)(ASD))
X
(EECMK))
A
ICGMK=(CM.K)((LAFK)(ASE)4(ITRF.K)(ASF)4(GF.*K)(ASG)+(8SF.K)
X (ASH)) (MSRF.K) (EECMK)
A MSRFK=CLIPf ISRFK,l,IC0fVCKqO)
A ICOMC.K=(LAF.K) CASE)+(ITRF.K)(ASF)+(GF.K)(ASG)+(BSF.K)fASH)
A ITM.K=(PEGPF.K) (1+UGF.K) (AS I)+(ENVCF.K)(ASJ))(EECM.K))
C AAS=l
C BAS=1
C CAS=1
C CAS=1
C EAS=l
C FAS=1
C FAS=1
C IAS=1
C JAS=1
C KAS=1
C LAS=1
C ?'AS~l
C NAS=1
C CASIl
C PAS=1
C CAS=1
C RAS=1
C TAS=1
C UAS=1
C ASA=1
C
ASB=1
C ASC=1
C ASD=1
C ASPE=1
C ASF=l
C ASG=1
C ASH=1
C ASI=1
C ASJ=1
NOTE
NOTE
NOTE
INITIAL VALUES
NOTE
NOTE
N I-POP1=10000
N HPOP2=1500
N HPOP3=1500
N HPOP4=3OOO
N MPOP1=76000
N MPOP2=125OO
83
PAGE
9
DELMARVA
MCCEL
5/06/772
N MPOP3=107000
N MPOP4=22000
N LPOP1=44000
N LPOP2=11000
N LPOP3=63CO
N LPOP4=13C00
N AG=22500
N AM=14000
N CM=19500
N TM=5500
N BS=23500
N HS=32500
N G=22500
N LUJT=O
N LH=6000
N ODH=370CC
N HE=5000
N
BE=BEN
C BEN=85000
N TAMIG=O
NOTE END CF MOCEL
DIRECTIONS
NOTE
A PLTPER.K=STEP(1,10)
PLCT
TOTPOP=P/TOTEMP=E/AVGU=U
PLOT HPOP3=3/HU=U/MIGH3=0/HPOP2=2/IGH2=Y
PLOT MPOP2=2/MPOP3=3/MU=U/MIGM3=0/MIGM2=Y
PLOT
LPOP2=2/LPOP3=3/LU=U/MIGL3=0/MIGL2=Y/LH=H/ODH=X
PLOT AG=A/AM=F/OM=C/TM=T/G=G/BS=B/HS=H
PLOT EQI=E/BER=B/HER=H/IPTR=T/PPTR=P/GR=G/RCR=R/ISRF=S
SPEC DT=1/LENGTH=50
BASIC4
RUN
84
85
BIBLIOGRAPHY
Delaware State Planning Office and University of Delaware, The Delaware
Population and Economy, 1968
Delmarva Advisory Council, Overall Economic Development Program--The
Delmarva Peninsula, 1967
Forrester,
J.
W.,
Industrial Dynamics,
M.I.T. Press,
1961
Forrester, J. W., Principles of Systems, Wright-Allen Press, 1968
Forrester, J. W., Urban Dynamics, M.I.T. Press, 1969
Hamilton, et. al.,
1969
Systems Simulation for Regional Analysis, M.I.T. Press,
Industrial Dynamics Newsletter,
May,
1969
Pugh, A. L., Dynamo II User's Manual, M.I.T. Press, 1970
Rubin, D. L. , 'The Recreation Potential of the Delmarva Peninsula",
Unpublished Master's Thesis, M.I.T., 1966
Samuelson, P. A., Economics, Eighth Edition, McGraw-Hill, 1970
State of Maryland, Maryland Statistical Abstract, 1970
University of Virginia, Statistical Abstract of Virginia, 1970