W H .^^^h. ^^^^k ^M *
USDA
EPA
United States
Department of
Agriculture
Economic Statistics and
Cooperative Service
Washington DC 20250
United States
Environmental Protection
Agency
Office of Environmental
Engineering and Technology
Washington DC 20460
EPA-600/7-80-146
August 1980
Research and Development
An Introduction to the
COALTOWN Impact
Assessment Model
Interagency
Energy/Environment
R&D Program
Report
-------
EPA 600/7-80-146
August 1980
AN INTRODUCTION TO THE
COALTOWN IMPACT ASSESSMENT MODEL
by
Lloyd D. Bender
The Economics and Statistics Service
Montana State University
Bozeman, MT 59717
George S. Temple
Department of Agricultural and Applied Economics
University of Minnesota
St. Paul, MN 55101
Larry C. Parcels
Department of Agricultural Economics and Economics
Montana State University
Bozeman, MT 59717
EPA IAG Number
79-D-X-0519-EG
Project Officer
Paul Schwengels
This study was conducted by
The Economics and Statistics Service
The United States Department of Agriculture
500 12th St. SW
Washington, D.C. 20250
Office of Environmental Engineering and Technology
Office of Research and Development
U.S. Environmental Protection Agency
401 M St. SW
Washington, D.C. 20460
-------
DISCLAIMER
The conclusions and views expressed are those of the authors alone and
do not represent policy of The Economics and Statistics Service or the U.S.
Environmental Protection Agency.
ii
-------
FOREWORD
Coal mining and conversion in the coal-rich Northern Plains create
social and economic change in rural areas of the region. Coal development
is expected to accelerate due to the low sulfur content of the reserves and
their relative ease of mining as utilities throughout the Nation turn to coal
for an alternative source of energy. Renewed Federal leasing of these vast
reserves and the application of new coal conversion technologies could add
greatly to the impacts in the few rural parts of the region with the easiest
access and greatest reserves.
This report summarizes the first phase of a research plan designed to
improve the accuracy of local economic impact estimates that are due to coal
development in the Northern Plains. Very little attention has been directed
to problems of estimating rapid employment and population changes in rural
localities. Procedures designed for urban areas and large regions are in-
applicable to small and isolated rural economies. The accuracy of impact
analyses for rural areas has suffered.
Future research will be directed to estimating more precisely the timing
of adjustments and their spatial distribution — elements which are especially
critical to impact assessments but which have received no research attention.
That research will relate local economic changes in the past to changes in
current periods, and will be directed to the general case of rapid rural
change. It's successful application will be useful to evaluate numerous
types of rapid change in many different rural places — a generic application
not confined to the-Northern Great Plains.
This research has been conducted with_ the cooperation of Montana State
University, North Dakota State University, and the University of Minnesota
through special agreements with the Economics and Statistics Service of the
U.S. Department of Agriculture. Numerous other technical and lay reports
relating to coal development and community impacts are available from this
research effort.
Paul Schwengels, Project Officer
Office of Environmental Engineering
and Technology
Office of Research and Development
U.S. Environmental Protection Agency
Kenneth L. Deavers, Director
Economic Development Division
Economics and Statistics Service
U.S. Department of Agriculture
iii
-------
EXECUTIVE SUMMARY
COALTOWN simulates future employment, population, wage levels, migration,
State and local tax receipts, intergovernmental transfers, and local government
expenditures for counties in Montana, Wyoming, and North Dakota. The model is
designed to assess impacts by comparison of results when one or more energy
projects are included with baseline results.
The model has three parts — socioeconomic, State and local government
revenues, and local government expenditures. The structure of COALTOWN is dif-
ferent from an economic base model although modified economic base concepts are
used. Stochastic estimates of the major parameters of the model are used. A
multiplier is not calculated by the model. Rather, predictions of 'ancillary'
employment are made by use of equations. The model is dynamic in that it uses
lagged variables, and because interrelationships among variables in the equa-
tions of the model produce reverberations in years following an initial change.
Although the coefficients in the equations of the model are representative of
Northern Plains counties, the application to a specific county yields results
which will be different from that of counties with other background conditions.
The model is designed for near term prediction and assessment purposes.
Principal uses are:
1. Predict absolute levels of socioeconomic aggregates for planning
purposes.
2. Assess impacts on socioeconomic aggregates for purposes of evalua-
tion of a facility.
3. Test the sensitivity of socioeconomic and fiscal aggregates to key
policy measures.
The predictive accuracy of the stochastic estimating equations in the model
is extremely good. The accuracy of results of the full model deteriorates for
successive years included in the analysis, a characteristic common to all simu-
lation models.
The model has limitations and shortcomings. First, it estimates only
aggregate parameters because predictive accuracy declines with the level of
disaggregation. Second, accurate results are very much dependent on the accu-
racy of data supplied by users as inputs. Third, no capability now exists to
estimate the distribution of new people among counties, and local governments
within a county. Finally, the level of aggregation to some extent prevents an
analysis of the distribution of impacts among residents in the area.
The COALTOWN impact assessment model is a prototype research model in its
current form. Each part of it can be used separately. The model requires main-
tenance each year in order to be up-to-date, and the model parameters should be
re-estimated periodically. The general structure of the COALTOWN model appears
to be a useful approach to impact assessments, and is a departure from other
models used for that purpose.
iv
-------
CONTENTS
Foreword iii
Executive Summary iv
Figures vi
Tables vi
Introduction 1
General Characteristics of the COALTOWN Model 1
The Socioeconomic Component . 1
Ancillary Employment 3
Employment Participation by Residents 4
Migration and Wages 4
Dynamic Interactions 5
State and Local Revenues 6
Local Government Expenditures 7
Principal Uses of the COALTOWN Model 7
Information Supplied by the User 8
Data Generated by the Model 8
The Predictive Accuracy of Estimating Procedures 10
Limitations of the Model 12
References 14
Appendices
A. Data Supplied to COALTOWN 16
B. Characteristics of Data Used in Estimating COALTOWN 19
C. Stochastic Estimation of COALTOWN Parameters 21
D. Predictive Accuracy of Estimates for Selected Counties ... 25
E. The System of Equations 28
-------
FIGURES
Number Page
1 General structure of the socioeconomic segment of COALTOWN .... 2
2 General structure of the revenue segment of COALTOWN 6
3 Example of summary output of COALTOWN, example county - Montana
Northern Great Plains scenarios - with new mines 9
4 Example of detailed revenue of COALTOWN, example county - Wyoming
Northern Great Plains scenarios - without new mines 10
TABLES
Number Page
1 Equation estimates and observed values, population 1970-77,
seven Northern Plains counties with coal mining and/or
conversion 11
Appendix C
1 Estimating equation for ancillary employment in Northern Great
Plains counties 22
2 Estimating equation for employment ratio in the Northern Great
Plains counties 23
3 Estimating equation for net migration in Northern Great Plains
counties 24
Appendix D
4 Equation estimates and observed values, ancillary employment,
non-farm proprietors and employment per 100 population
1970-77, seven Northern Plains counties with coal mining
and/or conversion 26
5 Simulated predictions of employment-population ratio and
population, and percent deviation from observed 1971-74,
seven Northern Plains counties with coal mining and/or
conversion 27
vi
-------
INTRODUCTION
Recent increases in coal mining and projections of future development
and utilization of Northern Plains coal reserves have created concern that
major social and economic changes will follow (2). JY Federal, State and
local leaders need assessments of the effects of coal development on local
economies and governments. Impact assessments aid in the evaluations of
public and private policies and the impacts of specific projects, and can
guide local and regional planning.
The COALTOWN model simulates future employment, population, wage levels,
migration, State and local tax receipts, intergovernmental revenue transfers,
and local expenditures for counties in the Northern Plains States of Montana,
Wyoming, and North Dakota. COALTOWN enables an analyst to estimate and com-
pare conditions "with" and "without" a project over a period of time. The
project's impact is the difference between each of the forecasted indicators
"with" and "without" the project.
COALTOWN is designed to reproduce conditions existing in the current
economy and to modify those relationships as growth and change occur (11).
Dynamics are built into the model's statistically estimated equations. The
reverberations following initiation of a project and the continuing shocks
within the system can be traced over a period of years.
GENERAL CHARACTERISTICS OF THE COALTOWN MODEL
COALTOWN estimates three sets of impacts — aggregate socioeconomic
indicators, governmental revenues, and governmental expenditures. Results
from each can be used separately or in combination with the others for assess-
ment purposes. Each has its own methodology, strengths, and weaknesses. The
simulation begins with data supplied by the user for an initial year. Data
for succeeding years are supplied by the model itself as well as the user.
The number of years to be included is determined by the user, but the model
is most appropriate for near-term analyses.
" /
The Socioeconomic Component
Estimates of employment, jobs per resident, migration, population, and
wages of ancillary employees are generated by the socioeconomic equations of
COALTOWN. The definitions, methods of estimating and relationships among the
elements of the model are quite different from that of an economic base model
even though the rationale is similar.
\J Underscored numbers in parentheses refer to references listed at the
end of this report.
-------
Primary employment, past economic events in the local economy, and the
events in and the characteristics of the economies in adjacent areas all drive
the socioeconomic part of the model (Figure 1). The model is dynamic in more
than one respect due to this. A local economy may change even though primary
employment may not change. In addition, a change in primary employment can
alter local economic conditions in future years (10, 1).
One year prior to the
year being estimated
Year being estimated
Key:
f Primary
V. employment
Primary
employment
Adjacent
primary
employment
Adjacent
primary
employment
Ancillary
employment
Ancillary
employment
Total employment change
Wage rates
and
town size
Employment
participation
rates
Employment
participation
rate
Population
level
Population
Net
migration
Net
migration
Wage rates
Local
government
spending
exogenous variable
endogenous variable
estimated relationship
Figure 1 — General structure of the socioeconomic segment of COALTOWN
^wsssgte^1^*-
-------
Primary employment is composed of all energy-related construction and
operation employment plus employment in agriculture, manufacturing, heavy
construction and Federal establishments. These industries by assumption ex-
port their production to people outside the county. 2j The user must supply,
for use in the model, the number of workers in energy activities and in other
primary industries for each year simulated. What has happened previously in
a local economy establishes a foundation upon which future changes are built.
The levels of primary employment, wages, employment participation and migra-
tion in prior years are a part of that economic foundation which influences
current and future changes. The size of the largest town in a county, the
distance to a regional trade center, the size of the dominant competing cen-
tral place in an adjacent county, and the amount of primary employment in
adjacent areas help to determine ancillary employment in a county. In turn,
these also affect the future place of a county in the central place hierarchy.
The socioeconomic part of COALTOWN contains three essential equations
which are used to estimate ancillary employment, the ratio of employment to
population, and migration and wages. A brief description of what factors
affect each of these impact elements followed by an illustration of some of
the dynamic behavior will serve to introduce the COALTOWN model.
Ancillary Employment
The number of ancillary jobs in the aggregate is estimated by the COAL-
TOWN equations for each simulation year (10). An employment multiplier is not
estimated directly. _3/ Estimation of ancillary employment in a forecasting
equation rather than calculating a multiplier each year requires less data,
allows the introduction of dynamic features, and allows the implied marginal
multiplier to be different from the average and to be non-linear. Ancillary
employees include workers in wholesale and retail trade, finance, insurance,
real estate, personal and business services, State and local government, and
the portions of the transportation and construction sectors which serve local
people, kj
Ancillary employment is estimated as a function of the amount of primary
employment in the current and prior year, ancillary employment in the prior
year, the wage level of the prior year, the characteristics of the county
economy, and the spatial setting of the county economy. _5/ It is noteworthy
that ancillary employment in a county is described as responding to a host
of variables and not just primary employment. Ancillary employment may in-
crease due to secular change in service production technology in the Nation
2J 'Primary' employment does not encompass all basic employment in a county
as economic base theory would dictate. For instance, service activities pro-
vided to people commuting from other counties is not included as 'primary'
employment.
j}/ The number of ancillary jobs in relation to the number of primary jobs
is an implied multiplier, but its magnitude will not be the same as multipliers
calculated with other definitions.
_4/ Ancillary employment in the transportation and construction sectors are
calculated using location quotients.
_5/ The stochastic equation is in Appendix C, Appendix table 1.
-------
or to increases in wages in a county (I). Furthermore, changes in prior
years affect ancillary employment in succeeding years. The characteristics
and spatial setting of a county which influence ancillary employment are the
distance to a regional trade center, the size of the largest town in a county,
and the activities in adjacent counties. For these reasons the number of
ancillary workers in relation to primary workers varies from county to county,
and through time.
Employment Participation by Residents
The demand for new workers in a county can be met by residents entering
the labor force or by migration. The potential supply of local workers de-
pends upon how much of the population is already employed, and how many would
be attracted into the labor force if new jobs were available.
An estimate of the ratio of employment to population is used to calculate
new entrants of local residents into the labor force as labor demand increases. 6_/
The ratio of employment to population tends to increase and to remain high in
tight labor markets characteristic of growing economies. To the extent that
local people continue to enter the labor force, then the ratio of employment
to population increases, migration is unnecessary, and population increases do
not occur as employment expands. As the employment to population ratio reaches
its upper limits, then migration occurs to satisfy demand for new labor. Once
employment increases cease, the employment to population ratio slowly declines
as migrants replace local residents in the labor force, and it continues to
decline until the proportion of residents with jobs returns to a normal level.
The number of jobs per person (the employment-population ratio) typically
varies a great deal across counties and through time, but can be estimated
quite accurately.
The model has a special programmed option for specifying an employment to
population ratio for inmigrants different from that which is estimated within
the model for the resident population. The feature is extremely important
during construction periods when a temporary labor force is brought to the
site of a facility. Temporary construction workers often leave their families
elsewhere, and a high ratio of employment to population is observed for them.
The result is a much less severe impact on population than might otherwise be
expected.
Migration and Wages
Migrants fill those jobs not taken by local residents. Some construction
and skilled operating employees are brought into a county through contracts
and nationwide hiring. Other workers migrate to the area in response to job
opportunities, higher wages, and local economic conditions.
COALTOWN explicitly assumes that wages will rise to the level required
to induce net migration sufficient to cover the shortfall or surplus from
employment demand not met by the local labor force. Net migration is a func-
tion of population of the county, migration in the prior year, wage level,
b] The stochastic equation is in Appendix C, Appendix table 2.
4
-------
employment change, and relative employment participation. _7/. Net migration
into a county is a flow of people. Some migration will come about as a re-
sult of the population size of an area without change in the economy. Other
migration will occur as a result of prior changes including prior migration,
employment changes in a prior period, and changes in employment participation.
The equilibrating force is a change in wages — increases hold residents and
attract migrants, and decreases precipitate some outmigration of residents and
tend to discourage inmigrants (3)., The model calculates the ancillary wage
necessary to attract or to discourage enough migrants to fill jobs not taken
by local residents and the migrants who responded to economic signals in prior
periods.
Dynamic Interactions
Interrelationships among the variables of the model are much more compli-
cated than Figure 1 implies (10, 11). The estimating equations, for instance,
include lagged variables which allow the model to capture some of the dynamics
of the adjustment process from the old levels to the new levels of population
and employment.
COALTOWN summarizes the dynamic features of the adjustment process. These
features can be highlighted by an examination of the equations themselves and
by an example. The equation for ancillary employment and its estimated coef-
ficients imply (a) incremental employment multipliers which are larger for
small communities than for large communities; (b) variations in employment
multipliers depending upon the spatial setting of the county; (c) increases in
employment multipliers through time; and (d) discrete changes in multipliers
following a growth shock (10).
An evaluation of the equation estimating the ratio of employment to popu-
lation shows that (a) the ratio will increase given rapid increases in labor
demand, and (b) then will gradually trend toward a ratio that is slightly less
than the U.S. level as labor demand stabilizes.
A brief example also can characterize some of the dynamic relationships
accompanying adjustments. Assume an initial rapid increase in primary employ-
ment. Ancillary employment, the employment participation of residents, migra-
tion, wages, and population levels all change in that year. The effects of
these changes will reverberate through the system the next year, and in
succeeding years. Variables which affect the estimates in succeeding years
are identified in Figure 1.
Most new employment at high growth rates is filled by migrants rather
than increases in employment participation. Because migration accelerates,
population grows more rapidly than when local residents supply the bulk of
the labor.
The dynamics of the socioeconomic component of COALTOWN subsequently feeds
into the local government expenditure and revenue parts of the model.
]_l A stochastic net migration equation is in Appendix C, Appendix table 3.
It is used to estimate an ancillary wage index in the model.
-------
State and Local Revenues
State and local government revenues and intergovernmental revenue flows
are calculated through a series of accounting identities (Figure 2). Each
State has a different tax and aid system, so separate models are required
(12, 13, 14). The revenue accounting programs of COALTOWN are adopted from
earlier modeling by Thomas Stinson at the University of Minnesota. jB/ The
tax systems in each State also change as their respective Legislatures act.
State, county, city, and school revenues are calculated for each year in the
simulation as are all formula aids available to local governments. Inter-
governmental revenues distributed by categorical grant or those which become
part of a trust fund are totalled separately insofar as possible.
The revenue calculations are dependent upon mill levies supplied by the
user. These conventionally are assumed to be at or near the levels existing
in the base year. If revenues are deemed inadequate for a function, then the
necessity of a mill levy increase is implied. If a revenue surplus is in-
dicated, then a decrease in taxes at some time in the future is implied,
depending upon the flexibility of the State and local tax system.
Figure 2—General structure of the revenue segment of COALTOWN
User inputs
Inputs from : Model
socioeconomic : outputs of
segment of model : revenues by source
Tax related
characteristics of
energy project
Initial assessed
values in county
State & local tax laws
Mill levies
Total employment
Ancillary wages
Population
State revenues
Severance
Electricity production
Property
School
Income
Sales
County revenues
Property
School
Sales
School revenues
City revenues
Property
Sales
Intergovernmental
transfers
J5/ The revenue accounting programs are adapted from the ENGYTX Model
developed by Thomas F. Stinson (7_, 8). Tax information for each State was
prepared by Stanley W. Voelker (14), Layton Thompson (12, 13) and Stinson (9)
These subsequently have been updated for publication by Stanley W. Voelker.
jlSSSSS-SB'iS
-------
Interrelationships among the tax and the socioeconomic components of the
model are complex. Energy projects affect assessed values, employment, wages,
and population which in turn influence government revenues as well as expen-
ditures.
Local Government Expenditures
Expenditure estimates for counties and schools are estimated using each
period's anticipated population (11). Expenditure estimates are a weak com-
ponent of the model due to the data which are available, but the estimating
equations are statistically significant. COALTOWN compares local revenues
and expenditures but the user is cautioned against inappropriate interpreta-
tions from the estimates. In most instances, the revenue data should serve
as a guide against which planned rather than estimated expenditures are
compared.
Expenditures for capital items and the timing of capital expenditures
and expected revenues are critical to assessments. COALTOWN provides guidance
on the timing and extent of population change but makes no attempt to forecast
the level or timing of capital expenditures by local governments.
PRINCIPAL USES OF THE COALTOWN MODEL
COALTOWN is designed to assess the impact of future energy development
projects. Results from a baseline scenario without the proposed energy pro-
ject are compared to the results from scenarios which include the energy
projects in order to assess impacts on employment, population, migration,
and revenues which result from a project.
A clear distinction should be made between an economic assessment and
an economic forecast. An economic forecast is conditional on the accurate
prediction of those factors which are used as data in the model. For ex-
ample, one may not be able to accurately predict the future fiscal impact
of an energy development project because future tax laws are unknown. How-
ever, one can assess the fiscal impacts taking the existing tax legislation
as given. These results help assess the adequacy of the current tax structure
for meeting future needs.
The user may test the sensitivity of the results to special background
conditions by assuming alternative data inputs. This procedure helps to
assess the importance of certain policy variables in addition to yielding a
forecasted range for the economic indicators of interest. It should be em-
phasized that uncertainty about background conditions tends to cancel out in
the comparison of the baseline results and the results which include the
energy-related facility.
The three principal uses of the COALTOWN simulation then, are as follows:
1. It can be used to predict the absolute levels of population, employ-
ment, migration, and other impact variables for planning purposes.
-------
2. It can be used to assess impacts due to a specific project for
purposes of evaluation.
3. It can be used to assess the sensitivity of the results to various
measures as an aid to policy analyses.
INFORMATION SUPPLIED BY THE USER
The user supplies two types of information: data regarding the energy
project itself, and background information related to the region where the
development will take place. These are labeled 'exogenous variables' in
Figure 1. Information about the project itself includes the number of
workers directly employed in operating and constructing the proposed facility
in each year, the megawatts of electricity generated or the tons of coal mined
each year, and optionally, the employment ratio of the migrant population.
Necessary background information regarding the region includes the
initial population of the county, projected State and local mill rates,
assessed value of property, the number of people currently in the ancillary
and basic sectors, distance to a regional trade center, and the size of the
largest town in any adjacent county. A detailed list of input variables is
presented in Appendix A.
DATA GENERATED BY THE MODEL
The output generated by COALTOWN is, to a large extent, self-explanatory.
An example of the printed output is illustrated in Figure 3. The user has an
option of choosing a summary output which lists the major economic indicators
and fiscal impacts for each year, or a complete output which includes detailed
revenue statistics for each year.
The COALTOWN output generates the following information for each year of
the simulation run: the number of workers employed by primary industries, the
number of workers employed in ancillary jobs, the number of non-farm proprietors
(owners of businesses and self-employed persons), total employment, relative
real wages in the service sector as a percent of the base year, migration,
population, the employment to population ratio, and the number of school chil-
dren. Summary statistics for State, county, school and town revenues are also
given along with estimates of school and county spending.
The detailed tax output (Figure 4) breaks revenues into the following
categories: coal severance, gross proceeds, taxes on electricity generation,
other taxes generated by mines and generators, taxes on people and businesses
(for example, income, liquor, cigarette, and auto registration taxes), and
intergovernmental flows. Information is also given regarding revenue sources
peculiar to each State, such as the Wyoming sales tax, the school foundation
program, or trust funds.
-------
Figure 3—Example of summary output of COALTOWN, example county - Montana
Northern Great Plains scenarios - with new mines
.
: Energy
: All :
: : project : econ. base :
Year
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
Year
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
: Tons mined
8699999.
11699999.
11699999.
12699999.
14199999.
17000000.
17500000.
18000000.
21500000.
23399984.
25000000.
: Labor mkt.
: index
1.4764
1.0701
1.1054
1.3286
1.4386
.9047
1.0701
1.0701
1.3316
1.2257
1.2007
: workers
1541.
675.
552.
880.
1797.
2476.
2967.
2432.
1399.
1289.
1306.
.
: Migration
2741.
-276.
-184.
509.
1961.
1427.
1011.
-255.
-285.
-59.
101.
: employment :
2680.
1806.
1675.
1995.
2904.
3576.
4059.
3516.
2476.
2358.
2368.
; ;
: Emp/pop. :
.4624
.4009
.4022
.4225
.4425
.4617
.4778
.4613
.4063
.4085
.4160
Ancillary
employment
1827.
1965.
2051.
2176.
2386.
2667.
2946.
3157.
3268.
3424.
3602.
Town size
3585.
3370.
3245.
3813.
5836.
7336.
8430.
8263.
8066.
8094.
8282.
: :
: :
: Proprietors:
326.
333.
340.
341.
331.
315.
303.
306.
323.
329.
332.
: :
: School kids:
2744.
2687.
2655.
2804.
3335.
3729.
4016.
3972.
3920.
3928.
3977.
Total
employment
4833.
4103.
4066.
4512.
5621.
6558.
7308.
6979.
6066.
6111.
6302.
Population
10451.
10236.
10111.
10679.
12702.
14202.
15296.
15129.
14932.
14960.
15148.
:::::: :Per capita
Year
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
: State :
: revenue :
11756551.
15348542.
15415457.
16840624.
19203488.
22782224.
23787392.
•25299696.
29706400.
31608928.
33345296.
County :
revenue :
872082.
1220390.
936882.
779505.
943750.
712809.
727580.
865689.
1059055.
1081122.
1099309.
School : Town
revenue : revenue
2919474. 180484.
3050717. 180882.
3240355. 177572.
3130495., 192040.
3642712. 248178.
4063318. 294704.
4330040. 331543.
4457834. 335793.
4645040. 335459.
4677916. 342211.
4742510. 353620.
: County
: spending
1167721.
1152104.
1143006.
1184081.
1324652.
1423826.
1493764.
1483226.
1470717.
1472506.
1484432.
: School :
: spending :
2286656.
2240318.
2213475.
2335542.
2769914.
3091306.
3325111.
3289508.
3247422.
3253427.
3293576.
town
revenue
50.34
53.67
54.72
50.37
42.53
40.17
39.33
40.64
41.59
42.28
42.70
All values are in 1970 dollars (or that specified by a user).
All revenue is reported for the year in which the tax liability is generated. No
allowance is made for lags in assessment or payment.
-------
Figure 4—Example of detailed revenue of COALTOWN, example county - Wyoming
Northern Great Plains scenarios - without new mine
1975 revenue amounts by type and jurisdiction
Sev. or GP taxes
Other mine taxes
People taxes
Total taxes
Intergov. flows
Net revenue
Wyoming
437759.69
256678.44
4058922.00
4755359.00
-3003154.00
1752205.00
109439.88
23707.44
580528.19
831624.44
165703.75
997328.19
School district
191337.38
41448.56
3446778.00
3679563.00
2245920.00
5925483.00
Town
.00
.00
608013.06
864776.94
591531.69
1456308.00
The county share of local sales tax revenue is: 117949.13
The town share of local sales tax revenue is: 256763.88
State aid available for local impacts, but not automatically rebated,
amounts to 437759.69 of State taxes generated locally.
Note: All taxes are reported for the year in which the tax liability is incurred.
THE PREDICTIVE ACCURACY OF ESTIMATING PROCEDURES
Differences in employment and population due to spatial patterns and
temporal variations in economic activity appear to be summarized well by
COALTOWN equations. These equations account for a very large proportion of
the variations in reported values of employment, employment participation,
migration and population of the 181 sample nonmetropolitan counties for the
period 1971-74.
Seven counties in the Northern Plains are destined to be major coal
producers in the next decade. Population values estimated by COALTOWN equa-
tions are compared with observed values for these counties in Table 1 for the
1970-77 period. Other estimated values are reported in Appendix tables 4-5.
The spatial and temporal influences of mining and conversion are accounted
for in the counties.
The accuracy of the estimates appears to be especially noteworthy be-
cause these major coal counties exhibited different growth patterns. These
patterns range from stability to rapid change as indicated by observed employ-
ment over the period. McLean, North Dakota had stable employment, and esti-
mated population tracks well. Both Big Horn, Montana and Converse, Wyoming
10
-------
Table 1—Equation estimates _!/ and observed values, population 2] 1970-77,
seven Northern Plains counties with coal mining and/or conversion
Year
1970
1971
1972
1973
1974
1975
1976
1977
: Big
: est.
10,213
10,203
9,967
9,998
10,240
10,681
10,663
10,199
Horn
obs.
10,063
10,109
10,330
10,352
10,498
10,934
10,590
10,675
: Rosebud
: est.
6,196
6,233
6,195
6,427
7,013
11,899
9,239
9,480
obs.
6,044
6,106
6,419
6,857
7,826
9,652
9,886
10,503
: McLean
: est.
11,799
11,538
11,472
11,331
11,316
11,810
11,903
12,630
obs.
11,322
11,671
11,563
11,443
11,472
11,600
11,851
12,272
: Mercer
: est.
6,370
6,253
6,136
6,192
6,550
6,762
6,147
6,713
obs.
6,170
6,184
6,180
6,169
6,279
6,404
6,720
7,039
: Campbell
: est.
13,587
12,978
13,226
12,233
12,794
14,231
14,542
17,521
obs.
13,049
12,820
12,489
12,295
12,035
13,103
14,540
16,759
: Converse
: est.
5,943-
6,903
6,528
6,416
7,177
8,117
8,596
10,072
obs.
6,072
6,630
6,672
6,866
7,162
8,029
9,363
10,687
: Sheridan
: est.
18,106
18,260
17,810
17,856
19,517
20,175
20,380
21,951
obs.
17,865
17,984
18,123
18,832
19,177
19,924
21,012
21,619
_!_/ Coefficients from Temple (1978) applied to revised BEA 1969-77 data. Coefficients were estimated from
unreyised data for 181 nonmetropolitan counties 1970-74 (11).
2_/ All current year values are estimated and any lagged values in estimating equations are observed values
of~revised BEA data 1969-77.
-------
exhibit a relatively stable employment until 1974. Estimated population is
very close in each case despite the change in later years. The Decker mine,
at that time one of the largest in the Nation, was just starting operations
in Big Horn County. No new energy projects were initiated in Converse County
in this period. Rosebud, Montana and Mercer, North Dakota were the sites of
power plant and mine construction, and coal mining was being initiated in
Campbell County, Wyoming in this period. Population in Rosebud County was
overestimated in 1975, the year of completion of construction of power gener-
ators. Despite those causes of rapid change, the population estimates are
extremely close. Finally, Sheridan County, Wyoming is the site of a city and
the recipient of impacts from Campbell and Big Horn Counties. Although the
economic conditions in these counties were different and certainly uncharac-
teristic of the region, the COALTOWN equations performed extremely well in
estimating population.
Total population is found by dividing estimated total employment by the
estimated employment to population ratio. Total employment is the sum of
primary employment and the estimated values for ancillary and non-farm propri-
etors. An inaccuracy in any one of the estimates in the population calculation
will produce errors. That is illustrated by the 1974-75 population estimates
in Campbell County when unusual conditions prevailed there but for which no
adjustment was made. Workers commuting into Campbell County distorted the
employment participation rate. For most accurate results when unusual condi-
tions are known to exist, COALTOWN users should incorporate that information
beforehand into the model.
LIMITATIONS OF THE MODEL
Any simulation model has inherent limitations. The model's structure
and the validity of its coefficients are basic considerations. COALTOWN'S
structure is purposely simple and uses aggregate variables which can be
predicted most accurately. Even though it is simple, dynamic interrelation-
ships among variables often make it difficult to follow through the logic of
a change in one part of the model. Thus, the aggregate level of prediction
and the simplicity of structure allow more accurate forecasting but at a loss
of some detail.
A common limitation of all simulation models is that their accuracy
declines for each year into the future for which predictions are made. In-
accurate estimates in early years tend to accumulate and to reduce the
accuracy of later estimates. In addition, structural changes in the economy
which are not anticipated by the model are likely to occur in the long run.
An example is the trend upward of the service to base ratio and the employment
ratio. These trends probably will not continue at the same pace in the long
run. Thus, the model should be viewed as a near term assessment model.
The amount and accuracy of input data supplied by the user is important
for the same reason. Inaccurate input data produce inaccurate estimates in
early years, and these become inputs in each successive year so that errors
build up over time. Some examples may illustrate this problem. Data for the
12
1
-------
employment directly associated with a project and with future projects are
critical for an accurate evaluation, but they are seldom known with certainty.
The labor force participation rates of new migrants coming into the region are
even more difficult to anticipate in the future. They depend on the duration
of the project, the characteristics of the local housing stock, the possibility
of building temporary housing for construction workers, the size of families,
and many other factors. As mentioned previously, this type of uncertainty can
be evaluated by testing a range of alternative values for input variables.
Uncertainty associated with particular input variables which may affect the
outcome of a baseline run (for example, the projected growth rate of other
basic sectors) will not be as important when doing an impact assessment com-
paring a "with" and "without" simulation as when forecasting the absolute
levels of the impact variables for use in planning.
Another important shortcoming of COALTOWN is its inability to allocate
new population to locations within the county and region. This information
would prove useful in assessing fiscal impacts on school districts and towns.
The problem is compounded when energy impacts spill over.to surrounding
counties, because the magnitude of the spillover cannot be estimated and
allocated by the model. The user must make a judgement about the distribu-
tion of workers within the county.
COALTOWN does not predict the industries which may move into a region as
a result of energy development. This type of secondary development could prove
to be significant in some areas. For example, energy intensive industry might
be drawn to a region to take advantage of low power rates. Manufacturing in-
dustries which serve the needs of the energy sector might also be drawn to a
region undergoing intensive coal development. The model will underestimate
future growth to the extent that secondary industries are attracted to a
location.
COALTOWN uses employment as a key variable although income is conceptually
preferable. Employment is used because county agricultural income data are
not reliable, and COALTOWN applies to rural counties where agriculture is im-
portant. Each job is implicitly assumed to have the same impact on the region.
For instance, when the effects of income associated with coal royalty payments
to individuals are investigated, the user must translate these payments into
equivalent employment units.
The distribution of impacts among people and the social strains which
emerge because of this is not addressed by COALTOWN. Definitive statements
about different impacts of coal development among people cannot be made. The
importance of such considerations should not be minimized. Residents of a
region would be very interested in knowing how the benefits of future devel-
opment might be distributed between current residents and newcomers. The
impact of new development also may be quite different for each socioeconomic
class. This information is relevant in assessing the benefits and costs, and
the political implications of development.
13
-------
REFERENCES
(1) Bender, Lloyd D.
1980 "The effect of trends in economic structures on population change
in rural areas." Chapter 6, pp. 137-62 in David L. Brown and
John W. Wardwell (eds.), New Directions in Urban-Rural Migration:
The Population Turnaround in Nonmetropolitan America. New York:
Academic Press.
(2) Bender, Lloyd D., Thomas F. Stinson, George S. Temple, Larry C. Parcels,
and Stanley W. Voelker
1980 "Impacts of coal mining and conversion in Northern Plains States."
ESCS Staff Report. Washington, D.C.: U.S. Department of Agri-
culture, The Economics, Statistics, and Cooperatives Service.
(3) Bender, Lloyd D., George S. Temple, and David O'Meara
1977 "Relationships among migration streams and local economic condi-
tions in the Northern Plains." Staff Paper 77-11. Bozeman:
Montana State University, The Department of Agricultural Economics
and Economics.
(4) Conopask, Jeff V.
1979 "Modeling coal mining growth and change: Geographic and temporal
perspective." Working Paper 7906. Washington, B.C.: U.S. Depart-
ment of Agriculture, The Economics, Statistics, and Cooperatives
Service.
(5) Conopask, Jeff V.
1978 A Data Pooling Approach to Estimate Employment Multipliers for
Small Regional Economies. Technical Bulletin 1583. Washington,
D.C.: U.S. Department of Agriculture, The Economics, Statistics,
and Cooperatives Service.
(6) Myers, Paul R., Fred K. Hines, and Jeff V. Conopask
1977 A Socioeconomic Profile of the Northern Great Plains Coal Regions.
Agricultural Economics Report 400. Washington, D.C.: U.S. Depart-
ment of Agriculture, The Economics, Statistics, and Cooperatives
Service.
(7) Stinson, Thomas F. , and Stanley W. Voelker __..
1978 Coal Development in the Northern Great Plains States: The Impact
on Revenues of State and Local Governments. Agricultural Economics
Report 394. Washington, D.C.: U.S. Department of Agriculture,
The Economics, Statistics, and Cooperatives Service.
(8) Stinson, Thomas F.
1978 "The effects of state taxation on coal mining in the Northern
Great Plains." Proceedings of the American Institute of Mining
Engineers, Council of Economics, (June)pp. 11-18.
14
-------
(9) Stinson, Thomas F.
1978 State and Local Taxation of Mineral Deposits and Production.
Rural Development Research Report 2. Washington, D.C.: U.S.
Department of Agriculture, The Economics, Statistics, and
Cooperatives Service.
(10) Temple, George S.
1979 "The spatial and temporal distribution of service activities among
small rural areas." Paper presented at Western Regional Science
Association, San Diego, California (February).
(11) Temple, George S.
1978 "A Dynamic Systems Community Impact Model Applied to Coal Devel-
opment in the Northern Great Plains." Ph.D. Dissertation.
Bozeman: Montana State University.
(12) Thompson, Layton S., and Willard D. Schutz
1978 Taxation and Revenue Systems in Wyoming. Bulletin RJ127. Laramie:
University of Wyoming, Wyoming Agricultural Experiment Station.
(13) Thompson, Layton S.
1978 The Taxation and Revenue Systems of State and Local Governments
in Montana. Bulletin 701. Bozeman: Montana State University,
Montana Agricultural Experiment Station.
(14) Voelker, Stanley W., Fred R. Taylor» and Thomas Ostenson
1978 The Taxation and Revenue System of State and Local Governments in
North Dakota (Revised 1978). Agricultural Economics Report 128.
Fargo: North Dakota State University, The Department of Agricul-
tural Economics.
15
-------
APPENDIX A
DATA SUPPLIED TO COALTOWN
The following is a comprehensive list of all exogenous data variables
which the user inputs into the COALTOWN model. Each variable is described
briefly.
Identification
The title of the scenario.
County name.
The number of scenarios to be analyzed for a particular county.
The number of counties to be analyzed.
The one digit code associated with Montana (1), North Dakota (2), or Wyoming (3)
The last year of the scenario (the first year of the scenario is assumed to be
1975 as the program now stands).
Definition of Scenario and Setting of the County
The tons of coal mined in the county in each year of the scenario.
The number of jobs in the energy sector during each year of the scenario. This
includes miners, power plant operators, and energy project construction workers.
Other primary workers in agriculture, manufacturing, mining other than coal
mining, Federal government, and the basic portions of the transportation and
construction industries.
All wage and salary employees not in the primary sectors. _!_/
Number of nonfarm proprietors. _!/
The ratio of total employment (including proprietors) to total population. _!/
Net migration. _!/
The ratio of total ancillary income (in thousands of dollars) to the number of
ancillary employees plus nonfarm proprietors. _!_/
County population. _!/
_!_/ Provided for the initialized year of the model only.
16
-------
The number of workers employed in the primary industries in adjacent county
with the largest primary employment.
Distance (in miles) from the largest town in a county to the Rand McNally
Regional Trade Center.
Population of the largest town in the county. \j
Population of the largest town in any adjacent county.
Revenues and Expenditures
The actual sales price of a ton of coal measured in 1970 dollars.
County sales tax applicable only to Wyoming counties.
Projected mill rates in each year for the State, county, school district, and
town respectively.
Average assessed values for: (1) detached homes, (2) mobil homes, (3) apart-
ments, (4) cars, and (5) trucks respectively.
The ratio of actual to estimated county spending (this correction factor makes
estimated expenditure figures more realistic for the particular county being
investigated).
The ratio of actual to estimated school spending (this correction factor makes
estimated expenditure figures more realistic for the particular county being
investigated).
The ratio of commercial property value (in dollars) to ancillary employment.
A vector with two values tells the number of cars per capita and the number of
trucks per capita respectively.
Total taxable value of all property in the county.
The amount (measured in megawatts) of electric power generating capacity in the
county in 1974.
The projected amount (measured in megawatts) of electric power generating capac-
ity in the county for each year of the scenario.
Data for Special Options
Whether the user desires a detailed tax printout.
The employment/population ratio if migrants are assumed to have a different one
than the existing population in the county.
\J Provided for the initialized year of the model only.
17
-------
Whether new basic energy workers will not live within the county's largest town.
Whether energy development will include the construction of thermogenerating
plants.
Whether the user will input rates of growth for the basic sectors in the object
county as well as the basic sectors in the adjacent counties.
Whether the user will input a vector of coal royalty payments (this may be the
case if coal development will occur on Indian land - the royalty payment will
be converted into employment equivalents by dividing total yearly payments by
the average basic yearly wage).
Coal royalty payments applicable to coal development on Indian land.
Projected values for the employment/population ratio of migrants. In cases
where there is a large influx of temporary workers and a shortage of family
housing units, this value will tend to be much higher than the employment/
population ratio of current county residents.
The fraction of migrant workers not living in the major town in the county in
each year of the scenario.
The annual rate of growth of the basic sectors in the adjacent county with the
largest number of basic employees.
The annual rate of growth (or decline) of the basic sectors (other than energy
related sectors) in the county being studied.
The projected growth rate of the largest town in any adjacent county.
18
-------
APPENDIX B
CHARACTERISTICS OF DATA USED IN ESTIMATING COALTOWN
Data used for estimating the principal parameters of the model have cer-
tain advantages, but also imply limitations. Employment, income, and popula-
tion estimates, 1970-74 are from the Bureau of Economic Analysis, Department
of Commerce. They are estimates which are revised successively as information
becomes available, and the coefficients of the model are subject to error from
that source. A combined cross section and time series analysis is dictated
for this reason (4_, _5).
The observations are 181 Northern Great Plains counties which are nonmet-
ropolitan in character — rural counties. The economies of these counties are
relatively uncomplicated (6) yet provide a range of conditions for application
of the modified economic base approach.
Employment
Employment data for counties are compiled by the Department of Commerce
from several sources. Wage and salary statistics are the most thorough and
accurate of these because they are derived from unemployment compensation
records filed by firms. Estimates of the number of proprietors are from these
and other sources such as the Internal Revenue Service. Farm labor and espe-
cially farm proprietors and their incomes are the least accurate due to the
coverage and nature of the reports available.
Employment is the number of jobs reported by an establishment. Second
and part-time jobs are included. JDouble counting of unknown magnitude is
inherent in the data.
The county location of the reporting establishment rather than residence
of the worker is reported. Although commuting across county boundaries is not
common in the rural portions of the Northern Great Plains, it can be charac-
teristic of rapid growth areas, especially during construction of major facil-
ities. For these reasons, the exact distribution of the population in and
among counties is not known.
The industry detail of employment data in this study is the one-digit SIC
level. Disaggregation below that at the county level is probably unreliable.
Income
Income of wage and salary workers by industry, and farm and non-farm pro-
prietors also is reported by the Department of Commerce. Income data are pre-
preferred for economic analysis in most economic base studies. Income data
appear to be inappropriate in counties dominated by agriculture. First,
coverage of the agricultural wage and salary employment is incomplete. Second,
farm income is allocated to the county level from estimates of state totals
19
-------
each year, hence, is an inaccurate measure. Finally, farm income is a poor
measure of economic activity in agriculture because it varies greatly even
when farm production and household expenditures do not.
Population
Population estimates reported by the Department of Commerce are those
generated to allocate Federal revenue sharing funds. The estimates are an
average of several estimates and are adjusted to agree with state totals.
Regression estimates are based upon variables such as school enrollments and
vehicle registrations. Other estimates are based on vital statistics, medical
records and tax data. The estimation method may vary from state to state and
estimates are revised periodically.
Changes in size of family, presence of school children and age composi-
tion of families in rapid growth areas can result in inaccurate population
estimates. The model allows user specification of different employment to
population ratios for inmigrants in order to reflect rapid growth conditions.
20
-------
APPENDIX C
STOCHASTIC ESTIMATION OF COALTOWN PARAMETERS
21
-------
Appendix table 1—Estimating
employment in Northern Great
equation for ancillary
Plains counties, I/ 2/
Equation
: Estimated : F
:coefficients: statistics
Definition of variables
Dependent variable
ANCEMP.
Independent variables
& coefficients
a
1 ANCEMP
t-1
a
2 BASE.
BASEt D
ADJBASE
6 BASEt_1
a7 BASE(._1 D
9 ADJBASE j
110 Waget_1
'It
Ancillary empl.; total empl. minus
basic empl. in year t
-32.91643 Constant
1.04194 3/ ANCEMP in prior year
0.42970 79.06 BASE is defined as no. of agricultural
employees & farm proprietors, empl.in
mining, forestry & fishing, manuf., &
Federal govt. plus any employees in
trans.& const, over the regional avgs.
-0.00305 7.58 The distance to a Rand McNally Region-
al trade center multiplied by BASEt
- .000005 0.83 Distance to trade center squared
multiplied by BASEt
0.12597 23.70 BASEt which is largest in any of the
adj.counties multiplied by the ratio
of the size of the largest local town
to the largest town in any adj. county
-0.42446 70.50 BASE in the prior year
0.00338 8.87 Distance to a trade center multiplied
by BASEt_i
0.000005 0.63 Distance to a trade center squared
multiplied by BASEt_l
-0.13680 28.04 ADJBASE in prior year
0.00008 0.17 Mean salary & earnings, thou.per yr.of
ancillary employees & proprietors mul-
tiplied by size of largest local town
Error
Statistics
R2
F
S.E.E.
Mean of ANCEMP
d.f.
0.99
3/
126.07
2353.67
713.00
JL/ Two parallel ancillary employment equations were subsequently estimated for use
in the COALTOWN model. One equation has wage & salary ancillary workers as its depen-
dent variable & the other uses non-farm proprietors as a dependent variable.
2J Coefficients were estimated using two-stage least squares on a data base of 181
rural Northern Great Plains counties for 4 years, 1971-1974.
_3/ Larger than 10,000.
22
-------
Appendix table 2—Estimating equation for employment ratio
in the Northern Great Plains counties, _!_/
: Estimated : F !
Equation 21 :coefficients: statistics
Definition of variables
Dependent variable
RELLFPRt
Independent variables
& coefficients
RELLFPR
t_1
PCEMP
0.037
0.961 6493.58
0.602 122.98
The county employment population ratio
relative to the U.S. ratio, in year t
Constant
RELLFPR in prior year
Percent change in total employment
from prior year to year t
b PCEMP^
62t
Statistics
R2
F
S.E.E.
Mean of RELLFPR
d.f.
-2.314 44.48 Square of PCEMPt
Error
0.90
2173.24
0.05
1.008
720.000
JY Coefficients estimated using two-stage least squares on a data base of 181
counties for 4 years, 1971-1974.
2/ All estimated values rounded.
23
-------
Appendix table 3—Estimating equation for net
migration in Northern Great Plains counties, _!/
Equation
: Estimated : F :
:coefficients: statistics :
Definition of variables
Dependent variable
Net migration in year t
Independent variables
& coefficients
CQ 81.8002
cl POPINt -0.0080 25.17
c, MIG. , 0.5961 266.13
<£ L~ 1
c3 EXPONENT^ 60.3945 198.35
CA EMPCHt 0.4719 24.90
c5 EMPCH2 -0.0001 14.79
c, RELLFPR^ , -72.0162 0.89
o t— 1
e3t
Statistics
R2 0.68
F 259.49
S.E.E. 293.94
Mean of MIG 50.32
d.f. 717.00
Constant
Population in year t, prior to
migration
MIG in prior year
Base e carried to the power
{2(wage-5.0)} 2/
L. '
Total employment change from prior
year
Square of EMPCH
RELLFPR in prior year _3/
Error
'
\J Coefficients estimated using two-stage least squares on a data base of 181
rural Northern Great Plains counties for 4 years, 1971-1974.
2J Wages defined as mean salary and earnings, thou. per year, of ancillary
employees and proprietors.
_3/ RELLFPR is defined as the county employment ratio relative to the U.S.
ratio in year t.
Note: In the actual COALTOWN Simulation Model this equation was used to estimate the
wage level. Migration was used as an independent variable (see appendix E).
24
-------
APPENDIX D
PREDICTIVE ACCURACY OF ESTIMATES FOR SELECTED COUNTIES
25
-------
Appendix table 4—Equation estimates JL/ and observed values, ancillary employment,
non-farm proprietors and employment per 100 population, "2J 1970-77,
seven Northern Plains counties with coal mining and/or conversion
Year
Big
est.
Horn
obs.
Rosebud
est. obs.
McLean :
est. obs. :
Mercer :
est. obs. :
Campbell
est. obs.
Converse
est. obs.
Sheridan
est. obs.
- Ancillary employment -
1970
1971
1972
1973
1974
1975
1976
1977
1970
1971
1972
1973
1974
1975
1976
1977
1,389
1,760
1,828
1,744
1,818
1,908
2,015
2,066
290
305
253
272
266
281
288
273
1,684
1,757
1,678
1,724
1,821
1,916
1,954
2,136
300
249
267
264
279
287
276
291
1,138 1,110
1,160 1,301
1,361 1,423
1,490 1,587
1,670 1,803
1,973 2,320
2,408 2,324
2,410 2,256
237 228
231 233
236 215
218 227
229 269
258 328
342 334
345 352
2,015
2,083
2,052
2,059
2,056
2,244
2,460
2,617
-
482
448
412
389
385
406
408
413
1,998
1,997
1,966
1,989
2,159
2,304
2,388
2,549
Non-farm
441
405
384
379
400
403
411
432
1,081 1,051
1,083 1,090
1,126 1,211
1,274 1,353
1,450 1,549
1,641 1,558
1,558 1,659
1,732 1,931
proprietors -
251 232
236 209
212 204
206 204
204 218
219 222
229 230
233 242
2,533 2,619
2,691 2,642
2,784 2,721
2,844 2,826
3,007 3,068
3,326 3,688
4,002 4,244
4,704 4,982
479 472
488 446
453 460
470 443
447 448
445 470
465 502
486 528
1,376
1,467
1,501
1,627
1,752
1,936
2,210
2,479
297
295
287
306
306
326
353
398
1,349
1,468
1,611
1,649
1,781
2,042
2,264
2,487
292
281
299
302
324
350
397
418
4,475
4,597
4,818
5,141
5,435
5,543
5,897
6,508
833
857
841
868
875
905
907
908
4,545
4,688
4,998
5,094
5,200
5,497
5,862
6,164
827
830
848
863
906
907
928
977
- Employment per 100 population -
1970
1971
1972
1973
1974
1975
1976
1977
36.10
38.20
37.96
37.32
37.00
35.36
34.72
35.47
38.63
38.35
36.29
35.97
37.76
34.58
34.98
37.23
40.86 41.64
42.68 45.21
46.79 45.58
48.20 46.28
48.23 44.94
33.29 53.83
49.39 46.05
42.68 38.50
40.22
40.89
39.46
40.27
40.67
40.18
43.12
45.69
41.70
39.99
38.90
39.58
40.53
41.12
42.89
46.62
39.50 40.50
39.99 40.31
41.77 42.10
44.78 45.85
47.83 51.66
50.98 52.95
51.24 47.21
49.87 49.73
41.26 43.29
39.46 39.93
41.80 43.94
45.14 44.75
46.66 50.02
48.85 57.16
58.89 61.10
61.61 67.27
44.06
43.94
46.45
46.27
45.34
46.11
50.72
50.34
42.93
45.52
46.29
43.31
45.99
48.49
47.63
47.72
44.03
44.66
46.39
47.50
46.32
45.93
48.21
49.16
44.79
45.52
46.08
44.90
45.52
46.38
46.65
47.25
_!_/ Coefficients from Temple (1978) applied to revised BEA 1969-77 data. Coefficients were estimated from
unrevised data for 181 nonmetropolitan counties 1970-74 (11).
2/ All current year values are estimated and any lagged values in estimating equations are observed values
of~~revised BEA data 1969-77.
-------
Appendix table 5—Simulated predictions of employment-population ratio
and population, and percent deviation from observed 1971-74,
seven Northern Plains counties with coal mining and/or conversion _!/
to
Year
1971
1972
1973
1974
1971
1972
1973
1974
Big
pre.
Horn :
% dev. :
Rosebud
pre. % dev.
: McLean :
: pre. % dev. :
Mercer
pre. % aev.
: Campbell :
: pre. % dev. :
Converse
pre. % dev.
Sheridan
pre. % dev.
- Employment-population ratio -
.3839
.3910
.4051
.4228
-1.01
7.39
10.24
9.53
.4243 -7.21
.4391 -5.82
.4615 -1.39
.4752 3.76
.4367
.4393
.4590
.4646
2.93
6.39
7.54
6.08
.4150 -2.06
.4255 -4.24
.4491 -7.49
.4693 -12.71
.4026 4.12
.4230 -8.10
.4395 -7.47
.4576 -14.43
.4375
.4413
.4385
.4582
-1.97
-2.10
1.33
0.37
- Population -
10,269
10,150
9,869
10,097
1.57
-1.46
-4.56
-3.69
6,276 2.78
6,369 -0.48
6,364 -7.70
6,501 -15.20
11,554
11,422
11,314
11,307
-1.39
-1.53
-1.81
-1.66
6,372 2.63
6,372 2.77
6,380 2.52
6,822 6.53
12,934 -3.76
13,465 15.09
13,206 7.66
13,701 15.55
6,723
6,659
6,392
6,721
1.57
-0.61
-7.43
-5.83
.4347
.4379
.4545
.4669
r
18,295
18,096
17,791
18,061
-2.20
-1.36
1.03
1.05
1.02
-0.57
-5.75
-6.19
_!/ Simulated predictions using system-generated values of estimating equations.
-------
APPENDIX E
1.
2.
3.
4.
5.
6"
7.
8.
9.
10.
11.
12.
THE SYSTEM OF EQUATIONS
ANCEMPt = aQ +
ANCEMP^ +
BASEt +
BASEt x DIST +
BASEt x DIST +
ac ADJBASE + a, BASE , + a., BASE
,
t-1
,
t-1
x DIST + a0 BASE , x DIST + a_ ADJBASE
0
8
,
t-1
WAGEt_1 x TOWN +
RELLFPRt = bQ +
RELLFPR^ +
POPI
c, RELLFPR,. , +
b t-i
*/
—
In GOVlt
+ d±1 In POPNt +
PCEMPt + bj PCEMP. +
C e2 (WAGEt ~ 5) + c
1,2,3
Q, = ANCEMP + BASE
Qst ' Qdt
= POPIt x RELLFPRt x USLFPRfc
MW = MIG x RELLFPR x USLFPR
POPNt = POPIt
CEMP = Q -
MIG
,
t-1
CEMP
where:
ANCEMP = ancillary employment
BASE = economic base
DIST = distance to trade center
ADJBASE = adjacent economic base
WAGE = ancillary earnings divided by ancillary employment
TOWN = town size
RELLFPR = employment participation rate relative to the U.S. rate
PCEMP = percentage change in employment
^J Equation 3 was used in the COALTOWN Simulation Model to predict the wage level.
Net migration was computed using identities 5 through 9 and the predicted values for
ANCEMP and RELLFPR .
28
-------
MIG = net migration
POPI = indigenous population
CEMP = change in employment
GOV = government spending
POPN = total population
Qd = labor demand
Qs = labor supply
MW = migrating workers
LOCW = local workers
USLFPR = U.S. employment participation rate
Equations 1 through 4 are stochastic, 5 through 12 are identities. This gives
12 equations in 12 unknowns: ANCEMPfcJ LFPRt, PCEMP , MIGt, WAGEt> CEMPt> GOVit ,
POPN , Q, , Q , MW , LOCW . Equations 1, 2, and 3 were estimated using 2SLS.
29
US GOVERNMENT PRINTING OFFICE 1981-757-064/0303
-------
Environmental Protection
Agency
Information
Cincinnati OH 45268
Fees Paid
Environmental
Protection
Agency
EPA-335
Official Business
Penalty for Private Use, $300
Special Fourth-Class Rate
Book
i--f, uOuO.i?'? _„ ,,--r--rv
it S {- •-', V I K t'Pil 1'bC f i i-''- A OH '•- C ¥
PM.IHJ 5 MSRAKI p
p
*-' i *" £- '*
11-
Please make all necessary changes on the above label,
detach or copy, and return to the address in the upper
left-hand corner
If you do not wish to receive these reports CHECK HERE D,
detach, or copy this cover, and return to the address in the
upper left-hand corner
EPA-600/7-80-146
------- |