Materials prepared for forecasting energy facility impacts on local government

[From the U.S. Government Printing Office, www.gpo.gov]

INSTITUTE

for

URBAN STUDIES

TD UNIVERSITY OF MARYLAND
195
E5 College Park
M38
1977 20742

CONTENTS

I. THE CEIP IMPACT MODEL: TECHNICAL ASSISTANCE MATERIALS

II. THE CEIP IMPACT MODEL: TECHNICAL MANUAL

III. CEIP IMPACT FORECASTING REVIEW MATERIALS

IV. ISSUES IN ENERGY FACILITY IMPACT FORECASTING

MATERIALS PREPARED FOR FORECASTING
ENERGY FACILITY IMPACTS
ON LOCAL GOVERNMEN;/

LA
W,

Prepared for the
office of Coastal Zone Management, NOAA
Contract No. 7-35714

Institute for Urban Studies
Woods Hall
University of Maryland
College Park, MD 20740
October 1977

PREFACE

These materials were originally prepared to aid the
Coastal Energy Impact Program of the Office of Coastal Zone
Management undertake forecasts of impacts of energy facilities
on local governments. Because the model developed may have
wider use for forecasting impacts on local government from any
major investment, we are considering a revision of the materials
and development of our own computer program for its use, followed
by making the materials widely available. Any comments on
either technical aspects, modes of presentation, or the use-
fulness of the materials to local governments will be appreciated.

Robert L. Bish

,j"e,

THE CEIP IMPACT MODEL:
TECHNICAL ASSISTANCE MATERIALS

Prepared By
Dr. Robert L. Bish, Dr. John D. Wolken
and Candis L. Brown in cooperation
with OCZM Staff

Prepared for the
office of Coastal Zone Management, NOAA
Contract No. 7-35174

May 1977

CONTENTS

Pages

I. Explanation and Instruc'tions for the
CEIP Impact Model Data Schedules 2

Introduction 2

Instructions for Completing Data Schedules 3

Data Schedules

1. Energy Facility Description -
Construction Phase 9
2. Energy Facility Description -
Operations 11
3. Local Area Description 13
4. Government Revenue 16
F 5. Government Expenditure 20

Ii. CEIP Impact Model Forecasting Procedures 21

Baseline Forecasts 21
Post-Impact Forecasts 22
Summary 24

III. Criteria for Alternative Models for Energy
Facility Impact Forecasts 25

Appendices:

A - Federal-State-Local Population Estimation
Agencies

B - Bureaus of Business and Economic Research in
Coastal States

C - Bureaus of Governmental Research
Local and State Agencies

D - U. S. Department of Labor, Bureau of Labor
Statistics, Cooperating State Agencies

CONTENTS

Pages

1. Explanation and Instructions for the
CEIP Impact Model Data Schedules 2

Introduction 2

Instructions for Completing Data Schedules 3

Data Schedules

1. Energy Facility Description -
Construction Phase 9
2. Energy Facility Description -
Operations 11
3. Local Area Description 13
4. Government Revenue 16
5. Government Expenditure 20

Ii. CEIP Impact Model Forecasting Procedures 21

Baseline Forecasts 21
Post-Impact Forecasts 22
Summary 24

III. Criteria for Alternative Models for Energy
Facility Impact Forecasts 25

Appendices:

A - Federal-State-Local Population Estimation
Agencies

B - Bureaus of Business and Economic Research in
Coastal States

C - Bureaus of Governmental Research
Local and State Agencies

D - U. S. Department of Labor, Bureau of Labor
Statistics, Cooperating State Agencies

I. EXPLANATION AND INSTRUCTIONS
FOR THE CEIP IMPACT MODEL

INTRODUCTION

The CEIP Impact Model forecasts the fiscal impact of an
energy facility and its associated population on a government.
To estimate the net fiscal impact of an energy facility,
several separate forecasts must be made. These forecasts are:

1) Baseline Revenues and Expenditures - Government
revenues and expenditures anticipated without any
energy facility.

2 Expenditures after the impact of the energy facility.

3) Revenues after the impact of the energy facility. .

4) Net Fiscal Impact - The difference between expenditure
and revenue after the impact of the energy facility.

Estimation of net fiscal impact may be an important deter-
minant of the payback schedule of loans made through the CEIP
prograra. To assist in making forecasts for small local governments,
we have reviewed studies of more than 300 impacts of new economic
activities on their surrounding areas before designing a relatively
simple impact model which captures the most essential elements of
industrial impact processes. The model is generalized to accommo-
date a variety of governments. Each stage of the model is clearly
defined and can be easily modified to take into account unique
local conditions. Unlike other models used for similar purposes,
it is not merely a "black box" into which data goes and forecasts
emergepLay, without being able to understand the calculations and
assumptions inherent in the forecasts. However, effective use of
such a model requires a cooperative effort by state and local
government officials, private interests, and persons familiar
with the consequences of economic impacts in local areas. The
processes through which this cooperation is to be achieved include:

1) Local officials complete the Data Schedules. Some of
the data requires cooperation of officials of companies
proposing the energy facility.

2) Using the data collected by local officials, OCZM
technical assistance personnel will use the CEIP Impact
Model to make baseline and impact projections.

3) After preliminary projections are made, OCZM personnel
will meet with local officials to discuss the projections
and the data and assumptions upon which they are based.
During this meeting it will be possible to alter data

or assumptions and see how much difference is made
in the projections. This will enable local officials
to understand the potential impacts of the energy
facility and the methods by which the forecasts are
made. It will also insure that unique local conditions
that affect the forecasts can be taken into account.

It is believed that through common dialogue the fiscal impact
forecasts may be calculated in as accurate and equitable a
manner as is currently possible.

Every effort has been made to keep data requirements to a
minimum. To assist with all data collection, commonly available
sources are indicated in the following instructions. In addi-
tion, Appendix B lists members of The Association for Business
and Economic Research and Appendix C lists members of The Associa-
tion of Government Research. These member institutes in a state
may be able to provide data or recommend data sources to assist
local officials to complete the Data Schedules.

INSTRUCTIONS FOR COMPLETING DATA SCHEDULES

Most of the data required will be available from local
government fiscal records, the environmental impact statement
for the energy facility, or by phone from energy facility company
officials and other knowledgeable citizens in the community. The
most likely sources for each item are indicated in the following
instructions. There are also a couple places where a hand calcu-
lator for calculating percentages will be useful, and there are
some estimates where the best sources of information may be the
general consensus of government officials, realtors, bankers,
and/or other knowledgeable citizens.

There are several specific instructions that apply to all
schedules.

1) Most data will be filled in on lines or in columns which
have a specific number of spaces for entries. Please
fill in these places with one letter or number on each
space, with entries adjusted to the right margin. For
example, in Schedule 1, item 1.1, entries for an esti-
mated 1979 construction workforce of 2,100, with an
additional 300 new employees in local firms supplying
the construction activity would look like this:

1.1 Construction Workforce: Number of New Employees
In Local Business Supplying
Year* Number (CFE) Construction (ICFE)

1 9 7 9 2 1 0 0 3 0 0

2) In filling in columns, begin with the earliest year
at the top. Thus, columns requiring historical data
will begin with oldest historical data and finish
with current data. Columns requiring future estimates
will begin with current year data and terminate with
future data.

3) Where there are lines or blank spaces for *writing in
answers instead'of a specific number of spaces, please
print or type.

4) Where something is not applicable or unclear, make the
entry you feel is best and make a note that you have
placed a comment or question on the back of the page.
Before data is entered into a computer, forms will be
checked for omissions and comments so that instructions
or the format of the schedules can be improved in the
future.

Following instructions are suggested data sources for each
schedule.

SCHEDULE 1 - ENERGY FACILITY DESCRIPTION - CONSTRUCTION PHASE

Data to complete this schedule can be derived from the
energy facility Environmental Impact Statement or directly from
an official of the company proposing the energy facility. This
schedule, along with Schedule 2, is also the same for any govern-
ment impacted by the energy facility, so a cooperative effort
among impacted governments or completion by a regional or state
agency may be desirable.

1.1 - The number of construction employees is self-
explanatory. The estimate of new employees in local
businesses supplying energy facility construction
materials is very important. This estimate should be
obtained directly from energy facility company offi-
cials or from managers of local construction materials
supply firms. It may be helpful to obtain an estimate
of construction materials to be purchased from the
energy facility company official to help local suppliers
make new employee estimates.

1.2 - For land purchases indicate the estimated cost of land
to be purchased during each year. For total costs of
completed parts of the facility indicate all non-land
costs. Define completed as that part of the facility
completed so that it can be assessed for property tax
purposes.

5
1.3 - These figures will be estimates, but they are
important if construction materials are taxed.
If construction materials are not taxed, this
data may be omitted.

SCHEDULE 2 - ENERGY FACILITY DESCRIPTION - OPERATIONS
Schedule 2 is similar to Schedule 1, except that it is
for operation of the facility instead of its construction.
2.1 - Similar to 1.1. Note that for the last line in the
table -- years 11-30 -- an estimate of the average
annual employment for those years is all that is
needed.

2.2 - Similar to 1.3.

SCHEDULE 3 - LOCAL AREA DESCRIPTION

3.1 - Data on population concentrations at different
distances from the energy facility may be obtained
from the Census of Population volume for the state
within which the facility is located. In some cases
good highway department maps will contain all the
information needed. Simply draw a set of circles
around the facility location and add up the popula-
tion of centers within the respective rings. Sub-
tracting out any population within the governmental
unit for which the Fiscal Management Schedule is
being prepared. Be sure the population data for the
rings is for the same year as the data for the popula-
tion "within the Local Government" even if data is
several years old.

For small governments, such as cities or towns,
simply estimate the distance from the center of town
to the energy facility. For large governments, such
as counties which contain several separate population
concentrations, it will be necessary to determine the
distance from the weighted average of subarea popula-
tion concentrations. The weighted average of several
population concentrations is calculated as follows:

Center 1 - population x distance =
Center 2 - population x distance =
Center 3 - population x distance =

Total population Sum
Divide the sum by the total population to obtain the
distance from the population center of the government
to the energy facility.

3.3 Employment and unemployment for county areas from
January through December 1976 is published in
State and County Employment and Unem@loyment
January - December 1976 from The National Technical
Intormation Service. A list of state agencies
responsible for employment data on each state is
included at the end of these instructions as
Appendix D. Finally, in smaller areas educated guesses
on the number of jobs in the government and the number
of employed and unemployed persons residing in the
government will have to be made. In addition to data
in possession of the local or county government
planning office, it may be useful to check with the
Chamber of Commerce, an economic development district,
or similar organizations in the area.

3.4 Population data for all counties is available from
1970 on in "Estimates of the Population of Counties"
from the Bureau of the Census. Data for before 1970
for large counties is available in "Population
Estimates for Selected SMSAs and Their Counties" also
Bureau of the Census. Smaller governments will need to
use their own estimates, data from a state agency, or
perhaps the Rand McNally Commercial Atlas, published
annually. See Appendix A for state agencies which may
be able to provide assistance with population data and
estimates.

Per capita personal income by counties is available
from the "Survey of Current Business" and "Local Area
Personal Income" (Table 3), both published annually.
The "Survey" is available from the Bureau of Economic
Analysis, U. S. Department of Commerce; "Local Area
Personal Income" is from the National Technical Infor-
mation Service. The state agency responsible for
employment data may also have income data. Use local
government data if it is available, otherwise use
county area data.

3.6 - If the state or local government makes long range
population forecasts, that forecast may be reported
here and it will be used for making baseline revenue
and expenditure forecasts in the model.

3.7 - Complete school enrollment figures for past 10 years
only if applying government is a school district.

3.8 - School district enrollment forecasts (without the
energy facility) may be used for baseline forecasts
if the school district has such forecasts available.

SCHEDULE 4 - GOVERMENT REVENUE

This data will generally come directly from local government
fiscal records. Be sure and give the dates of the local govern-
ment's fiscal year in the blank at the top of the page.
4.1 - The column 2 list of total revenues should exclude
revenue from borrowing and revenue from grants for
specific projects. Revenues should include all tax
revenues, special assessments, user charges, fines
and fees, and revenues received from other governments
such as shared taxes, formula grants or revenue sharing.
Revenues from selling packages of services to other
governments may be either included or excluded; if
included also include expenditures for performance of
such services in total expenditures in 5.1; if excluded
also exclude expenditures for performing such services
from total expenditures in 5.1.
4.2 - List the major taxes and their current rates used by
the government. List property tax rates in percent
of assessed value instead of in mills. (To a77 =is,
simply place a decimal between the hundreds and tens
numbers in the millage designation. That is, 175
mills equals 1.75 percent, to be entered as 1.7 5)
Based on past trends in tax rates, estimate @7h_at_future
tax is likely to be for each tax (without the energy
facility).

4.3 - Check with the assessor or tax collector to determine
whether or not property taxes are collected during the
same fiscal year assessments are made.
4.4 - The assessor will know if a state agency makes such
studies. If not, the assessor should have an idea
of the appropriate percentages.

4.5 - The assessor will have this information.

4.6 - If a sales tax is used, indicate whether or not pur-
chases by a business which do not become a physical
part of the final product are taxed under it. In some
states, as much as one-third of the retail sales tax
revenues are derived from this source. A similar
situation exists for construction materials which are
sometimes taxed under the retail sales tax, unless the
constructed facility is itself to be sold.

4.7 There are unlikely to be previous studies of tax
exporting by businesses or tax revenues paid by
tourists. The reason for attempting to isolate
@hese revenues is that they cannot be expected to
increase in response to energy facility induced
growth along with taxes paid by local businesses
or local residents. Consult with the tax assessor
for an estimate of property taxes paid by businesses
who sell their products outside the local government.
Consult with major retailers or hotel-motel owners
and other retailers serving tourists for an estimate
of tourist based revenues.

4.8 User charges vary among local governments. Be sure
and consider utility charges. Contact. an energy
facility company official if data is needed on
activities and requirements of the energy facility
to assist in making the estimates.

4.9 - Estimate as closely as possible.

SCHEDULE 5 - GOVERNMENT EXPENDITURE

As with revenue data, the source will be general government
fiscal records.

5.1 - Include general expenditures for both operating and
capital projects. Exclude expenditures from project-
related grants or fro-F-5-orrowed funds. Check with
4.1 to see if consistent.

Energy Facility Name Code

Name of Government

SCHEDULE 1: ENERGY FACILITY DESCRIPTION - CONSTRUCTION PHASE

1.1 Construction Workforce

Year Number (CFE) Number of New Employees in Local Bus-
inesses S..upplying Construction* (ICFE)

1.2 Construction Costs (Energy Facility Components Within the
Government only)
Year Land Total Cost of Parts Total
of the Facility Com- (L)
pleted During the Year
(Exclude land)

- - --- - - - - -

- - - - - - OP - - -

- - - - - --- - -

1.3 Construction Materials to be Purchased:
(Conplete only if construction materials are subject to
taxation.)

Year Cost (BT)

Energy Facility Name Code

Name of Government

SCHEDULE 2: ENERGY FACILITY DESCRIPTION - OPERATIONS

2.1 Operations Work Force

Year Number (OFE) Number of New Employees Likely in Local
Businesses From Which Operations
Materials Will be Purchased (ICFE)

Years 11 30

2.2 Materials Which Do Not Become a Physical Part of the Product
or Products: (Compl-ete only if such materials are subject to
taxation by the government.)

Year Cost (BT)

Years 11 30

Energy Facility Name Code

Name of Government

Fiscal Year

SCHEDULE 3: LOCAL AREA DESCRIPTION

3.1 Population Distribution Around Energy Facility Site

Population

Within 10 Miles (POP10)

Over 10 to 20 (POP20)
Exclude population within
Over 20 to 30 (POP30) the local government for
which the fiscal management
Over 30 to 40 - - - (POP40) schedule is being prepared.
Over 40 to 50 (POP50)

Over 50 to 60 (POP60)

Within Local
Government (POPG)

Year of Population Data . . . .
Distance From Energy Facility Site to Population Center of
Government (DIST)

3.2 Is the Energy Facility Located Within the Government?

Yes No
3. 3 Employment Year of
Within Government Within County Data

Number of Jobs (J)

Number of Residents
Employed (Even if
jobs are outside of
area) (E)

Number of Residents
Unemployed (U) - - -

3.4 Population of Government for Past 10 Years

Year Population (P) Per Capita
Personal Income (Y)

(current)

3.5 Area For Which Per Capita Personal Income Estimates Were Used
in 3.4

Governmental Unit County SMSA BEA Area
3.6 Population Forecasts Without Energy Facility For Up to 20 Years
Year Population (P) Year Population (P)

3.7 School Enrollments (To be completed only if government is
a school district.)

Year Enrollment (S)

(Current)

3.8 School District Forecasts of Future Enrollment, Without
Energy Facility - For Up to 20 Years

Year Enrollment (S) Year Enrollment (S)

Energy Facility Name Code

Name of Government

Fiscal Year

SCHEDULE 4: GOVERNMENT REVENUE
4.1 Total Revenues for Past 10 Years (Excludes borrowing and grants
for specific projects.)

Year Revenue (BLR)

(Current)

4.2 Major Taxes

Name of Tax Current Expected Rates
Tax Rate (In Percent) (T)
(In Percent) (T) In 5 Yr In 10 Yrs In 15 Yrs
% - - - % - - - % - - - %
% - - - % - - - % %
% % - - - % %
% % - - - % %
% % - - - % %
% % - - - % - - - %
% % % %

4.3 When are property tax revenues received relative to
assessments made?

Same Fiscal Year Following Fiscal Year

4.4 What is the ratio of assessed value to market value for
industrial property similar to the proposed energy facility?
- -- % (g)

4.5 What proportion of property tax revenues accrue from
residential property? . . . % (q)

4.6 If retail sales taxes are used, does the tax base include:
(Yes or No)

a. Items purchased by a business which do not become a
physical part of the business's finaf-product? . . .

b. Construction materials7 If not included in sales
tax, what tax rate, if any, applies to construction
materials? %

4.7 Revenues received by a local government are not all paid by
local government citizens. Taxes paid by nonresidents are
called exported taxes. Exported taxes are of two basic kinds.
First, taxes paid by local businesses whose revenues are
derived from products sold outside the local government and,
second, taxes paid by local businesses whose customers are
tourists. Please estimate below the proportion of government
revenues which may be characterized as exported taxes.

Proportion of Revenues Exported. % W

4.8 Are there any specific user charges the new energy facility
will be subject to and, if so, please indicate the estimated
revenues below:

Kinds of Charges

Estimated Revenue

Year Revenue (UT)

Years 11 30 - - - per year

19
4.9 If the energy facility will be subject to an inventory-type
tax (personal property), pipeline royalties, or any other
kind of tax not listed as a major revenue source in items
4.2, please provide an estimate of the revenues to be received
from the energy facility in future years.

Kinds of Taxes

Estimated Revenue

Year Revenue (OT)

Years 11 30 per year

20
Energy Facility Name Code

Name of Government

SCHEDULE 5: GOVERNMENT EXPENDITURE

5.1 Total General Expenditures for Last 10 Years (Excludes
expenditures from project-related grants and borrowing.)

Year Expenditure

(Current)

II. CEIP IMPACT MODEL FORECASTING PROCEDURES

To assist users to*understand the CEIP Impact Model, a brief
description of each forecasting process is presented here. This
description is for general users. Analysts interested in the
equations and the computer program may obtain a technical manual
directly from OCZM in-the near future.

The model is designed to make three final and three intermediate
forecasts. The final forecasts include (1) baseline expenditures
and baseline revenues, (2) post-energy facility impact expenditures,
and (3) post-energy facility impact revenues. The net fiscal impact
of the energy facility can be calculated from the post-impact expen-
diture and revenue forecasts. The three intermediate forecasts are
(1) baseline population, (2) population after the energy facility
impact, and (3) per capita personal income. Each forecast is made
for each year for 2.0 years.
The population forecasts are important intermediate steps
because expenditures and revenues are forecast partially on a per
capita basis. The forecast of personal income is necessary because
revenue forecasts are tied to personal income growth. The basis of
each. of these forecasts will be described in turn.

BASELINE FORECASTS

Baseline Population
Forecasts of baseline population are made by simply projecting
the historic trend into the future. The local government can
substitute its own population projections if it desires.

Baseline Per Capita Income
Forecasts of baseline income are made by simply projecting the
@istoric trend into the future. The local government can substitute
its own per capita income projections if it desires.

Baseline Revenues and Expenditures
The revenues forecast are revenues from all general sources
including taxes, user charges, special assessments, fines and fees,
and revenues received from other governments such as shared taxes,
formula grants, or revenue sharing. Excluded from forecasts of
revenues are revenues from borrowing or revenues from grants for
specific projects. It is assumed that revenues are spent and, thus,
the expenditures forecast are expenditures from general revenues
excluding expenditures from project-related grants or expenditures
from orrowed funds.

The baseline revenue forecast is made by estimating the
increase in revenues associated with past increases in population
and increases in per capita personal income, and then projecting
future increased revenues on the basis of the baseline population
and per capita income forecasts. Baseline expenditures are assumed
@o equal baseline revenues because the same basic variables - changes
in population and per capita income - are also the strongest deter-
minants of government expenditure increases over time.

POST-IMPACT FOMCASTS

Forecasts of population and revenues after energy facility
impacts are more complex than baseline forecasts.

Post-Impact Population Forecast
The number of employees at the energy facility and new employees
in local firms directly servicing the-energy facility are added to
determine the total new direct employment. These employees are then
allocated to geographic areas, including the area of the local govern-
ment for which the impact forecast is being prepared, within commuting
distance of the energy facility. The formula for allocation is
inversely related to distance (the further the distance the fewer the
employees who will be located there) and directly related to existing
population concentrations (the larger an existing population concen-
tration the greater the number of employees who will reside there).
The weights of each variable in the allocation equation are based on
previous studies of the residential location of employees around a
facility. The forecasts of the employment residential distribution
are one element of the model that will be specifically discussed with
local government officials after preliminary forecasts have been made.
After employees have been allocated and the number expected to
locate within the local government area known, a multiplier is used
to estimate the number of secondary jobs that will be created in
response to the higher incomes and new employee population attracted
to the energy facility. The multiplier used varies from 1.05 to 1.5,
depending on the number of jobs in the existing community. (A
multiplier indicates the total number of jobs generated from one new
energy facility job, e.g., a multiplier of 1.05 indicates 1 energy
facility job and .05 secondary jobs per energy facility job -- with
this multiplier it takes 20 energy facility jobs to generate 1
secondary job. With a multiplier of 1.5, it takes only 2 energy
facility jobs to generate a secondary job.) The fewer the number
of existing jobs in a community the smaller the multiplier, up to
5,000 jobs where the multiplier becomes 1.5. This is because
secondary employment in all but the most isolated communities will
tend to occur in areas where business activity already exists - not
in purely residential areas.

After the total direct and secondary jobs are estimated,
an estimate of the number of jobs that will be filled by new
residents in the community is necessary. This estimate is made
by subtracting .3 of the unemployed - who it is assumed find
either energy facility or secondary employment - and if the
employee/population ratio in the community is lower than the
national average, it is also assumed that some residents not
formerly employed will enter the labor force. The further the
local area employment/population ratio diverges from the national,
the more local residents who enter the labor force. If the area
has an employment/population ratio equal to or greater than the
national ratio, no additional old residents are assumed to enter
the labor force. These adjustments for unemployment and labor
force entry result in a forecast of the number of jobs which will
be held by residents new to the community.
Following estimation of the number of new resident job holders,
the increase in total population is estimated by multiplying the
number of new resident job holders by the average population per
employee. This estimate of impact population is then added to
baseline population estimates to obtain the post-impact population
forecast.

Post-Impact Revenue Forecast
The post-impact revenue forecast involves several steps. First
revenues from baseline revenue forecasts are adjusted to exclude
revenues from businesses whose products are sold outside the local
government or whose customers are primarily tourists or nonresidents.
These revenue sources are excluded because having new population
does not automatically result in increases in these two revenue
sources. A second adjustment in revenues is made by lagging and
adjusting residential property tax revenues from the new population.
The lag is based on the construction-assessment-collection lag
within local government and the rate of new population growth. The
greater the rate of new population growth, the lower the residential
property tax revenues per capita because of the increased likelihood
that new residents will occupy apartments or mobile homes. These
adjustments for taxes from nonresidents and in residential property
taxes from new residents provides an estimate of revenues to be
expected from new residents and regular businesses serving them.
Revenues expected from the energy facility itself must be added
before a final post-impact revenue forecast is achieved.

Revenues anticipated from the energy facility are calculated
by applying current or estimated future tax rates to the actual tax
bases created by the energy facility. This is a series of simple
calculations depending on the tax structure in use by the local
government. These revenues and the revenues generated by new
residents and existing service businesses are added to the baseline
revenue forecast to obtain the post-impact revenue forecast.

Post-Impact Expenditure Forecast
The Post-Impact Expenditure Forecast is obtained by multi-
plying the increased expenditure associated with -an increase in
population times the increase in population due to the energy
facility impact and adding this estimate of increased expendi-
tures to the baseline expenditure forecast. Because the impacts
of the energy facility and its associated population on revenues
and expenditures may differ, it is unlikely that forecast post-
impact revenues and expenditures will be equal to one another.

Net Fiscal Impact
The net fiscal impact forecast is made by subtracting forecast
post-impact revenues from forecast post-impact expenditures. The
net fiscal impact may be either positive or negative and in many
cases it will be negative in early years and positive in later years.

SUMMARY

CEIP Impact Model Forecasts are simple. Each step must be
understood by users, however, to be sure that unique local condi-
tions are taken into account and modifications made where neces-
sary to improve the forecasts.

The most important steps to pay attention to include (1) the
allocation of employees to different areas around the energy
facility depending on commuting distances and existing population
concentrations, (2) the estimation of secondary impacts from multi-
pliers, (3) the adjustment based on a comparison of local and
national employee/population ratios and the employment of the
unemployed to determine the number of jobs to be filled by new
residents, and (4) the lags and adjustments in collection of the
residential property tax from new residents or the energy facility.
The assumptions used are based on previous impact analyses; but if
something regarding one of these factors in a local government area
is unique, the forecasts could be in error unless an adjustment
based on local knowledge is made. Any of the assumptions can be
easily modified so that the difference made in final forecasts can
be easily identified. With cooperation among officials who possess
local knowledge, energy facility company officials and OCZM personnel
familiar with the impacts of energy facilities and other economic
developments, it should be possible to make reasonably accurate
forecasts with the CEIP Impact Model.

III. CRITERIA FOR ALTERNATIVE MODELS FOR
ENERGY FACILITY IMPACT FORECASTS

Many governments have developed their own forecasting models
which may potentially be used for energy facility forecasts.
These models may be an acceptable alternative to use of the OCZM-
developed model. In general, alternative models should meet the
following conditions:

(1) Take into account all significant revenues including
state shared taxes, formula grants, federal revenue
sharing, and other federal formula grants such as aid
to federally impacted school districts. Borrowing and
project-related grants may be excluded as long as
expenditures from borrowed funds or grant-financed
projects are excluded.
(2) Expenditures should be forecast on a per capita marginal
cost basis, if possible. Marginal costs, however, should
not generally exceed average costs. Debt repayment
should be considered an expenditure.
(3) The model must include an allocation of new employment
and population around the energy facility location in
relation to existing population concentrations and ex-
pected commuting patterns unless the applying local
government is so large as to include all potential employees.
(4) Multipliers for induced employment and population should
@ave an empirical base taking into account different impacts
in areas of different size. Empirical bases should rely
primarily on actual impact studies and not just cross-
sectional analyses. Both direct and indirect population
and employment impacts are lower in actual impact analyses
than forecasts with cross-sectional data based multipliers.
(5) The model must be able to forecast (a) baseline expendi-
tures and revenues, (b) population impact of the energy
facility, (c) revenues with the facility, and (d) expendi-
tures with the facility.

(6) All data and assumptions upon which the model is based must
be available to OCZM technical assistance personnel as
requested.

The quickest and easiest way to have an alternative model
accepted for forecasting purposes is to simultaneously provide data
necessary for calculating the CEIP Impact Model, with an accompanying
explanation of how the models differ.

APPENDIX A
LIST OF PARTICIPATING STATES IN THE FEDERAL-STATE COOPERATIVE PROGRAM FOR LOCAL POPULATION ESTIMATES,
ALONG WITH OFFICIAL AGENCIES AND OFFICIAL CONTACTS AND/OR PARTICIPANTS: NOVEMBER 1975
AN asterisk (*) denotes agencies which contributed to the estimates previously published in Current Population Reports,
Series P-26. A dagger (.) denotes key technical person)

State Agency State Agency
Alabama Alabama Development Office District of Office of Planning and Management
509 State Office Building Columbia Executive Office of the Mayor
Montgomery, Alabama 36104 Room 113 - District Building
Mr. R.C. Bamberg. Director 14th and E Streets, N.W.
Center for Business and Economic Research Washington, D.C. 20004
Mr. Gangu Ahujs
Graduate School of Business
University of Alabama Florida Division of Population Studies
Box AK Bureau of Economic and Business Research
Calversity, Alabama 35486 College of Business Administration
University of Florida
Mr. Edward Rutledge, Director Gainesville, Florida 32611
Ms. Carolyn Sawyer Dr. Carter C. Osterbind, Director
Alaska Division of Policy Development and Planning Dr. Madelyn Lockhart
Office of the Governor Mr. Jack D. Doolittle
Pouch AD Mr. Bart Lewis
Juneau, Alaska 99811
Mr. Robert S. Weeden, Director Georgia Office of Planning and Budget
Research and Analysis Section 270 Washington Street, S.W.
Alaska Department at Labor Atlanta, Georgia 30334
Box 3-7000 Mr. James T. McIntyre, Jr., Director
Juneau, Alaska 99301 Mr. Richard B. Cobb. Deputy Director
Mr. David L. Gale Mr. Ronald G. Crowe, Planner, State Data Center
Arizona Department of Economic Security Hawaii *Department of Planning and Economic Devolopment
Bureau of Planning Post Office Box 2359
Post Office Box 6123 Honolulu, Hawaii 96804
Phoenix, Arizona 85005 Mr. Robert C. Schmitt, State Statistician
Mr. Jack Kronenfeld, Demographic specialist State Department of Health
Arkansas Industrial Research and Extension Center Post Office Box 3376
University of Arkansas Honolulu, Hawaii 90801
Post Office Bx 3017 Mr. Shigeo Tengan
Little Rock, Arkansas 72203
Dr. Barton A. Westerland, Director Idaho Bureau of Vital Statistics
Dr. Forrest Pollard, Head of Population and Idaho Department of Health and Welfare
Manpower Studies Statehouse
Dr. Jong No Rhew Boise, Idaho 83720
California Population Research unit Ms. Janet Wick, Chief
State Department of Finance Illinois Illinois Department of Public Health
1025 P Street 335 West Jefferson Street
Sacramento, California 95814 Springfield, Illinois 62761
Dr. Walter P. Pollmann, Chief Mr. Clyde A. Bridger, Chief Statistician
Mr. Nelson Rasmussen, Demographer
Ms. Isabell Hambright, Demographer Indiana Indiana State Board of Health
1330 West Michigan Street
Colorado Colorado Division of Planning Indianapolis, Indiana 46206
Rom 670 Dr. William T. Poynter, State Health Commissioner
Columbine Building Dr. Robert A. Calhoun, Director of Public
1245 Sherman Street Health Statistics
Denver, Colorado 80203
Mr. Phillip M. Schmuck, Director Iowa Records and Statistics Division
Mr. Arthur Thompson Iowa State Health Department
Mr. Lee Whitney State Office Building
Mr. Kenneth Prince Des Moines, Iowa 50319
Mr. Jams Taylor
Connecticut Vital Statistics Section Mr. Steve Boal
State Health Department Ms. Hazel Shearer
79 Elm Street
Hartford, Connecticut 06115 Kansas Kansas Department of Economic Development
Dr. Douglas S. Lloyd, Commissioner Division of Planning
Mr. Hal Burde 1258-W State Office building
Mr. Robert O'Dell Topeka, Kansas 66612
Delaware State Planning Office Mr. John F. Halligan, Director
Thomas Collins Building Population Research Laboratory
530 South Dupont Highway Kansas State University
Dover, Delaware 19901 Manhattan, Kansas 66506
Mr. David R. Keifer, Director Dr. Cornelia Flora
Ms. Mellen Galof Ms. Karen Schwartz
The District of Columbia is participating on an informal basis.

APPENDIX A--Continued
LIST OF PARTICIPATING STATES IN THE FEDERAL-STATE COOPERATIVE PROGRAM FOR LOCAL POPULATION ESTIMATES.
ALONG WITH OFFICIAL AGENCIES AND OFFICIAL CONTACTS AND/CR PARTICIPANTS: NOVEMBER 1975-- Continued

(As esterick (*) denotes agencies which contributed to the estimates proviously publidhrf in Current Population Reports,
Series P-25. A dagger (') denotes key technical person)

State Agency State Agency

Kentucky *Urban Studies center Nebraska nebraska Department of Economic Development
University of Louisville Post Office Box 94666
Gardencourt campus State Capitol
Alta vista Road Lincoln, Nebraska 68509
Louisville, Kentucky 40205 Mr. Ronald J. Mertons, Director
'Dr. Mike Spar
*Bureau of Business Research
Louisiana *Research Division The University of Nebraska
College of Administration and Business Lincoln, Nebraska 68508
Louisiana Tech University 'Ms. Vicki Stepp
Ruston, Louisiana 71270 Nevada
Dr. Don C. Wilcox, Director *Bureau of Business and Economic Research
'Ms. Barbara Denton University of Nevada
Reno, Nevada 89507
Maine *Research and Vital Records Dr. Robert C. weem, jr., director
State Department of Heath and Welfare dr. shih-fan chu
Augusta, Maine 04330 'mr. samuel malos
'Mr. Dale Welch, Director
New Hampshire Office of Comprehensive Planning
Maryland Maryland Center for Heath Statistics executive Department
State Department of Health and Dental Hygiene State House Annex
O'Connore Building Concord, New Hampshire 03301
201 West Prestin Street Mr. James
Baltimore, Maryland 21201
'Mr. Ira Rosenwalke
Mr. Luther Frantz, Jr.

Massachusetts *Bureau of Research and Statistics
Massachusetts Department of Commerce and
Development
State Office Building
100 Cambridge street
Boston, Massachusetts 02202
'Mr. William P. Tsaffaram, Director

Michigan *Office of the Budget
Lewis Cass Building
Lansing, Michigan 48913
Mr. Tom Clay, Director
*Mr. Bill O'Hare

Minnesota *Minnesota State Planning
101 Capitol Square Building
550 Cedar street
St. Paul, Minnesota 55101
'Ms. Hazel Neinhardt

Mississippi *Department of Sociology
Mississippsi State Univeristy
Post Office Drawer C
State College, Mississippi 139782
Dr. John Saunders, Department Head
*Ms. Ellen Bryant, Assistant Syciologist
'ms. Eayly chaney

MIssouri *State Planning and Analyiss Division
Office of Administration
State Capitol
Post Office Box 809
Jefferson City, Missouri 65101
'mr. Michael Boxberger

Montana Bureau of Business and Economic Research
University of Montana
Missouis, Montana 59801
Ms. Maxine C: Johnson, Directore
'Ms. Susan Solig wallwork

APPENDIX A- Continued

PARTICIPATING STATES IN THE FEDERAL- STATE COOPERATIVE PROGRAM FOR LOCAL POPULATION ESIMATES,
ALONG WITH OFFICIAL AGENCIES AND OFFICIAL CONTACTS AND/OR PARTICIPANTS: NOVEMBER 1975- Continued

(An asterisk (*) denotes agencies which contribute to the estimates previously published in Current Population Reports,
Series P-26. A dagger (') denotes key technical person)

State Agency State Agency

Oklahoma Research and Planning Division Utah Utah Department of Employment Security
Oklahoma Employment Security Commission 174 Social Hall Avenue
Will Rogers Building Salt Lake City, Utah 84111
Oklahoma City, Oklahoma 73105 Mr. Richard J. Arnold, Director of Reports and
Mr. W.J. Bowman, Chief Anaysis
Mr. William Hunter, Assistant Chief Mr. Kenneth Jensen
Mr. Roger Jacks
Vermont Division of Public Health Statistics
Oregon Center for Population Research and Cesus
Portland State University- Box 751
Portland, Oregon 97207
Dr. James

Pennsylvania Offic of State Planning and Development
Post Office Box 1323
Harrisburg, Pennsylvania 17120
Mr. A. Edward Simon, Executive Director
Ms. Natalie Sato State Department of Health
115 Colcheater Avenue
Puerto Rico Puerto Rico Planning Board
Government Center
North Building, De Diego Avenue
Post Office Box 9447
Santurce, Puerto Rico 00908 Burlington, Vermont 05401
Mr. Walter L. Cooley,Chief

ESTIMATES PUBLISHED IN SERIES P-26 REPORTS SINCE 1970

(Reports Issued under the Federal-State Cooperative Program for Population Estimates, jointly prepared by the
Bureau of the Census and designated State agencies)

Report No. Report No.

State 1973 and 1972 and 1971 and State 1973 and 1972 and 1971 and
provisional provisio 'nal provisional provisional provisional provisional
1974 1973 1972 2974 2973 1972

Ala .......... 125 76 48 Pont ........ 109 53 19
Als:1a (1) (11) (3) Xcbr ......... 104 58 25
Ar I."':.* 94 50 *11 IN c v ......... 117 67 29
Ark.. .. 115 70 33 N. I I........ 107 52 Is
CalIf:..:..,: 119 (2) -41 X . J........ 135 92 20

Colo ......... 203 62 17 N. Mex ...... 123 85 (3)
Conn ......... 116 79 (3) N. Y..._.. (1) (2) (3)
Del ........... 121 57 15 X. C........ 114 68 44
Fla ........ 130 90 46 N. Dak ...... 102 60 (3)
Ga ........... 124 92 37 Ohio ........ 122 80 -40

Rawall 105 56 23 Okla ........ 112 63 24
Idaho.:--.'. lOG 51 9 Oreg ........ (1) 74 (3)
III .......... 128 78 27 Pa .......... 136 93 -39
Ind ........... 113 75 14 R. I ........ 98 65 22
Iowa ......... 138 72 31 S. C ........ 108 71 34

safts ......... 229 64 43 S. Dak ...... 101 61 *12
Ky ........... 120 84 35 Tenn ........ 133 83 47
'IA ........... 97 54 *16 Tex ......... (1) (2) (3)
Maine ........ 99 59 28 Utah ........ 96 55 10
Md ........... (2) (2) (3) Vt .......... 95 49 *13

KasX ......... 137 91 42 Va .......... 127 88 36
Mich ......... 110 69 32 Wash ........ (1) 66 (3)
uInn 132 :7 38 W.,Va ....... 121 89 30
131 (3)wi ......... 126 81 26
me ........... 134 767 45 W'yo ......... 100 73 (3)

-First year only. For second year, see Series P-25, No. 517,
$County or county equivalent astimates for 1973 and provisional 1974 are published in Series P-25 for the following
States: Slaryland. No. 596; washington, No. 597; New York, No. 599; Oregon, No. 602; Alaska, No. 604; and Texas.No. 609.
'County or county equivalent estimates for 1972 and provisional 1973 are published in Series P-25 for the following
Btates: New York. No. 527: Maryland. No. 520; Alaska, No. 531: California, No. 532; and Texas, No. 535.
$County estimates for this State for 1972 and provisional 1972 are published In Series P-25. No. 517.

APPENDIX B

BUREAUS OF BUSINESS AND ECONOMIC RESEARCH
IN COASTAL STATES

ALABAMA

CENTER FOR BUSINESS AND ECONOMIC RESEARCH
Graduate School of Business
The University of Alabama
Box AX
University, Alabama 35486
205/348-6191

ALASKA

INSTITUTE OF SOCIAL, ECONOMIC AND GOVERNMENT RESEARCH
University of Alaska
Fairbanks, Alaska 99701
907/479-7436

CALIFORNIA

INSTITUTE OF BUSINESS AND ECONOMIC RESEARCH
156 Barrows Hall
University of California, Berkeley
Berkeley, California 94720
415/642-1922

BUREAU OF BUSINESS RESEARCH AND SERVICE
School of Business and Administrative Sciences
California State University, Fresno
Fresno, California 93740
209/487-2068

DIVISION OF RESEARCH
University of Southern California
Bridge Hall
Los Angeles, California 90007
213/74 6-5202

BUREAU OF BUSINESS AND ECONOMIC RESEARCH
School of Business Administration, BA-410
San Diego State University
San Diego, California 92182
714/286-6838

APPENDIX B (continued)

FLORIDA

'BUREAU OF ECONOMIC AND BUSINESS RESEARCH
University of Florida
221 Matherly Hall
Gainesville, Florida 32611
904/392-0171

GEORGIA

DIVISION OF RESEARCH
College of Business
University of Georgia
Athens# Georgia 30602
404/542-4085

OFFICE OF RESEARCH AND SERVICES
School of Business Administration
Georgia State University
Atlanta, Georgia 30303
404/658-4256

ILLI140IS

CENTER FOR RESEARCH AND SERVICE
College of Business and Administration
Southern Illinois University
Carbondale, Illinois 62901
618/453-3328

BUREAU OF ECONOMIC AND BUSIN8SS RESEARCH
408 David Kinley Hall
University of Illinois
Urbana, Illinois 61801
217/333-2330

INDIANA

DIVISION OF RESEARCH
Graduate School of Business
Indiana University
Bloomington, Indiana 47401
812/337-5507

APPENDIX B (continued)

LOUISIANA

DIVISION OF RESEARCH
College of Business Administration
Louisiana State University
P. 0. Box 17350-A
Baton Rouge, Louisiana 70803
504/388-5830

DIVISION OF ADMINISTRATION AND BUSINESS RESEARCH
P. 0. Box 5796
Louisiana Tech University
Rustont Louisiana 71270
318/257-3701

DIVISION OF BUSINESS AND ECONOMIC RESEARCH
College of Business Administration
University of New Orleans
Lakefront
New Orleans, Louisiana 70122
504/288-3161, ext. 248

MARYLAND

BUREAU OF BUSINESS AND ECONOMIC RESEARCH
University of Maryland
Room 4118, Tydings Building
College Parke Maryland 20742
301/454-2303

MASSACHUSETTS

DIVISION OF RESEARCH
Harvard Graduate School of Business Administration
230 Morgan, Soldiers Field
Boston, Massachusetts 02163
617/495-6334

BUREAU OF BUSINESS AND ECONOMIC RESEARCH
Northeastern University
Boston, Massachusetts 02115
617/437-3252

APPENDIX B (continued)

MICHIGAN

DIVISION OF RESEARCH
Graduate School of Business Administration
The University of Michigan
Ann Arbor, Michigan 48109
313/764-1366

BUREAU OF BUSINESS AND ECONOMIC RESEARCH
Division of Research
5-J Berkey Hall
Michigan State University
East Lansing, Michigan 48824
517/355-7560

MINNESOTA

BUREAU OF BUSINESS AND ECONOMIC RESEARCH
114 Social Sciences Building
University of Minnesota, Duluth
Duluth, Minnesota 55810
218/726-7298

BUREAU OF BUSINESS AND ECONOMIC RESEARCH
School of Business
Mankato State College
Mankato, Minnesota 56001
507/389-2711

BUREAU OF BUSINESS AND ECONOMIC RESEARCH
Mankato State University
Mankato, Minnesota 56001
507/389-1623

MISSISSIPPI

BUREAU OF BUSINESS RESEARCH
University of Southern Mississippi
Southern Station, Box 94
Hattiesburg, Mississippi 39401
601/266-7247

DIVISION OF BUSINESS RESEARCH
College of Business and Industry
Mississippi State University
P. 0. Drawer 5288
Mississippi State, Mississippi 39762
601/325-5244

APPENDIX B (continued)

BUREAU OF BUSINESS AND ECONOMIC RESEARCH
University of Mississippi
University, Mississippi 38677
601/232-7481

NEW JERSEY

BUREAU OF ECONOMIC RESEARCH
Rutgers University
The State University of New Jersey
New Brunswick, New Jersey 08903
201/932-7451

NEW YORK

BUSINESS RESEARCH INSTITUTE
St. John's University
College of Business Administration
Jamaica, New York 11439
212/969-8000, ext. 480

MANAGEMENT RESEARCH CENTER
School of Management
Syracuse University
129 College Place
Syracuse, New York 13210
315/423-2052

NORTH CAROLINA

ECONOMIC DEVELOPMENT CENTER
School of Business
Western Carolina University
Cullowbee, North Carolina 28723
704/293-7492

BUREAU OF ECONOMIC AND 8USINESS RESEARCH
College of Business
Appalachian State University
Boone, North Carolina 28608
704/262-2148

INSTITUTE OF APPLIED BUSINESS AND ECONOMIC RESEARCH
Graduate School of Business Administration
University of North Carolina
Chapel Hill, North Carolina 27514
919/933-8301, ext. 225 or 221

APPENDIX B (continued)

OHIO

CENTER FOR MANAGEMENT DEVELOPME-NT AND RESEARCH
Case Western Reserve University
School of Management
Cleveland, Ohio 44106
216/368-2042

CENTER FOR BUSINESS AND ECONOMIC RESEARCH
The Ohio State University
1775 College Road
Columbus, Ohio 43210
614/422-5967

CENTER FOR BUSINESS AND ECONOMIC RESEARCH
Graduate School of Business Administration
Kent State University
Kent, Ohio 44242
216/672-2093

OREGON

BUREAU OF BUSINESS RESEARCH
140 Gilbert Hall
University of Oregon
Eugene, Oregon 97403
503/686-3376

PENNSYLVANIA

BUREAU OF ECONOMIC AND BUSINESS RESEARCH
Temple University
School of Business Administration
Philadelphia, Pennsylvania 19122
215/787-8101 or 8102

BUREAU OF RESEARCH AND COMMUNITY SERVICES
School of Business and Administration
Duquesne University
Pittsburgh, Pennsylvania 15219
412/434-6229

CENTER FOR RESEARCH
College of Business Administration
801 Business Administration Building
The Pennsylvania State University
University Park, Pennsylvania 16802
.814/865-7669 or 7660

APPENDIX B (continued)

SOUTH CAROLINA

BUREAU OF BUSINESS AND ECON014IC RESEARCH
College of Business Administration
University of South Carolina
Columbia, South Carolina 29208
803/777-2510

TEXAS

BUREAU OF BUSINESS RESEARCH
P. 0. Box 7459
University St4tion
The University of Texas at Austin
Austin, Texas 78712
512/471-1616

VIRGINIA

TAYLOE MURPHY INSTITUTE
The Colgate Darden Graduate School of Business Administration
University of Virginia
P. 0. Box 6550
Charlottesville, Virginia 22906
804/924-7451

WASHINGTON

OFFICE OF FACULTY RESEARCH AND PUBLICATIONS
Mackenzie Hall, DJ-10
University of Washington
Seattle, Washington 98195
206/543-4598 or 4599

WISCONSIN

BUREAU OF BUSINESS RESEARCH AND SERVICE
110 Commerce Building
University of Wisconsin
1155 Observatory Drive
Madison, Wisconsin 53706
608/262-1550

APPENDIX C

BUREAUS OF GOVERNMENTAL RESEARCH

-Local and State Agencies

Alabama California

MONTGOMERY BERKELEY
Alabama League of Municipalities (1935)
535 Adams ave 36104, University of California 94184
Telephone: (205) 263-1042 Graduate School of Public Affailrs
John F. Watkins, Executeive Director Telephone: (415) 642-4670
Dan O'Dowe, Jr., Pub. Mgr. 2607 Herst
D. N Hamilton, Gen. Counsel Aaron Wildavsky, Dean

Alabama Chamber of Commerce Institute of Governmental Studies
Research Division Telephone: (415) 642-8722
P.O. Box 76 39101 Eugene C. Lee Director
Telephone: (205) 262-7319 Stanley Scott Assistant Director
Harry C. McMillan, Director Barbara J. Hudson, Librarian
Phyllis Barush
State of Alabama Ora Huth
Legislative Reference Service 36104 Dorothy Tompkins
Telephone: (205) 269-6438
Louis G. Greene. Director

Program Development Office Institute of Business & Economic Research,
304 Dexter Avenue 36104 Northern Section (1941)
Telephone: (206) 269-7171 156 Barrows Hall
J.E. Mitchell, Jr., Director Joseph W. Garbarion, Director

UNIVERSITY Institute of Urban & Hegional Development
Center of Real Estate and Urban Eco-
University of Alabama 35486 nomics
Bureau of Public Administration (1938) 260 Stephens Memorial Hall 94720
Telephone: (205) 348-5980 Telephone: (415) 642-2491
Robert B. Highsaw, Director Wallace F. Smith, Acting Chairman
L. Franklin Blitz
Coleman B. Ransone Western Governmental Research Association
William H Stewart (1537)
James D. Thomas 109 Moses Hall
University of California 94720
Telephone: (415) 642-6722
Alaska Kenneth Hunter, President
Stanley Scott, Executive Secretary
JUNEAU
BURLINGAME
State of Alaska
Local Affairs Agency Governmental Research Council of
Peach AB 99801 San Mateo County
Telephone: 586-6221 1299 Bayshore Hwy, # 217 94010
Byron I. Mallott, Director Robert D. Harrison, Jr., Executive Director

DAVIS Property Owners Tax Association of
California (1931)
132 West First Street, Room 228
University of California Davis, Istitute of Telephone: (213) Madison 9-3366
Government Affairs 95616 Melvin Horton, Executive Vice- President
Telephone: (916) 752-2042 Milton Harker, Research Director
Lloyd D. Musolf. Director
Richard W. Gable. Associate Director
Nedjelko Suljak, Librarian Southern California Research Council
1600 Campus Road 90041
LONG BEACH Telephone: (213) 265-5151
Joseph E. Haring, Research Coordinator
City of Long Beach 90807
Budget & Research Division
Telephone: (213) 486-9041 Town Hall (1937)
Randall J. Verrue, Director Biltmore Hotel. 515 South Olive St. 90013
Roger Keast, Senior Analyst Telephone: (213) Madison 8-8141
Rolland D. Headlee, Executive Director
LOS ANGELES

Bureau of Municiple Research (1931) University of California at Los Angeles
117 West 9th Street 90013 Institute of Government and Public Affairs
Telephone: (213) Madison 7-3383 405 Hilgard Avenue 90024
James O. Stevenson, Director Telephone: (213) 825-7117
Werner Z. Hirsch, Director
California Taxpayers Association (1926)
659 Mobil Building
612 South Flower Street 90017 Graduate school of Business Administration
Telephone: (213) Madison 7-9001 Housing, Real Estate & Urban Land
J. Roy Holland, Regional Director of Local Studies Program
Affairs Telephone: (213) 825-3977
City Administrative Office (1951) Fred E. Case, Director
350 City Hall 90012
Telephone: (213) Madison 4-2121
C. Erwin Piper, City Administrative Officer Department of Political Science
470 City Hall 90012 Winston W. Crouch
Telephone: (213) Madison 4-5211
A. C. Estes, Chief Legislative Analyst Public Affairs Service/Local
Alfred Purvis, Legislative Analyst Telephone: (213) 825-1942
County of Los Angeles Dorthy V. Wells, Local Documents
Chief Administrative Office Librarian
Managment Services Division
Telephone: (213) Madison 5-3611 University of Southern California
Douglas R. Steele, Chief von KleinSmid Center of International &
Public Affairs 90007
League of California Cities (1898) Telephone: (213) 746-2241
Los Angeles Office Henry Reining, Jr. Dean
702 Hilton Center
Telephone: (213) madison 4-4934
Murray Brown, Editor, Western City Mag- MARTINEZ
azine
Contra Costa Taxpayers Assocition, Inc.
Los Angeles Chamber of Commerce (1888) (1929)
Vincent A. Bordelon, Manager, Government P.O. Box 72, 820 Main St. 94553
Relations Department: Telephone: (514) 223-5610
James J. Carroll, Executive Vice President
(Washington Office)
1000 Vermont Avenue N.W.
Washington, D.C. 20005 OAKLAND
Eleanor Buhler, Administrator
Alameda County Taxpayers Association Inc.
Municple Reference Library 1404 Franklin Street 94612
City Hall 90012 Telephone: (415) 393- 2341
Room 1003 Edouard B. McKnight, Executive Vice-
Telephone: (213) 485-3791 President
Bert Maze, Field Representative

SACRAMENTO

California Farm Bureau Federation
Public Affairs Division
Room 531, 11th & L Bidg. 95814

Telephone: (916) 446-4647
J.A. Juneelli, Governmental Affairs
Specialist

California Reatailers Association
1127 Eleventh Street 95214
Telephone: (916) 444-6670
John T. Hay, Executive Vice President

Economic Development & Research Depart-
ment
Jack Smith, Director

Insurance Department
W. Edward Couhc, Director

Commonwealth Club of California (IS03)
California Taxpayers Association Monadrock Arcade,681 Market Street
11th & L Buliding. Suite 713 95814 Telephone:(415) Douglas 2-
Telephone: (916) 443-8163 Durward S. Riggs,Executive Secretary
Robert C. Brown. Executive Vice President Michael J. Brassington, Assistant Executive
Arlen K. Bean. Director of Research Secretary
Richard P. Simpson, Regional Director.
Local Affairs San Francisco Bay Area Council, Inc.
World Trade Center 94111
League of California Cities Telephone: (415)Yukon 1-6405
1108 "0" Street 95814 Stanley E. McCaffrey, President
Telephone: (916) 444-5790 Angelo Siracusa. Vice-President
Richard Carpenter, Excutive Director & Kenneth Evansco, Director of Research
General Counsei,

State of California
Council on Intergovernmental Relations San Francisco, Bureau of Governmental
1400 Tenth Street 95814
Telephone:: (916)445-7366 Research (1916)
Telephone:' (415) 362-8715
James A. R. Johnson. Executive Director 58 Sutter Street 94104
Department of Finance
1145 State Capitol *Louis Clisbee, Directoor
Telephone: (916)445-4141 San Francisco Chamber of Commerce
Verne Orr. Director 400 Montgomery Street, 94104
Dept. of Housing and Community Telephone: (415) 392-4511
Development W. E. Dauer, Executive Vice-President
1120 O Street. Room 3344 95814 San Francisco Planning and Urban Renewal
Telephone: (916) 445-4775 -Association
Donald F. Pinkerton. Director 126 Post Street 94103
Telephone: (415) SUtter 1-8726
Department of Human Resources John H. Jacobs, Executive Director
Development Michael L. Fischer, Associate Director
800 Capitol Mall 95814
Norman Blacher, Deputy Director
Legislative Budget Committee SANTA ROSA
306 State Capitol 95814 Sonoma County Taxpayers Association (1946)
Telephone. (916) 445-4656
Analyst Telephone: (707) 542-0442
A. Alan Post, Legislative 2403 Professional Dr., Suite 105
Legislative Counsel Bureau (1913)
3021 State Capitol, 95814
Telephone: (916) 445-2731 STANFORD
George H. Murphy, Legislative Counsel Stanford University
State Board of Equalization The Hoover Institution on War. Revolution
1020 N. Street 95814 and Peace 94305
Telephone: (916) 445-3956 Telephone: (415) 321-2300
Herbert F. Freeman, Executive Secretary Director
W. Glenn Campbell.
Roger A. Freeman. Senior Fellow
Slate Library Thomas L. Glenn, Research Associate
Library & Courts Building 95814
Telephone: (916) 445-2585
(Mrs.) Carma R. Leigh, Librarian

VENTURA

Ventura County Taxpayers Association
SAN DIEGO 1068 East Main Street, Room 210
San Diego State College 82115 P. 0. Box 818 93001
Telephone: (305) 643-6166
Burea of Buusiness & Economic Research
(1937) *Daniel J. Montero. EXeCUTiVe Secretary
John McFall. Director
Public Affairs Research Institute 92115
Telephone: (714) 266-6224
W. Richard Bigger, Director
San Diego Taxpayers Association
1330 U.S. National Bank Building 92101
Telephone: (714) 234-6423
Stanley If. Coombs.Manager
Charles.E. Stine. Associate Manager
SAN FRANCISCO Connecticut
Civic League of Improvement Clubs and HARTFORD
Associations (1995)
859 Flood Building Connecticut Business & Industry
870 Market Street 94102 Association, Inc.
Telephone: (415) 781-4480 60 Washington Street 16103
Augusta G. Haas, Executive Seccretary Telephone: (203)547-1661

Connecticut Public Expenditure Council, Inc. STORRS
(1942)
21 Lewis Street 06103, University of Connecticut
Telephone: (203) 527-3177 Institute of Public Service
*Carter W. Atkins P. 0. Box U-14 06268
*Robert H. Franklin, Executive Director Telephone:(203) 429-3311
*Richard W. Lafferty, Director Municipal Beldon H. Schaffer, Director
Consulting Service Edward T. Dowling, Assistant Directo-
*Russell G. Mobley, Director,Membership George E. Hill
Field Service Rosaline Levenson
*Mark T. Geodrich, Senior Researcher George H. Murray
*Durward D. Wakefield, Researcher Patricia Stuart
*Charles L. Miller. Senior Researcher Myron E. Weiner
*Arthur E. Schlos, Senior Researcher
Institute of Urban Research
Connecticut State Library Telephone: (203) 429-3311. Ext. 883
Legislative Reference Unit Morton Tenzer, Director
231 Capitol Avenue Or; 66115
Telephone: (203) 566-4544
George Adams, Chief

Greater Hartford Chamber of Commerce, Inc,
250 Constitution Plaza 66103 Delaware
Telephone: (203) 566-4544
Research and Governmental Affairs DOVER
Donald W. Goodrin, Manager Delaware League of Local Governments
P .O. Box 484 19901
Telephone: (302) 678-0991
David L. Press, Executive Director

State of Delaware
State of Connecticut Department of Community Affairs
Budget Division and Economic Development
Capital Avenue 06115 Division of Housing
Telephone: (203) 527-6349 55 The Green 19901
Fred A. Schuckman, Budget Director Robert S. Moyer, Director
Maurice C. Wintrode, Assistant Budget Director
Director
Frank J. Reilly, Assistant Budget
Director

Department of Community Affairs NEWARK
1179 Main Street, P.O. Box 786 06101
Telephone: (203) 566-3318/9 University of Delaware 19711
Donald T. Dorsey, Commissioner Divisoin of Urban Affairs
Telephone: (302) 738-2397
State Welfare Department (1935) C. Harold Brown, Director
1690 Asylum Avenue Peter M. Ross, Assistant Director
James Morrison, Chief of Welfare Staff
Services James L. Cox, Political Scientist
Francis X. Tannian, Senior Economist
Marvin Brams, Economist
NEW HAVEN Robert A. Wilson, Sociologist

Connecticut Conference of Mayors
956 Chapel Street 06310 WILMINGTON
Telephone: (203) 772-2168 Committee of 39, Inc.
Joel Cogen, Executive Director 909 Orange Street 19201
Telephone: (302) 656-0766
New Haven Taxpayers Research Council, Inc. Claude Corty, President
(1933)
P. 0. Box 1784 06507 Delaware State Chamber of Commerce
Telephone: (203) 777-7659 Governmental Affairs Division
Francis J. Kelly, Executive Director 1102 West Street 19801
Irene E. Trejsner Telephone: (362) 653-7221
Ross E. Anderson, Jr., Executive Vice-
Yale University President
Department of Political Science
if. Bradford Westerfield, Chairman Greater Wilmington Development Council,
Inc.
Institute of Social Science 390 Delaware Avenue 19801
John Perry Miller, Director
Telephone: (302) 658-3263
Peter A. Larson. Executive Vice President
Robert W. Lang. Administrative Director
STAMFORD Robert D. Stoddard. Education Director

Stamford Area State of Delaware
Commerce & Industry Association. Inc. Depqartment of Housing
Washington Bldg. One Bank Street 06901 601 Delaware Avenue 19801
George E. Cunningham. Secretary
Telephone: (203) 348-6246

John Mitovitch, Executive Director
Mrs. Pobie Johnston

Florida
CORAL GABLES

University of Miami 33124
Telephone: (305)284-5155
*Henery K. Stanford, President

GAINESVILLE

University of Florida 32601
Public Administration Clearing Service
Telephone: (904)0392-0279 or (904)392-0248
Ernest R. Bartley, Director
Bureau of Economic & Business Research
(1930)
221 Matherly Hall 32601
Carter C. Osterbind, Director

JACKSONVILLE

Florida State Chamber of Commerce
P.O. Box 8046 32211
Telephone: (904) 724-2400
Ronald S. Spencer. Jr., Executive Vice-
President

Jacksonville Area Chamber of Commerce
Governmental Affairs
604 Hogan Street 32201
Telephone: (904) 353-6161
Richard E. Johnston, Acting Director

MIAMI

Greater Miami Chamber of Commerce
1200 biscayne Boulevard 33132
Telephone: (305) FR 7-4711
Lester Freeman, Executive Vice-President

TALLAHASSEE

Florida State University 32306
Institute of Social Research
Telephone:(904)599-2015, 599-4570
Charles M. Grigg, Director
E. Lester Levine, Associate Director

Florida League of Citites
P.O. Box 431 32302
Telephone: (904) 224-8160
Raymond C. Sittig, Executive Director
Dwynal B. Pettengill, Director of Research

State of Florida
Department of Community Affairs
301 Office Plaza 32301
Telephone: (904)877-3185
M. Athalie Range, Director

TAMPA

Flordia Taxpayers Association, Inc. (1933)
3430-A W. Kennedy Boulevard 33609
Telephone: (313)876-4286
R.L. Newman, Jr., Executive Director

Georgia
ATHENS

University of Georgia, The 30601
Institute of Government
Terrel Hall
Telephone: (404)542-2736
Morris W. H. Collins, Jr., Director
*J.D. Weeks, Head, Legal Section

Division of Research, College of
Business Administration (1928)
New College
Telephone: (404)542-1721
W.B. Keeling, Director

Institue of Community of Area
Development
Old College
Telephone: (404) 542-3463
Cameron Fincher, Director

ATLANTA

Association of County Commissioners
of Georgia
205 Forsyth Building 30303
Hill R. Healan, Director

Atlanta Chamber of Commerce (1938)
Governmental Affairs Department
1301 Commerce Building 30303
Telephone: (404)JAckson 1-0845
Jim King, Director

Emory University
Department of Political Science
Telephone:(404)377-2411, ext. 7567
Lewis Bowman, Professor

Georgia Municipal Association, Inc. (1934)
501 Fulton Federal Building 30303
Telephone: (404)688-0472
W. Elmer George, Executive Director
Henry J. Wise
Marsha Buttram

Georgia Chamber of Commerce
Governmental Department
1200 Commerce Building
Telephone: (404) 524-8481
Glenn Anthony, Manager

Georgia State Library
301 Judicial Building
40 Capitol Square, S.W. 30334
Telephone: (404) 656-3468
John D.M. Folger, State Librarian

Georiga State University
Bureau of Business & Economic Research
(1950)
33 Gilmer Street, S.E. 30303
Willys R. Knight, Director

Southern Regional Council
5 Forsyth Street, N.W.
Telephone: (404)522-8764
Paul Anthony, Executive Director

State of Georgia
Bureau of State Planning and Community
Affairs, Office of the Governor
270 Washington Street, S.W., Room 611
30334
Telephone: (404)656-3821
Tom Linder, Jr., State Planning and
Community Affairs Officer

Hawaii

HONOLULU

Hawaii Employers Council
P.O. Box 9663 26320
Telephone: (808) 841-6141
Betty F. Hirozawa, Director of Research

Municipal Reference Library
305 City Hall 96813
Telephone: (808)546-7578
(Mrs.)Jean K. Mardfin, Municipal
Librarian

State of Hawaii
Department of Budget and Finance
P.O. Box 150 96819
Hiram K. Kamaka, Director
Legislative Budget Office
Iolani Palace 96313
Clinton Tamimura, Legislative Auditor

Office of the Governor
State Capitol Building 98613
Telephone: (808) 548-2378
Hirobumi Uno, Special Asst. on Humen
Resources

Tax Foundation of Hawaii (1953)
680 alexander Young Building 96813
Telephone: (808)536-4587
*Fred W. Bennion, Executive Director
Nell Cammack, Research Assistant

University of Hawaii 90822
Legislative Reference Bureau (1943)
Telephone: (808)536-7372
Henry N. Kitamura, Director
Samuel B. K. Chang, Deputy Director

Economic Research Center
Walter Miklius, Director
Social Science Research Institute (1959)
William P. Lebra, Director

Illinois

CARBONDALE

Southern Illinois University 62901
Community Development Institiute
511 South Grand
Telephone: (618)453-2491
Richard M. Rhomas, Director
Howard R. Delaney, Assistant Director
Robert K. Knittel
Donald E. Voth
Raymond E. Wakeley
Malcolm T. Walker

Department of Government
J.F. Isakoff

Public Affairs Research Bureau
David Kenney, Head

CHICAGO

Better Government Association
75 East Wacker Drive 60601
Telephone: (312)641-1181
J. Terrence Brunner, Executive Director

Chicago Association of Commerce
and Industry
Governmental Affairs Division
130 South Michigan Avenue 60603
*Preston E. Peden, Director

Chicago Crime Commission (1919)
79 West Monroe Street 60603
Telephone: (312)FRanklin 2-0101
Harvey N. Johnson, Jr., Operating Director

City of Chicago
Department of Purchase, Contracts
and Supplies
Room 400, City Hall 60602
Telephone: (312)744-4900
*John F. Ward, Purchasing Agent

Civic Federation, Inc.,
29 East Madison Street 60602
Telephone: (312)263-3237
Norman J. Beatty, Executive Secretary
*D. Daniel Baldino, Director of Public
Affairs
*Lavern W. Kron, Director of Research
*William J. McGlone, Director of Develop-
ment
*Richard F. Elberfald, Research Analyst
Lorraine Woods, General Counsel

Illinois State Chamber of Commerce (1919)
Tax Department
20 North Waker Drive 60606
Telephone: (312) FRanklin 2-7373

Loyola University
Center for Research in Urban Government
820 North Michigan Avenue 60611
Telephone: (312)944-0300
Joseph Small, Acting Director

Metropolitan Housing and Planning Council
(1934)
53 West Jackson Boulevard 60604
Telephone: (312)922-5616
(Mrs.)Frederick H. Rubel, Director

Municipal Reference Library ROCKFORD
1004 City Hall 60602
Telephone: (312)744-4994 Civic League, Inc. of Winnebago County
Joyce Miden, Librarian 604 Rock River Savings Building 61101
401 West State Street
Union League Club of Chicago (1879) Telephone: (815)963-7114
65 West Jackson Blvd. 60604 *Arthur D. Logan, Executive Secretary
Telephone: (312)IIArrison 7-7000
*Robert W. Bergstrom, President
*Roger E. Henn, Director, Public Affairs SPRINGFIELD
Edward M. Martin, Director, Emeritus Illinois Municipal League (1914)
1220 South 7th Street 62703
University of Chicago Telephone: (217)525-1220
Population Research Center Steven Sargent, Executive Director
1413 East 60th Street 60637
Telephone: (312)753-2571 Illinois State Chamber of Commerce
Philip M. Hauser, Director Government Operations Department
415 Illinois Building
607 East Adams 62701
Telephone: (217) 544-1787
University of Illinois at Chicago Circle
Center for Urban Studies
Box 4348 60680
Telephone: (312)663-8722
Stuart Scher, Director

EAST ST. LOUIS
St. Clair County Taxpayers' Association State of Illinois
622 First National Bank Building 62201 Dept. of Local Government Affairs
Telephone: (618)875-2250 325 West Adams Street, Room 206 62706
John R. Henne, Exectuive Vice-President Telephone: (217)525-6436
Robert J. Lehnhausen, Director
Legislative Council (1937)
EDWARDSVILLE M-0 State House 62706
Telephone: (217)525-6851
Southern Illinois University 62025 William L. Day, Director of Research
Regional & Urban Development Studies & H. William Hey, Associate Director
Services James T. Mooney, Deputy Director for
Telephone:(618)692-3032 Legal Research
William J. Tudor, Director Paul W. Reeder
Robert G. Granda
William M. Bleakley

EVANSTON Danee R. Wright
Gerald L. Gherardini
Northwestern University Dorothy A. Nadasdy
The Transportation Center
1818 Hinman 60204 Office of the Governor
Telephone: (312)492-3220 205 State Capitol Building 62706
John A. Bailey, Director Telephone: (217)525-6330
Duane F. Marble, Director of Academic Ronald D. Michaelson, Asst. to the Governor
Programs (Local Affairs)
Ricard M. Michaels, Director of Research
Edward K. Morlok Taxpayers' Federation of Illinois 62702
Patrick M. O'Sullivan 525 W. Jefferson, Suite 506
Peter L. Watson Telephone:(217)522-6818
Maurice W. Scott, Exec. Vice President

HARVEY URBANA

South Suburban Chamber of Commerce University of Illinois 61801
and Industry Bureau of Urban and Regional Planning
15328 Center Avenue 60426 Research
Telephone: (312)333-1720 1202 West California
C. Frohman Johnson, Executive Vice- Telephone (217)333-3020
President Eric Freund, Director

Bureau of Economic & Business Research
403 David Kinley Hall
PEORIA Telephone: (217)333-2330
V. Lewis Bassie, Director
Peoria Association of Commerce
307 First National Bank Building 61662 Institute of Government and Public Affairs
Telephone: (309)676-0755 1201 W. Nevada
H. N. Johnson, Executive Vice-President Telephone: (217)333-3340
*Samuel K. Gove, Director

State of Indiana
Civil Rights Commission
1004 State Office Bldg. 46204
Indiana Telephone:(317)633-4855
BLOOMINGTON C. Lee Crean, Director
Indiana University 47401
Institute of Public Administration (1963) Legislative Concil (1967)
Telephone: (312)337-6505 301 State House 46204
Telephone: (317)633-6570
Edison L. Thuma, Executive Director

EVANSVILLE MUNCIE

Metropolitan Evansivill Chamber of Muncie-Delaware County Chamber of
Commerce Commerce
Governmental Affairs Division 500 N. Walnut Street 47305
Southern Securities Building 47708 Telephone: (317)288-6681
H. F. Tim Hines, Executive Vice President Robert L. Brock, Manager
John Munger, Director Charles E. McGrigg, Mgr. Tax Research
Dept.

FORT WAYNE RICHMOND

Chamber of Commerce of Fort Wayne Richmond Board of Realtors
Public Affairs Department Tax Research Bureau (1937)
826 Ewing Street 46802 208 Medical Arts Bldg. 47374
Telephone: (219)742-0135 Telephone: (317)962-5144
C. James Owen, Dir. Civic Affairs J. F. Wiechman, Executive Director
Mary Johnson, Research Assistant

Taxpayers Research Association (1934)
826 Ewing Street 46802
Telephone: (219)743-4892
*R. Dean Hall, Executive Director TERRE HAUTE

Indian State University
Center for Governmental Services
Telephone: (312)232-6311
*William Harader, Director
GARY

Gary Chamber of Commerce
Governmental Affairs and Tax Research
Dept.
583 Broadway 46402
Telephone: (219)885-7407 Louisiana
George Uzelac, Director
Gloria Walton, Tax Assistant BATON ROUGE

HAMMOND Council For A Better Louisiana
Fidelity National Bank Building
Hammond Chamber of Commerce P.O. Box 2978 70321
429 Fayette Street 46320 Telephone: (304) 342-5229
Telephone: (219) WEstmore 1-1001 *Edward W. Stagg, Executive Director
Walter Ford, Executive Vice President O. Fred Loy, Jr., Assistant Director

Louisiana Municipal Association (1937)
INDIANAPOLIS 301 Capitol House Hotel
Telephone: (504)343-9571
Indiana Association of Cities and Towns Marvin L. Lyons, Executive Director
408-10 Ista Center
159 W. Market Street 46204 Louisiana State Law Institute
Telephone: (317) MElrose 5-8616-17 Telephone: (504) 389-6370
Ivan H. Brinegar, Executive Director J. Denson Smith, Director
William F. Bailey, Coordinator of Research
Indiana State Chamber of Commerce and Revisor
Taxation Department Carol N. Blitzer
Board of Trade Building 46204
Telephone: (317)634-6407 Louisiana State University 70803
*Edward J. Bowman, Director Institute of Government Research
Telephone: (504)388-2142 and 388-2141
Indianapolis Chamber of Commerce *Louis E. Newman, Director
Bureau of Governmental Research (1923) Department of Political Science
320 North Meridian Street 46204 *Louis E. Newman, Associate Professor
Telephone: (317) MElrose 5-6423
*Donald L. Robinson, Director Public Affairs Research Council of
Louisiana, Inc.
Attorney's Building, Suite 200 70801
200 Louisiana Avenue, Post Office Box 3118
70821
Telephone:(504)343-9204

*Edward J. Steimel, Executive Director
*Arthur R. Thiel, Research Director
*Emogene Pliner, Director of State Studies
Charles Saunders, Research Associate
Ricard Keller, Research Associate
Tom Farley, Research Analyst
*Hubert C. Lindsay, Research Analyst
Sylvia McCracken, Research Analyst
Reilly Stonecipher, Research Analyst
Jackie Ducote, Research Librarian

NEW ORLEANS

Bureau of Governmental Research (1933)
4308 Richards Bldg. 70112
Telephone: (504)525-4152
*Louis D. Brown, Executive Director
*George W. White, Research Director

Chamber of Commerce of the
New Orleans Area
Area Development Department
P. O. Box 30240 70130
Telephone: (504)524-1131
J. Ferdie Herbert. Jr., Director

City of New Orleans
City Hall 70112
*Bernard B. Levy, Chief Administrative
Officer

Metropolitan Crime Commission of New
Orleans (1934)
1107 National Bank of Commerce Building
70112
Telephone: (504)524-3148
Aaron M. Kohn, Managing Director

Tulane University of Louisiana, The 70118
School of Law
Telephone: (504) 863-7711, ext. 302
Joseph M. Sweeney, Dean

Urban Studies Center
William W. Shaw, Director

Maine
AUGUSTA

State of Maine 04330
Housing Authority
State Office Bulding, Room 219 04330
Telephone: (207)289-2546
Eben L. Elwell, Director

Legislative Reference Bureau (1917)
Telephone: (207)289-2754
Edith L. Hary, State Law Librarian

Legislative Research Committee
Telephone: (207)289-2101
Samuel H. Slosber, Director
David S. Silsby, Asst. Director

BRUNSWICK

Bath-Brunswick Regional Planning
Commission
98 Main Street 04011
Telephone: (207)725-4233
Dana A. Little

Bowdoin College 04011
Public Affairs Research Center
Telephone: (207)725-8731

HALLOWELL

Maine Municipal Association (1937)
89 Water Street 04347
Telephone: (207)623-8429
John L. Salisbury, Executive Secretary

ORONO

University of Maine at Orono 04473
Bureau of Public Administration
Telephone: (207)381-7744
Dana R. Baggett, Director
*James J. Haag, Research Supervisor

Maryland

ANNAPOLIS

Maryland Legislative Council (1939)
16 Francis Street P.O. Box 348 21404
Telephone: (301)267-5561
Carl N. Everstine, Secretary and Director
of Research

State of Maryland
Department of Legislative Reference (1917)
16 Francis Street 21404
P.O. Box 348
Telephone:(301) 267-5561
Carl N. Everstine, Director
Ruth D. Eaton, Librarian

Maryland Department of Fiscal Services
P.O. Box 231-Treasury Building 21404
Telephone: (301)269-0790
*Paul D. Cooper, Director of Department
Pierce J. Lambdin, Director-Division of
Audits
Kenneth N. Bragg, Director-Division of
Budget Review
William S. Ratchford, II, Director-
Division of Fiscal Research

Maryland Municipal League
76 Maryland Avenue 21401
Telephone: (301)268-5514
*Peter B. Harkins, Executive Director

BALTIMORE

Chamber of Commerce of Metropolitan
Baltimore
Business Research Department (1930)
22 Light Street 21202
Telephone:(301)LExinton 9-7600
M.R. Bourn, Manager

Citizens Planning and Housing Association
(1940)
330 North Charles Street 21201
Telephone:(301)529-1369
Christopher C. Hartman, Executive Director

City of Baltimore
Department of Legislative Reference (1906)
City Hall 21202
Telephone: (301) PLaza 2-2000, Ext. 385
Leon A. Rubenstein, Director

Commission on Governmental Efficiency Commonwealth of Massachusetts
and Economy, Inc. (1929) Department of Corporations and Taxation
330 North Charles Street 21201 Bureau of Planning and Research
Telephone: (301) 727-0910 100 street,
Eugene M. Thomas III. Director Daniel B. Breen, Chief
Jerrietta R. Hullinger. Research Associate
Joint Committee on Taxation
Johns Hopkins University State House, Room 237 02133
Department of Political Science Robert H. McClain, Jr.
Telephone: (301) 366-3300
Frances E. Rourke Legislative Research Bureau
State House, Room 236, 02133
Morgan State College Daniel M. O'Sullivian
Urban Studies Institute
Coldspring lane & Hillen Rd. 21212 Greater Boston Chamber of Commerce
Telephone: (301) 323-2270 Ext. 312 125 High Street 02110
Telephone: (617) 426-1230
COLLEGE PARK James G. Kelso, Executive Vice President

University of Maryland 20742 Community Development Department
Bureau of Governmental Research William F. chouinard, Manager
Telephone: (301) 454-2506
Franklin L. Burdette, Director Public Affairs Department
Clarence N. Stone, Director Thomas J. Moccia, Director
Research Groups: Director, Maryland
techincal Advisory Service Harvard University
Grove E. Nash County Managment; Graduate School of Busines Administration
Associate Deputy, Director, Maryland Division of Research (1912)
Technical Advisory Service Telephone: (617) 495-1000
M. Henry eppes Soldiers Field 02163
Edward D. Kelleher Richard E. Walton, Director
John E. Rouse, Jr. Robert N. Anthony
Carl T. Richards Joseph L Bower
James E. Skok John W. Drake
Daniel R. Thompson Ray A. Goldberg
Regina Herzingler
Bureau of Business & Economic John W. Praft
Research (1947) Howard Raiffa
John W. Dorsey, Director James C. Wayne Jr.

Department of Government & Politics
Telephone: (301) 454-2248 Telephone: (617) 742-2334
Don C. Piper, Chairman Kenneth E. Pickard, Executive Director
Ernest A. Chaples John F. Dacey, Jr. Director, Legislative
M. Margret Conway Services
Donald J. Devine
Conley H. Dillon
Parris Gelndening Massachusetts Taxpayers Foundation, Inc.
145 Tremont Street 62111
Joseph L Ingles Telephone: (617) 357-8500
Charles Levine Richard A. Manley, Executive Vice
Earlean McCarrick President
Eugene B. McGregor Francis Blunt, Librarian
Thomas Murphy Lyman H. Ziegler, Director- Technical
Ralph A. Ronald Services and Municiple Consultant
Mavis M. Reeves Susanne E. Tompkins, Senior Research
James C. Strouse Associate
Doris P. Paul, Research Associate
Massachusetts Edward H. Diott, Research Associate
Nancy S. Serafini , Research assocaite
BOSTON John C. Driscoil, Special assistant to
Executive Vice President
Boston Finance Commission (1909) Francis M. Keane, Legislative Counsel
Room 820, 3 Center Plaza 02108 Robert C. O'Day, field director
Telephone: (617) LAfayette 3-1622
Thomas J. Murphy, Executive Secretary Metropolitian Area Planning Council
Chandler W. Smith, Analyst 44 School street 02108
Robert G. West, Analyst Telephone: (617) 523-2454
Louis R. Sacco, Analyst Richard M. Doherty, Executive Director
James A. Miller, Director of Planning
Boston municiple Research Bureau Paul E. McBride, Director of Metropolitian
294 Washington Street 02108 Projects
Telephone: (617) HUbinard 2-3626
Josephin R. Barresi, Executive Secretary
Naomi B. Isler, Senior Research Associate
Carl A. Prusing, Research Associate

The New England Council for
Economic Development, Inc. (1925)
Building 02116
Telephone: (617) 542-2580
A. Thomas , Executive Vice-
President

Northeastern University NEWTON
Bureau of Business & Economic Research
(1953) Newton Taxpayers Association
Huntington Avenue 02115 313 Washington Street 02158
Dean S. Ammer, Director Telephone: (617) 4-7614
Lorenz F. Muther, Jr. executive Director
Curriculum in Public Administration
David W. Barkely New England School Development Council
55 Chapel Street 02160
State Library Telephone: (617) 969-1150
State House 02133 Robert Ireland, Executive Secretary
Telephone: (617) 727-2590
I. Albert Matkov, State Librarian PITTSFIELD

University of Massachusetts Association of Business and Commerce of
Institiute for Governmental Services Central Berkshire County Inc.
85 Devonshire 02109 46 West Street 01201
Telephone: (617) 723-7820 Telephone; (413) 443-9117
Maurice A. Donshue, Director Daniel J. Courtney, President

BROOKLINE Quincy Taxpayers Association Inc (1935)
1 Cliveden Street 62169
Brookline taxpayers Association Inc. (1935) Telephone: (617) GRainte 2-3586
7 Harvard street 02146 Harry E. roemer, Executive Director
Telephone: (617) AS 7-6038-9
Ray Alden, Executive Director
TAUNTON
Advisory Committee
City Hall 03138 Taunton Area Chamber of Commerce Inc.
Telephone: (617)876-8200 (1959)
Paul J. Frank, Executive Director 39 Taunton Green 02780
Telephone: (617)824-4068
Harvard University 02138 Charles E. Volkmann, Executive Vice-Pres.
John Fitzgerald Kennedy SChool of
Government WELLESLEY
Telephone: (617) 495-5000
Don K. Price, Dean Wellesley College 02181
Department of Political Science
Joint Center for Urban Studies of Telephone: (617) 235-0320
M.I.T and Harvard University Alan H. Schechter, Chairman
Church Street 02138
Telephone: (617) 868-1410 WORCESTER
Bernard J. Frieden, Director and Chairman,
Executive Committee Citizens' Plan E Association (C.E.A) The
Charles M. Hear, Vice Chairman (1947)
Executive Committe 32 Franklin Street Room 407 01608
Joseph F. Connolly, Administrative Officer Telephone: (617) 757-4832
Barbra C. Kohln, President
CHICOPEE
Worcester Area Chamber of Commerce
Chicopee Taxpayers Association (1955) 90 Madison Street 01608
48 Center Street 01013 Telephone: (617) 753-2924
Telephone: (413) LYceum 4-9075 Roland L. Theriault Manager, Govn't Dept.

HOLYOKE Worcester Taxpayers Association (1931)
Room 702, 29 Pearl Street 01603
Holyoke taxpayers Association Inc. (1932) Telephone: (617) 755-0721
225 High Street 01040 Malcolm D. MacLeud, Executive Director
Telephone: (413) 532-1600 John H. Mahoney, Consultant
Charles M Headley, Jr. Executive Director

NEW BEDFORD

New Bedford Taxpayers Assoc. Inc.
628 Pleasant Street 02740
Telephone: (617) 992-3638
Clair F. Carpenter, Executive Director

Wayne State University
Department of Political Science
856 Mackenzie Hall
ANN ARBOR Telephone: (313) 577-2634
Michigan Municipal League (1899) Charles James Parrish, Chairman
1675 Green Rd. 48105
Telephone: (313) NOrmandy 2-3246
Robert E. Fryer, Director EAST LANSING
Robert L. Hegel, Manager
Publication Research Division Michigan State University
Shirley S. Smith. Staff Associate Institute for Community Development
R. Thomas Martin. Staff Assistant Kellogg Center, Room 27 48823
Telephone: (517) 355-0100
University of Michigan *Duane L. Gibson. Director
Center for Research on Economic
Development
309 South State Street 43104
Telephone: (313) 764-9490 FLINT
Ellot J. Berg, Director
Civic Research Council of Flint (1939)
Graduate School of Business Administrator 505 Metropolitan Building 48502
Bureau of Business Administration Telephone: (313) CE 4-4664
Telephone: (313) 764-1366 G. Keyes Page, Executive Vice-President
H. Paul Root, Director G. Keyes Page. Executive Vice-President
Institute of Public Policy Studies (1914) :Manufacturer Association of Flint
1510 Rackham Building 43104 Mott Foundation Building 48502
Telephone: (313) 764-3491 Telephone (313) ce4-4664
John P. Crecine, Director G Keyes Page. Executive Vice President
Joel Aberbach
Russell Hill
Stephen Pollock
Donald Shoup KALAMAZOO
Jack L. Walker
Kenneth Wertz City of Kalamazoo
Sidney Winter Bureau of Municipal Research (1934)
City Hall
Legislative Research Center Telephone (616) 381-5300 William J. Pierce, Director ~_~r~q) ~'
David M. Bradford. Director

c~s~lern M~@c~l
DETROIT Institute of Public Affairs, The
Hall ~q49~q0~q0~q1
Citizens Research Council of Michigan (1916) ~t~q6~q1~8q03~q3~0~1-~q1~q8~q89
1526 David Stott Building ~qR~o~hert W. Kaufman. Director
1150 Griswold Street 48226 ~qH~ei~e~nan L~e%vi~s, Research Associate
Telephone: (313) 961-5377
*Robert E. Pickup, Executive Director
*Robert L. Queller, Research Director
*William A. Carter, Senior Research
Associate
*Kathleen R. Kepner, Senior Research ~qC~qi~iize~n~s Research C~oun~qd~ql of Michigan ~q(1~q9~q1~q6~q)
Associate LANSING OFFICE
*Paul Timmreck. Research Associate ~834 Michigan National Tower 48933
T~c~!~q!~cphon~e: (51~1.) 43~q5-9444
Civic Searchlight, Inc. (1912) 'Francis A. W~q@~eel~er, Director, State A~ql~ci~rs
2337 Commonwealth Building 48226
Telephone: (313) WOodward 1-1330
*William H. O'Brien, Executive Secretary

Metropolitan Fund, Inc.
Detroit Bank & Trust Bldg. 48226
211 West Fort Street
Telephone: (313) 961-7887
Kent Mathewson, President

Municipal Reference Library (1945)
1004 City-County Building 48226
Telephone: (313) 224-3885
Gertrude Pinkney, Chief
Diana Franco, Asst.

State of Michigan
Executive Office, Office of Community
Affairs
7310 Woodward Avenue 48202
Telephone: (313) 222-3257
Roy Levy Williams, Special Assistant for
Urban Affairs

UAW-International Union
Research Department
8000 East Jefferson 48214
Telephone: (313) 926-5261
Carrol L. Coburn, Director

MUSKEGON

Civic Affairs Research, Inc. (1959)
931 Third St. 49440
Telephone: (616) 722-2581
*David H. Walborn, President State of Minnesota
Office of Local and Urban Affairs
Muskegon Area Development Council and Capital Square Bldg. 55101
Chamber of Commerce Telephone: (612) 221-3091
4th Street at Webster Avenue 49441 James J. Salem, Director
Telephone: (616) 722-3751
John Chapman, Executive Vice-President State Planning Agency
Research & Environmental Development Capital Square Bldg., Room 802 53101
Division Telephone: (612) 221-6562
*Max D. Petersen, Director A. Edward Hunter, Deputy Director

Minnesota Mississippi
DULUTH JACKSON

Governmental Research Bureau, Inc. (1921) Mississippi Economic Council
907 Alworth Building 55802 P.O. Box 1849. Standard Life Building
Telephone: (218) 722-6544 30205
*David J. Hagelin, Executive Secretary Telephone: (601) 355-4721
Bob W. Pittman, General Manager
MINNEAPOLIS Research Department
Glyde McLeod, Director
Citizens League (1952)
530 Syndicate Building 55402 Mississippi Municipal Association
Telephone: (612) 338-0791 Downtowner, Suite 411
Ted Kolderie, Executive Director P.O. Box 234 39205
Paul A. Gilje, Research Director Telephone: (601) 355-3791
Calvin W. Clark, Research Associate W.J. Caraway, Executive Vice-President
Clarence Shallbetter, Research Associate
State of Mississippi
League of Minnesota Municipalitics (1913) Research and Development Center
3300 University Ave. S.E. 55414 P.O. Box 2470 39205
Telephone: (612) 373-9992 Telephone: (601) 982-6456
Dean A. Lund, Executive Secretary Kenneth C. Wagner, Director

Greater Minneapolis Chamber of Commerce
Legislative Department
15 South 5th Street 55402
Telephone: (612) 339-8521
Lloyd L. Brandt, Manager

Minneapolis Taxpayers Association (1924) New Hampshire
625 Second Avenue, Room 419 55402 CONCORD
R.T. Oakes, Director New Hampshire Municipal Association
64 South Street, P.O. Box 617 03301
University of Minnesota Telephone: (603) 224-7117
Municipal Reference Bureau (1913) David L. Mann, Executive Director
3300 University Ave. S.E. 55414
Telephone: (612) 373-9992 New Hampshire State Library 05301
Dean A. Lund, Director Legislative Service (1935)
Telephone: (603) 271-2239
ST. PAUL Philip A. Hazelton, Law Librarian
Richard M. Serena, Legislative Research
Metropolitan Council of the Twin Cities Area Librarian
Capital Square Bldg.
Cedar at 10th 55101
Telephone: (612) 227-9421 DURHAM

Minnesota Taxpayer's Association University of New Hampshire 03924
812 Minnesota Building 55101 Department of Political Science
Telephone: (612) 224-7477 Public Administration Service
*Charles P. Stone, Executive Director Telephone: (603) 868-5511
*Harold T. Miller, Research Director Lawrence W. O'Connell, Director
Gary W. Bostian, Research Analyst

Saint Paul Area Chamber of Commerce
Governmental Research Department
Osborn Building, Suite 300 53102
Telephone: (612) 222-5361
David L. Schoeneck, Director

New Jersey

JERSEY CITY TRENTON

Jersey City Chamber of Commerce (1890) Trenton-Mercer County Chamber of
911 Bergen Avenue 07306 Commerce
Governmental Affairs Department
Telephone: (201) 653-7400 104 N. Broad St. 08908
*Edward C. Babock, Director of Govern- Telephone: (609) 393-4143
mental Research *Bruni Fiabane, Research Associate

New Jersey Manufacturers Association
MADISON Sullivan Way, P.O. Box 2708 08607
Telephone: (609) 883-1300
Drew University 07940 Leonard C. Johnson, President
Department of Political Science
Telephone: (201) 377-3000 New Jersey State League of Municipalities
Julius Mastro, Chairman (1915)
433 Bellevue Avenue 08618
Institute for Research on Government Telephone: (609) 695-3481
Telephone: (201) 377-3000 Robert H. Fust, Executive Director
Robert G. Smith, Director John E. Trafford, Research & Information
Associate

NEWARK New Jersey Taxpayers Association (1931)
104 North Broad Street 08608
Greater Newark Chamber of Commerce Telephone: (609) EXport 4-3116
1180 Raymond Boulevard 07102 *Frank W. Haines, Jr., Executive Director
Telephone: (201) 624-3333 Phillip W. Blaze, Secretary
Charles G. Hall, President *Maurice S. Shier, Director of Research
*Alan D. Levine, Vice Pres. for Research & David C. Dare, Office Manager
Govt. Affairs
Joan A. Rohifs, Assoc. Research Dir.

New Jersey State Chamber of Commerce
(1911)
54 Park Place 07102
Telephone: (201) 623-7070
Donald H. Scott, Exec. Vice-President New York
ALBANY
Department of Governmental and
Economic Research Citizens Public Expenditure Survey, Inc.
*Gerald D. Hall, Director (1938)
100 State Street 12207
Telephone: (518) HO 5-4506
NEW BRUNSWICK James E. Finke, Executive Vice-President
John M. Quimby, Director of Research
Rutgers--The State University 08980 Gertrude Wilber, Research Analyst
Bureau of Governmental Research Robert W. Englehardt, Research Analyst
Telephone: (201) 932-3642
*Ernest C. Reock, Jr., Director, Bureau of Empire State Chamber of Commerce
Government Research 150 State Street 12207
Raymond D. Bodnar, Director, Govt. Telephone: (518) 472-9166
Services Training Program John J. Roberts, Executive Vice-President
Philip H. Burch, Jr. Arthur M. Arnold, Director of Taxation
Harris I. Effross and Governmental Affairs
William G. Rae Sanford H. Bolz, General Counsel
Robert White
*Wesley R. Westmeyer New York State
Civil Service Commission
Eagleton Institute of Politics The State Campus 12201
Donald G. Herzberg, Executive Director Telephone: (518) GL 7-2487
Allen Rosenthal (Mrs.) Ersa H. Poston, President

Department of Audit and Control
PRINCETON The Governor Alfred E. Smith State Office
Building
Greater Princeton Chamber of Commerce and Telephone: (518) GR 4-4044
Civic Council Arthur Levitt, State Comptroller
44 Nassau St. 08540 Martin Ives, Deputy Comptroller
Telephone: (609) 921-7676
Education Department
Princeton University 08540 State Capitol 12201
Telephone: (609) 452-3000 Telephone: (518) GR 4-3878
Lorne H. Woollatt, Associate Commissioner
Research Center for Urban and of Education (Research)
Environmental Planning
Dorothy E. Whiteman, Assistant Director

Department of Politics
W. Duane Lockard, Chairman

Woodrow Wilson School of Public and
International Affairs
John P. Lewis, Dean

ITHACA
Legislative Commission on Expenditure Cornell University
Review Telephone: (607) AR5-5014
111 Washington Avenue 12210 Thomas R. Reifers, Office of Director,
Telephone: (518) 474-1497 Sponsored Research
*Troy R. Westmeyer, Director Graduate School of Business and Public
*Ray D. Petriel, Assistant Director Administration
*Neil C. Blanton Justin Davidson, Dean
*Richard E. Brown Edward S. Flash
*Robert Fleischer College of Agriculture
*Stuart Graham *E.A. Lutz, Professor of Public Adminis-
*Harry Moscatello tration
*Richard C. Spaulding Department of Government
Arch T. Dotson, Chairman
Legislative Reference Library (1890)
Telephone: (518) GR4-5945 JERICHO
William P. Leonard, Librarian Bureau of Government Research, Division of
The Long Island Assoc. Of Commerce &
Office for Local Government (1959) Industry
155 Washington Avenue 12210 131 Jericho Turnpike 11753
Telephone: (518) GR4-4210 Telephone: (516) 333-9300
*Richard A. Atkins, First Deputy *John Brewer, Director
Commissioner Mrs. Marion King, Research Librarian
*Franklin M. Bridge, Director, Municipal
Management Services NEW YORK CITY
Academy of Political Science of New York
Division of Economic Opportunity 1108 International Affairs Bldg.
107 Washington Avenue Columbia University 10027
Albany, New York 12210 Telephone: (212) 280-3642
Telephone: (518) 474-3642 Robert H. Connery, Executive Director
Richard A. Wiebe, Director
Bernard M. Earuch College of the
New York State Conference of Mayors (1910) City University of New York, The
6 Elk Street 12207 17 Lexington Avenue 10010
Telephone: (518) 463-1185 Telephone: (212) ORegon 3-7700
Raymond J. Cothran, Executive Director Clyde Winfield, President
J. Omer Laplante, Assistant to Director
Donald A. Welch, Counsel Brooklyn College of the City University
Donald F. Larson, Attorney of New York
John H. Galligan, Administrative Assistant Department of Political Science
Brooklyn 11210
State University of New York Telephone: (212) 780-5306
135 Western Avenue 12203 Albert Gorvine, Chairman
Telephone: (518) 472-5362 Martin Landau
Ernest L. Boyer, Chancellor Sungjoo Han
Graduate School of Public Affairs Peter Gluck
L. Gray Cowan, Dean Dennis Palumbo
*Joseph A. Zimmerman Richard Styskal

BUFFALO Citizens Budget Commission, Inc. (1932)
Buffalo Area Chamber of Commerce (1844) 110 East 42nd Street 10017
238 Main Street 14202 Telephone: (212) 687-0711
Telephone: (716) 852-5400 *David Bernstein, Acting Exec. Dir.
C.F. Light, Executive Vice-President Herbert J. Ranschburg, Assistant Executive
Research and Education Department Director
Kurt Alverson, Manager Richard Morris, Research Analyst
Tax and Legislative Service Department
Herbert Berry, Manager Citizens' Housing and Planning Council of
New York, Inc.
Greater Buffalo Development Foundation, 20 West 40th Street 10018
Inc. Telephone: (212) 563-5990
136 Rand Building 14203 Roger Starr, Executive Director
Telephone: (716) 856-2708
Lee Norton, Director Citizens Union of the City of New York
Henry E. Wyman, Director, (1897)
Government Research Dept. 15 Park Row 10038
Telephone: (212) BAarclay 7-0342
HEMPSTEAD Gary H. Sperling, Executive Secretary
Hefstra University 11550 *George H. Hallett, Jr., Legislative Repre-
Center for Business & Urban Research sentative
Telephone: (516) 560-3297
Lois Blume, Director Citizens Union Research Foundation, Inc.
15 Park Row 10038
Telephone: (212) BArclay 7-0342
Dana Converse Backus, President
*George H. Hallett, Jr., Director of Research

City Club of New York, The (1892)
5 West 48th St. 10036
Telephone: (212) LT 1-2485
Robert Conrad, President

City of New York Officers and Senior
Finance Administration *Luther R. Galick, Chairman of the Board
Fiscal Research Dept. Lyle C. Fitch, President
Municipal Building. Rm. 506 10007 Mark W. Cannon, Director
Telephone: (212) 566-5213 Robert H. Kirkwood, Assistant to the
*John Fava, Deputy Finance Administrator President
Joan Russell Perry Howard N. Mantel, Assistant Director and
(Mrs.) Sue Papish Counsel
Jeanne Griffo Ruth P. Mack, Director, Economic Studies
Peter Shalleck Xenia Duisin, Library Director
Charles Sandmel Summer Myers, Director, Urban Systems
Municipal Service Administration Studies (Washington)
Municipal Building Room 2139 10007 Annmarie Hauck Walsh
Ramiro Cabezas M. (Peru)
Telephone: (212) 366-4446 Randolph L. Marshall (Uganda)
Martin J. Hodanish, Director, Performance Albert A. Mavrinac (So. Vietnam)
Planning & Management Charles S. Ascher, International Represen-
tative
Civil Service Reform Association (1877)
315 Fifth Avenue 10016 Metropolitan Regional Council
Telephone: (212) MUrray Hill 9-3544 1 World Trade Center, Suite 2437 10048
Alfred Kleinfield, Executive Director Telephone: (212) 466-3850
Robert P. Slocum, Executive Director

Columbia University
Department of Political Science
Fayerweather Hall 10027 Metropolitan Transportation Authority
Telephone: (212) 280-3644 1700 Broadway 100119
Wayne A. Wilcox, Chairman Telephone: (212) 757-4040
William J. Ronan, Chairman
Lawrence R. Bailey Harold I. Fisher
Legislative Drafting Research Fund Leonard Braun Mortimer J. Gleeson
5 West 10 Law School William L. Butcher Frederic B. Powers
Frank Grad, Director Donald H. Elliott Eben W. Pyne
Justin N. Feldman William A. Shea
Robert R. Prince, Secretary and Counsel
Sidney Brandes, Executive Officer for
Department of Economics Construction Administration
521 Fayerweather Hall James B. Huff, Controller
Telephone: (212) 280-2494 Sidney J. Frigand, Public Affairs Director
*C. Lowell Harriss
Municipal Reference & Research Center
Commerce and Industry Association of New 2230 Municipal Building 10007
York, Inc. Telephone: (212) 556-4285, 6
99 Church Street 10007 Eugene J. Bockman, Director
Telephone, (212)732-5200 Thelma E. Smith, Deputy Director
Ralph C. Cross, President Frieda W. Chait, Chief, Reference &
Research Services
Downtown-Lower Manhattan Association, (212) 566-4284
Inc. Eve Thurston, Chief, Technical Services
(212) 566-4283
120 Broadway-Room 1043 10005 Devra Zetlan, Public Health Librarian
Telephone: (212) REctor 2-4000 (212) 566-3169
*John B. Goodman, Executive Vice President Solomon Jacobson, Legislature Reference
Librarian (212) 675-2700

Economic Development Council of New York New School for Social Research
City, Inc. Center for New York City Affairs
230 Park Avenue 10017 65 West 12 Street 10011
Telephone: (212)684-2300 Telephone: (212) 675-2700
Paul Busse, Executive Vice-President Henry Cohen, Director
*Roland J. Delfausse, Vice President for Blanche Bernstein, Director of Research
Government Research Urban Social Problems
*Robert W. Schleek, Senior Research Robert Hearn
Associate Jacob B. Ukeles, Chairman, Dept. of Urban
Affairs
Governmental Research Association. Inc. Jerome Liblit, Associate Dean
(1914)
P.O. Box 387 New York Chamber of Commerce
Ocean Gate, New Jersey 08740 65 Liberty Street 10005
Telephone: (201) 269-3489 Telephone: (212) REctor 2-1123
*Troy R Westmeyer, Secretary-Treasurer Thomas Stainbach, Exec. Vice-President
Sandra J. Leibrick, Assistant Secretary
Frank A Brady, Fiscal Economist
Hunter College of the City University of Peter Lynch, Director, Industrial Relations
New York Research
Department of Urban Affairs Kenneth E. Placek, Research Assistant
790 Madison Avenue 10021
Telephone: (212) 360-5594 New York City Housing & Development
Seymour Mann, Chairman Administration
Bertram Gress 100 Gold Street 10007
Robert C. Weaver Telephone: (212) 566-4440
Donald Sullivan Albert A. Walsh, Administrator
Herbert Hyman Joseph Polser, Asst. Administrator for
Peter Salins Public Affairs
William Stafford

Institute for Public Service (1915)
329 88th Street 10023
Telephone: (212)LE4-7403
William Allen, Jr., Director

New York City Oif-Track Betting Corporation Rochester center for Governmental &
1501 Broadway 16036 Community Research, Inc.
Telephone:(212) 621-5461 37 South Washington Street 14608
Howard Samuels, President Telephone(716) 325-6360
Roger J. Herz, Assistant to Vice President Craig M. Smith, Director
of Administration and Facilities Freidrich J. Grasberger, Associate Director
Robert Sullivan, Director of Reseach (Mrs.) Eleanor C. Parfitt, Administrative
Assistant

New York University Alan J. Taddiken, Senior Research Analyst
Graduate School of Public Administration Donald E. Pryor, Senior Reseach Analyst
Washington Square North 100003 (Mrs.)Jeraldine L. Braff, Research Analyst
Telephone(212) 598-2441 John F. Burke, Research Analyst
Dick Netzer, Dean David J. Coons, Research Analyst
William H. Boise (Mrs.)Joan K. Ford, Research Analyst
Junn M. Capozzola (Mrs.)Patti J. Kingston, Research Analyst
Charlton F. Chute (Miss)Nancy H. Orr, Research Analyst
Kevin C. Hilling David J. Wirschem, Research Analyst
Sterling D. Spero, Emeritus
Troy R. Westmeyer Rochester Chamber of Commerce
Governmental Action Task Force
Port of New York Authority 55 St. Paul Street 14604
111 Eighth Avenue 10011 Telephone(716) 454-2220
Telephone:(212) 620-7207 Peter O. Allen, Manager
Daniel L. Kurshan, Director of Adminis-
tration SCHENECTADY
Edward Gallas, Director of Personnel Schenectady Bureau of Municipal Research,
Inc. (1927)
Queens College of The City University of 202 State Street 12305
New York Telephone (512) Frankling 4-1343
Department of Policial Science 11367 Charles K. Bens, Executive Director
Telephone:(212) Hickory 5-7500
Henry W. Morton, Chairman STEWART AIRPORT, N.Y.

Regional Plan Association, Inc. Mid-Hudson Pattern for Progress
235 East 45th Street, 10017 Building 702 12550
Telephone(212) 682-7730 Telephone(914) 562-1346
John P. Keith, President C. David Locks, President
William B. Shore, Vice President
Boris S. Pushkarev, Vice President SYRACUSE
Richard T. Anderson, Asst. to the President City of Syracuse
& Director, Environmental Publications Bureau of Research
Robert P. Starseth, Director, Management 218 City Hall 13202
& Finance Telephone:(315) 473-6600
Stephen Carroll, Director, Regional Clinton C-Byers, Director
Development (Mrs.) Patricia Deacon, Reseach Assistant
John E. Mahoney, Director, Public
Transportaion County of
Max Schwartz, Director, Informations Executive Department
Systems Division of Research and Development
Edward F. Sullivan, Director, Systems 603 County Office Building 13202
Planning & Highways Telephone (315) 477-7645
Frank T. Wood, Jr., Director
OLEAN
Olean Chamber of Commerce Greatez Syracuse Chamber of Commerce
225 Exchange Bank Building 14760 1700-One Mony Plaza
Telephone: (716) 372-4423 100 Madison St. 13202
Jonathan B. Bates, Executive Vice Presi- Telephone:(315) 422-1343
dent James R. Schneider, Manager, Govern-
mental Relations Council
ROCHESTER
Syracuse Governmental Research Bureau
Citizens' Tax League of Rochester and Mon- (1948)
roe County New York, Inc. (1935) 809 Loew Building 13202
432 Powers Bldg. 14614 Telephone:(315) 471-4310
Telephone:(716) 546-4340 Thomas A. Dorsey, Executive Director
Robert J. Menzie, Executive Director (Mrs.)Sophie Pulah, Assistant Director
(Mrs.)Eugenia Dammers, Secretary

Syracuse University 13210
Telephone(315) 476-5541
Maxwell Graduate School of Citizenship
and Public Affairs
Alan K.Campbell, Dean
Frank Marini, Associate Dean, and
Director of Public Administration

CINCINNATI
CHAPEL HILL
Better Housing League of Cincinnati
University of North Carolina 25714 2200 Reading Road 45202
Department of Political Science Telephone: (513)721-3160
John D. Martz, III Chairman *Charles G. Stocker, Director
Institute for Research in Social Science Linda I. Strauss
Post 0ffice Box 1167.
Telephone: (919) 933-1214 Charter Research Institute
James W. Prothro, Director 102 Carew Tower 45202
Institute of Government Telephone: (513) CHerry 1-0303
Telephone: (919) 933-1304 *Forest Frank, Director

John L. Sanders, Director City of Cincinnati
Princicipal Reference Library
CHARLOTTE 224 City Hall 45202
Telephone: (513)421-5700
Charlotte Chamber of Commerce Hila 0. Foley, Librarian
222 South Church Street 28202
Telephone: (704)377-6911 Greater Cincinnati Chamber of Commerce
Charles Crawford, Executive Vice- 55 Central Trust Building 45202
President Telephone: (513) 721-3300
George C. Hayward, Director, Planning &
Developement
CULLOWHEE
Department of Governmental Affairs
Western Carolina University Telephone: (513) 721-3300
Office of the President *Frederick E. Ewing, Director

P.O. Box 103 28723 University of Cincinnati 45221
Telephone: (704) 293-7313 Department of Political Science
Alex S. Pow, President Telephone: (513) 475-4245
S. Aaron Hyatt. Director for Institutional *C.A. Harrell
Research and Development
Institute for Urban Information Systems
Telephone: (513) 475-3649
*Fred J. Lundberg, Director
DURHAM

The L.Q.C. Lamar Society
P.O. Box 4774, Duke Station 27706 CLEVELAND
Telephone: (919) 684-6774
Thomas H. Naylor, Executive Director Citizens League, The (1996)
1010 Euclid Bldg.. Room 502 44115
Telephone: (216) CHeery 1-5340
RALEIGH *Zstal E. Sprlin, Director
*Blair R. Kost, Executive Assistant
North Carolina Citizens Assocciation *Robert Amstutz, Business Manager
P.O. Box 1430 27602
Telephone: (919) 828-O758 Case Western Reserve University
Edward L. Rankin, Jr., Executive Director Graduate Program in Pbulic Management;
and Secretary Science (1984)
Telephone: (216) EN 8-2424
North Carolina League of Municipalities Nathan D. Grundstein, Director
Post Office Box 3069 27602
Telephone: (919) 334-1311 City of Cleveland
S. Leigh Wilson, Executive Director Office of Budget and Management
Room 111 City Hall 44114
State of North Carolina Telephone: (216) 694-2434
Office of Community Resources Kimber A. Wald, Director
P.O. Box 27687 27611
Telephone: (919) 829-3174 David G. Currie, Budget and Management
Irvin Aldridge, Director Analyst
Robert Drelfort, Budget and Management
Analyst
Tom Farnsworth, Budget and Management
Analyst
Cleo Jordan, Accountant II

Cuyahoga County Mayors and City
Managers Association
Cleveland State University AKRON
2323 Prospect Avenue 44115
Akron Area Chamber of Commerce Telephone: (216) 687-2135
Bureau of Research
137 South Main Street 44308 Governmental Research Institute (1943)
Telephone: (216) 253-9181 1010 Euclid Building, Room 502 44115
*John A. Earle, Director Telephone: (216) CHerry 1-3340
*Pstal E. Sparlin, Director
*Harold R. Kust, Research Associate
City of Akron *Robert C. Mayer, Research Associate
Department of Finance *Robert Amstutz, Business Manager
City Hall 44308
Telephone: (218) 375-2317 Greater Cleveland Associated Foundation
*Dan P. Zeno, Director 769 National City Bank Building 44114
Telephone: (216) 861-3810
Departement of Public Service James A. Norton, President
City Hall 44308
Telephone: (216) 375-2270
*David W. Zimmer, Director

Greater Cleveland Growth Association (1932) Taxation. Research & Statistics Section
Tax & Legislation Research Department 68 Gay Street 43215
690 Union Commerce Building 44115 Telephone: (614) 469-3960
Telelphone:(216)MAin 1-3300 James K. Hunter, Jr. Director
Gilbert D. Richmond, Manager Martha L. Saenger, Administrative
Municipal Reference Library (1913) Specialist
211 City Hall 44114 Welfare, Statistics and Research
Telephone:(216) 694-2656 Raymond F. McKenna
Lee Wachtel,Librarian

DAYTON
COLUMBUS Community Research, Inc. (I957)
Rm. 444, 333 W. First Street 45402
Citizens Research. Inc. (1938) Telephone: (513) 224-9656
21 East State Street. Suite 1000 43215 Jeptha J. Carrell. Executive Director
Telephone: (614) 221-4459 William J. Schneider. Research Associate
*Paul E. Hadinger, Executive Director James J. Giandfield, Associate Dir.-
Corrections
Columbus Area Chamber of Commerce, The John W. Kessler, Associate Dir.-Courts
(1834) Gary Pence. Associate Dir.-Police
Research Department (1930) Herbert J. Shubick-, Research Associate
50 West Broad St., P.O. Box 1527 43216
Telephone: (614) 221-1321 Dayton Area Chamber of Commerce
Sneraton-Dayton Hotel 45402
Ohio Chamber of Commerce
Telephone: (513) 224-9601
Taxation and Research Department (1929) Marvin E. Purk, Executive Vice-President-
820 Huntington Bank Building 43215
Telephone: (61-0223-4201
Norran H. Baker, Director
James E. O Leary. Fiscal Specialist
KENT
*Edmond M. Loewe, Governmental Affairs
Specialist
1. John Reimers. Tax Specialist Kent State University
Center for Urban Regionalism
C. Emory G. Lander, Tax Counsel
Lowry Hall 44242
Joann Davidson, Research Librarian
Telephone: (216) 672-2232
Eugene A. Werininger, Director
Ohio Citizens' Council for Health and
Welfare, The
22 East Gay Street 43215 LIMA
Telephone: (614) Capital 4-8146
Lima Area Chamber of Commerce, The
W. James Greene. Execultive Director 53 Public Square 45201
Thane Griffin, Associate-Director Telephone: (419)222-8045
Government Relations
Richard A Anthony, Associate- Director, Robert L. Tracht.Executive Tanager
Edward Hanks, Ass't Manager
Community Relations (Mrs.) Carol Fry, Statistician
Leonard E. Ford, Consultant
Ohio Municipal League, The OXFORD
60 East Broad Street 43215
Telephone: (614) 221-4349
Miami University
John P. Coleman, Executive Director Department of Political Science
John E. Gotherinan. Jr., League Counsel Telephone: (513) 529-3151
Herbert Waltzer, Chairman
Ohio Public Expenditure Council (1941)
50 South Third Street 43215
Telephone: (611) 212-7673 TOLEDO
Charles A. Calhoun. Executive Director
Jack L. Whitmore, Research Director City of Toledo
Office of City Auditor
Ohio State University City Hall 43624
Division of Public Administration Telephone: (410) 233-1500
1775 South College Road 43210 John J. Sheehy, City Auditor
Telephone: (614) 422-8896
Clinton V. Oster, Associate Dean and Commission of Publicity and Efficiency
Director Municipal Reference Library of
208 Fire and Police Alarm Building 43624
Telephone: (419) 255-1500 Ext. 471, 172
State of Ohio
Edward L. Ways, Director
Auditor of State
State House 43216 Toledo Area Governmental Research
Telephone: (614) 469-4971 Association
William L. Williams Cummunity Services Building
Department of Urban Affairs I Stranalian Square 43604
8 East Long Street 41213 Telephone: (419) CHerry 1-8621
Telephone:(614) 469-5462 'Frank L. Britt. Executive Secretary
Bruce L. Newman. Director Putrick J. Kessler. Research Asst.
Legislative Service Commission
State House WARREN
Telephone: (611) 1469-3615 Warren Area Chamber of Commerce
David A. Johnston. Director
P. 0. Box 1147, 182 High St., N.E. 14-132
Telephone: (216)393-2565
Harold J. Naills. Manager

Oregon BETHLEHEM
Pennsylvania Economy League, Inc.
EUGENE Lehigh Valley Branch (1935)
(Includes Lehigh &- Northampton Counties)
Loaguool Oregon Cities 520 East Broad St. 18013
Post Office Box 5177 97403 Telephone: (215) 867-9532
Telephone: (503)342-1411
A. M. Westling, Planning and Public Works BUTLER
Consultant
University of Oregon 97403 Pennsylvania Economy League, Inc.
Telephone: (503) 342-1411 Butler County Branch (1937)
Bureau of Governmental Research and 403 Mellon Bank Building 16001
Service (1933) Telephone: (412) 237-5610
Kenneth C. Tollenaar. Director Robert W. Cyphert, Executive Director
Bureau of Business and Economic Research
Donald A. Watson, Director GREENSBURG

Pennsylvania Economy League. Inc.
PORTLAND Region I
712 First National Bank Building 15601
City Club of Portland (1916) Telephone: (412) 634-3360
505 Woodlark Building 97205 *Dennis R. Adams, Executive Director
Telephone: (503) 123-7231 *Howard J. Barnhart, Assislant Director
Mrs. W. E. Naylor, Executive Secretary
Oregon Tax Research (1935)
1104 Loyalty Building 57204 HARRISBURG
Telephone: (503) 227-1149
George J. Annala, Manager Commonwealth of Pennsylvania
Christopher L. Dudley, Research Director Office of Administration
Ronald G. Lench, Secretary
Departmnent of Education
Education Bldgy. 17126
Lerue of Oregon Cities
(1925) Telephone:(717) 787-528820
270 Cottaue Strcet. N.E. 97301 John C. Pittenger. Secretary of Education
Telephone: (503) 535-697 Neal V. Musmanno. Deputy Secretary of
*Donald L. Jones. Executive Secretary Education
Karl A. Van Asselt, Assistant Executive Office of Educatioral Research and Statistics
Secretary Paul B. Carinbell. Director
Gary F. Carlson Bureau of Educational Research
David G. Finiran Robert B. Hayes, Director
Oregon State Library (I905) 97310 Department of Community Affairs
Telephone: (503) 378-4243 South Office Building
Eloise Ebert, Librarian *William H. Wilcox, Secretary
Dorothea B. Kelsay Bureau of Research and Program
Stale of Oregon Development
Local Government Relations Division Telephone: (717)787-7300
Room 320, Public Service Building 97310 James W. Guest, Director
Telephone: (50i) 373-3722 Pennsylvania Economy League, Inc. (1932)
Robert K. Logan, Administrator Stale Division
Post Office Box 105 17108
Telephone: (717) 2341-3131
*John W. Lngram, Director
*Robert S. Lewis. Assistant Director
Pennsylvania *William F Zaun. Research Analyst
'Lewis B. Lee, Research Analyst
ALTOONA Pennsylvania League of Cilies
2608 North Third Street-P. 0. Box 5096
Pennsylvania Economy League, Inc. 17110
Blair County Branch (I948) Telephone: (717) 236-9469
1207 12th Avenue 16601 Richard G. Tarden. Executive Director
Telephone: (814) 942-1776 William B. Harral. Assistant Executive
Paul C. Dau, Executive Director Director for Legitation
*Robert J. Middleton. Assistant Executive
Director for Research and Information
AVOCA Patrice A. Lenker
Economic Development Council of Pennsylvania Municipal Authorities Associa-
Northeastern Pennsylvania tion
P.O. Box 777 18641 22941 North Front Street 17110
Telephone: (717) 4587-44526 Telephone: (717) 42343-8696
Howard J. Grossman, Executive Director J. Edwin Slupecke, Executive Director
BEAVER Pennsylvania Municipal Utilities Association
127 Locust Street 171l0
Pennsylvania Economy League, Inc. Marian Schwalm Furman
Beaver County Branch (1343) Pennsylvania Slate Association of Boroughs
208 Beaver Trust Bldg. 2094441 North Front Street 17110
P.O. Box 325 15009 Telephone: (717) 24306-8246
Telephone: 2(410212) 774-64046 Charles F. LeeDecker, Executive Director
Roger A. Perhacs, Executive Director Patricia Crawford, Assistant Executive
Director for Research

Pennsylvania State Chamber of Commerce
222 North Third Street 17101 Creator Philadelphia Chamber of Commerce
Telephone: (717) 233-0441 1528 Walnut Street 19102
Robert Hibbard, Executive Director Telehone: (215) PE, 9320
Research Bureau (1916) Robert S. Harr. Executive Director,
*Nevin A. Schall *Harry A. Stutzman Research & Publications Bureau
*John R. Whipple
Greater Philadelphia Movement
State of Pennsylvania 920 Western Savings Fund Building
State Tax Equalization Board Broad and Chestnut Street 19107
Room 513. Finance Building 17103 Telephone: (215) Kingsley 5-2752
Telephone: (717) 737-1950 *William L. Rafsky, Executive Director
Warrcn H. Barton, Director James A. Lineberger. Associate Executive
Director
LANCASTER Pennsylvania Economy League, Inc.
Pennsylvania Economy League, Inc. Eastern Division (1933)
Lancaster County Branch (1935) LiBerty Trust Buildig
30 West Orange Street 17603 Broad and Arch Streets 19107
Telephone: (215) LOcust 4-6250
Telephone: (717) 397-8919 *Edwin Rothman, Director of Research
*William C. Wagner, Il. Executive Director Mitchell J.Hunt, Supervisor, County
Branch activities
NEW CASTLE *John N. Carson, Senior Research Associate
Greater New Castle Association. Inc. (1939) *Edgar Rosenthal, Senior Research Associate
First Federal Plaza *Marjorie L. Jacob, research
25 N. Mill Street 16101 A.L. Gehman, Consultant
Telephone: (412) 654-5593 *Ellen Brennan, Librarian
Victor J. Andrew, Executive Vice Presidet:
Temple University 19122
Telephone: (215) 787-7309
NEWTOWN William G. Willis, Vice-President and
Secretary
Pennsylvania Economy Leacgue, Inc. (ED) Bureau of Economic & Business Research
Bucks County Branch (1952)
10-B South State Street l3940 Telephone: (215) 787-8101
Telephone: (213) WOrth 8-3862 Michael H. Moskow, Director
*Michael P. Tyler. Branch Manager
University of Pennsylvania 19104
NORRISTOWN The Fels Center of Government (1970)
Telephone: (215) 594-8212
Pennsylvania Economy League, Inc. Julius Margilis, Director

Montgomery County Branch (1932) Government Study Center
400 West Johnson Highway 19401 Morton Lusug, Administrator
Telephone: (215) 279-6894
*George F. Sears, Branch Manager
PHILADELPHIA PITTSBURGH

Bureau of Municipal Research (1908) ACTION-Housing. Inc. (1957)
Liberty Trust Building 2 Gateway Center 15222
Broad and Arch Streets 19107 Telephone: (412) 281-2102
Telephone: (215) LOcust 4-6250 Bernard E. Loshbough, Executive Director
Edwin Rothman Secretary
(For other staff see Pennsylvania Econom Allegheny Conference on Community Devel-
League, Eastern Division) opment
200 Ross Street 15219
Citizens' Budget Committee Telephone: (412) 281-1890
920 Western Saving Fund Bldg. Robert B. Pease. Executive, Director
Broad and Chestnut Streets 19117 John J. Grove, Assistant Director
Telephone: (215) MU-6-6140
City of Philadelphia Chamber of Commerce of Greater Pittsburgh
Department of Finance Chamber of Commerce Bldg. 15219
1420 Municipal Services Building 19104 Telephone: (412) 391-3400
Telephone: (215) MU-6140 John H. McLain, Executive Vice President
*Lennox L. Moak, Director Ruth Ann Nickel, Librarian

Committee of Seventy, The (1904) Civic Club of Alleheny County (1895)
Suite 910, 1420 19102 William Penn
Telephone: (215) KI 5-7017 Telephone: (412) 281-5343
Michael von Moschzisker, Executive Mrs. Jacki Garger, Administrative Secretary
Secretary Duquesne University
Bureau of Research in Business, Commun-
Crirme Prevention Association of Philadelphia nity & Government Affairs (1957)
(1932) 600 Forbes Avenue 15219
250 South Broad Street 19102 James Acklin, Director
Telephone: (215) KI 5-5231
Arthur Gewirtz, Executive Director Health and Welfare Association of
Allegheny County
Free Library of Philadelphia 200 Ross Street 15219
Department of Public Documents Telephone: (421) 261-6010
Logan Square 19103
Elmer J. Tropman, Executive Director
Telephone: (215 MU 6-5330
(Mrs) jeanne H. Mahler, Head
Clifford Crowers

Pennsylvania Economy League. Inc. UNIVERSITY PARK
Western Division (1933)
200 Fourth Avenue 15222 Pennsylvania State University. Tho
Telephone: (412) 471-1477 Institute of Public Administration
Howard B. Stewart, Director 208 Social Sciences Building 16802

*Emery P. Sedak. Assistunt Director Telephone: (814) 865-2336
and Director of Research Robert 1. Mowitz. Director
*Richard L. Conawny Director-Urban Robert LaPorte. Jr. Assistant Director

Transportation Research
*William H. Eisinger. Assistant Research
Associate WASHINGTON
*Armistead L. Guthery, Director-Urban
Research Pennsvlvania Economy League, Inc.
J. Paul Riden. Jr.. Assistanr Director of Re- Wasbington-Greene County Branch (1943)
search and Director of Branch Activities 617 Washinton Tourist Building 15301
"Frank J. Volpe, Director Municipal. Telephone: (4112) 222-2190
Research *David J. Kolesky. Executive Director
Keith C. Robb. Research Associate
Pittsburgh Regional Planning Association WEST CHESTER
564 Ferbes Avenue 15219 Pennsylvania Economy League. Inc.
Telephone: (412) 391-4120 Chester County Branch (1935)
William R. B. Froehlich, Executive Director 7 Green Tree Building 19380
University of Pittsburgh Telephone: (215) Owen 6-2217
Graduate School of Public and Peter K. Rosengarten, Branch J11anager
Internafional Affairs
704 Bruce ll523 WILKES-BARRE
Telephone: (412) 621-3500. Extension 71194
*Lawrence C. Howard, Dean Pennsylvania Economy League, Inc.
Thomas J. Davy, Associate Dean Central Division (I940)
Joseph 1. Coffey. Associate Dean 706 First National Bank Building 18701
Donald C. Stone, Dean Emeritus Telephone: (717) ?24-3539
*Raymond R. Carmon. Director
Department of Urban Affairs *William D. Jonathan, Research Associate
Joseph E. Nlclean, Acting Director 'Harold R. Heesch, Research Analyst
Gwendolyn Bell
Clifford C. Ham Wilkes College
Earl Oncue
Clark D. Rolers Institute of Rational Affairs (1951)
Telephone: (717) 824-4851
Anatole A. Solow *Andrew Shaw, Director
Institute of Urban Policy and *Philip R. Tuhy, Associate Director
Administration M44) Walter H. Nieipoff, Associate Director
Joseph A. James, Director
Department of Public Administration
William F. Matlack, Director
Department of International Affairs RHODE ISLAND
Daniel S. Cheever. Director
of Economic end Social KINGSTON
Development University of Rhode Island 02881
Hamlim Robinson. Acting Director Bureau of Government Research
Graduate Center of Public Works Telephone: (401) 792-2153
William D. Brincklop, Director John 0. Stitely, Director
School of Education James C. Pritchard. Asst. Director
*David H. Kurtzman Joseph Coduri
Anna G. flwggarty
READING Robert W. Sutton Jr.
Pennsylvania Economy League, Inc. PROVIDENCE
Berks County Branch (1956)
18 North 5th Street 19601 Brown University 02912
Telephone: (215) 374-2145 Curriculum Public Administration
*Charles W. Vatters, Executive Director Telephone: (401) 421-0493
Elmer E. Cornwell Jr. Chairman, Political
SHARON Science Department
Rhode Island Public Expenditure Council
Pennsylvania Economy League, Inc. (1943)
Mercer County Branch (18345) 130 Francis Street 022003
811 E. State St. 16146 Telephone: (0401) 4211-9493

Telephone: (412) 342-3074 Roger L. Slater. President
Harry McIndue. Executive Director 'Ronald R. Blair. Director of Research
'Thomas R. Farley, Senior Researcher
UNIONTOWN Rhode Island State Library
Legislalive Reference Bureau (1907)
Pennsylvania Economy League. Inc. State 02903
Fayette County Branch (1238) Telephone 401 277-2473
5149 Ga4llatum National Bank Building 15401 Elliott E. Andrews. State Librarian
Telephone: (412) 48348-04241
*Michael D. Costa, Executive Director

State of Rhode island and Providence Texas Research League
Plantations P.O. Box 12456 78711
Department of Administration 403 East l5th Street 78701
Division of Budget Telephone: (512) 472-3127
111 State House 02903 *James W. McGrew. Executive Director
Telephone: (401) JAckson 1-7100 *Glenn H. Ivy. Research Director
John C. Murray. Budget Officer *Homer E.Scace. Senior Research Associate
Department of Community Affairs *Robert E. Norwood. Research Associate
289 Promenade Street 029O8 *Alan E. Barnes. Reasearch Associate
Frederick C. Williamson. Director *Leighton Bearden. Research Analyst
Department Of Social Welfare *Bill W. Bownds. Research Analyst
40 Fountain Street 02903 *Wilburn French. Research Analyst
Ruth A. Coogan. Research Supervisor *Lynn M. Moak, Research Analyst
House Finance Coommittee *John R. Kennedy, Research Analyst
William J. Denuccio. Fiscal Advisor *N. David Spurgin. Research Analyst
*Brenda Lee, Publications Assistant

University of Texas
Lyndon B. Johnson School of Public Affairs
South Carolina Sid Richardson Hall 78712
Telephone: (512) 471-5711
CHARLESTON John A. Gronouski, Dean
Alexander L. Clark, Associate Dean
Charleston Trident Chamber of Commerce Nicholas P. Thomas, Director, Office of
P.0. Box 975 29402 Research
Telephone: (803) 577-2510 Bureau of Business Research (1926)
Robert L. Frank, Research Director Stanley A. Arbingast, Director

DALLAS
COLUMBIA
Greater Dallas Planning Council
Municipal Association of South Carolina 2021 Fidelity Union Tower 75201
Suite 900, Columbia Building Telephone: (214) 748-2274
P.0. Box 306 29202 William L. Moore, Executive Director
Telephone: (803) 256-8368
J.N. Caldwell, Jr., Exccutive Vice HOUSTON
President

State of South Carolina Civil Service Commission
Division of Local Government 900 City Hall 77002
Governor's Office. State House 29201 Telephone: (713) 222-3542
Telephone: (803) 758-3606 H.S. Lanier, Director
Woody Brooks, Executive Director
Houston Chamber of Commerce
University of South Carolina 29208 Public Affairs/Trnsportation Dept.
Bureau of GovernmentalResearch Chamber of Commerce Building
Telephone: (803) 777-8156 P.O. Box 53600 77052
James E. Larson. Director Telephone: (713) CApitol 7-5111
Robert Stoudemire, Associate Director *Frank R. Kemfield. Manager
W. Hardy Wickwar
C. Blease Graham Houston-Galveston Area Council
3311 Richmond Aveneu 77006
Telephone: (713) 521-9573
James E. White. Jr., Staff Assistant
Texas
Southwest Center for Urban Studies
1200 Southmore 77004
AUSTIN Telephone: (713) 526-8801
*Ralph W. Conart. Director
Legislative Reference Library George W. Strong. Assistant Director
State Capitol 78711
Telephone:(512) GReenwood 5-4626 Tax Research Association of Houston and
James R. Sanders. Director Harris County, Inc.
414 Capital National Bank Building 77002
Southwest Educational Development Telephone: (713) 222-0349
Laboratory *George L. Nichols. Executive Director
Commodore Perry Hotel. Suite 550
Telephone: (512) 476-6261 University of Houston
*Charles Rodman Porter, Asst. Dep. Exec. Institute of Urban Studies
Dir. (Support Department) 33801 Culler Blvd, 77004
Telephone: (713) 748-6000. ext. 685
Texas Legislative Council (1949) *Ralph W. Conant, Director
Capitol Station-Box 12128 *John E. Bebout. Program Director
Telephone: (512) 476-2736 George W. Strong. Assistant Director
Robert E. Johnson, Executive Director
John T. Potter. Asst. Director LUBBOCK
(Mrs.) Julia T. Potter. Asst. Director
Research Texas Tech University 79409
(Miss) Floy M. Johnson. Director of Curriculum in Public Administration (1936)
Special Projects Telephone: (806) 743-3121
W.B. Wilmot. Director Affairs Jack Hopkins. Charman Government Dept.

Texas Municipal League SAN ANTONIO
801 Vaughn Building 78701
Telephone: (512) GReenwood 8-6601 Research and Planning Council (1948)
Dick Brown. Acting Executive Director Three Americas Bldg. Suite 626 78205
*Telephone: (512) CApital 7-2501
*Walter Stoneham, Executive Vice-Presi-
dent

TEXAS CITY Washington

Galveston County Research Council (1959)
622 Sixth Street North MERCER ISLAND
P.O. Drawer D 77590
Telephone: (713) 948-1724 Tax Research of Washington
*Craig Foster. Executive Director 9724 Mercerwood Drive 98040
*George T. Odom. Research Associate Telephone: (206) 232-1630
(Mrs.) Billie C. Macik. Research Assistant *John H. Current

Virginia OLYMPIA

CHARLOTTESVILLE State of Washington 98504
Department of Revenue
University of Virginia Telephone: (206) 753-5512
Institute of Government George Kinnear, Director
207 Minor Hall 22903 Donald R. Burrows. Asst Director
Telephone: (703) 924-3396 R. Donn Smllwood, Chief, Research &
Weldon Cooper, Director Statistics
Office of Program Planning & Fiscal
RICHMOND Management
Public Health Building
Commonwealth of Virginia Telephone: (206) 753-3431
Advisory Legislative Council (1936) *Donald L. Sorte. Program Coordinator
State Capitol 23219
John B. Boatwright, Jr., Secretary Washington State Libray (1953) 98501
Telephone: (206) 733-5390
Division of State Planning and Maryan E. Reynolds. State Librarian
Community Affairs Gene Bismuti. Chief of Readers Services
1010 Madison Bldg. 23219 Donald P. Duncan. Head, Reference &
Telephone: (703) 770-3785 Interlibrary Loan
Robert H. Kirby. Director
Washington State Legislative Council
Department of Taxation Legislative Building 98504
Division of Research (1929) Telephone: (206) 753-6326
W.B. Harvie, Jr., Director James W. Guenther. Executive Secretery
John B. Welsh, Jr., Attorney
Greater Richmond Chamber of Commerce Tim Burke
Research Department Victor B. Moon
616 East Franklin St. 23219 Stan Finkelstein
Telephone: (703) 549-0373
E.M.C. Quimby. Director of Research Washington State Research Council
1659 Capitol Way 98501
Meril System Council (1943) Telephone: (206) 357-6643
300 State Finance Building 23219 *W. Phillip Strawn, Executive Director
Telephone: (703) 770-3309 William B. Pilkey. Research Director
W. Richard Lawrence, Supervisor Lawrence K. Martin. Research Analyst
William C. Baber. Exam. Sup.
PULLMAN
Virginia Municipal League (1905)
700 Travelers Building 23219 Washington State University
Telephone: (703) 643-0264 Curriculum in Public Administration 99163
Harold I. Baumes, Executive Director Telephone: (309) 335-4613
Paul L. Beckett, in charge
Virginia State Chamber of Commerce (1924) James A. Thurber
Research Department K.T.W. Swanson
611 East Franklin Street 23219 Bureau of Economic & Business Research
Telephone: (703) 643-7491 John A. Guthrie. Director
Edwin C. Luther, III. Director, Public Department of Political Science-Division
Affairs & Research Of Governmental Studies & Services
Telephone: (509) 335-3329
James A. Thurber, Director

SEATTLE

Association of Washington Cities (1933) 93105
4719 Brooklyn Avenue, N.E.
Telephone: (206) 543-9050
Chester Biesen, Executive Director

Evergreen Safety Council
622 John Street 98109
Telephone: (206) MAin 2-1670
M.O. Christman, Exec. Vice Pres.

Municipal League of Seattle and King
county (1911)
725 Central Building 98104
Telephone: (206) MAin 2-8333
Walter W. Davis, Executive Secretary
*William L. Massey, Editor & Research
Secretary

Municipal Reference Library University of Wisconsin 53706
(Branch of Seattle Public Library) Institute of Governmental Affairs
307 Municipal Building 98104 Telephone: (608) 262-3150
Telephone: (206) 583-2617 Edward V. Schten, Director
Harold D. Wilson. Librarian Ruth Baumann
Marjorie R. Henry James R. Donoghue
Erica Wilhelm A. Clarke Hagensick
Albert D. Hamann
University of Washington 98105 Richard I. Stauber
Division of Community Development Donald B. Vogel
316 Lewis Hall John A. Martin
Harold L. Amoss. Director John C. Roberts
Fred A. Wileman
Bureau of Governmental Research Frank J. Crisafi
and Services
3935 University Way N.E. Wisconsin State Chamber of Commerce
Robert H. Pealy, Director 411 W. Main Street 53701
Telephone: (608) Alpine 7-1088
Center For Urban and Regional Research Kenneth W. Haagensen. Executive Vice
Telephone: (206) 543-7793 President
Edward L. Ullman. Acting Director Phil Sellinger, Director of Research

Department of Political Science Wisconsin Taxpayers Alliance (1932)
201 Engineering Annex 335 West Wilson Street 33700
David W. Minor. Chairman Telephone: (608) 255-4581
C.K. Alexander, Exec. Vice President
Graduate School of Public Affairs James R. Morgan. Vice President, Research
226 Smith Hall Services
Brewster C. Denny, Dean *John P. Reynolds, Vice President, Field
Services
Institute for Administrative Research Beulah M. Poulter. Research Associate
262 Smith Hall Rindert Kiemel, Jr., Research Associate
George Shipman, Director Donald T. Ripple, Educational Coordinator

SPOKANE MILWAUKEE

Municipal League of Spokane (1951) Citizens' Governmental Research Bureau, Inc.
W. 921 Sprague. Room 10 99201 (1913)
Telephone: (309) MADison 4-6213 125 East Wells Street 53202
Charles A. Wendtland. President Telephone: (414) 276-8240
Donna Kuder. Executive Secretary *Norman N. Gill, Executive Director

Spokane Taxpayers Association City Club of Milwaukee (1909)
West 704 First Avenue 99204 759 North MIlwaukee Street 53202
Telephone: (309) Riverside 7-3171 Suite 523
William D. Roberts, Executive Director Leo Tiefenthaler, Civic Secretary

TACOMA University of Wisconsin-Milwaukee 53201
Department of Political Science
Pierce County Taxpayers Association (1931) Cornelius Cotter, Chairman
619 Security Building 98402
Telephone: (206) MArket 7-0318 Department of Urban Affairs
668 Bolton
Telephone: (414) 228-4751
Wisconsin Harold Rose, Chairman

Institute of Governmental Affairs
MADISON 354 Bolton
Telephone: (414) 228-4754
League of Wisconsin Municipalities (1898) A. Clarke Hagensick, Associate Director
433 West Washington Avenue 53703 Donald B. Vogel, Assistant Director
Telephone: (608) 255-7291 Sarah C. Ettenheim
Ed Johnson, Executive Director
Wisconsin Manufacturers' Association
Public Expenditurs Survey of Wisconsin 324 E. Wisconsin Avenue 532002
(1939) Telephone: (414) 271-9428
615 East Washington Avenue 53703 *Paul E. Hassett, Executive Vice Presiden
Telephone: (608) ALpine 3-6767
*Glenn D. McGrath, Director
Duane Riggert, Assistant Director
Bernard C. Sullivan
Arch Ely. Administrative Advisor

State of Wisconsin
Legislative Council (1947)
State Capitol 53702
Telephone: (603) 286-1304
Bonnie Reese. Acting Executive Secretary

Legislative Reference Library (1901)
H. Rupert Theobald, Chief

APPENDIX D

U.S. Department of Labor APPENDIX D
Bureau of Labor Statistics

REGION I. BOSTON REGION II NEW YORK REGION III PHILADELPHIA REGION IV ATLANTA
Jon Fitzgerald Kennedy Federal B 1515 Broadway Suite 1400 1515 M Street 71 Street N
Government Center Room 1603 A New York, NY 10036 P O Box Atlanta, GA 30309
Boston, Mass. 02202 Philadelphia, PA

REGION V. CHICAGO REGION VI DALLAS REGIONS VII & VIII KANSAS CITY REGIONS IX & X SAN FRANCISCO
230 S. Dearborn street 555 G 2nd fl 911 Walnut Street 450 Gate Avenue, Box 06017
Chicago, Ill. 00604 Dallas Tex 75202 Kansas City, 6410 San Fransisco, Calif 94102

COOPERATING STATE AGENCIES
State and Local Area Unemployment Statistics Program (LAUS). Current Employment Statistics Program (CES), and
BLS Labor Turnover Statistics Program ( TS)
Region

IV ALABAMA -Department of Industrial Re Industrial Building Montgomery 36104
X ALASKA -Employment Security Division Department at PO Box 3 1000 Jeneau 99802
IX ARIZONA -Department of Economic Security P 0 Box 29026 Phoenix 86038
VI ARKANSAS -Employment Security Division, Department of Labor PO Box 2981 Little Rock 72203
IX CALIFORNIA -Employment Development Department PO Box Sacramento 95808 (LAUS and CES)
VIII COLORADO -Divison of Employment, Department of Labor and Employment, Room 222. 1210 Sherman Street
Denver 80203
I CONNECTICUT -Employment Security Division, Labor Department 200 Brook Boulevard Wethersfield 06109
III DELAWARE -Department of Labor. 301 West Street, Wilmington 19899
III DIST. OF COL. Office of Administration and Management Source Man Administration Room 676.
500 C Street. NW Washington 20001
IV FLORIDA Divistion of Employment Sercurity Department Building Taliahassee 37304
IV GEORGIA Employment Security Agencies Department of Labor 254 Washington Street, SW Atlanta 30334
IX HAWAII Department of Labor and Industrial PO Box 3680 Honolulu 96811
X IDAHO Department of Employment PO Box 35, Bouse 83707
V ILLINOIS Bureau of Employment Security Department of Labor 910 South Michigan Avenue, Chicago 60605
V INDIANA Employment Security Division 10 North Senate Avenue Miniapolis 46204
VII IOWA Employment Security Commission 1000 Avenue 503
VII KANSAS Employment Security Division Department of Labor 401 Topeka Boulevard, Topeka 66603
IV KENTUCKY Department of Employment Security, PO Box 44994 Cap Station, Raton Houg 70804
Employment Security Commission Department of Manpower Affairs 20 Union Street, Agusta 04330
I MAINE Department of Human Resources, 1100 North Street, Baltimore 21201
III MARYLAND Division of Employment Security Ch Employment Building Government Center
I MASSACHUSETS Boston 02114

V MICHIGAN Employment Security Commission Department of Labor 7 0 W Avenue Detroit 48202
V MINNESOTA Department of Employment Security 390 North Robert Street, St. Paul 55101
IV MISSISSIPPI Employment Security Commission PO Box 39205
VII MISSOURI Division of Employment Security Department of Labor PO Box 59 Jefferson
City 65101
VIII MONTANA Employment Security Division Department of Labor and Industry PO Box 1728 Helena 59601
VII NEBRASKA Division of Employment Security Department of Labor PO Box 94600 State Station Lincoln 68509
IX NEVADA Employment Security Department PO Box 602 City 89
I NEW HAMPSHIRE -Department of Employment Security 37 South Main Street Concord 03301
II NEW JERSEY Department of Labor and Industry 202 Trenton 08625
VI NEW MEXICO Employment Security Commission PO Box 1928 A 87101
II NEW YORK Division of Employment NY State Department of Labor, State Campus Building 17, Albany 17201
IV NORTH CAROLINA -Employment Security Commission PO Box 25901 Raleigh 27611
VIII NORTH DAKOTA Employment Security Bureau PO Box 1537
V OHIO Division of Research and Statstics Bureau of Employment Services 1455 Front St. Colombus 43276
VI OKLAHOMA Employment Security Commission W Office Building Oklahoma City 73105
X OREGON Employment Division Department of Human Resources Room 402 Labor and Industries Building,
S 97310
III PENNSYLVANIA Bureau of Employment Security Department of Labor and Industry Seventh and Forster Streets,
Harrisburg 17121
I RHODE ISLAND Division of Statistics and Census Department of Labor Room 117,235 Promenade Street, Providence 02903
(CES) Department of Employment Security 24 Mason Street, Providence 07903 (LAUS and LTS)
IV SOUTH CAROLINA Employment Security Commission PO Box 995 Colombia 79702
VIII SOUTH DAKOTA Department of Labor PO Box 1730 A 57401
IV TENNESSEE Department of Employement Security Room 519 Office Building Nashville 37219
VI TEXAS Employment Commission TEC Building 15th and Congress Avenue Austa 78878
VIII UTAH Department of Employment Security PO Box 1749 Salt Lake City 84147
I VERMONT Department of Employment Security PO Box 488 Montgomery 05602
III VIRGINIA Division of Research and statistics Department of Labor and Industries PO Box 12064 Richmond
23241 (CES) Employment Commission PO Box 1358 Richmond 23211 (LAUS and CTS)
X WASHINGTON Employment Security Department 1007 South Washington Street, Olympia 98501
III WEST VIRGINIA Department of Employment Security State Office Building 12 Califorina Avenue Charleston 25305
V WISCONSIN Department of Industry Labor and Human Resources PO Box 7344 Madison 53707
VIII WYOMING Employment Security Commission PO Box 2760 Casper 82601

THE CEIP IMPACT MODEL

TECHNICAL MANUAL

Prepared by

Dr. Robert L. Bish and Dr. John D. Wolken

and OCZM Staff

Prepared for
The Office of Coastal Zone Management, NOAA
Contract No. 7-35174

JUNE 1977

CONTENTS Page

1. Introduction 1

Ii. List of Variables 2

III. Equations and Calculations 8

IV. Comments 14

V. Coding Forms (to be added by OCZM)

Vi. Computer Program (to-be added by OCZM)

I. INTRODUCTION

This manual provides the technical elements of the CEIP
Impact Model. Variables are listed in the order they are used
in the equations. Data sources or derivations for each variable
are also indicated.

Equations are listed in the order they are used in the
Impact Model. Each equation is listed under a heading indicating
the purpose for which the equation is used.

Copies of coding forms and the computer program used for
calculations are included so that analysts may verify how the
variables and equations are utilized in the Impact Model.

II. LIST OF VARIABLES

BLRt ....... baseline revenues, excluding revenues derived
from borrowing or project-related grants

source: t=l,..........10 Schedule 4.1 data
t=ll,...,30 Equation 3 forecast

BLRt-1 ........... baseline revenues, as above, lagged one year

Yt ............ per capita income for specific locality (or its
county area)

source: t=l, ... ,10 Schedule 3.4 data
t=ll,...,30 Equation 1 forecast

Pt ....... local population

source: t=l,..........10 Schedule 3.4 data
t=ll,...,30 Equation 2a forecast

St ...... number of students

source: t=l,.....10 Schedule 3.7 data
t=ll,...,30 Equations 2a and 2b

Pt ....... defined as Pt - Pt-1

Yt ...... defined as Yt- Yt-l

BLXt ....... baseline expenditures, excluding expenditure of
project grants or borrowed funds

source: t=l,...,30 Equation 4
Eit ............... ... disturbance term of in the equation

t ...... time, range 1 to 30. Year 10 is PRESENT YEAR

A
c3 ....... estimated coefficient (from Equation 3) of the
effect of a change of one person on revenues
collected, i.e.,

A
BLR t /@Apt = c 3

source: Equation 3

CFEt ..... construction employment

source: t=ll,...,30 Schedule 1.1 data

OFEt ...... operating employment

source: t=ll, ... r30 Schedule 2.1 data

ICFEt ....... indirect construction facility employment

source: t=ll,, ... 30 Schedule 1.1 data

IOFEt indirect operating facility employment

source: t=ll, ... 30 Schedule 2.1 data

FE t ....... total facility employment

source: t=ll,...,30 Equation 5c

DFEt ....... direct facility employment

source: t=ll,...,.30 Equation 5a

IFEt ....... indirect facility employment (new employees in
local businesses supplying the energy facility)

source: t=llr...,.30 Equation 5b

FEGt ....... facility employment in local jurisdiction

source: t=ll,...,30 Equation 6

DIST .... distance from energy facility site to population
center of government

source: Schedule 3.1 data

z 0..0.0. gravity distance

source: Equation 6a

POPJ ..00.4. population within the J th ring
J = 1, 2, 3, 4, 5, 6 (e.g. POP30=population
within the 20 to 30 mile ring)

source: Schedule 3.1 data

POPG ...... population within the government for gravity
model year

source: Schedule 3.1 data

SUMY ....... calculation for gravity model

source: Equation 6b

J .... jobs within the local community

source: Schedule 3.3 data

k employment multiplier

source: Equation 7

RFE t residential employment from facility
source: Equation 8

U ... unemployment

source: Schedule 3.3 data

e "labor market tightness" coefficient

source: Equation 9

PNat'l ....... population nationally

source: use 215,396,000

ENat'l ....... employment nationally

source: use 96,817,000

E employees residing in local jurisdiction (may
work elsewhere)

source: Schedule 3.3 data

NRFEt new residential facility employment

source: t=ll,,..,30 Equation 10

NPt new population associated with the energy
facility

source: t=ll,...,30 Equation 11

WPt ....... total population with the energy facility

source: t=ll,,...,,30 Equation 12

S student-population multiplier

source: use .25

NSt ... new student population

source: t=ll,...,30 Eauation 12a

RPTt ....... residential property tax revenues

source: t=ll,...,30 Equations 13a, c, d

M ....... proportion of t axes exported

source: Schedule 4.7 data

q ....... proportion of taxes from residential property tax

source: Schedule 4.3 data

ht ....... adjustment for property tax base lag with large
population growth

source: Equation 13b

Lt ....... value of land purchased for energy facility in
given year t OR value of completed physical
facility in yi;-ar subject to property tax (if
both occur, then the sum)

source: t=ll,...,30 Schedule 1.2 data

9 ....... assessment ratio for business property

source: Schedule 4.4 data

-T l't ....... business property tax rate

source: t=ll,...,30 Schedule 4.2 data

BPTt ....... business property taxes

source: t=111 ... 30 Equations 14a, l4b

RETt ....... real est ate transfer taxes

source: t=ll,...,30 Equation 15

T2,t ....... real estate transfer tax rate

source: t=l'L,...,,30 Schedule 4.2 data

ST t .... sales taxes
source: t=ll, ...,30 Equation 16

Tn,t ...... other tax rates
n=3,...,J where J is the total number of taxes

source: t=ll,...,30 Schedules 4.2 and
4.6b data

BTn ....... other tax bases
n=3,...,J (e.g., sales tax base, etc.)

source: Schedules 1.3 and 2.2 data

UTt ...... user charges, in appropriate year

source: t=ll,...,30 Schedule 4.8 data

OTt ... other revenue sources from taxation, not
explicitly covered in property tax, sales tax,
etc.

source: Schedule 4.9 data

OBTt ...... other business taxes

source: t=ll,...,30 Equation 17

WXt expenditures with the energy facility impact

source: t=ll,...,30 Equation 18

WRt ..... revenues with the energy facility impact

source: t=ll,...,30 Equation 19

NFIt ..... net fiscal impact

source: t=ll,...,30 Equation 20

111. EQUATIONS AND CALCULATIONS

SECTION 1. BASELINE FORECASTS

Forecast BLRt, Yt, Pt, and BLXt for t = ll,...,30.
Use ordinary least squares to estimate Equations 1, 2a and 3.
Then apply the estimated equations to predict the above variables
for t = 11, 30.

Equation 1. Forecast per capita income, t=ll,...,30.
(1) In Yt = a I + b 1 + E it

Equation 2a. Forecast population, t=ll,...,30.
(2a) In Pt = a2 + b2t + E2t

Equation 2b. Forecast student enrollments.
(2b) St = Pt in Equations 2a and 3.

Equation 3. Forecast baseline revenues, t=ll,...,30, given above
forecasts for the independent variables:
(3) BLR t = a3 + b3BLR t-l + c3 Pt + d3 Yt + E 3t

Equation 4. Forecast baseline expenditures, t=ll,...,30.
(4) BLX t = BLR t

(NOTE: Save estimated coefficient above)

SECTION II. WITH IMPACT FORECASTS (CALCULATIONS)

Step 1. Forecast new population (impact) as result of energy
installation:

Equation 5. Total facility employment.
(5a) DFEt = CFE t + OFEt definition

(5b) IFEt = ICFE t + IOFEt definition

(5c)- FEt = DFEt + IFE t definition

Equation 6. Allocate new employment to the local jurisdiction.
Allocation by gravity model and given data:

(6a) IST if DIST < 20
Z =
120 + 3(DIST - 20)] if DIST > 20
(6b) SUMY = (POP10/5) + (POP20/15) + (POP30/35) + (POP40/65)

+ (POP50/95) + (POP60/125) + (POPG/Z)
(6c) FEG t = POPG/Z FE
I SUMY I t

Equation 7. Employment multiplier.
(7) If J t < 50 then k = 1.0
if 50< J t < 200 then k =1.1
If 200< J t < 500 then k =1.2
if 500 J t < 2000 then k =1.3
If 2000< J t < 5000 then k =1.4
if J t > 5000 then k =1.5

Equation 8. Residential employment from facility.
(8) RFEt = k FEG t

Equation 9. Labor market tightness coefficient.

(9) if P/E)/(PNatll/ENatl) < 1 then e = 0
> 1, < 1.05 then e = 0.005
1.05 then e = 0.01

Equation 10. New residential facility employment.
(10) NFREt RFE t -0.3U - ePt

Equation 11. New population.
(11) NPt NRFE t(PNat'l/ENat1l)

Equation 12. Total population with the energy facility:
(12) WPt = Pt+ NPt

(12a) In the case of school districts, then

NSt sNPt

Step 2. Forecast new residential property tax revenues (RPTt
Equation 13. Property tax revenues.

Equation 13a. Property tax revenues, first year:
(13a) t 11: RPT 11 = NP 11 (1-m-)BLR 11 / P11

Equation 13b. Define proportion of new residents paying property
tax coef ficient (h) :
(13b) If (WP t+1 - WPt < .1 then h = q
.1 to .2 then h = .8 q
WP t+1 .2 to .4 then h .6 q
> .4 then h .4 q

Equation 13c.l. If property tax receipts in next fiscal year, use
Equation 13a for RPT 12 (derive from data), and
subsequently, for t=13 .... ,30 use Equation 13d
for RPT t (t=l3,..., 30) .

Equation 13c.2. If property tax receipts in same fiscal year, use
for t=l2,...,30.
RPTt = NP t(1-m-q)BLR t /P t + NP t-l* h-BLRt/Pt

Equation 13d. If property tax receipts following fiscal year, in
third year and later, use following equation
(t=13,...,30). See note at Equation 13c.l.
(13d) RPTt NPt(1-m-q)BLR t /Pt + (NP t-2) h (BLR t /P t)

Step 3. Forecast (calculate) energy facility business property
taxes.

Equation 14a. Business property taxes if tax revenues received
in SAM fiscal year (data, t=ll, ... 30.
(14a) For t = 11, BPT 11 = 0.5Lt (g)(T 1111)

for t = 12,...,30
BPTt
Equation 14b. Business property taxes if tax revenues received in
following fiscal year (data), t=ll,...,30.
(14b) For t = 11 BPT 11 = 0
for t = 12 EPT 12 = 0..5L 11 (g)(T 1, 12)
for t = 13,...,30

Step 4. Other business taxes.

Equation 15. Real estate transfer taxes (if applicable).
(15) RETt = Lt T 2, t

Equation 16. Sales and other such taxes.

(16) ST t= ZT n, t - BT nt
n=3
where n is the type of tax, and J is total number of such
taxes + 2.

Equation 17. All non-property taxes.
(17) OBTt = RET t + ST t+ UT t + OT t
(Note: UT is user charges, and OT other taxes. This data
given in schedules.)

Step 5. Calculate expected tax revenues and expenditures with
the impact of the energy facility.
Equation 18. Expected expenditures with the energy facility.
(18) WXt.= BLXt + c 3NPt

Equation 19. Forecast expected revenues with energy facility.
(19) WRt = BLRt + RPTt + BPTt + OBTt

Equatio n 20. Net fiscal impact.
(20) NFIt WRt WXt

IV. COMMENTS

The general description of forecasting procedures is contained
in the Technical Assistance Materials along with the data schedules.
The comments presented here are supplementary to clarify certain
technical aspects of the model.

BASELINE FORECASTS

Data limitations prevent making independent estimates of base-
line revenues and baseline expenditures. Hence the baseline reve-
nues are estimated as a function of revenues the previous period,
changes in population and changes in per capita income. Revenues
from borrowing or project related grants are excluded. Baseline
expenditures, excluding expenditures of borrowed funds or project
related grants, are then assumed to equal baseline revenues. This
assumption is warranted in that after "lumpy" expenditures and
revenues are eliminatedl, revenues generally come very close to
equaling expenditures for local government units.
The CEIP Impact Model uses only a simple continuation of
trends in forecasting per capita income and population. If alter-
native estimates are available they should be utilized.

IMPACT FORECASTS

The impact forecasts are a series of calculations which are
added to the baseline revenue forecasts. Assumptions and calcula-
tions underlying four of the more important steps in the impact
forecast are explained below.
1) Gravity Model - The gravity mode" is based on previous
empirical work. The assumptions are that the residential location
of facility employees varies directly with the existing population
in an area and inversely with the distance from the facility to
the local area. The decline in relation to distance is direct up
to 20 miles and three times the additional distance beyond 20 miles.
This formulation may overstate the number of employees close to the
facility and understate the number of employees distant from the
facility during its initial years. This is because new employees
will commute longer distances until they feel their jobs are perma-
nent, after which they move closer to the facility.
2) After the number of "new" jobs within the local government
area are estimated with the gravity model and multiplier, an at-
tempt is made to determine how many holders of new jobs will be
new residents. The adjustment for labor market tightness (Equation
9) assumes no new entrants to the labor force if the population-
employment ratio in the local area is lower than the national

average. If the local population-employment ratio is up to 5
percent higher than the national average, .5 percent of the exis-
ting residential population are assumed to be new entrants to the
labor force filling energy facility related jobs. If the local
population-employment ratio is more than 5 percent higher than the
national average, one percent of the existing residential popula-
tion is assumed to join the labor force in energy facility related
jobs. A second adjustment is made by assuming that 30 percent of
the currently unemployed in the community find jobs. These calcu-
lations reduce the need for new residents in the community to fill
energy facility jobs, and hence reduce the new population impact
from the facility.

3) Property Tax Lags - It is assumed that no new residential
property tax revenues accrue during the first year of energy fa-
cility activity. Beginning in the second year new residents pay
the same amount of residential property tax as old residents if
a) property taxes are collected in the same year as they are
assessed and b) the rate of new population growth was less than
10 percent. If there is a one year lag between assessments and
collections, new residential property taxes do not accrue until
year three. If population increases are large, the amount of res-
idential property tax paid by new population is decreased by the
factors indicated in Equation 13b, i.e., if growth is between 10
and 20 percent, new residents only pay 80 percent as much property
tax as old residents.
Business property tax receipts from the energy facility are
also lagged if there is an assessment-collection lag. In addition,
during.the first year of a new business property tax assessment,
only 50 percent is estimated to accrue. This is an "expected
value" in that if the facility is in'place early in the year, the
amount would be 100 percent but if in place only at the end of the
year, the amount could be 0. This 50 percent assumption can be
modified to be either 0 or 100 percent by substituting 0 or 1 for
.5 in Equations 14a and 14b.
4) Tax Rates for Estimating Energy Facility Revenues - In
Schedule 4.2 local officials are asked to indicate current tax
rates and tax rates 5, 10 and 15 years in the future for-major
taxes. Revenues from the energy facility will be sensitive to
future tax rate estimates so it may be desirable to run the model
more than.once with a different estimate for rates for taxes in
the future.

PRELIMINARY URA.VT

CEIP IMPACT FORECAST REVIEW MATERIALS

Prepared by
Dr. Robert L. Bish and Candis L. Brown

Prepared for the
Office of Coastal Zone Management, NOAA
Contract No. 7-35174

June 1977

CONTENTS

Pages

I. Introduction 1

Ii. Alternative Data Sources 1

Recommended for CEIP-OCZM Special 1
Library Collection

General Sources 2

III. Review Questions for Schedules 2

IV. Review Questions for Forecasts 3

NOTE: The Review Questions have been prepared prior to develop-
ment of the computer program or actual use of the model
for forecasting. Consequently, the list of review questions
is relatively short. The list will need to be supplemented
after some experience with the operation of the model is
obtained.

I. INTRODUCTION

Three kinds of reviews of CEIP Impact Models and forecasts
can be made. First, data provided in schedules can be verified by
checking alternative data sources as listed below. Second, some
checks for internal consistency can be made; and third, forecasts
can be examined to see if they are reasonable. Each of these
review processes will be presented in turn. OCZM staff, however,
should follow their own inclinations and also maintain a log of
important questions or techniques for checking so that a more
detailed, systematic review process can be developed after some
experience with the program.

II. ALTERNATIVE DATA SOURCES

Recommended for CEIP-OCZM Special Library Collection

1. Advisory Commission on Intergovernmental Relations (ACIR).
SIGNIFICANT FEATURES OF FISCAL FEDERALISM 1976-1977, Vol. II.
2. STATE AND COUNTY EMPLOYMENT AND UNEMPLOYMENT JANUARY-DECEMBER
1976. NTIS (Dept. of Commerce). microfiche #3.00, paper-
back $28.75.

3. Bureau of the Census. "Population Estimates and Projections/
Estimates of the Population of Counties;" 1970, 71, 72, 73,
74, 75.

4. Bureau of the Census. "County Business Patterns."
5. Bureau of Economic Analysis. "Local Area Personal Income."
6. Bureau of the Census. "Finances of County Governments."
(GF series, Vol. 4, No. 3).
7. Bureau of the Census. "Finances of Municipality and Township
Governments." (GF series, Vol. 4, No. 4).
8. Bureau of the Census. "Compendium of Government Finances."
9. Commerce Clearing House. STATE TAX REPORTER, Vol. I, II.

General Sources

1. Official state agencies who participate in federal-state
cooperative program for local pppulation estimates.

2. Directory of bureau members of the Association for University
Business and Economic Research (see Appendix B, THE CEIP IMPACT
MODEL: TECHNICAL ASSISTANCE MATERIALS).

3. Directory of local and state agency members of the Government
Research Association, Inc. (see Appendix C, THE CEIP IMPACT
MODEL: TECHNICAL ASSISTANCE MATERIALS).

4. RAND MCNALLY COMMERCIAL ATLAS.

5. Bureau of the Census. "State Reports on State and Local
Government Finances." (GF series, Vol. 6, No. 2).

6. Bureau of the Census. "Government and Census Depository
Libraries Holding Census Bureau Reports."

III. REVIEW QUESTIONS FOR SCHEDULES

Energy Facility (Schedules 1 and 2)

1. Che ck to see that the totals in column 4 of Schedule 1.2 are
equal or slightly less than the cost of inputs, i.e. number of
employees from 1.1 x an estimated wage ($16,000 to $20,000),
plus the cost of land (1.2) and construction materials (1.3).

2. Be sure that 1.3 has been completed if the answer to 4.6b is
yes. If 4.6b is no, Schedule 1.3 may be uncompleted.

3. Be sure that 2.2 is completed if the answer to 4.6a is yes.
If 4.6a is no, Schedule 2.2 need not be completed.

Local Area Description

1. Check to see that the local government's population for the
year given in 3.1 corresponds to the population data in 3.4.

2. Check to see that the sum of the number of residents employed
and number of residents unemployed from 3.3 is one-third to
one-half of the total population for the year of the data.

3. If population or school enrollments forecasts are provided
(3.6 or 3.8), examine them for comparability to data for past
10 years (3.4 or 3.7).

Government Revenue and Expenditure

1. Compare data on revenue (4.1) with expenditure data (5.1).
The way revenues and expenditures are defined, they should be
very close to one another each year.

3
2. If expected tax rates in 4.2 are not increasing, check to see
that either 1) revenues are not increasing very much; or 2)
population is increasing rapidly.

IV. REVIEW QUESTIONS FOR FORECASTS

1. Calculate the per capita revenues for the current year by
dividing total revenue (4.1) by population (3.4). Compare
this with the value of coefficient c as estimated in Equation
A 3
3. c3 should be less than the average per capita revenues;
if it is greater, the forecast is extremely suspect. (83 is
the marginal revenue or expenditure from an additional person
historically, taking into account income and the previous
year's revenues or expenditures.)
2. Examine the population and income data in Schedule 3.4 for any
trends that would not be picked up in a linear equation. Com-
pare the predicted population and income growth with historical
experience.

3. Compare the taxes used with the changes in impact revenues.
a) Property taxes will build slowly and level off upon com-
pletion of facility and stabilization of population.
b) Sales taxes on construction materials will cause an early,
sharp revenue rise followed by a decrease.
c) Sales taxes on operating inputs will parallel increases in
production and then level off.
4. See if there is a boom effect on expenditures. If there is a
sharp population increase followed by a population decrease,
the expenditure forecast after the population decline may be
a little low. This is because unless population has decreased
in the past, the estimating coefficient for the effect of
c
population on expenditures ("Y will be based on increases
rather than decreases and decreases are likely to be less
than increases.

ISSUES IN ENERGY FACILITY IMPACT FORECASTING

Prepared by
Dr. Robert L. Bish and Candis L. Brown

Prepared for the
Office of Coastal Zone Management, NOAA
Contract No. 7-35174

June 1977

CONTENTS

Page

Introduction 1

1I. The Resident-Commuter Split 2

111. Employment Multiplier 5

Geographic Size of the Area 5

Size of the Facility Work Force 5

Diversity in the Local Economic Activity 6

Current Growth 6

Forward and Backward Linkages to Industry 6

Payroll Leakage 7

Underemployment 7

Excess Business Capacity 8

Unfilled Vacated Jobs 8

Increased Participation in the Labor Force 9

Unemployment 9

Relation to Cross-Sectional Data Based Models 10

IV. Fiscal Impacts 11

Table 1 14

V. Abstracted Bibliographies 16

Copies of Studies of Economic Impact Issues

I. INTRODUCTION

This manual provides information to increase understanding
of energy facility economic impacts. It is based on studies of
rural industrialization throughout the U. S. and energy-impacted
communities in the Rocky Mountain and Northern Great Plains states.
Some concepts and assumptions implicit in the forecasting pro-
cedures and specific findings of empirical data are also explained.
This is necessary because many of the issues are not well known
or understood. Questioning of procedures or use of data may be
expected from local government officials, due mostly to not under-
standing or misunderstanding the factors involved. In addition,
there are potentially important factors which are not used in
forecasting procedures due to limitations of available data or
methodological difficulties. These factors may change certain
expected forecasts and are noted in their discussion. In communi-
cation with the applying local governments, these factors can be
discussed to provide information for adjustment of the forecasts
for that community.

The narrative is divided into three sections, treating issues
of (1) the community/resident split and population impact on small
local areas, (2) employment multipliers, and (3) fiscal impacts.
The first two sections are directly related to the most important
issues in the forecasting model. The third section is also re-
levant,'but includes description of less related fiscal impact
experience to illustrate misconceptions that may exist among local
officials regarding fiscal benefits from industrialization.

Other parts of the manual include an annotated bibliography,
and copies of important studies of economic impact issues.

II. THE RESIDENT-COMMUTER SPLIT

The commuting radius of employees to an energy facility is
likely to exceed the boundaries of small local governments. Thus,
an allocation of increased employment must be made among local
areas. While this problem is critical for forecasting impact on
local governments and is not a problem for large area forecasting
models, this issue has not received significant attention as a
forecasting problem. Thus, the technique utilized, while the best
available, must be viewed with caution until more evidence on this
problem is obtained.

The allocation of employment to geographic areas around a
facility is based on the gravity concept. This concept holds that
the interaction between two points or places is a function of
population and distance. It is directly proportional to its
population and inversely proportional to the distance between the
two places. For our purposes the interaction is commuting to
work. The object of the formula is to forecast the distribution
of direct employment to the local governments surrounding the
facility site. The gravity concept applied to commuting means that
the facility attracts employees from surrounding areas in direct
proportion to the population of a particular local government.
The larger the population, the greater the number of employees who
will live there. And, the facility attracts commuters from sur-
rounding areas inversely related to the distance between the
facility and the area. The farther the local government from the
facility site, the fewer the number of employees who will live
there.

The distribution forecasting formula used in the CEIP Impact
Model is the result of several case studies' findings and the
analyses of twelve different specifications (5, p. 125). These
studies looked at commuting in nonmetropolitan areas. Conditions
were similar to the expected conditions of energy impacted in
coastal areas. Distance could be used as a substitute for travel
time. The evidence from these studies suggests that in rural areas
there is a propensity to remain in established residences and a
willingness to commute long distances to work. Rural and small
town residents commute long distances with the opportunity to work
in an industrial plant.
Commuting patterns in one small nonmetropolitan area were
studied with data collected from a total of 1,645 employees from
two firms. The two patterns were compared and their characteristics
analyzed. There was a major difference between the two employee

3
groups' commuting patterns. The average one-way commuting
distance of the fiber plant employment was 17.5 miles. This
compares with the shirt factory's much smaller average one-way
distance of 6.7 miles. The median distances were 13 miles for
the fiber plant and 4 miles for the shirt factory employees.
Approximately 54 percent of fiber plant workers lived within
15 miles of the plant. Of the shirt factory workers, 80 per-
cent lived within 15 miles of the factory. The state average
for workers living within 15 miles of their work place is 77
percent. Thus, the fiber plant work force is drawn from dis-
tances farther than are most workers in the state; the shirt
factory draws most of its work force in a smaller radius than
both the fiber plant and the state average4 Slightly more than
15 percent of the fiber workers travel 35 miles or more to work,
while less than 1 percent of the shirt factory employees commute
that far. In fact, 7 percent of the fiber plant workers com-
muted over 60 miles to the factory. The comparison of the two
plants' commuting patterns shows that there is a significant
difference in the distances traveled to work for the two groups.
The labor-shed, arbitrarily defined in this case to include the
closest 90 percent of the two factories' labor force, is nearly
twice as extensive for the fiber plant as for the shirt factory:
38 miles and 20 miles.

Wage differences are the primary factor explaining the
significant difference between the two commuting patterns. The
wages paid by the fiber plant were substantially above those in
the surrounding area and the state. The shirt factory wages were
below both area and state wages. Previous studies have left
researchers in dispute over the relationship of wages to distances
commuted. However, the comparison of the lower and the higher
wage groups within the same community suggested that wages have a
significant impact on commuting. But it was evident that only
when wages were compared with those in the immediate area did
they affect the willingness to commute long distances. Several
other studies were conducted in similar economically depressed,
small nonmetropolitan areas. A comparison of the existing area
opportunities and median one-way distances commuted in these
studies with those of the fiber and shirt factory shows that wages
do have a significant influence on the willingness to commute
longer distances, particularly in the "lower wage" environments.
In this respect energy facilities will have "high wages" and, thus,
draw on a very large labor market area. Thus, commuting distances
forecast in the model for an energy facility may be longer than
existing employment commuting, but this result is warranted by
previous studies.

A second important study finding is the tendency, over time,
to move closer to the place of employment. Nearly 4 of the fiber
plant workers and 1/6 of the shirt factory workers had moved
closer to their place of employment since they began work there.

4
And other employees who had not-relocated indicated future
intentions to do so. The median distance commuted since the
opening of the fiber plant dropped from 28.8 miles to 13 miles.
The shirt factory shift was less, mostly because it was located
inside the town (the fiber plant was 7.miles outside the town).
The existing road networks were also found to influence the
commuting pattern. Each of the 1,645 employees of the fiber and
shirt factories plotted their residences on a map provided. The
effect of road networks is evident from the residential locations.
They extend farthest out along main or radial roads.
In addition to case study findings, the results of 12
different exponents of distance were tested to obtain the best
prediction. Both time and mileage were used as measures of
distance, but one was found to be as good a measure of distance
as the other. Mileage, however, serves forecasting purposes
better because it is more easily determined. While the models
fit very well, each understated or overstated the actual numbers
contained in the various zones by some amount. In an attempt to
account for the deviations between the model and actual distribu-
tiont several other factors were tested.
Per capita income, population density, and intensity of
agricultural employment of the local area were found to have an
effect on the commuting patterns of the fiber workers. These
three variables explained a major portion of the deviation from
the expected distribution. Those districts generating more com-
muters than expected were low (population) density areas, had
lower per capita incomes, and a high percentage of the labor force
was employed in the agricultural sector. These three factors,
which are not accounted for in the forecasting formula, may
indicate an adjustment from the forecasted distribution.
To summarize, the forecasting model should give good results
if the energy facility has higher than area-average wages and
travel time and distance are approximately equal in different
commuting directions. The over forecasts, however, should be
discussed with local officials to discovt!r any conditions unique
to a particular community.

III. EMPLOYMENT MULTIPLIER
Industrial development in rural areas A often expected to
result in many new jobs and to stimulate the local economy. How-
ever, the evidence indicates@ that the secondary employment affects
from development are relatively small. The range of reported
multipliers for small areas is 1.00 to 1.71, the majority of which
are less than 1.2. These figures are lower than those generated
by regional impact models. These multipliers range above 2.0.
State or regional models and models based on cross-sectional data
consistently predict much more secondary employment than is evi-
denced from case studies of small areas impacted by industrial
development.

The following sections discuss the principles that signifi-
cantly @ffect the mutliplier. Several factors with a less signif-
icant effect are also discussed to give a more complete description
of the multiplier effect.

GEOGRAPHIC SIZE OF THE AREA

Since secondary jobs tend to locate around already existing
business activity, smaller areas with fewer existing jobs will
have fewer additional jobs and a smaller multiplier effect. Very
small areas have small multipliers. However, smallness is not
important after a county-sized area is included. The effect of
geographic size, beyond that of a single county, on the size of
multipliers was the subject of two case studies. Expecting to
find size an important factor, one study extended its considera-
tion of one-county area to a four-county area, and the other study
extended its boundaries to an eight-county area. They assumed
that extension of the geographic boundaries would increase the
degree to which secondary employment effects would be internalized.
But the impact was not significantly larger due to the size change
at the county-area scale.

SIZE OF THE FACILITY WORK FORCE

The size of the facility work force is a factor associated
with the indirect and induced employment growth. The size, how-
ever, is not directly related to the size of the multiplier. For
example, Box Elder County, Utah, with its rocket fuel and missile
fuel development, had a total direct employment of 5,688. This
is large when compared with other industrial plants. This figure
is also high for most energy facilities in the beginning of oper-
ation. The multiplier was low -- 1.34. In contrast, Braxton County,
West Virginia had 77 employed in the particle board plant, and a
multiplier of 1.50.

The following sections discuss industry and local economic
conditions, which have a more significant impact on the size of
the multiplier than do the size of the industry work force and
the geographic size of the area. Diversity of local economic
activity, forward and backward linkages of the industry, payroll
leakage, underemployment, excess business capacity, and the num-
ber of unfilled vacant jobs, all have significant impact on the
size of the multiplier.

DIVERSITY IN THE LOCAL ECONOMIC ACTIVITY

Diversity in local business activity has a significant im-
pact on the number of new jobs generated by the facility. There
are several ways in which diversity is important. First, there
is an affect of the size of the existing commercial and business
sectors on the amount of trade carried on within the local market.
Communities with only a few or no commercial and industrial estab-
lishments are more dependent on imports, and do not seem to gain
many indirect or induced jobs through increased business activity
generated by new industry. This is a major reason why small areas
have smaller multipliers. They do not have the existing commer-
cial and business capacity to promote higher growth of secondary
employment..

The second aspect of diversity important to the number of
indirect jobs generated by new industry is the size of the communi-
ty's existing manufacturing sector. There is empirical evidence
that industrialized areas with manufacturing activity have higher
multipliers.

CURRENT GROWTH

When areas contain both a large manufacturing sector and a
high growth rate, multipil-ers tend to be high. Studies of impacts
in county areas with these characteristics indicate multipliers
of 1.65 and 1.68 -- close to the top range of multipliers identi-
fied in several hundred studies.

FORWARD AND BACKWARD LINKAGES TO INDUSTRY

Nonmetropolitan communities are also limited to small multi-
pliers by linkage to external markets. Backward linkages are the
suppliers of inputs to production. Forward linkages are the con-
nections with external markets for the manufactured product.
Industries which depend upon local business to supply the raw
materials and services for production, and whose product is consumed
on the local market, produce more of an employment impact in those
businesses than if the industry were linked to external markets.
From the increased economic activity employment is induced in those
sectors which do not directly service the industry, in addition to
those which do. An example of a small area with a high multipli-
er is Braxton County, West Virginia. Braxton was able to supply
n6arly all timber and coal to the particle board plant located
there. As a result of the internally supplied raw materials,
Braxton had a high multiplier of 1.50. Box Elder County, Utah,

7
in the other extreme, was little more than a labor supply for the
rocket fuel and missile fuel industries. Nearly all the raw
materials were "imported" into Box Elder, and the product was
distributed to external markets. The secondary employment growth
was moderately small -- 1.34. The researchers who studied Box
Elder attributed the small multiplier effect to the lack of inter-
action between industry and local businesses. Energy facilities
are characteristic of the latter kind of linkage. As with the
rocket and missile fuel industry in Box Elder, secondary employ-
ment growth in the local business and service sectors is expected
to be small because raw materials are imported and products
exported.

PAYROLL LEAKAGE

Payroll leakage refers to the facility wages and salaries
paid to nonresidents. These employees commute to work and tend
to spend their income in their place of residence. For some areas
this does not present a serious problem, since the direct employed
are community residents and the number of commuters are small.
In these cases there is little of the facility income "leaked"
out of the local area. But there are communities where a sub-
stantial number of the facility employees are not local community
residents. Studies of these cases have found substantial leakage
evidenced by low multipliers. One study reported 30.8% of the
nonresident employees spent about 40% of the factory income out-
side of the community. In this instance, the purchasing power
added through industrial employment leaked out and did not con-
tribute to the creation of new jobs. The lack of respending had
a restricting `41-ffect on the number of jobs generated by the new
factory. In the case of an energy facility, the multiplier is
expected to be lower during the construction phase, due to the
higher number of commuting construction workers. Commuting is
also expected to be significant in the case of the energy facility
operation phase due to the lack of available labor in the community
with the skills required for the job. This labor must be "imported"
to the facility location. The problem presented by the lack of
local labor with the necessary skills is an important component
of the total number of unemployed who will be hired for indirect
employment, and will be discussed further in the section explaining
the unemployment issue.

UNDEREMPLOYMENT

The amount of existing underemployment is an important factor
of growth in indirect employment. To the extent that local
businesses can handle increased business without hiring additional
employees or increasing the capital stock, there will be no sig-
nificant increase in secondary employment. This is easy enough
to understand. The problem lies in the measurement of underemploy-
ment. Underemployed include those working less than full-time

hours and those employed in jobs for which they are over qualified
based on previous experience, skill, and education. There is no
systematic method for detecting the amount of underemployment.
What little is known about the extent of underemployment was
collected through surveys conducted in studies of particular local
areas. No methods of identifying or measuring the underemploy-
ment have resulted from the studies. The best estimates of the
extent of underemployment in a community are obtained from local
businessmen or business associations. Some communities will have
a better idea of the existing conditions than others. But an
estimate for this factor is important, since this has a signifi-
cant effect on the number of jobs which will be generated by the
new facility.

EXCESS BUSINESS CAPACITY

In addition to the problem of detecting and quantifying the
existing underemployment, there is an additional effect on second-
ary employment growth of excess business capacity. Excess capacity
will absorb economic business activity and decrease the number of
jobs generated by direct employment. This effect was noted in
one case study of new industry in five small communities. The
multipliers ranged from 1.00 to 1.18, and the excess capacity in
capital stock of the supporting goods and services was cited.
There was particular excess capacity in the construction industry,
where there was little induced and indirect employment growth.
Historical data for the community are helpful in determining the
communities likely to have excess capacity in business and com-
mercial sectors. These are communities which have experienced
economic and population declines in the past 10 years or so.
The variability of this factor is why direct impact estimates by
local businessmen are used in the CEIP Forecasting Model.

UNFILLED VACATED JOBS

Another factor which contributes to the low multipliers found
in small communities is that jobs vacated by employees taking jobs
with the new facility often are left unfilled. Empirical data
show a substantial amount of unfilled vacancies, particularly when
the vacant jobs are paid a lower wage or salary than jobs with the
new direct and induced activities. In a study of employment pat-
terns, employers were interviewed and asked the previous employ-
ment status of their employees. The study reported most employers
answered that there was considerable hiring of workers from other
industries. Figures as high as 19.3 percent of the vacated jobs are
reported unfilled. This is one factor that few models take into
account in their calculations of the multiplier effect of new
industry. It is important to recognize that not replacing employ-
ees who go to work for the energy facility can have a substantial
role in reducing the size of the multiplier. This factor is
probably not recognized by local officials as a contributor to a

lower secondary employment effect. But this information, like
the underemployment and excess capacity data, is not systemati-
cally collected. It is another reason, however, for the use of
small multipliers in the CEIP Model.

INCREASED PARTICIPATION IN THE LABOR FORCE

Increased participation in the labor force is even more dif-
ficult to adjust for than is unemployment. Very few studies have
measured the potential labor force in an area, nor have specific
variables associated with increased participation been identified.
The result is that there is no specific data available to deter-
mine for a given area who will enter the labor force and under
what conditions. However, the studies do suggest explanations
for the increased participation in those areas experiencing in-
creases. The most evident explanation is the opening of job
opportunities on the local market. Empirical results point to
increased participation as new opportunities are made available.
Participation rates seem to be more a function of the demand for
workers and wages than of the number of existing and potential
labor force. While this observation is helpful in developing a
theoretical understanding of labor force participation, it does
not provide a method for determining the number of those expected
to enter the labor force. The national employment/population ratio
has been a basis to compare the amount of labor force participa-
tion on the local level. The rational here is that the national
ratio is an average or expected participation rate, and divergence
from this rate indicates the amount of additional participation
which can be expected with an increase of employment opportuni-
ties. The studies of labor force participation report marked in-
crease in participation in the communities, with pre-industry
rates much lower than the national ratio. The lower the labor
force ratio compared with the national average, the greater the
probability potential members will become active. One study of
industry employees found the proportion of new industry employees
not previously in the labor force was substantial -- 25%-34%.
The increase of local participation in the labor force is most
likely in areas of economic and population decline. This is an
indication that there is potential, although local business and
civic leaders who know their community are the best sources for
the estimates. As with the other factors of economic and employ-
ment growth, which is not well documented, the national-local
labor force participation adjustment is not perfect but it is
feasible to use with the information available.

UNEMPLOYMENT

Predicting the distribution of the secondary jobs between
local and new residents includes an assumption that 30% of the
unemployed are hired in direct or secondary jobs. Previously, it
was assumed that new industry locating in a declining area would
hire many of the unemployed; substantially raising economic con-
ditions in the local area. But the results of studies of rural
industrialization have not supported this belief. New industry

does not significantly reduce the number of unemployed. And in
some instances, unemployment increases. One of the main reasons
for this is the hiring practices of employers. other applicants
are preferred to.the unemployed, who are viewed as a risk. Immi-
grants, commuters, returnees to the area, and those already em-
ployed who quit to take a job with the new industry,- are hired
before the unemployed are. The higher educational levels and
skills attained of the incoming and already employed people are
the reasons cited for the preference. In most cases studied, the
unemployment rate decreased, but only by about 2 percentage
points. The number of direct jobs filled by previously unem-
ployed persons was small. The range was 1.0 percent to 43 per-
cent, and only in three instances was the proportion above 14
percent. The only studies which concluded the unemployment rates
fell substantially (more than 2 percent) were those of EDA pro-
grams, which provided manpower training, direct financial support,
and employment-related requirements by industry for program
funding. A second reason for such a small decrease in the unem-
ployment rate when new industry locates in a community is the
mismatch of skills between industry demand and readily available
labor in the area. Case studies have reported that the higher
@7age, higher skill industries draw more of their employees from
immigrants and commuters and less from the unemployed, than do the
lower wage, lower skill industries. Since both the construction
and operation of the energy facility require particular skills,
the conditions for mismatch are expected in hosting communities.
Based on the evidence supporting these expectations, .3 of the
unemployed indirect labor force, are expected to join the direct
and indirect labor force. If local officials believe the unem-
ployed in their community are comprised of higher skilled and
educated people required for direct and induced employment,
additional adjustment may be advisable.

RELATION TO CROSS-SECTIONAL DATA BASED MODELS
The CEIP Model uses the small multipliers actually identi-
fied in case studies of economic impacts on small communities.
Two major sources of the difference with higher multipliers
estimated from cross-sectional data are 1) the lack of employers
refilling jobs vacated by employees who are hired by the energy
facility; and 2) the smallness of the areas impacted. We
believe that to use multipliers based on cross-sectional data or
multipliers based on large areas will grossly overestimate im-
pacts of energy facilities on local communities. This is likely
to be a major point of difference between the CEIP and alterna-
tive models. From all evidence from actual impact studies, the
CEIP Model assumptions are supported E-y the evidence whi
exists at the current time.

IV. FISCAL IMPACTS

Nearly all growth in public revenues depends on growth in
the private sector. Studies of fiscal impacts on local govern-
ments show that whatever the gains made in the public sector,
they were small in comparison with those achieved in the private
sector. Furthermore, if the benefits of industrialization were
better channeled, they could have made a more significant impact
on local government fiscal well-being. Most studies of rural
industrialization find the costs to local governments higher than
necessary. This is because financial inducements to industrial
locations are not fully recovered. These inducements may be one-
time costs or they may be in the form of services provided to
industry at less than cost.

Locational costs include advertising expenses, tax holidays,
low interest financing, 'Land acquisition, and site preparation.
If the local government purchases the land, there is the loss of
previous revenue since government property is not taxed. Tax
holidays, which relieve industry of paying any or all taxes lasting
as long as 20 years, have been cited by industrialization studies.
And it is common practice to tax industry at a lower rate, inducing
industry to locate in the area. Site preparation includes exten-
sion and improvements of access roads, utility connections, land-
scape modification, and construction of buildings. -
Service provision has been another high cost to local govern-
ments. In providing public services like police and fire protec-
tion, water and sewerage, electrical and/or gas, and access road
maintenance, payments do not always equal the costs of providing
them. In some cases, the local government has funded and built
utility or sewerage treatment facilities for the industry. Environ-
mental damage has also required public expenditures. Case studies
have found that runoff from development has caused serious problems
with water systems. Capital expenditures for new or expanded
storm sewerage systems were necessary. All of these subsidies are
actually costs to the community. In the past it was believed that
these*costs would be recovered indirectly through the increased
business and personal incomes generated by the additional economic
activity but empirical evidence disputes this. In some instances
the costs are recovered over time, but more often they are not.
Industry's indirect effect on the public sector is through
@opulation growth and change. The first effect is the increase
in personal income in the local area. Increases in personal income
make their way into the public financial sector through two avenues.
Property tax revenue is increased. The extra earnings are put into
upgrading the standard of living either through home (property)
improvement or through a new home purchase. Secondly, there is an
increase in retail sales tax revenue or business taxes for local
governments using these tax sources. Increased income generates
more retail sales or businessf which is accompanied by an increase
in tax revenue from those sales. But emDirical evidence shows that

increases in public revenues resulting from increased income
is often not as significant as the income growth itself.
While public revenues increase less than private incomes,
studies consistently report increases in local tax revenue. The
major increases are in retail sales tax revenues, intergovern-
mental transfer payments, and property tax revenues. For example,
the property tax has been observed in many studies as being
especially unresponsive to economic growth in the private sector.
This presents a serious problem in many local governments. They
lack the operating and the "front-end" capital for expansion of
facilities which are warranted by residential growth. There are
two reasons evident for the lack of growth of local property tax
revenues, particularly residential property tax revenues. One
is the conditions determining construction and development of
residential property. The other is the "lag" associated with
property tax assessment and collection. Residential property
tax growth is dependent upon several factors in the housing market.
The distance to other housing markets influences residential
construction and development. Neighboring communities "compete"
to provide housing for employees new to the area. Potential
residents are lost to nearby housing markets. A second factor is
the availability of existing housing. Those who can find vacant
housing will have no need to construct homes. Thus, the amount
of vacant housing and nearby housing markets consequently minimize
growth of property tax revenues.

Another factor which affects residential property revenues
is the amount of commuting to work. The more people'who commute
into the area, specifically for direct (facility) employment,
the fewer the number of new residents. Although there is a tendency
to move closer to the place of employment over time, the increases
in property tax revenues from residential development are potential,
at best. As studies of energy-impacted communities in the western
states have noted, assessed valuation in residential properties
rose very little in response to the economic development. There
was little increase of those revenues in inflated dollars and none
at all in terms of real dollars.

There are two "lags" associated with property tax assessment
and collection. Local governments may be affected by one of these
or both. The effect is called a lag because of the time that
elapses between the value increase of the property and tax receipts
accruing to the government. The first lag occurs with property
assessments. Property is assessed periodically at a specified time
period. If Cesidential building construction or other property
development is completed after the assessment date, property will
not be assessed until the next year. The second lag occurs between
the assessment and collection of the tax. Often the tax "bill" is
not collected during the same fiscal year the assessment is made.
Over the years some states have changed fiscal years-, while assess-
ment and collection dates remain the same. These lags do not

actually diminish revenues. Rather they limit the available
revenues during the first years of construction and operation
of a new facility, precisely when new government expenditures
may be needed to service the facility and its expected popula-
tion. In Table 1, a summary of revenue sources and their
implications for revenue growth in response to energy develop-
ment is presented. While The Tax Lead Time Study (6, Sec. 3)
was prepared for the state of Colorado and is specific to certain
rates and taxable goods and services, it still offers basic infor-
mation on the responsiveness to private sector growth of various
taxes.

In many case studies it has been discovered that additional
revenues are often not sufficient to cover increased demands for
basic services. First, with the increased incomes generated by
industrial development, historical empirical data show an in-
crease in the quantity and quality of demand for public services.
Second, an increase is evident due to population growth, often
requiring capital outlay. This has been especially true of
utilities such as water and sewerage treatment and schools.
Existing capacities are overloaded by new population, so new or
extended facilities are necessary. Usually the increase in user
charge revenues for utilities and property taxes and state aid
for schools does not cover the capital costs. This puts a burden
on the finances of the government, particularly on capital expan-
sion which is necessary to provide services to temporary residents.
As is the case with most energy development, there is an employ-
ment and population decline after construction. This often leaves
the permanent residents bearing the financial burden of the ex-
tended, and now underutilized, service capacity.

Predicting the response of a local government to population
growth is extremely difficult. In the CEIP Model the historical
increase in expenditures added by each new person is estimated,
and this estimate is then used to forecast the increase in expendi-
tures associated with new population. This technique is better
than simply multiplying average per capita expenditures by the
expected population increase -- but it is still a very rough
estimating procedure for large population changes.

Increases in revenues are forecast in a similar manner. The
historical increase in revenues associated with new population is
estimated and used for forecasting, while taking into account pro-
perty tax revenue lags. In addition, each taxable element of the
energy facility itself is forecast and revenues calculated. In
general, as much importance should be given to the forecast
differences between revenues and expenditures in the CEIP Model
as to ttheir absolute levels. It must also be remembered that
the revenue and expenditure forecasts depend upon previously
estimated population changes, which in turn depend on multiplier
and residential-commuter employee split estimates. At each step

EEL--=- 0011-

TABLE I

Revenue Alternatives For Colorado Local Governments

GENERAL SALES SHECTIVE USE TAX AD VALOREM GENERAL SPECIFIC USER FEES SEVERANCE iOCAL HAL ESTATE SITE VALUE LAND VALUE
TAX SALES TAX PROPERTY TAX OCCUPATIOt4 LAX OCt:UPATION TAX TAX INCOME TAX TRANSFER tAX TAX INCREMENT TAX

. ... . .. ..... .... ...
DESCRIPTION -.- .1 1- .. ..
..... .. . .. .... . . p....

........... ..
-.1. yp.. f .11 1-1- 1. VI.-
... . .. . .....

l-- I... c-,
YIELD .. .. ...
p_:,
.;,b
.I Y-d bl. I.--
.. ......... .... ....
it, I, ..
. ..... ......
It Z .. I. ..... ...
I.. .. ..... 2-1/2 1 I'D= bbl/d@y -,--
I. .. .... .....

LEGALITY .. . ... ... . . .... ......
.-l ... ....... .
-d - , -.1 -d - :4 ...... 1 .1 1-1 .It- 1*
'Y .. . ... ... .
(-I... It- ... .. ... . ... .. . . ... -11- 'W-12 '1::l b@
1, -1. d-

ELASTICITY L
n
b;'
I .... I b, .11 1,

INCIDENCE
-.1 1,. 1. -11, 1,: bft@ d ... 1,,
I'T "P1.1111.1 1. -id- d .... 11-- -y P- L'

A It., I. b .... d

CONTROL ......
d
ol
1.1-Im d:cl ..... .III ............ -

MARKET SIDE L!7 ...... ...... -d t11-1-11 -1.1 .1d.-
d-.-., f ... d I@ C-1.4.. ...... ..
EFFECTS .... .. ......
4. 1. 1,
-h 1.1.14 1.
-.1 ... I. ---
.-I
. . . ... .... ...

CERTAINTY/ ......
p-c'
PREDICTABILITY 2"U' !bl*@1;'.r-I%- I

ADMINISTRATIVE
l.. ........ "
COST . .......... b I..- I I I I
:1 dt. 'I 1'@
4

.p
Z.-
2:
'41" -.:
CITIIEN ....... .. . . t.. . ... l-I c-11-1
1 1. - - Illy
ACCEPTANCE
,pp..", n
It .. .....
. . .. ...... .

the CEIP Model utilizes existing evidence, but it remains a
relatively simple model of a very complex process. It should
be treated as a useful guide and is probably as good as any
existing alternative models, but weaknesses in the state of
the art for forecasting industrial and fiscal impacts on local
governments must be recognized.

V. ABSTRACTED BIBLIOGRAPHIES OF
PRINCIPAL INFORMATION SOURCES

Advisory Commission on Intergovernmental Relations. SIGNIFICANT
FEATURES OF FISCAL FEDERALISM 1976-1977, Vol. II. Washington,
D. C., March 1977.

This report provides detailed information on the federal-
state-local revenue and debt structures. The material includes
major state and local tax rates and bases; major revenue producers
of federal, state, and local finances; federal aid to state and
local governments; state aid to local governments; and state and
local government debt. This volume is intended to provide the data
necessary for a comparison among states of alternative policies in
the area of revenue and debt.

Braschler, Curtis, and John Kuehn. "Estimation of Employment
Multipliers for Planning in Ozark Nonmetropolitan Counties."
SOUTHERN JOURNAL OF AGRICULTURAL ECONOMICS, July 1976, pp.
.187-192.

In determining employment multipliers for small areas, this
study differs from previous approaches in two respects. The
first difference is the grouping of counties by population.
Statistical tests indicated estimation was improved by grouping
based on population. This recognizes the importance of size in
determining an area's secondary employment growth. Secondly,
the regression analysis equation separated basic employment into
sectors giving separate multipliers for each of the sectors.
This recognizes the differences among basic sector impacts.
Different industries produce different effects. The reported
tables support findings of this and other studies which report
significantly lower employment multipliers for nonmetropolitan
areas than are projected by regional impact models. This article
also notes multipliers should be adjusted to individual areas
for more accurate community-specific estimates.

Garrison, Charles B. "New Industry in small Towns: The Impact on
Local Government" National Tax Journal, 24, no. 4: 493-500.

This article reports the results of a study which analyzed the
net impact of new industry on the local economy and government of
five small towns in Kentucky.Regarding the public sector, the school
districts received individual analysis. This was the only component
of the public side which experienced a growth-related negative impact.
And in only one town was the impact significant. Garison used two
methods of assessing the impact on the local government. Some dis-
agreement exists as to how the one-time or "transitory" costs (and
revenues, if any) to the local government should be accounted for.
These are the costs associated with plant location. The first cal-
culation includes all costs and revenues. The second or alternate
calculation in effect eliminates those one-time costs. Comparison
with five control communities shows industry did affect significantly,
the local economies, and in a positive way.

Gilmore, John S. and Mary K. Duff, Boom Town Growth Management:
A case study of Rock Springs - Green River, Wyoming, Westview
Press, Inc., Boulder, Colorado, 1975.

This book is the result of a case study of two communities
experiencing "boom" or rapid growth. Gilmore and Duff found the
economic and social framework within the communities were seriously
affected. The housing market, public service provision, and stability
of local labor supply were strained by the rapid growth, and unable to
respond adequately to accomodate the increased demands. It was evident
to the researcher that the traditional processes regulating economic
growth were not operating well. The serious issue raised by the
experiences with boom town growth is that of growth management.
Following discussion of the "boom" phenomenon, growth management
principals were presented. Identification of tools and methods of
implementing objectives were included in that section.

Lonsdale, Richard E. "Two Commuting Patterns in North Carolina"
Economi c Geography, 42, no. 2: 114-138.

This article reports the findings of a study which compared
and analyzed two commuting patterns of two manufacturing plants
within the same community. Differences between the two patterns
were observed and variables were introduced to explain the differ-
ences. Following the analysis and comparison of the two patterns,
probability models based on gravity concepts were constructed using
population and distance as the variables. Seven models using distance
measures and five models using time measures tested the two variables'
predictive power. The estimates of the models were compared with the
actual distribution obtained in the fiber plant commuting pattern.

Lemont, William, George Beardsley, Andy Briscoe, John Carver,
Dan Harrington, John Lansdowne and James Murray, Tax Lead Time
Study, Colorado Geological Survey, State of Colorado Department
o
T-1
TaNtural Resources, Denver, Colorado, 1974.

This study presents the revenue sources available to the
State of Colorado and its local governments. Included in the
study is a discussion of the revenue alternatives and of techniques
to deal with problems caused by rapid population growth. This study
was prepared for the Regional Development and Land Use Planning Sub-
Committee of the Governor's Committee in Oil Shale Enviromental Pro-
blems to provide recommendations for new legislation to improve the
financing operations available to local governments. As stated in
the preface, the intended users of this study are the local govern-
ment officials, their staffs, citizens of the oil shale area, and
the Colorado legislature.

Summers, Gene F. and Jean M. Lang "Bringing Jobs to People:
Does It Pay?" Small Town, 7, no. 3: 4-11

This article provides a concise summary of important issues
determining net impacts to the local private and public sectors.
The summary of the issues presented here was taken from their book,
"Industrial Invasion in Nonmetropolitan America". Direct employment
hiring practices, employment multipliers, income effects, and popu-
lation growth are discussed as they contribute to net impacts. The
information and conclusions reported in the sections represent the work
of over 100 case studies in 245 locations and 34 states. The conclusion
is that the structure of the community, actions of the local public
officials, and the character of the industry determine what impact
industry will have on a community. Employment, population growth,
and economic prosperity are not automatic and predictable gains to
the host community.

Summers, Gene F.j Sharon D. Evans, Frank Clemente, G. M. Beck
and Jon Minkoff, Industrial Invasion of Nonmetropolitan America:
A Quarter Century of Experience, Praeger Publishers Inc., New York,
N. Y., 1976.

This book is the summary of 25 years of studies of specific
plant locations in nonmetropolitan areas in the U. S. The purpose
of this work was to determine the effects of industrialization on
small towns. Several basic issues were addressed. One is the
validity of procedures used with regard to nonmetropolitan industrial
development as a tool for promoting the general welfare. Costs and
benefits to the public and private sectors provided the framework
with which to determine net impacts. The fact that this book presents
local community experiences from the local perspective sets this study
apart from previous studies, most of which analyzed impacts on the
nonlocal private sector.

NEW INDUSTRY IN SMALL TOWNS: THE IMPACT
ON LOCAL GOVERNMENT

CHARLES B. GARRISON*

ABSTRACT approach is used; the communities studied
The establishment of new manufactur- are live small towns in Kentucky in which
ing plants in five rural towns in Kentucky new manufacturing plants located during
typically resulted in a negative direct im- the period 1958-63.

pact in local government finances. This The local government impact is con-
impact was usually small, however, since sidered as two distinct effects-the pri-
it of the new plants added few new mary and the secondary. The primary ef-
residents to the community and there was fect involves, on the one land, the addi-
therefore very little increased demand for tional direct tax revenues derived from
local government services. The school sys- the new plant and, of the other, expen-
tem was the unit of government most ditures or changes in services by local
likely to be significantly affected by a large government for the express benefit of, or
negative impact resulted if property taxes directly attributable to, the few plant. The
were substantially avoided and large num- primary effect is summarized by the quan-
bers of new residents were brought to the tity "net primary benefits to local govern-
community. The negative impacts tended ment." This quantity may be either positive
to become positive after a few years. or negative, and is given by the excess
(deficiency) of the new firms revenue
NEW industry in rural areas is gaining effect over the expenditures effect. The
increased acceptance as a solution for secondary effect involves the impact of the

many of the nation's social and economic plant's nontax expenditures on local gov-
ills. Persons concerned with alleviating ernment revenue, expenditures, and ser-
rural economic stagnation and poverty see vices.
The benefits of new Industry to the
the dispersion of manufacturing plants and local private economy also include a prim-
jobs to tile countryside a perhaps their
best hope of making rural communities ary effect, i.e., the employment and pay-
economically viable again. Those con- roll of the plant itself, and a secondary
cerned with problems of the major cities effect, i.e., the impact on thc local con-
see rural development as a way of reduc- sumption (or "not basic) sector of the
ing population pressures in urban areas. community's economy.
In addition, new industry is thought by An attempt was made to ensure that
many to be a solution, the problems no major economic developments other
of rural local governments. New industry than location of new manufacturing plants
occurred the study towns. Accordingly
it is hoped, will produce new tax revenue. the criteria used in selecting the study
That new industry may also produce new
towns were that the), be located outside
costs for local government may be over-
Standard Metropolitan Statistical Areas,
looked, however. This article reports the
results of an effort to determine the con- that they be small (a 1960 population
ditions under which these additional costs between 1,000 and 5,000), and that at
least one new plant employing at least
may equal or exceed additional revenues.
Further, the costs to local government are 100 people had been established in thee
compared with the benefits accruing to community during 1958-63. In addition,
the local private economy. The case study towns tied to the economics of neighbor-
ing Larger cities were eliminated from
#Assistant Professor of Economics, The Uni- consideration. The five communities
versity of Tennessee. The article reports a por- selected are described in Table 1. The new
tion of findings of a study supported by the plant produced a variety of products, al-
Ecnomic Research Service, U. S. Department though three of the eight manufactured
Agriculture, and the Bureau of Business Re-
apparel of some type.
search, University of Kentucky. 493

NATIONAL TAX JOURNAL Vol. XXIV
494

TABLE 1
DESCRIPTION OF STUDY TOWNS AND NEW MANUFACTURING ACTIVITY
Community Population Distance to Number New Plants
(thousands) Nearest
Larger City Employment Year
(miles) Established
A 2.0 60 2 115 1959
B 4.0 87 1 90 1962
C 2.0 45 1 100 1962
D 4.8 62 3 200 1959
135 1958
E 1.3 91 1 125 1959

U.S. Census of Population, 1960.
A city with a population of at least 50000.

The economics of the five communities and D-3). In these cases the cities issued
were characterized in 1958 by low incomes industrial revenue bonds and with the
and high rates of unemployment or, more proceeds purchased the plant sites and
typically, underemployment in agriculture. constructed the plant buildings. The man-
Per capita incomes in the five study coun- ufacturing firms make monthly rental
ties ranged from 596 (29 per cent of payments to the cities sufficient to cover
the national average). Agriculture principal and interest payments on the
typically was the largest single source of bonds. In two cases "favorable assess-
personal income, accounting for 30 per ments" on real property resulted in mini-
cent or more of total income in four of mal revue (C. and E.). The three plants
the five counties, but both average farm city limits and were not subject to city
size and average value of farm products taxes.
sold per farm were low. Manufacturing The amount and cost of the new public
was a relatively unimportant source of in- services attributes to location of the new
come, in 1985 three of the five counties plants depended in large measure on the
had fewer than 100 manufacturing em- number of new residents brought to the
ployees. community. New residents mean new
school children, and if the previous level
1. Net Primary Benefits to Local Govern- of local support is to be maintained, ad-
ment ditional revenue is required. It is also
The direct effects of the new plants the new residents who force expansion in
on local government revenues and costs fire and police protection and other basic
are given in Table II. Only two of the services, if they are needed. A community
eight new plants producted significant new may also incur a cost in providing services
revenue, i.e. revenue in excess of that direcly to the plant itself, such as provi-
yielded by the property prior to the plant sion of water services or traffic control.
location. In three cases the plant was In six of the eight cases reported here,
owned by the city and was therefore not additional costs to lcoal governemnt ex-
subject to real property taxes (A-2, B, ceeded addtional revenue, i.e. net primary
benefits were negative. The additional costs

No NEW INDUSTRY

TABLE II

ANNUAL NET PRIMARY BENEFITS TO LOCAL GOVERNEMENT OF NEW
MANUFACTURING PLANTS LIVE RURAL COMMUNITIES IN

Community Additional Cost of Additional Net Primary
and Plant Revenue Services Benefits
A-1
$2,505 $2,675 170
(Average,
1960-63)

A-2 372
(1963) 28 3,400
B-1 -24 511 535
(1963)
C-1 54 953 899
(Average,
1960-63)
D-1 187 187
(1963)
D-2 517 1320 1837
(Average,
1960-63)
D-3 530 1,200
(Average,
1962-63)

42 0
E
(Average,
1960-63)
Total $2,805 $10,059
Does not include a cost to the city government of $92,000 assigned entirely to the year 1962.
The expenditure was made from the proceeds of a tax-supported industrial bond issue approved by
city voters in 1957, and represents the donation of the plant site and the construction of an elevated
water tank and sewage disposal plant on the site.
b C, Does not include one-time costs to the city of $1,200, $8,000, and $10,000, respectively, for
the extension of water lines.
Source: Municipal, county, and company records and interviews.

were largely due to the addition of new students. The small number of new resi-
students to the local school systems. How- dents had important implications for local

ever, the typical plant location studied here government: the direct effect on public
resulted in few new residents in the com- expenditures as well, as revenues was typ-

mmunity, and therefore very little increased ically small. Thus, whether net primary
demand for public services, including edu- benefits were negative or positive, they

cation. Indeed, a major location factor in were likely to be small.
each case was the availability of an ade- The impact was by no means uniform

quate local labor force. The largest num- among the several units of local govern-
ber of new residents, and accordingly the ment studied. The units most susceptible
largest cost to local government, occurred to a negative effect were the school dis-
tricts (Table III). To estimate the cost

in Community A, where each of the two
companies brought in 15 managerial and of new students, it was assumed that the

supervisory employees. Company officials cost to the school district of educating an
estimated that each plant accounted for additional student WithOllt reducing the

an increase of 25 students in the local quality of education received by other stu-
school system. The remaining six plant dents is equal to the average local revenue
locations involved the transfer of only 19 per student. It might be argued that, with
new employees and a total of 27 new the exception of Community A, the num-

496 NATIONAL TAX JOURNAL [Vol. XXIV

TABLE III
ANNUAL NET PRIMARY BENEFITS OF NEW MANUFACTURING PLANTS, BY TYPE
OF LOCAL GOVERNMENT UNIT, FIVE RURAL COMMUNITIES IN KENTUCKY

Community School
and Plant County District City

A-1 370 $-1,563 $ 1,025
A-2 9 --2,380 -1,000
B-1 6 -18 -511
C-1 8 -197 -709
D-1 52 135 0
D-2 144 -1,693 0
D-3 148 -818 0
E-1 18 23 1

Total $ 455 $-6,511 $-1,194

One time costs omitted from calculation; see Table II footnotes.
Source: Municipal, county, and company records and interviews.
ber of new students was so small that they senting actual outlays which will recur
could be absorbed without diluting the well into the future. The effect is to elim-
quality of education. Indeed, alternate Mate from consideration one-time costs in-
treatments Could be defended for a num- curred at the time of plant location (in
ber of revenue and expenditure items en- cases B-1, D-1, D-2, and D-3). Further,
tering into the calculation of net fiscal in case C-1 an annual outlay incurred by
impact. These ambiguities in large part the city was completed in 1968." In the

disappear, however, if the calculation is other direction, the cost associated with
made for a later year. Such an "Improved" plant A-2 was i creased in 1965 by a
calculation is shown in Table IV, which further addition of 15 new residents 'and

differs from Table 11 in the following
20 new school children.
The alternate calculation to a consider-
respects:
able extent removes from the analysis the
Tax concessions initially granted by
transitory" cost and revenue effects, i.e.,
local government but later removed result
those associated with the actual plant lo-
in larger revenue effects. Specifically, real cation process or applicable for only a

property assessments were increased sub- limited time period following the plant
stantially for plants C-1, D-1, and E-1.
location. By this calculation modest gains
In the opposite direction, correction of an
accrue to three of the communities and

assessment errot discovered in 1966 re- only in Community A is there a signifi-
cant negative impact. Community A was
duced the revenue yielded by plant A-1.
2. An alternate treatment of the reve- the only study town receiving a sizeable
nue calculation is accorded plant D-2. This

number of new residents; payment of all
firm moved into a building previously oc- taxes by plant A-1 was not sufficient to
curred by a manufacturing concern which
offset the added cost of new students.
had left the community in 1957. When the
new firm acquired the property, the assess-

ment was reduced, apparently reflecting the
11. Benefits to the Local Economy
purchase price. It may reasonably be argued
Small towns which recruit new industry,

that the resulting decline in tax revenue obviously consider the stimulus to the local

should not be attributed to location of
the new firm but to the loss of its prede-
The "one-time" cost of $92,000 incurred by
cessor. Accordingly, Table IV treats the
the city in case B-1 might be considered all
total taxes paid by the new plant as ad- annually recurring cost in the amount of the tax
ditional revenue." required to support the industrial bond issue.
3. The cost of new students, except in At any rate, the bond issue was retired, and the
pporting tax was eliminated, in 1966; ac.
Community A, is considered to be zero.
cordingling, no cost is assigned to the post-1966
The costs which remain are those repre- period.

No. 41
NEW INDUSTRY 497

TABLE IV
ANNUAL NET PRIMARY BENEFITS TO LOCAL G0VERNMENT
OF NEW MANUFACTURING PLANTS,FIVE RURAL COMMUNITIES
IN KENTUCKY: ALTERNATE CALCULATION

Community Additional

Cost of Additional Net Primary
and Plant Revenue Services Benefits

A-1 (1966)
A-2 (1965) $1,655 $2,675 $-1,020
29
5,360
-5,332
B-1 (1967) -24 0 24
C-1 (1968) 875 0 875
D-1 (1964) 906 0

906
D-2 (1964) 1,667 0
0 1,667
D-3 (1964) 530 530
E-1 (1967) 1,285 0 1,285

Total $6,922 $8,015 $--1,115

Source: Municipal, county, and company records and interviews.

private economy as the major benefit to be residents. The estimated multipliers for
derived. It is of interest, then, to provide communtities A through D are, respectively,
an estimate of such benefits in the case 1.46, 1.73, 1.43, 2.02 and 1.26. The in-

studies reported here. Table V gives the terpretation is that, in Community A, an
estimated impact of the new plants on Increment of $100 in new industry wages
I personal income in the five communities paid to local residents led to an increase
(where the unit of study is actually tile of $46 In nonbasic income.

county). The total impact consists of two The secondary impact on employment

components: (1) the direct or primary was relatively smaller than that on income.
effect, which represents the increase in the For the five counties combined, employ-
community's basic income, and (2) the ment of the new plants was 1,517 in 1963,
secondary effect, which represents the in- 1,177 of whom were county residents. But
the associated increase in nonbasis employ
crease in nonbasic income.
The distinction between "basic income" ment was estimated as only 98 jobs. This

and "nonbasic income" derives from the estimate Involved calculating for each
concept of an economic base. The basic county the ratio of the increase in basic
income of a community is earned in those income required to generate one addi-
tional nonbasic job. The example, in Com-
activities which export goods and services
to other areas. Noribasic income, on the munity A an increase of $30,830 in basic

other hand, is earned in the local consump- income during the study period was re-
tion sector of the county's economy. This quired per additional nonbasic job. The
sector is dependent on the responding lo- implication for new industry is that for
cally of basic income. The increase in basic each $30,830 in annual wages paid to
income here attributed to new industry is county residents, one additional job was
measured by the 1963 plant payroll, less created in the county's nonbasic sector.

the earnings of employees who commuted Apparently the small secondary effect on

from other counties the total incease,

income due to new industry is equal to
The multiplier for a county was calculated
the community income multiplier times the
as the ratio of the total increase in annual in-
new industry payroll accruing to count), come to the increase in annual basic income,
with 1958 serving as the base year and 1963,
as the terminal year. Calculation of the multi-
Basic activities in the counties studied here plier thus involved separating the county's per-

in some cases, certain other components such as nents. While subject to serious limitations if
include agriculture, mining, manufacturing, and, sonal income into basic and nonbasic compo-

in some cases, certain other associated with tour-
applied to complex economics, this type of

ism, income earned by county residents commut- analysis appears well suited to small economics

ing to jobs outside the county, and transfer pay- characterized by a minimum of interindustry

ments. relationships.

NATIONAL TAX JOURNAL [Vol. XXIV

TABLE V
ESTIMATED INIPACT OF NEW INDUSTRY ON COUNTY INCOME, 1958-63,
FIVE RURAL COMMUNITIES IN KENTUCKY

Increase in Increase in
Basic Annual Nonbasic Annual Total Impact Ott
unity Income Income Annual Income

(Thousands of dollars)
A 1,007 466 1,473
B 326 238 564
C 663 287 950
1,076 1,098 2,174
E 463 120 583
)tal 3,535 2,209 5,744

urce: AL101017'S estimates.

loyrnent is explained by underutiliza- dQUbtlessly also came from the local labor
of employees in the local consumption force. (Population estimates indicate a de-
)r prior to the location of the new cline of 2.9 per cent for the five counties
its. This sector could then accommodate during the 1958-63 period.) Further, an-
ased sales without a commensurate in- alysis of construction industry data and in-
,c in employment. This explanation is terviews with local businessmen indicated
)orted by the minor role of the con- very little investment in new business or
tion industry in the secondary impact residen6al construction during the study
of 98 new nonbasic jobs); appa rent 1), period. One would thus expect a net sec-
communities' capital stock was also un- ondary effect on local government of no
tilizcd. The lack of a significant effect increase in the demand for local govern-
nonbasic employment is demonstrated ment services and little or no increase
her by the fact that the income multi- in the revenues, since the local revenue
r effect on wage and salary Income was base was dominated by the assessed value
Her than the effect on proprietors' and of ,real and personal property.4 This con-
)Crty income. clusion tends to be supported by an
analysis of local government data cov-
ering the five study communities land a
Secondary Impact on Local Govern- group of five "control" communities which
had similar economic characteristics but did
lie calculation of new inclustry's effect not receive new industry (Table VI). For
local government has considered only all local government units combined, the
direct or primary impact, while it was relative increase in direct general expendi-
ted out that the impact on the local tures from 1957 to 1962 was somewhat
ate economy consisted of both a pri- greater in the study communities than in
and a secondary effect. The possibil- the control group, but the range of in-
hus exists that tl;c multiplier effect on creases within the groups was even larger.
nonbasic sector might result in a signifi- And if the analysis is extended to 1967,
secondarv effect on local government the percentage increase in expenditures was
nues or expenditures. actually larger in the control group. There
he evidence suggests that such an effect
he five case studies reported here, 'if GAt the state government level, Legler and
xists at all, is quite limited. As noted Shapiro have observed that the responsiveness of
revenue to economic growth of a particular tax
e, the economic impact did not include varies according to whether the income increase
pulation increase; the new plants them- is due to per capita improvement or to popula-
es brought in very few new residents, tion growth. See John B. Legler and Perry Sha-
piro, "The Responsiveness of State Tax Revenue
the relatively small employment ex- to Economic Growth," National Tax journal,
sion in the local consumption sector XXI (March 1968), pp. 46-56.

J t@

No. 4 NEW INDUSTRY 499

TABLE VI
COMPARISON OF STUDY AND CONTROL COMMUNITIES: CHANGE IN
LOCAL GOVERNMENT REVENUES, SERVICES, AND EXPENDFIURES

Study Communities Control Communities

Per cent Per cent
Item Change Change Change Change
I. All Local Government Units (1957-62)
General revenue ($1,000) 2,638 57.0 1,932 55.0
Per capita 35 61.2 33 60.0
Direct general expenditures ($1,000) 2,998 66.6 1955 58.0
Per capita $ 39 70.2 $ 33 62.2
Employment 31 2.5 141 16.0
1. School Districts (1959-64)
Enrollment -359 -1.8 -411 -2.6
Number of teachers -1 -0.1 -3 0.5
Full market value of taxable
property ($1,000) 73,483 32.1 50,787 39.4
Local revenue ($1,000) 103 7.4 78 60.6
III. County Governments (1959-64)
Operating expenditures ($1,000) 191 14.o 98 25.4
IV. Municipal Governments (1958-63)
Assessed value of property ($1,000) 2,437 15.6 n.a. n.a.
Property tax revenue ($1,000) 18 11.6 n.a. n.a.
Total revenue ($1,000) 167 49.7 n.a. n.a.
Expenditures ($1,000) 131 39.9 n.a. n.a.

Not available
Source: For 1, U. S. Bureau of the Census, Census of Governments: Kentucky VI (1957), Table
36 and VII (1962), Talles 27 and 28. For 11, Kentucky Department of Education, Re.
port of Superintendent of Public Instruction, (Frankfort: Kentucky Department of Edu-
cation), XXVII (1959) and XXXIII (1964) and Kentucky Department of Revenue.
For 111, Kentucky Auditor of Public Accounts, "Report on Examination,--- 1959 and 1964.
For IV, Municipal records of the five study communities.

is some evidence of relatively greater de- ernment," was usually small. Most of the
pendence on nonlocal sources of revenue by new plants studied here added few new resi.
the control communities during the study dents to the community; the availability of
period. This is perhaps explained by the local labor was in fact a major reason for
act that incomes were lower in these tile locations. For this reason there was
counties than in the study group. The very little incrased demand for local gov-
analysis of the private economic impact ernment services, and this factor served to
indicates that about 15 per cent of the per keep the magnitude of large negative impact
capita income difference between the two relatively small. A large negative impact

groups as of 1963 was attributable to the resulted for the school system only if prop-
study communities' new manufacturing erty taxes were substantially avoided and
plants. significant numbers of new residents (and
school children) were brought to tile com-
I IV. Summary and Conclusions munity. A sizeable negative impact on the
municipal government occurred if a large
The establishment of new manufacturing nontax inducement (e.g., provision of wa-
plants in five rural towns in Kentucky dur- ter or sewer services or donation of land)
ing the period 1958-63 typically resulted was combined with property tax avoidance.
in a negative direct impact on local gov- There was a tendency in the towns stud-
ernment finances. Of equal importance, ied here for the negitive impact to be con-
however, this impact, summarized in the verted into positive net primary benefits
quantity "net primary benefits to local gov- (although rather modest in magnitude) a

few years after the plant location. A vari- benefits to local government ranging from
ety of reasons accounted for this result, $535 to $6,352 per year, or with "one-
including (1) the climination of tax con- time" costs incurred at the time of plant
cession in the form of low property assess- location limited to $10,000 or less, except
ments granted at the time (if location; in one case in which $92,000 was ex-
(2) the fact that some costs incurred by pended.
Municipal governments are applicable to The secondary Impact on local govern-
only the year in which the new plant was ment was apparently quite small, if indeed
established or are amortized over a rela- it existed at all. This conclusion is sug-
tively few years; and (3) more rarely, the gested by comparing local government
later imposition of new types of taxes, such revenues, expenditures, and services in the
as occupational taxes applicable to the em- study communities which did not attract in five rural
ployees of new industry. communities which did not attract new in-
Whether net primary benefits to local dustry during the study period, and by in-
government are calculated for the period terviews with local civic leaders, who con-
immediately following plant location or for tended that the new plants had placed no
a later period, and whetherr they are posi- strains on local government services. As
tive or negative, they tend to be very small further indirect evidence of the adequacy
relative to new industry's benefits to the
local private economy. These benefits were of local public facilities and services, each
calculated to include' both the primary im- of study towns was seeking further new
industry at the time of the study, and three
pact (i.e., the plant payroll) ind the sec- (communities B, C, and D) had already
ondary impact (i.e., the incrcase in income
succeeded. New industry attracted to small
induced by local spending of the plant
payroll). The total impact of the private rural towns mainly by the availability of
economy, as measured by the increase in local labor does not produce significant
County personal income, ranged from population growth; a small impact on lo-
$564,000 per year to $2,174,000. This may cal government services is therefore not
be compared with negative net primary surprising.

TWO NORTH CAROLINA COMMUTING PATTERNS

Richard E. Lonsdale
Dr. Lonsdale is assistant professor of geography at the University of
North Carolina, Chapel Hill. Research was supported by a grant from
the University's Institute for Research in Social Science.

HIS study examines the com- the area. The study wits conducted
muting patterns of production in the spring of 1964.
workersat two industrial estab- The specific objectives in this study
lishments in eastern North Carolina.., are fourfold: (1) to analyze and com-
Each of the two plants investigated is pare the characteristics of the two
situated in the area of Kinston a city commuting patterns and identify the
of nearly 30,000 population, and to- respective labor market areas-, (2) to
gether they account for well over half observe the effect on commuting of such
of the manufacturing employment in in personal factors its wages, age, sex, and
the county centered on that city. One length of service; (3) to investigate the
of the establishments, a producer of significance of two geographic variables
synthetic fiber, employs approximately -population in distance-by con-
2200 persons and is noted for its" above- structing a series of probabilty models
average" wages; it employs workers based on gravity concepts; (4) to con-

who commute from points within a sider sonic other geographic variables
broad area extending out 40 miles and which may explain discrepancies be-
more. The other plant, a shirt facotry, tween the actual distribution of com-

employs about 900 persons, and is con- muters and those suggested in the
sidered " below-average " in its wages, it probability models. These probability
draws much of its labor from the area models are not universal, applicable,
in the immediate vicinity of the plant. but they may nevertheless provide

The two industrial facilities thus pro- tentative basis for estimating the po-
vide a broad spectrum of wages and tential availability of commuting labor
commuting distances and facilitate in in areas where conditions might be
examination of wages and other factors similar to those in eastern Nort

its variables affecting the pattern of Carolina.

commuting of industrial workers in Commuting studies have a proper and
important place in geography. Commut-

-ing distance can be used as a basis for

delimiting labor market areas or " labor-

sheds-the area supplying labor to

some central point. The labor-shed is
a regional conception-an extent of
space functionally organized about some-
nodal point such as an individual.

TWO NORTH CAROLINA COMMUTING PATTERNS 115

of the limited economic opportunities
factor, a group of plants, or a whole
complex of economic activities embraced in many of these areas. Given an opport-
by a city. Commuting or " journey- to- tunity to work in a nearby industrial
plant, rural and small-town dwellers
work" patterns could form the basis have shown a marked propensity to
maintain their established place of
for delimiting networks of overlapping
regions across the whole expanse of it residence and a willingness to commute
territory. Commuting studies offer geog-
raphers the opportunity to apply their great distances to work. Thus indus-
skills in seeking answers to some im- tries often draw their labor front re-
portant practical questions. For ex- markably road geographic areas. As
ample, the extent of the labor force might be expected, the higherwage
potentially available at some point is industries are able to attract labor front
directly influenced by the territorial much wider areas than are other indus-
extent of the labor-shed, i.e., by the tries; this point will be demonstrated
number of miles workers are willing later in this paper. An industry consider-
to commute. This may be an important ing a location in North Carolina can

consideration when all industry seeking compare the wages it is prepared to
a new location with an assured labor offer with those prevailing in specific
supply must decide aniong several al- area, and with all Understanding of
ternative places. continuting tendencies in this region, it
Commuting studies are particularly can proceed to estimate the size of
appropriate and pertinent in the case the labor force that might be marshalled
of North Carolina, a state striving to at any particular point.
attract new industrv in order to expand While this paper focuses on two indi-
employment and raise presently low vidual cases in it single region, tile study
income levels. The traditional attraction is intended to be of more than just local

for manufacturing concerns has been interest. Hopefully, it will be of use
the large supply of labor willing to to other analysts undertaking similar
work for wages which, though " modest " studies by pointing out some of the

by national standards, are above those problems encountered in conducting
prevailing in many non-industrial occu- such journey-to-work surveys. In addi-
pations in North Carolina. Surveys tion, the paper presents one method for
conducted by the Employment Security, analyzing a commuting pattern. Also,

Commission of North Carolina indicate by adding to the number of studies of
that a large reservoir of employable individual areas, the likelihood of de-
labor remains to be tapped the supply veloping a general theory on commuting
is especially abundant in rural areas is perhaps improved by some small
where out-magration has proceeded more degree. It can be reasoned that if an
slowly than might be expected in view acceptable general theory on travel-to-
work behavior is ever to be established.

116 ECONOMIC GEOGRAPHY

the key elements in the theory--e.g. the population is abuot 30 per cent urban,
relationships between commuting dis- nearly average for North Carolina, but
tance and wages, length of service, etc.- far below the national level. The Negro
will likely be discerned from a multitude population is rather uniformly high,
of individual empirical studies. averaging about 40 percent, and Negroes
account for about a quarter of the
THE STUDY AREA manufacturing labor force.
Income levels are generally low. In
Workers commuting to two indus- 1962 the average per capita personal
trial plants examined in this paper are income in the study-area counties ranged
mostly drawn from a ten-county area from about $900 to $1500, averaging
on the coastal plain of eastern North two-thirds the state mean and less than
Carolina (Table i and Fig. 1). There half the national level. Wages in manu-
are no major natural features such as featuring, which employs about 17 per-
mountains or large water bodies which cent of the area's labor force, are
seriously influence the general geogaphic generally above those in most other
pattern of commuting. Significant differ- branches of the economy, but still
ences in the density of paved roads do averaged scarcely $70 a week in 1963.
exist, however; the availability of such The leading branches of manufacturing
roads decreases as one moves south are food products, textiles, apparel.
and eastward (Table 1). This reflects wood products, and chemicals. The
diminishing population densities toward majority of food, textile, and apparel
decidedly less suitable for agriculture workers earn between $1.25 and $1.50
because of poorer soil and drainage an hour, while those at the large syn-
conditions. About one-third of the study thetic fiber plant near Kinston average
area's labor force is in agriculture (com- over $2.50 an hour. Local chambers of
pared with 7 per cent nationally); commerce boast of the "lack of labor
tobacco is by far the most important strife" and the prevailing absence of
crop, with peanuts, cotton, corn, and strong labor unions. But economic
soybeans also significant. opportunities are limited. unemploy-
The population of ten-county ment has been high, and many workers
area was approximately one-half million remain underemployed.
in 1960, up about 8 per cent from 1950
(compared with a national gain of THE QUESTIONNAIRE
19 per cent in the same period); four A travel-to-work, questionnaire (Fig.
counties experienced population losses 2) was given all production workers
in this decade (Table 1). The population (wage earners) at the two industrial
detisitv ranges from about 150 per plants. Salaried personnel were omitted
square mile in the northwest to less only because company officials preferred
than 30 in the south and east (Table they he excluded. The questionnaires
1). Five towns (Goldsboro, Greenville, were distributed and collected by plant
Kinston, Nww Bern. and Wilson) have supervisors, and there was it return rate
populations over 10,000: overall the of about 90 per cent. Of the returned
questionnaires about 10 per cent were
Ten counties supply approximately 98.5 per screened out because of incomplete or
cent of the community who identified their obvionsly false answers. The final sample
residential location on a questionaire. Small population consisted of 1052 workers-
numbers of coummities originate from four other at the syntheic fiber plant and 734
counties (Edgercombe, Martin, Ontow, and
Paplico).

'ent urbaii. Two NORT11 CARMANA CoMMU'ri-mi PATTER\.S H7
tI,
rolithi. bill V E 0 G C M 8
The Negr(p
P'ne s
nllv lligll. All,
id Ncuo,-,
Wilson
of
or
S P I T
low. Greenville
personal .00 Formvillp Washington
-ies rijilge(l
itno
From-on#
Faveraging,
less t1j;111 Ayden
G
S.O. H.11
in n1jinti- I
0 Golds oro*.% ....... %.
,ut 17 per
iorce, are / . ...... 0
PLANT
ost other La 6m.9o
Kinston
but still W A Y N E
in 1963. HIRT FACTORI
c E N
,,,facturing Mr. Vivo
%
apparel. L E 0 R
'als. The
New ern
apparel 0
linton
ind S1.50 James
warga@ Car
arge syn-
S
avera ........... J 0 N
ge a.
imbers of ftchlorids
of labor
D u L I N
)sence of
t Rose NIN
leconomic 0 N S L 0 W
workers Wait* Jacksonville Midway Pork

STUDY AREA
ire (Fig.
LOCATION MAP
NORTH CAROLINA
workers
Industrial
onlit led TOWNS 25009OVER PRIMARY
)referred PAVED ROADS
-IOWNS 1000-2500 .......... COUNTY
onnaires BOUNDARIES 0
(N. Plant
urn rate
turned
111-t were
-plete or. workers at the shirt factory, a total of 2. Workers hvinq a si@gnificant distance
salliple 1786 wage carners. 'riie questions asked
(over 3 miles) from the plant were also
1workers of the workers are indicated in the asked to locate their place of residence
ind 734 &iniple questionnaire shown as Figure
on a inal) included on the quest iorina ire.

TABLE I
SOME CHARAC-1 F.ILIS'r it's oy INDIVIDUAL COUNT11iS vi 711L STU,)Y AREA

Per cent Per Airicultural Per cent of Pjr,4
Dcxs;jY Per lif6on" rwd,.
Total chaxle per sq. text cent TOW Afanufac- Per Average labor forte IV63.
1950-1060 mi. urbax 40 !1- 1960-.1 turing capila ureekly In& unemployed .49.. Per
wh WaRes 196JA
No. P. r 1963b.6 196.M 1 196j".8 sq. mi.-
cepit

CLN I RM.
+20 141
..................... 55,276
4S 40 18.016 3,882 22 695.50 8.5 1.84
""I .... ................ 69,442 +10 5.()Oq
306 43 44 22.353 6.488 19 3.270 1.387 72.39 8.8 1.56
........ ............. 16.741 -7 62 0 so 4,331 2,831 63 132 1,288 S2.27 8.4 1.70

W;,Y0. ................... +28 14H 41 37 22.522 5,087 23 3,329 1.472 72.(.g
Wil .......................... 57.716 +6 155
j"Ital'I"11 ................. 936 50 40 19.596 4.386 22 6,6 1 74
. . 62. -5 79 20 12 3.915 1.510 67.34 11. 0 1: ", -7@
RN CIII-NIII'S 20.989 0.768 32 3.989 1,42, 59.SS 7.1 1.88
............. 1 40.270 -2 41) 0 .18
...... 13,251) 3,89A 44 2.085 1. 193
......... 11,00s 0 24 0 47 3,186 64. 40 4.0 1.74
J.467 46 144 902 49AS 6. Ob 0.68
E.%,11 WN Cw
............... A6.014 43 28 .17 11, 2,866 2t, ZAP* 1:265 53. 3 2 5.61, Os
................. I SX.773 +20 27 14,999 2.33.1 16 2.031 154t 68.61 6. 1.14

I QIW Ce", Id , "i l"'Pula li"It. V,-I. 1. Pal I 3s.
V,,,Ih Vor,wind Insured f1ge 1. ayments. 1963
i
ft.oo data 4-IS11M.41 by S...'sPaich. State ,I N.C.. Raj,1gj,. 1964.
wol dal., C. by of Tax U( - Es" I'll IY Ment Swaily CO'oloibsi,m of N.C., Hi'lligh. 1964. Table 5,
'Mily Commission of N.C.. Italeig1j.
I I-oo dala conavil,41 by platinitts: N.C. Stilke ilighway Camminsion.
by ..f

7-
7-
v.

71*
E.

Two NORTH CAROLINA COMMUTING PATTERNS 119

~10~1~1
The TWO SAMPLE POPULATIONS road map, whereas t~qi~t~(~- d~et~cr~ini~n~ati
The basic characteristics of the two ~of travel little r~e~(~qIt~ilr~es ~e~l~t~qh~er ~;~I l~qi~e~qld
s~t~irv~ey or an interview of workers
re indicated i~n Table ~I~c~t (lit~.~qHv d~o~l~l~i~qg~. tile ~co~l~l~i~l~l~it~i~t~qi~l~i~t~"~. The ~i~t~'~(~-
~t~-v~id~t~-~l~i~t~. tile ~t~qw~o populations
o~qt time ~i~ts a ~I~l~l~e~a~S~i~lr~e ~(~'~.~,~i~n he defended
~linilar with regard to a~v~e and (lit- ~qgr~om~id that ~i~t worker~'s w~i~ql~ql~i~l~i~qg-
~_~1~q,~t ~l~.~qj ~v~i~l~lp~qloym~ent (tile fiber plant ('~I~'
ness to commute presumably depends
(,,,I%- i~n ~q19~q53~; ~t~qhe average length
ice ~.of fiber workers should i~n- in part o~i~l t~qhe "effort" ~I~qm~-~qb~qlv~ed. Tell
wi ~.~Ih tile passage of tin~i~c), bUt "lil~es of "fast" open ~qhighw~i~tv requires
~@~@r~v~a~tlv i~l~l average wages and tile less effort than 10 ~ni~l~ql~es of "slow" ~citv
s~tr~e~ct~s or dirt roads, and in tills ~In~s~ta~l~ic ~c
~qhv~i~u~v~e~n ~s~e~x~t-~s. As would be tile number of ~1~1~q6~q1~CS ~NV~O~L~I~qld lik~e~q]v be a
,-d. tile shirt factory relies heavily
labor. most of whom receive poor ~n~ic~a~sur~e of ~co~l~l~i~t~i~l~l~i~t~in~qg distance~.
s located Along t~qhis line. o~n~e geographer has
~,~i~t h~ot~ir. The shirt fir~in i
recently suggested that commuting be
~Im ~t~qhe city of Kinston, ~enab~qlin-
~n~e~i~l~s~t~ir~cd i~l~l terms of ~"travel effort,"
~"~r ~l~l~t~l~i~l~l~ql~)~cr of employees to walk to I
~'H~i~c fiber plant.' o~i~l t~qhe other t~aki~qm~, into consideration t~qhe number
"I
of Stop signs, tUr~n~s, con~t ~C.
1, is situated i~n t~qhe count~i~rV~, ~S~eV~e~l~l g~e~s~t~i~on~, ~e~t
But t~qhe obvious difficulty i~n obtaining
~o~p~t~I~t~sid~e of Kinston, a~nd essentially
~4~.~1~1~1~q1~1~q1~(~py~c~e~s~-~c~o~l~l~i~l~l~i~t~it~e by automobile~. such data for all tile roads i~n t~qhe coni-
~I~I~I~L~lti~flg area will likely preclude I~lILICh
T~I~ll: MEASURE OF COMMUTING use of t~qhis ~i~lle~i~lS~Ur~e. Cost lias been
I ~q!~@c~r~v is some question as to what is suggested as a possible ~tn~e~a~s~t~ir~e of
satisfactory nicasur~e of com- (~70111~1~111.~1ti~ng. The ~3SS~L~I~I~I~l~qj)ti~O~l~1 i~s t~ql~l~l~t
~qj the expense involved ~inav, beyond it
~qI~l~i tile majority Of St~Udi~c~s a certain ~qli~n~lit, diS~C~OUr~i~qwe' ~(~-~O~l~l~l~l~l~l~t~lt~j~l~l~L~r~.
~.~1~%~:~qh~t m~qi~ql~c~a~i~qz~e measurement
IS "I I ~qlow~ev~er, ~li~t ~t~qhis st~udv area and i~n most
A few a~l~ia~qlv~s~t~s have measured
distill ~, Ill- other,.;, m~a~t~iv worker, join car pools,
~Ic~c in ~t~qi~l~qi~l~e (i.e.. ind for them tile cost is r~el~a~ti~v~e~qlv minor
The p~op~t~i~qla~r~qit~v of miles Over ~i
~11 compared with t~qh~e ti~l~l~i~c i~l~lv~o~ql\~,~-~ed~. On
~44q, understandable~; mileage call tile other hand, it worker driving alone
~!~:~A~*~.~I~:~@~t~I~r~V~(~ql dir~e~ct~qiv from an ~ordi~narv
~q30 ~I~l~lil~e~s each Will" would, at t~qhe rate
TABLE It of eight cents ~i~t mile, incur it d~i~ti~qly
~W I ~i k~l~%~TICS OF IWO CO~M~A~I~V~I~E~R GROUPS expense of almost five dollars.
~qI~li ~t~qhis study~, information o~i~l com-
Fib, r Plant Shirt F~a~a~, r~i
~I~l~l~ik~-~J~I~-~C ~d~1~ld ~ti~f~i~l~t~! W~i~I~S ~0~qbt~;~Li~l~I~Cd
~N
~u~.~,~g~e ~c~urn~er~A. 1.052 734 from t~qh~e questionnaires, The pr~e~s~unip-
.......... 875 71 t~i~on ~th~:~it one might provide it
........... 1~77 ~6~6~' ~4 'tire of distance than ~t~qh~e
employment ~9~9 ~1~1~1~0~s. ~9~2 ~1~1~1~0~A. better m~e~a~s I
~9~8 mos.
.......... ~9~6 other. To test tile relationship between
~. . ............ 97 mos. 81 n
............ 3~2.1 ~yr~s~q. 32.7 ~yr~s~q. ~t~qh~qe two, little and mileage ~q(~q1~q1~qS~qU~6qM~qC~qC
~.~.~q.~q..~q.~q.~q.~q.~q.~q.~q. 32.3 ~qyr~qs~q. ~q3 ~q1. ~q2 ~qy~qr~q@~q.
~8q4 ~q*~. ~q. I ........ 31.4 yr~qs~q. ~q3~q2.9 ~qv~qi~qg. values foi- a 1~8q0 per cent random sample
%.,Ice ....... ~ql. ~qS~q10~q9~q.~q1~q3 ~q$~q3 ~q1 .7~q@~8q@
. ........... ~0q'b~qer workers were plotted against
~q1~q1~q1~q.~q7~q7 ~q6~q0~q.~q0~q0 ~qo~8ql ~8qI~qt
...... ~q4~q6.~q5~q4 ~q5~q1~q.0~q6
One another o~qi~ql it scatter diagram (Fig.
. ............ ~q1~q,0~q3~q1 ~q608
.......I ..... I I "John 1). ~qXy~q@t~qu~qv~qn: A Measure ~q(~qlf Effective
. ..... I .... ~q0 122 Di~q@t~qanc~qe i~ql~ql Urban Travel, Abstracts ~q(~q,~q/ Papers,
........... 1 ~q0 ~q.~q1 20th ~0qIn~qt~qer~qm~qi~qti~q0~qi~qi~qal ~q(~q;~qv~qt~q)~qgr~qap~ql~qlic~q@~qd ~00qG~qm~qgr~qe~q@~q,~q, Loll-
(hill, ~q1904~q.

120 ECONOMIC GEOGRAPHY
TRAVEL-TO-WORK QUESTIONNAIRE 90

A University of North Carolina research group is anxious to find out how much time 80
workers spend travelling to and from work. The results of this and other studies
will contribute to an overall understanding of North Carolina's industrial labor 70
force, and will assist in developing plans for continued industrial progress.
60
Your cooperation in answering the questions below will be greatly appreciated.
After completion of this form, please deposit it in the box marked "Travel-to-Work 50
Questionnaire", which will be placed in a convenient location in your plant.
Thank you very much. 40

30
1. Name of company where you now work:
20
(a) How long have you worked for them? years.

10
2. Haw many miles from the plant do you live? miles.
3. How long does it take you to got to work? minutes. 0

4. How do you get to work? (auto, bus, walk, etc.)

5. Have you moved closer to work since taking your present job?
(a) If so, how many miles from work did you live previously? miles.
6. Are you now considering moving closer to the plant? FIG. 3.
7. Personal data: showing rela
(a ) age _ (b) sex (c) average weekly wage, and commu
before taxes: $
8. If you live more than three miles from where you work, place an "X" mark on the 3). As w
map below, showing approximately where you live: trend was
of correla
high
as satisfa
A regressi
the "X"
the "Y"
equation
indicates
commutin
per addit
"zero" 1
6.6 minut
a measur
time con
FIG. 2. getting O
from park
possible
over the
the degre
distributi

TWO NORTH CAROLINA COMMUTING PATTERNS 121

E 90
distributions suggested by probability
models based on gravity-model con-
much time 80 cepts. Twelve such models were con-
studies structed, using both miles and minutes
labor 70 as measures of distance; they are dis-
ss. cussed later in this paper. A closer fit
was obtained using mileage as a distance
ted. 60 measure, but at best this is highly
-to-work inconclusive evidence. In the proba-
t. 50 bility models the frictional value at-
tached to distance can be adjusted
40 ad infinitum. It is indeed likely, there-
fore, that fits better than those achieved
30 in this studv could be obtained. In this
study mileage is used more extensively
20 than travel time only because it facili-
tates comparisons with other commuting
10 y= 1.25x + 6.58 studies, most of which used mileage
exclusively.

0 10 20 30 40 50 60x CHARACTERISTICS OF THE TWO
miles COMMUTING MILES COMMUTING PATTERNS
FIG. 3. Scatter diagram and regression line The higher-wage fiber plant workers
showing relationship between commuting mileage commute considerably farther than do
and commuting minutes, based on a 10 per cent shirt factory workers. The spatial extent
sample of fiber plant workers of the two labor-sheds, in this case
3). As would be expected, at positive arbitrarily defined as the region encom-
k on the trend was clearly evident. The coefficient passing the nearest 90 per cent of an
of correlation between the two is a very individual plant's labor force, is shown
high +.96, suggesting that one is about in Figure 4. The fiber plant labor
as satisfactory a measure as the other. market area extends out almost twice
A regression line was established, with as far from the point of employment
the "X" axis representing miles, and and embraces an area about three times
the "Y" axis minutes. The regression larger than the labor-shed for the shirt
equation is Y = 1.25x + 6.58, which factory. Fiber plant workers travel an
indicates that for fiber workers the average of 17.5 miles each way and
commuting time increases 1.25 minutes require an average of 28.7 minutes to
per additional mile, and to commute cover this distance; median figures are
"zero" miles requires approximately approximately 13 miles and 27 minutes.
6.6 minutes. The latter is presumably By contrast, shirt factory workers travel
a measure of "terminal time" i.e., the an average of 6.7 miles one-way and
time consumed in starting the car, require an average of 18.2 minutes; the
getting out of the driveway, walking median values are about 4 miles and
from parking lot to plant gate, etc. The 17 minutes. The distribution of com-
possible superiority of one measure muters by five-mile and ten-mintute
over the other is perhaps indicated by zones for each of the plants is given in
the degree of association between actual Tables III and IV. As can be seen by
distributions of commuters and those the figures in the cumulative percentage

122 ECONOMIC (;I:O(;RAI'I(Y

..........
Zone
... ... ..........
... ............ ....
..........-...............
... ..... .....

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

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

..........
.......... 4..............
t S..... ........
..............
7..............
4W

..............
to ..............

columns of
the shirt wo
their place
.... ......
half of the
. .... .. .....
IVA distance fro
......... ....
cent of the
or more, w

. .. . ........-.... .
shirt worke,

. . . ................ ..
striking diffc
........... . .... ..
... ...............
ing habits
surprising it
two-to-one
of fiber worl,,,
0 10 20 of the shirt
more rural-@
miles qeven miles
N-If rhe coin
workers app
COMPARISON of LABORSHEDS @jverage, an
somewhat le
OF FIBER PLANT (F) AND SHIRT FACTORY (S) lished surve
Carolina D
Laborsheds delimited to include nearest iiiisslon, 77
ninety per cent of commuters Carolinians
13 miles of
Fiber plant laborshed (90% of workers within 38 miles) per cent wit'
within 15 ir
Shirt factory laborshed (90%of workers within 20milos) of the shirt
of the fiber
that ill ave
Fit;. 4. the fiber I
above tile
factory

TWO NORTH CAROLINA COMMUTING PATTERNS 123

TABLE III
DISTRIBUTION OF COMMUTERS BY MILEAGE ZONES

Fiber plant workers Shirt factory workers
Zone Miles
Percent Cumulative Per cent Cumulative
Number of total per cent Number of total per cent

1.......... 0-4 113 10.7 10.7 435 59.3 59.3
2.......... 5-9 258 24.5 35.2 71 9.7 69.0
3.......... 10-14 201 19.1 54.3 81 11.0 80.0
4.......... 15-19 82 7.8 62.1 72 9.8 89.8
5.......... 20-24 117 11.1 73.2 40 5.5 95.3
6.......... 25-29 61 5.8 79.0 25 3.4 98.7
7.......... 30-34 55 5.2 84.2 8 1.1 99.8
8.......... 35-39 78 7.4 91.6 1 0.1 99.9
9.......... 40-44 54 5.1 96.7 1 0.1 100.0
10.......... 45 & over 33 3.1 99.8 0 ... .....
Total ..................... 1,052 99.8 ... 734 100.0 .....

columns of these tables, 80 per cent of A federal government survey of 6000
the shirt workers live within 14 miles of households in 357 geographic areas of
their place of work, whereas just over the United States, conducted in October,
half of the fiber workers live a similar 1963, provides a kind of "national
distance from their plant. Over 15 per norm" with which the two commuting
cent of the fiber workers travel 35 miles patterns in this study can be com-
or more, while virtually none of the pared.10 According to this survey, 45
shirt workers commutes that far. The per cent of American workers commute
striking difference between the commut- up to four miles, and 76 per cent up to
ing habits of the two groups is riot 10 miles. For these distances the fiber
surprising in view of the better than plant percentages are 11 and 40, indi-
two-to-one wage differential in favor cating that its commuting distances are
of fiber workers, and the in-city location considerably above the national mean.
of the shirt factor as opposed to the The shirt factory percentages are 59
more rural site of the fiber plant some and 71, or much closer to the national
seven miles outside Kinston. average. The meaningfulness of such
The commuting distance of fiber comparisons call be seriously questioned,
workers appears to be above the state as the national figures reflect largely
average, and that for shirt workers urban conditions, dissimilar to those pre-
somewhat less than average. In unpub- vailing in eastern North Carolina.
lished surveys conducted by the North It is difficult to compare the commut-
Carolina Employment Security Com- ing patterns in this study with those
mission, 77 per cent of the North in previous studies of other areas in the
Carolinians they interviewed live within United States. Comparisons of this sort
15 miles of their place of work, and 93 suffer because of great differences in
per cent within 24 miles. By comparison, such circumstances its degree of urban-
within 15 miles of work are 80 per cent ization, city, terrain, availability
of the shirt workers but only 54 per cent of paved roads, model of transportation,
of the fiber workers. It is worth noting job opportunities, wage levels, etc. With
that in average weekly wages in 1964 these limitations in mind, a few com-
MEN the fiber plant (S109) is also much
above the state mean, and the shirt 10 Home-to-Work Travel, advance report, 1963
factor (S52) again below average. Census of Transportation, Bureau of Census,
Washington, 1965, p.6.

124 ECONOMIC GEOGRAPHY

TABLE IV

DISTRIBUTION OF COMMUTERS BY TIME ZONES

Fiber Plant workers Shirt factory workers

Zone Minutes
Number Percent Cumulative Number Percent Cumulative
of total per cent of total per cent

1......... Under 10 36 3.4 3.4 131 17.9 17.9
2......... 10-19 288 27.4 30.8 308 42.0 59.9
3......... 20-29 258 24.5 55.3 130 17.7 77.6
4......... 30-39 173 16.4 71.7 97 13.2 90.9
5......... 40-49 164 15.6 87.3 59 3.0 98.8
6......... 50-59 57 5.4 92.7 5 0.7 99.5
7......... 60 & over 76 7.2 99.9 4 0.5

Totals ................. 1,052 99.9 .... 734 100.0 ......
...........

parisons may be attempted. In a 1959 subsequently moved closer to the plant.
study of over 2000 workers at the In comparing fiber plant commuting
Maytag Company plant in Newton, with that observed in tqhe Kaiser study,
Iowa (Population 15,000), the median it is appropriate to consider the com-
distance was about seven or eight muting distances of fiber plant workers
miles,11 much below that for tqhe fiber when they first obtained employment.
plant workers. Although each labor- Qustionnaire data indicate that 260,
shed embraces a largely rural area, tqhe or nearly one-fourth, of the fiber plant
farm wages are lower and the alternate workers have moved closer to tile plant
job opportunities are more limited in since commencing their employment;
North Carolina; perhaps this partially they originally commuted in average
explains greater commuting distances of 28.9 miles one-way, comparcd with
in North Carolina. the present 8.4 mile average. This would
The commuting habits of Kaiser seem to indicate that the median dis-
Aluminum workers at the new Ravens- tance of fiber plant workers when first
wood, West Virginia (pop. 1175 at time hired was comparable to that of the
of plant establishment in 1956), plant West Vinginia aluminum workers shortly
were analyzed in a 1957 investigation. after their hire by Kaiser. Both plants,
This was a "depressed" and largely in similar "low-income" environments,
rural area where agricultural incomes initially attracted commuters from ex-

were low and "well-paying" industrial ceptionally wide areas.
jobs were as highly sought after as those
A 1948 survey of commuting at a

with the fiber plant in this study. 0ne spinning mill on the South Carolina
year after tqhe plant's opening, aqlummUln piedmont showed a median one-way
workers traveled a median one-way distance of six miles. 13 only slightly
distancc of about 20 miqles.12 or about
greater than that for tile shirt factory.
half again as fiber as fiber plant workers. While the South Carolina plant is in
But many of the aluminum employees
a more rural setting, tile rather sim-
11 C. A. Peterson: An Iowa Commuting ilarly-low median commuting ranges
Pattern and Labor Market Areas in General
Bureau of Labor and Management. State probably stems from the fact that both

University of Iowa City 1961), p. 1.
12Lat Nupply and Mebility in a Newly 12 J. M. Steep and J. S. Plaxico: The Labor
Industrialized Area 1 Bulletin 1261. U.S. Dept. Supply of a Rural Industry (South Carolina
Labor. Bureau of Labor Statistics, Washington, Agr. Experiment Station, Bulletin 376, Colum-
1960, p. 21. bia, 1948), p. 21.

Two NORTH CAROLINA COMMUTING PATTERNS 125

are in the "low-wage" category. Thus, and be labeled either way. There is
despite limited Job opportunities mid an absolute outer limit for any existing
low farm wages, neither plant attracted labor-shed, the isoline which includes
any large number of persons living a
I 100 per cent it of the commuters, How
Cumulative sizeable distance from the plant. ever, a mere handful of" persons com-
percent DELIMITING The LABOR-SHEDS muting unusual distances can cause
17.9 this outer limit to lie far beyond what
9.9 In the majority Of communiting studies might be termed the "effective limit"
77.6
90.8 few attempts have been made to de- of the labor-shed. This raises the ques-
98.8
99.5 limit accurately labor-sheds or labor tion of what constitutes such an effec-
100.0 market areas. Where they have been tive limit" in terms of the percentage
delimited, their boundaries usually ap- of commuters included. There is no
pear as perfectly concentric circles, or established norm, and any decision is
they are drawn to coincide with existing by subjective. A reasonable

o the plant. political boundaries. To the geographer, limit might be the isoline embracing
it proper delimitation would seem to be the nearest 90 per cent of the workers
aiser study a necessary step if the labor-shed is to (Fig. 4).
r the com be conceived its a region.but immedi- Commuting
Isoline mills were pre-
in t workers ately evident are several obstacles to pared for tile fiber plant and Shirt
mploment. such a regionalization. Labor-sheds over- factory labor-sheds (Figs. 5, 6, and 7).
that 260, lap, particularly in those zones inter- The data for constructing the maps
fiber plan, mediate between two plants or "nodes" were obtained from the questionnaires
the plant which are attracting labor. It may which, in addition to asking each
employment often happen, as it does in his study worker his one-way commuting mileage,

in average (Fi-. 4), that one labor-shed mav lie and little, also requested each person
ared with entirely within another, larger labor- commuting over 3 miles to locate
This would shed. -They cannot be conceived its his home on a map included in the
median dis- mutually exclusive entities. 0n the questionaire (Fig. 2). About 87 per
when first other hand, the labor-shed can he cent-of the fiber plant workers and
Rat of the thought of its a distinct region, but 98 per cent of those at the shirt factory
er shortly always as one whose limits: (1) diminish who returned questionnaires fully com-
th plants, by degree rather than abruptly and plied with this request. With this
ronments, (2) commonly overlap or encompass information it was possible to plot the
from ex- those of other labor-sheds. approximate place of residence of 926
The Outlines Of Suoh it "diminishing" fiber plant and 719 shirt factory workers.
region call be shown on maps by the Next to each place of residence oil the
ng at a
map were recorded the mileage and
Carolina use of isolines. Around each factory or
wav other nodal point attracting workers minutes indicated oil the questionnaire.
will be a series of commuting Isolines This provided tile control necessary to
factor. (isocoms)`. These isocoms can rep- construct the communting isolines, In
resent commuting mileage or time in- tervals of five miles and tell minutes
ant is in
tervals (ex., an isoline for every addi- were chosen arbitrarily. The isolines
are identified not only in terms of the
ranges
that both Each isoline can be translated into a miles or minutes from the plant, but
line indicating the cummlative per- also in terms of the percentage of
The Labor centage of commuters contained within all commuters contained within each
Carolinia
that line (e.g., the 25-mile isoline may isoline. Another approach, not em-
encompass 75 per cent of the commuters, employed here, Would be to select those

126 ECONOMIC GEOGRAPHY

distance isolines which would indicate in using isolties based on time rather
the percentage of commuters enclosed than on miles; tile added difficulty of
at regular intervals (e.g., 30 per cent, obtaining data on commuting time in
40 per cent, etc.). itself seems to recommend the use of
The commuiting mileage isoline maps Isolines based on mileage
for the two plants (Figs. 5 and 6) both The delimation of labor-sheds in
show the influence of the existing road terms of communig isolines call be
network orientation. Isolines extend objected to on the grounds that the
farthest out along major radial roads isolines do not necessarily identify the
in typical "spiderweb" fashion. The areas of densest commuter origins. For
fiber plant isolines appear rather ellip- example, Figures 4 and 6 tend to give
tical in shape with it northeast-south- different impressions of the distribution
west orientation. this apparently re- of shirt factor commuters. As is clear
flects the position of the fiber plant in Figure 6, in a much larger number of
on a main northeast-south west road shirt workers originates from the lower-
and the absence of a major east-west income areas South of Kinston, than
road in the immediate vicinity of the from the north, the labor-shed shown
plant. The shirt factory isolines are in Figure 4, delimited to include the
more nearly circular because of tile nearest 90 percent of commuters, in-
many roads raoiating out from Kinston includes areas north of Kinston, where
in all directions. In neither case are few workers reside, and excludes dis-
there any major distortions in the shapes tricts south of Kiniston (e.g., around
of isolines because of the lack of through Beulaville), where significant members,
roads in any area. File shirt factory of them live. A case can be made for
labor-shed is contained entirely within rather arbitrarily positioning the outer
the fiber plant labor-shed, an observa- limit of tile labor-shed so as to include
tion noted earlier in connection with all areas of denser commuter origins
Figure 4.The fiber plant labor-shed and then drawing in just those isolines
was extended to the 45-mile isoline, wholly or in part within tile labor-shed.
which encloses 97 percent of the WAGES AS A FACTOR INFLUENCING
commuters. For the shirt factory, it COMMUTING
was only necessary to extend the
labor-shed to the 30-mile isoline to A comparison of the fiber plant and
embrace 99 per cent of the workers. shirt factory labor-sheds cleafly sug,
A commuting time isoline map was gests that "higher wage" workers are
prepared for the fiber plant labor-shed more willing to travel farther and
(Fig. 7) for the purpose of comparing therefore give up more of their "free"
time isolines with those based on time. Presumably, evervone attaches
mileage. The coinmuting time lsolines some value to his little" and is reason-
appear more irregular than do the ably aware of the full cost of driving
mileage ones. The extend farther Out and maintaining all automobile. Thus,
along main roads, indicative of the one could assume that it more highly-
higher speeds possible on major arteries. paid worker is in a better position to
Nevertheless, the 60minute isoline very hear the additioinal expenditures of
roughly corresponds with tile 45.mile For a discussion of hi matter sev L,K
isoline, which is consistent with the
regression analysis. There (foes not Loewinstein: The Spatial Distribution of Resi
dences and Work Places in Urban Areas ( Dept.
of City Planning Univ of Pennsylvania

appear to he any clear-cut advantage Philidelphia 1962 pp. 4bj (mimieo

time and mon
have studied
cided differn
relationship
journey-to-wo

Two NoR-ru CAROLINA COMMUTING PATTER.Ni 127
time r ather
difficult". Of
ing time in
the use of

or-sheds in
ies can be
.s that the
t
dentifv the
rigins'. For
.-nd to give
Jistribution
As is clear @4
number of
the lower-
iston than
hed shown %
clude the
nuters, in- Q;z
lion, where
-ludes dis- pt.\
around
It numbers .614e
made for 11
the outer
to include
er origins
se isolines
Fbor-shed.

'E'NICING
klant and
arly sug- FIBER PLANT LABOR-SHED
Each small dot represents one commuter;
irkers are COMMUTING MILEAGE ISOLINES larger Circles feprtsent left of more
ther and commuters, with number 9i"n next to circle
i r " free " FIVE -MILE INTERVAL
attaches X Circle indicatesc mmu I
Figures in Pdf*nthese imd,cals Per Cent within three miles of Plant
s reason- of Commuters Enclosed by that Line
f driving Based am 0010 Obtained from Questionnaires, March. 1964 0 'a 20
-1c. Thus,
L highly- J
__ sition to Fi(;. 5.
-tures of tinie and money, Vet aniong those who gest that there is no relationship be-
have studied C0111111LItIng there are de-
@1 of Resi- "These differences were noted bv James 11.
:;I., (I)ept. cided differences of opinion as.to the *Yhonwsow Lakir Nkirket Ar(';L@ for Ximitif-m-,
relationship between watzes and Hit- turiiig Hams in W(-t Virginia (litireau (it
11o,ille" Rvsv@lrdl. We,t Virginia Cniv_ Mor-
journey-to-work.'-` Some analysts SLIg- g;tIltown, 1955), 1). 23,

128 ECONOMIC GEOGRAPHY

tween the two factors, and one an- wages and distance. A slightly higher

anlyst, C. A. Peterson, in a study of percentage of long-distance commuters
commuting in Iowa, found data which are younger, newly-hired workers (Table
suggested that there was an inverse VI), and thcir average wage is below

relationship between the two factors; that for the group as it whole. However,
i.e., more-distant commuters received, it sample inspection 1ndicated that even
on the average, it lower wage than with this latter factor consisidered, there
those living closer to the plant." is still an apparent lack of connection
.An examination of the fiber plant between wages and distance within tile
data alone indicates the lack of direct fiber p1ant group.
link between wages and commuting
It would be a mistake, however, to
distance. A scatter diagram with wages conclude from tile above that wages
plotted against distance, drawn from and commutinng distance are completely
it random sample of 100 fiber plant unrelated variables. It is true that
questionnaires, showed no discernible within the fiber plant group there is
trend. The fiber plant data were then no evident connection, but one has only
arranged to show the average wage to examine the commuting habits of

of workers in each commuting Wile the shirt factory workers for evidence
(Table V). To make certain that a that lower-paid workers in the same
possible relationship was not obscured region do not commute nearly as far
bv differences based on the sex of (Tables I I I and IV). The fact that a
much higher percentage of shirt factorv
workers, the data were also arranged
by sex. The only- conclusion that can workers are female than male is ap-
parently not it factor here; as shown in
be drawn from this table is that there
is no correlation whatsoever between Table V1., there are no significant
wages and distance within the fiber differences between male and female
plant group. Another factor the length Commuting In this area. Perhaps tile
'A of service of workers-was examined critical element is whether wages.are
above or below those prevailing in the
on the theory that it might be re- region being, examined the overall non-
sponsible for obscuring the link between
agricultural wage in the ten-countv
Peterson, op. cit., pp. 8-9. study area in 1964 was about S63 per

TABLE V
AVERAGE WEEKLY WAGE OF FIBER PLANT WORKERS* SH(RT
BY COMMUTING ZONES AND BY SEX COMMUTII
Zone 1 2 3 4 5 6 7 8 9 10

Miles 0-4 5-9 10-14 13-19 20-24 25-29 30-34 35-39 40-44 45 &over
All commuters
No 103 245 198 81 112 36 51 76 52 32
Wages &112.89 107.60 110.73 106.20 105.96 109.34 104.71 110.86 107.87 110.25
No. 89 195 172 70 96 47 36 59 43 29
Wages $115.91 110.68 112.72 108.07 111.09 113.11 107.44 114.97 111.35 111.69
No. 16 50 26 11 16 9 13 17 9 3
Wages $96.06 95.60 97.69 94.27 96.19 89.67 98.13 96.59 91.22 98.33
The size of this population (1008) is somewhat smaller than in some other tables due to the necessity for eliminating
a few questionnaires where the desired combination of data required for this table were not wholly provided.
SHIRT
COMMUTI
Figures
of Comm
Based on date
week compared
plant and 32 at
sibly, differences
failed to show up
sample because
F

1 0 Ntwi-u Cmim( iim; 121)
-1 slightty higher
NUICC commuters
i
d workers Table
t! wage is below
whole. f lowever,
icated that even
1..,onsidered, there
-k of connection
:ance within the

ke, however, to
,)%'c that wages
are completely
is true that
group there is I
-ut one has on1v
iting habits of
rs for evidence
4211
s in the saine
nearly as far
he fact that a
qqof shirt* factory
I) niale is ap-
-Ov; '1'i shomm in
110 Significant
e and feniale
Perhaps the
ier wages are
wailing in the
Jje overall non-

C ten-countv
ibout S65 per

SHIRT FACTORY LABOR-SHED
COMMUTING MILEAGE ISOLINES Each small dot represents
one commuter larger circles
Oak romintiters FIVE-MILE INTERVAL represent tenor more commuters,
Figures in parentheses Indicate Per Cent with number given neRt to circle
11'act's of Commuters Enclosed by that Line

Soled on Ocilla Obtained from Ouest@orvnoares , March. 1964 0 a 20
S06 06
9S: 60
97 . 69
94.27 6.
96.19
89.67 f
98.13
96.59 week, compared with S109 at the fiber Urs reciv Ivc ;I wage above the ten-county
91.22 plant and $52 at the shirt factory. Pos- average. The same was true of the 'shil't
96.33
siblv, differences in commuting habits factor\ where the great majority of
ty for elittlinating failed to show III) within the 11her Ifli;tut work-crs itre paid a wage below III(.
s
ample because virtually all 11hCr %vork- rct.,,lonal average. 'I'lils smggcsts tI1;l1

130 Economic GEOGRAPHY

there is a positive relationship between Sex does not seem to be a factor in
wages and communting, and that when explainins, comimiting; for both plant!;
a plant offers wages appreciably above the commuting habits of women are
those prevailing in it region, one call about the same its for those of the
expect, cetera Paribus, workers to exhibit men. As Peterson noted in his Iowa zone Miles
a greater willingness to commute long sturdy, "tile lack of ally consistent
distances. This is consistent with find- relationship between Sex and commuting
ings in the study of Kaiser aluminum behavior Is the only safe generalization I..... 0-4
2..... 5-9
workers at Ravenswood, West Virginia." that can be made." 3..... 10-14
4..... 15-19
S..... 20-24
OTHER PERSONAL FACTORS 6..... 25-29
CONSTRUCTION OF PROBABILITY 7......30-34
INFLUENCING COMMUTING 8.......35-39

Personal factors, those that vary 9.... 40-44
with the individual worker, which may A means of investigating the Sig- 10 .........45 & older
nificance of two geographic variables
influence and thus help to explain a population and distance is provided by
commuting pattern include, in addition
constructing gravity probability models. on commuting.
to wages, tile age, Sex, and length
service of workers. Data oil the latter The gravity concept holds that the gest means fo,
three factors are shown by commuting potential interaction between two points extent of labo
or areas is directly proportional to their
zone in Table vi. The average age of new industrial
workers declines somewhat with in- populations and inversely proportional ProbabilityI-
to the distance between them. In the the fiber plant
creased commuting distance; workers
living close to the plant tend to average Case of commuting. the gravity idea commuting pa"
four years older than those doing con- call be conceived as suggesting that distances and
siderable commuting. This is about an Individual plant (or group of plants) provides a mot
cqually true for both plants. Similarly, attracts commuters from Surrounding appraising the
areas in direct proportion to the popu- tion and distan
the average length of service for both
lation of the area and in inverse propor- -commuting that
groups diminishes with increased com-
muting distance, particularly in the tion to the distance between the-area more-restricted
and the plant. As is evident in Figures
case of the shirt factory. These findings 5, 6 and 7, the density Of commuter pattern.
are consistent with those of most other Before const
analysts who have noted a greater origins is not constant; nor does it was necessary
willingness (or necessity) of younger diminish at a constant rate with in- Oil methods and
persons to commute and it lower senioritv creased distance from the plant. It is models a direct
here assumed that much of this"uneven-
level of the averagelonger-distance ness,can be attributed to differences was postulited
commuter. These two observed facts in distance and the spatial arrangement commuters from
are probably related: younger person of population. oF ,Employing the gravitv population; (list
take., a job, commutes a considerable idea, a series of probability models withs are confined to
distance, and after Several years when Constructed, expermenting with various tile effect of d
fie commands it higher wage, decides Second, the exi
to buy a house closer to the plant. exponents of distance (measured in ships was used a
to this is the fact that about miles and minutes) in an attempt to
Testifying to this is the fact that about establish a model which most closely ingthe ten-cou

worker, and one-sixth of those at the approxmated the actual (list distribution of availibility
miles and minutes) in an attempt to
shirt factory have moved closer to the establish a model which most closely
approximated the actual distrbution of "A similar app
plant since taking their present job. Taaffle, and 0ther
commuters. It is reasoned that such a

I", Labor Supply and Mobility, up. cit., pp. to Work. A Georg
model provides a basis for judging the ton,III 1963), pp.
pp.
influence of population and distance
Peterson ep cit.,p 11.

TWO NORTH CAROLINA COMMUTING PATTERNS

AGE, SEX, AND LENGTH OF SERVICE OF WORKERS, BY COMMUTING ZONES

Average age Sex, shirt factory

Zone Miles
Fiber Shirt Male Female Percent Male Female Percent Fiber Shirt
plant factory female female plant factory
1... 0-4 34.2 33.9 96 17 15.0 44 391 89.9 110 95

2... 5-9 33.1 32.9 205 53 20.3 8 63 88.7 106 89

3... 10-14 32.5 30.8 174 27 13.8 4 77 95.1 98 63

4... 15-19 29.8 30.8 71 11 13.4 7 65 90.3 74 53

5... 20-24 31.6 29.0 101 16 13.7 4 36 90.0 97 31

6... 25-29 31.6 28.4 52 9 14.8 3 22 88.0 92 44

7... 30-34 30.3 29.1 40 15 27.3 1 7 87.5 89 79

8... 35-39 30.0 22.0 61 17 21.8 -- 1 100.0 95 48

9... 40-44 30.4 28.0 45 9 16.7 -- 1 100.0 89 144

10... 45 & over 30.0 ---- 30 3 9.1 -- --- ----- 90 ---

All Zones.............. 32.0 32.7 875 177 16.8 71 663 90.3 98 82

on commuting. Such models also sug- data, available on a township basis,
gest a means for estimating the potential were used despite a four-year time
extent of a labor-shed about a proposed difference; estimates of 1964 county
new industrial facility. populations fail to show any signifi-
Probability models are presented for cantly large shifts in population within
the fiber plant only. The fiber plant the study area since 1960. Fourth, the
commuting pattern, involving greater area under consideration was limited
distances and a much larger area, to those 74 townships shown to be
provides a more satisfactory basis for wholly or largely within a one-hour
appraising the significance of popula- commuting distance of the fiber plant
tion and distance as factors influencing (Fig.7). One-hour's distance was selected
commuting than does the geographically because it includes almost all com-
more-restricted shirt factory commuting muters and appears to represent a dis-
pattern. tance beyond which few workers would
Before constructing the models it consider commuting. As noted earlier,
was necessary to make several decisions the one-hour isoline corresponds rather
on methods and procedure. First, in all closely with the 45-mile isoline on
models a direct positive relationship Figure 5. Fifth, the approximate pop-
was postulated between the number of ulation center-of-gravity of each town-
commuters from an area and that area's ship was used to measure the distance
population; distinctions between models between a township and the plant;
are confined to varying expressions of distances were read off the commuting
the effect of distance on commuting. isoline maps, in miles or minutes (Figs.
Second, the existing network of town- 5 and 7). Sixth, in indicating the
ships was used as the basis for regionaliz- distribution of commuters suggested
ing the ten-county area because of the by a model, each of the 74 townships
availibility of population data for each is placed in an appropiate distance
township. Third, the 1960 population zone for purposes of simplicity and the
data presented by such zones; zones
"A similar approach was used by Edward J. were established over intervals of five
Taaffe, and others: The Peripheral Journey miles and six minutes, respectively. Six
to Work, A Geographic Consideration (Evans- minutes was chosen as the time interval
ton, III., 1963), pp. 36ff.

132
XONOMIC GEOGRAPHY

distribtition
ill each of
distance 70
distributions

ages
of th,
each model
associatioW"
.7 the degree
distribution
and the ac,
30(55%) nititers by diz,
4vd,,
el oil
;4 20(31%).
"rem retards conin
age.
to Inile,
"od

formulation
C = P/d with
of coninititer.,
I
20 N this-stti(
association is U,
distribution of c
ability inodel xv
distance zones,
total. Perrentag
distribtition are
other, and the ,i
differences divid
from nue. .\If i
soniewhere hetwi,
a complete lack-
olle indicates a
ellssioll of this
Methods of Re
pp.. 253,
FC011011lie Gcogf6:
pp. 595-597.

FIBER PLANT LA13ORSHED
COMMUTING TIME ISOLINES Each Small Cot f0presents one Commuteri
larger Circle$ represent too or rmare
TEN-MINUTE INTERVAL Zone .11ites
comimutorg, with riumaer given men? to circle

Filutell In ladicaft PW Con$ C.,Clo md,Cat*s Corrimutore
OF commatere Enclosed by that Lima mr-1h." th'te ,;#I of plant
I...... 0-4
909" an Date Obtained from Oveet.onna,,av. Match, 1964 0 10 go MILES 2_. . . 5-9
3...... 10-14
IrrL 4..... IS-19
S...... 20-24
7. 6...... 2S-29
7...... 30-14
It
1) ...... 40-44
(rather than ten as ill Figure 7) onlv equals 6.2.5 minutes, with six being tile 10...... 4 5 'k. -'Wel'
T,,tal ..............
because it regression analysis indicate('I closest round number.
c:@e1ficitent "i Rei'va
(,()Illllltltlllg tillic of 1.23 mintiteS per Ili tile first series of sevell nlodels 0 nk, i It actual di@ti
additional mile, 1.2.5 tinies 3 miles distance is meastired ill iii-iles. I'lic

TW0 NORTH CAROLINA COMMUTING PATTERNSdi

distribution of commuters suggested township under consideration, and of the
in each of these models is shown by distance in miles from the townships
distance zones in Table VIL The population center of gravity to the plant
distributions are expressed as percent- (read from Fig.5). Model one suggests
ages of the 74-township total. For that there are more commuters from
each model the coefficient of geografic the more distant zones and somewhat
association is calculated, measuring fewer from the nearer zones than is
the degree of coincidence between the actually the case. In other words, the
distribution suggested by the model retarding effect of distance is under-
and the actual distribution of com- stated beyond 23 miles, and overstated
muters bv distance zones. closer in. Nevertheless, a reasonably
Model one assumes that distance high (.851) coefficient of geographic
retards commuting in direct proportion association is attained.
to mileage. This is the most elementary In the second model the frictional
formulation of the gravity concept; effect of distance in discouraging com-
muting is increased by squaring tile
C = p/d with c representing the number
of commuters, p the population of the distance. This formulation, c = p d^2, is
rather commonly employed by those
In this Study the coefficient of geographic
association is used to compare the geografic constructing gravity models. In this

distribution of commuters suggested in a prob- instance the negative influence of dis-
ability model with the actual distribution by
tance is grossly exaggerated, and the
distance zones, expressed in percentages of the
total. Percentage values for each zone in involve coefficient of geographic association is
distribution are subtracted from values, in the a poor .728. In all but two closest
other, and the sum the positive (or negative)
differences divided by 100 is then subtracted zones the suggested number of com-
from one. All coefficients will have a value
somewhere between zero and one; zero signifies commuters is understated.
a complete lack of association, and a value of Model three seeks a better fit by
one indicates a perfect association. For a dis-
cussion of this measure see Walter Isard: attempting to combine the approaches
Methods of Regional Analysis (New York, in the two previous models. Since model
1960). pp. 253, 255; or John W. Alexander: one's suggested commuters in the closer
Economic Geography (Englewood Cliffs, 1963),
pp. 595-597. zones were somewhat below but close

TABLE VII

DISTRIBUTION OF FIBER PLANT COMMUTERS SUGGESTED BY PROBABILITY MODELS
(Distance measured in miles)

Actual
Zone Miles distri- Model I Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
bution Percent Percent Percent Percent Percent Percent Percent
Percent

1...... 0-4 7.56 6.12 24.74 9.30 1.64 1.69 7.00 7.63

2...... 5-9 31.86 27. 2 41.96 41.85 19.69 20.29 31.52 34.37

3...... 10-14 15.77 8.62 8.78 13.11 9.27 9.55 9.88 10.77

4...... 15-19 7.57 5.76 4.05 8.79 8.95 9.29 6.63 7.21

5...... 20-24 13.29 13.13 7.17 17.12 21.18 20.33 13.73
6...... 25-29 2.59 3.83 1.81 2.78 5.34 4.91 3.60

7...... 30-34 3.79 6.17 2.46 2.20 7.27 6.79 3.30 4.60
8...... 35-39 11.22 16.96 5.68 3.27 16.74 16.53 13.45 11.14
9...... 40-44 4.54 5.42 1.61 .75 4.76 4.97 4.13 3.36

10 ...... 45 & over 1.82 6.48 1.75 .78 5.17 5.69 4.79 3.83

Total ................ 100.0 100.01 100.01 99.95 100.01 100.04 100.03 100.01

Coefficient of geographic associa- .851 .728 .830 .754 .763 .919 .914
tion with actual distribution

134 ECONOMIC GEOGRAPHY

to the actual, moDEl three assumes that tances up to 20 miles,and beyond that
the number of commuters decreases at a proportional to twice the
directly with distance up to it range mileage (e.g., a distance of 30 miles
of 20 miles; beyond that tile distance would be assigned a %,;title of 20 + 10 x 2
is squared just as in model two. Overall, = 40).Employing this approach, a Zone
the result is an improvement over quite favorable .919 coefficient of .,,Co-
model two, with a .830 coefficient of graphic association is obtained. Never- I.........
2.........
geographic associatim, but the number theless, the proportion of longer-distance 3.........
of commuters in the more distant zones commuters was sufficient1y overstate(] C........
is even more severely understated. to warrant continued experimentation. 6.........
In models four and five, a different. Model seven utilizes the same ap--
approach is used; a f riction less zone is proach its model six, but tile frictional
Total ........
assumed. It is reasoned that since tile effect of distance beyond 20 miles is
average fiber plant worker drives over increased. Committing is assumed to Coefficient of
actual distri
17 miles, and since over one-fourth of diminish directly with mileage up to
all workers drive 20 miles or more, 20 miles, and bevond that at a pace *The " O-S
perhaps distance seriously discourages proportional to three times the mileage- six minutes to
commuting only beyond a certain point. In this case the suggested percentage
miles is I
Twenty miles was selected, somewhat of commuters from each mileage zone 33 miles
arbitrarily (but With the distribution is close to the actual distribution, and results in
in model one in mind), as the outer it very favorable .934 coefficient of superior t
limit of this frictionless zone. In model geographic association is realized. The model tw
four, calculations all distances over restraining effect of mileage could be two closes
20 miles are squared, and distances up further rcstated in subsequent models a favorable
to 20 miles all assigned a value of 400 and the coeficient of association prob- griiplic as,
(20 squared). In model five, distances ably improved somewhat, but the ap-
Model.
over 20 are tripled, and distanceS Lip proximate significance of mileage would model sev
to 20 miles all assigned a value of 20. appear to be already evident. assumed t,
The suggested distribution of com- Another set of five models was con-
muters by distance zones is about the structed. These differ from the others as the -
&-title in 'both models; the number of in that distance is measured in ternis as suggest
short-distance commuters is badly un- of commuting time (Table Vill). It bevond th
tile minit
derstatcd, with tile result that tile was reasoned that it might be possible did not a(
suggested number of medium and long- to achieve it better fit using this distance
distance commuters is generally too measure, and, if so, it might suggest of success
coefficient
high. In this instance it appears that that time is it better measure of com- Model
the assumption of a frictionless zone muting distance than mileage. on model
is inappropriate. I Models eight and nine, patterned frictional
Model six abandons any assumption after models one and two, employ the nine the
that there is a frictionless zone or that two most standard formulations of up to 18
distance discourages commuting pro- the gravity concept. In model eight eleven di
portional to some power (e.g., the distance is assumed to retard commuting
square) of the mileage. At tile same in direct proportion to the number
time. it is recognized that the retarding Of Minutes, and in model nine in direct commutir
effect of distance is accelerated toward relation to the square of the number of of 18 (e.g
the outer margin of the labor-shed. In minutes involved. Model eight results a value
model six calculations, commuting is are inferior to those of model one; the The resu
assumed to diminish directly with dis- proportion of commuters beyond 35

TWO NORTH CAROLINA COMMUTING PATTERNS
TABLE VIII
DISTRIBUTION OF FIBER PLANT COMMUTERS SUGESTED BY PROGBABILITY MODELS
(DISTANCE MEASURED IN MINUTES)

ZONE 1 2 3 4 5 6 7 8 9
MINUTES 6-11 12-17 18-23 24-29 30-35 36-41 42-47 48-53 54-60
ACTUAL DISTRIBUTION PERCENT
7.56 30.35 12.74 9.30 8.64 9.94 4.32 10.37 6.79

MODEL 8 PER CENT
3.37 21.28 6.25 6.03 7.42 12.59 7.07 20.60 13.40

MODEL 9 PER CENT
30.39 35.54 8.53 6.24 6.53 9.33 4.31 11.76 7.34

MODEL 10 PER CENT
4.10 25.83 7.59 7.31 9.01 13.78 5.95 15.85 10.53

MODEL 11 PER CENT
9.31 38.74 14.78 3.35 3.38 1.19 2.80 1.37
MODEL 12 PER CENT
35.63 33.41 6.61 3.99 3.86 5.01 1.22 3.72 3.54

TOTAL 100.01 100.01 899.97 99.97 100.02 100.00

Coeffient of geographic association with actual distribution
.758 .900 .849 .678 .689

The 0-3 minute zone is considered nonexistent As indicatd by the regression line in Figure 3. it requires about
six minutes to travel zero miles this is presumably a measure of terminal time.

miles is badly overstated and withiin
35 miles understated.By ontrast, the results in model nine are immensely
superior to those of he unsuccessful
model two. While the values for the
two closest zones are significantly high,
a favorable (.900) coeffiecient of geographic association is attained.
Model seven as a guide; distance is assumed to restrain communing directly
as the number of minutes up to 35
(as suggested by model eight ) and beyond that at the rate of three times the number or minutes. Model ten
did not acheive model seven's measure
of success and recorded a fair (.849) coeffiecient of geographic association
Model eleven attempts to improve on model nine by adjusting In model ine the suggested values were high
up to 18 miniutes Therefore in model eleven distance is presumed to retard
commuitind dircetly as the number of minutes up to 18 and beyond that at the rate of the square of values in excess
of 18 (e.g.25 minutes would be assigned
a value of 18+7 squarred. or 67) The results proide an example of how
a seemingly minor adjustment can severely change suggested distributions; model eleven registers a very poor (.678)
coefficient of geographic association. One final model was constructed.In the previously discussed regresasion anai
ysis comparing commuting mileage with minutes, a terminal time" of about six minutes was indicated. Perhaps a superior
measure of distanse would be attained by subtracting six minutes from all indicated comuting times.
thus specifying the time clapsed while actually travelling. Otherwise model twelve is similar to the
successful model nine i.e. commuting is assumed to be discouraged in direct proportion to the square of the
distance. The results in this model are disappointing: the very poor (.689) coefficient of geographic
association suggests that the subtraction of terminal time is unwarranted.
Despite the failure of attempts to improve on model nine, the best in this group it must be assumed that con
tinued experimentation could in all likelihood produce a somewhat closer fit. The construction of the probability
models demonstrates that geographic distributions similar to the actual ones.

Economic Geography

can be approximate, leading some tion, intensity of agricultural and manu-
credence to the assumption that the facturing employment, per capital in-
number of commuters will vary directly come, wages, levels of unemployment,
with population and inversely with and density of paved roads. One might
distance from a specified point. The consider other factors, such as levels of
significance of an irregular population education, land tenancy, and farm
distribution as a factor contributing to abandonment. All of these factors and
an uneven geography of commuter many more may have some influence
origins is evident in the models. Less on the tendency of willingness of
obvious is the specific impact of dis- workers to commute.
tance on commuting. The fact that a As a means of observing the possible
very high fit was attained in model significance of some of the variables
seven suggests that in this area distance noted above, Table IX was prepared,
does exert a greater restraining influence comparing the actual number of fiber
beyond about twenty miles, perhaps plant commuters from each of nine
proportional to the mileage be- counties with the number suggested
yond that point. As to whether the in probability model seven, the model
number of miles or the number of achieving the highest coefficiant of
minutes is the better measure of dis- geographic assocaiton. Model seven
tance, there s little evidence here to values are treated as the "expected
support one over the other, even though norm," and the percentage deviation
a somewhat better fit was obtained of the actual number from that expected
using mileage. in the model seven is inidicated. The southern
counties provide many more commuters
THE COSIDERATION OF OTHER than model seven suggests, and the
GEOGRAPHIC VARIABLES northwestern counties quite the reverse.
Populatin and distace are not the The deviations were compared with
only geographic variables which prob- the county data presented in Table 1.
able have a bearing on the spatial Clearly evident is a reasonable high
pattern of commuter origins. This would correlation betwee these deviations
partially explain the difficulty in con- (actual from expected commuters) and
structing models which consider only at least three geographic variables: per
these two variables. When probablility capita income, population density, and
model distributions were compared with intesity of agricultural employment.
actual distributions, discrepancies were To facilitate regional comparisons, for
noted. For example, a particular dis- each of the four variables the nine coun-
tance zone may be supposed to generate ties are ranked , one through nine
a specified number of commuters ac- (Table IX). In the case of agricultural
cording to some model, but in fact employment, where the correlation is
provides only a few. Perhaps some negative, the ranking is given in inverse
other factors are causing the distance order to maintain concordance.
zone to supply fewer commuters than the similarity of the four rankings
would be expected on the basis of the shown in Table IX is sufficient to suggest
population- distance relationship built that these three geographic variables
into the model. As can be seen in Table may have a significant effect on the
I, there are sizeable differences among commuting pattern of fiber plant work-
Labor-sized counties in such matters as ers. The moderatly high rank corre-
rates of population growth, urbaniza- lation coefficients attained (+.57 to

138 ECONOMIC GEOGRAPHY

about three times the area. Commuting labor-shed from which it can expect
isolines provide a promising means to draw labor.
of delimiting laboor market areas; this A series of probability models based
permits the labor-shed to be conceived on gravity concepts provided a useful
as a region diminishing by degree rather method of appraising the importance
than terminating abruptly at some of two geograpic variables, population CAPITA
arbitrarily-designated limit.Commuting and distance. The positive relation
isoolines can be based on miles or min- between the irregular population dis-
utes, but the additional effort required tribution and the uneven geographic
to obtain data on travel time in itself pattern ofo commuter origins is effectively Dr. Log
recommends the use of mileage in con- indicated by the high degree of associa-
structing isooline maps. As to the ques- tion between the distributions of com-
tion of which is the better measure of muters suggestedd by soome models and The dist
commuting distance-miles or minutes the actual geographic distribution. The activities
--the evidence in this study, while apparent significance ofo distance in concentra
inconclusive, does suggest that there is retarding coommuting in this sectin of ital city modes.
relatively little advantage in ne over eastern North Carolina was approxi- pattern to be ex
the other. mated through a lengthy process of Western country
Wages appear to be the primary experimentation with assorted valuations six Soveriegn Stat
factor explaining the acute differences of distance in the probability models. particulat, is hea
in the two commuting patterns. Wages Discrepancies between the actual dis- distributioon patte
at the fiber plant are, like their coommut- tribution of commuters and those desig- portance of variou
ing distances, much above the average nated by the more successful models. interventioon and
for the state and the ten-county study are possibly explained by spatial differ- tion in Australian
area. Within the fiber plant group, there ences in ther conditions. Districts of this paper are
is no correlation between wages and generating more commuters than a turing distribution
distance, but the critical point here model suggests tend to be areas with city levels and to
may be that almost all fiber plant a high percentage of the labor force in specialization of
workers receive a wage above the agriculture, low per capita income, low veloped. Because
study-area average. The shirt factory population density, and little if any the distrribution,
workers, with a below-average wage, recent population growth. In the final niques based on
commute a distance soomewhtat under analysis, an appraisal of any commuting factoring employee
the state mean. The prevalence of pattern requires a consideration ofo a Manufactoring i
female workers at the shirt factory does multitude of interrelated geoographic states, New South
not explain the variance in distance, as variables. and in the capital
there are no apparent differences in both respects its
the commuting habits of men and ACKNOWLEDGEMENTS is more concentrat
women in this area. A firm considering The kind of cooperation of EI du Pont offi- lation. Whereas N
a locatioon in this or similar district cials, and Sol Schechter of the Kinston Shirt Victoria in 1961
should compare its wage standards Company, and the advice and help given by 63 per cent of the
with those prevailing in the area before Hugh M Raper, Lonnie Dill and other ofo the they contained
making estimates on the size of the employment Security Commission of North manufacturing jol
Carolina are sincerely appreciated. tioon 1901, Vict

TWO NORTH CAROLINA COMMETING PATTERNS
TABLE
SOME GEOGRAPHIC VARIABLES POSIBLY RESOPONSIBLE FOR DISCREPANCY BETWEEN
MODEL SEVEN AND ACTUAL DISTRIBUTION OF FIBER PLANT COMMETERS

COUNTYS CENTRAL COUNTIES, LENOIR, PITT, GREENE, NORHWESTERN COUNTIES WAYNE, WILSON ,SOUTHERN COUNTIES DUPLIN,JONES EASTERN COUNTIES
BEAUFORT, CRAVEN
NUMBER OF COMMENTERS SUGGESTED IN MODEL 7 435 203 36 38 31 19 13 19 34

ACTUAL NUMBER OF COMMENTERS 368 261 57 13 7 51 23 19 64
PERCENTAGE DURATION ACTINAL FROM SUGESSTED -15 +29 +2 -17 -77 +165 +77 0 +19

3 7 5 2 1 9 8 4 6
3 5 6 4 2 8 9 7 1 +.62
3 4 6 2 1 7 9 8 5 +.73
3 6 9 4 2 7 8 5 1 +.37
Spearmans rank correlation coeffient with figures in fourth column
Only those counties within a one hourcommuting distance (arbitraru cutoff point in probabilitu models) are considered
Rank among nine counties here considered
Derieved from data in table1.

+73. wiould be much highr werit
not for Craven County's sizable rank
inconsistency. Craven is unique amont
study area counities inthat it possess
a large military installation (cherry
Point Marine Air Station ) employing
large numbers of civilians thus explain-
in part that county's highre per
capita income and lower intensity of
agricultural employment.
The generation of greater than ex
pected numbers of commuters from
lower infcome counties tends to sub-
stantiante teh observation made earlier
that when a manufacturing plant offers
wages apprrecialbly above those prevail
ing in a region one can expect the
workers to show a greater willingness
to commute longer distances. The data
for Duplin and Jones counties in Tables
I and IX suggest that the greater the
positive difference between a plant's

11 Further evidence of such a relaionship
can be seen in Figure 6 which shows the distribu-
tion of shirt workers. Dulpin county where
the average income is particurlarly low, supplies
manyu more workers that other higher income
counties equidstant from the factory.

wages an those prevalent inan area the greater the distznce workers will be willing to travel.
The counties with the highest percentage of labor force in agriculture are also the counties
with lower per capita incomes and stagnant or declining populations; such conditions are indicative
of poor or declininf agricultural opportunities and presumably stimulate commuting.22

SUMMARY
The commuting pattern of the higher wage fiber plant workers contrast sharply with that of th lower-wage
shirt factory employees. The former commute a mean distance of 17.5 miles
each way, while the latter average 6.7 miles Where the two labor sheds
are outlined to encompass the neaest 90 per cent of the respective commuters the fiber plant labor-shed covers
The greater temlency of workers to commute whee agricultural conditions are poor
was observed in upstate New York by Harold E. Conklin The Rural Urban Economy of the Elmira Corning (N.Y.)
Region, Jouern Land and Public Utility Economics, Vol 20,1944 p.3.

Triii@inp,, Jobs to People: Does It PayD?

I by GENE F. SUMMERS and JEAN M. LANG

This article was prepared by Gene F. Summers, Professor of and a good Supply of' laborers, presumably steeped in the
Rural Sociology, University of Wisconsin-Madison, and Jean American work ethic.
M. Lang, Editor and Science Writer, Institute for Environ- Industry's interest in rural factory sites has been strongly
mental Studies, Universitv of Wisconsin-Madison. I t is based
upon material in Gene @. Summers, Sharon Evans, Frank I encouraged by the eager solicitations of potential host com-
Clemente, Elwood M. Beck, Jr. and Jon Minkoff, Industrial munities and by federal policy. For example, nonmetro- I
In Pasion of Nonmetropolitan A meri(w; Praeger, 19 76. politan location of industry has been an explicit goal of
The University of Wisconsin Department of Rural Sociology recent federal anti-poverty legislation including the Eco-
issues a semi-annual list of "Publications in Print." Many of nornic Opportunity Act of 1964, the Public Works Act of
these deal with applied programs in Wisconsin, others with 1965, the Appalachian Regional Act of 1965, and the Rural
specific studies in community development and rural indus- Development Act of 1971.
trialization related to problems discussed in this article. For Tile apparent logic behind this interventionist strategy is
a copy of the publications list, write Gene Summers at the fairly simple. Both rural poverty 4nd urban socioeconomic
Department 'of Rural Sociology, 603 WARF Building, problems are seen as products of a geographic mismatch of
University of Wisconsin, Madison, Wisconsin, 53706.
labor supply and demand. The mismatch has been caused
Over the last twenty-rive years manufacturing industries by a decline in economic opportunities in rural areas and an
have been moving out of the city and into the countryside at increase of the same opportunities in urban areas. One means
an ever increasing rate. Between 1960 and 1970 manufac- of correcting this imbalance is to stimulate the rural
turing employment in nonnietropolitan areas grew by 2.) economy, thereby increasing job opportunities and halting
percent while manufacturing jobs in metropolitan areas grew the exodus of rural labor to the city.
only four percent. An industry, particularly a manufacturing plant that
Industries have had their own reasons for expanding into generates a direct flow ofmoney to the local community, is
rural areas: lower local taxes, cheaper land and water costs, considered an ideal stimulus for the rural economy. Indus-

Nam.
Mow

7"!

A.
r
.. ft@s
.A.

7
FIE

This aerial photo shows the village of Hennepin, Illinois. and the Putnam County Court House (center, right) -the oldest
in continuous use in Illinois -with a new Jones & Laughlin Steel plant in the background. The plant produces cold rolled
and galvanized steel sheets. This, and the photo an page 10 are courtesy of the Jones & Laughlin Steel Corporation.
47

MW%
943% R,, - L E
try's presence is expected to spark income growth, popula-
5'
tion redistribution. housing improvements, better com- R E 56, Q% R E T_T_ 84'
munity services, and other amenities. It is exactly these
I presumed benefits that make large industry so attractive to
the small community. But are these benefits being delivered?
Do rural communities really profit from industry's arrival,
or are there undesirable side effects?
In a study sponsored by the Economic Development
Administration, U.S. Department of' Commerce, a team of
sociologists attempted to answer these questions.'
Our group reviewed almost 100 case studies of' the im-
pacts of industrial location on nonmetropolitan comilluni
ties. The case studies encompassed more than 700 manu-
facturing plants in 245 locations and 34 states. The N
predominant industries were metals production and fabri- Zoning laws prohibit subdividing farms in this Wisconsin
cation, chemicals manufacture and wearing apparel assembly. township so developers get around this rule by creating
The factories ranged in size from those with less than ten 5-acre "farmettes." University of Wisconsin photograph
workers to plants with over 4,000 employees. The majority by Jim Larison.
of factories were located in the Midwest and the South.
Although the studies included a great diversity of' indus- Table I
tries and locations, they did not constitute a representative Percentage of New Plant Workers
sample and should be judged accordingly. Previously Unemployed

Emplojyment - Direct Hiring % of Jobs
Filled ky
There is no question that industry brings new jobs to a NO. of Previously
community. Some of the jobs come front direct hiring of StudY Site IndustrY Jobs Ernernplo.yed
plant personnel, and others follow indirectly as the new Linton, Ind. Aluminum chairs too 25.0%,
industry stimulates growth in existing sectors of' the local @Vyn,c. Ark. Apparel; copper
economy. The important question is who gets the new jobs. tubing 1,900 11.2
Our study revealed that new factories generally did not Rochester, Minn. Business machines 1,862 14.0
hire the local unemployed. In the majority ol'cases only, a Ravenswood. W. Va. Aluminum 894 11.0
small portion of the jobs were filled by local disadvantatied E. Oklahoma Comm. 12 plants (mixed) 554 7.7
or unemployed persons (Table 1). There was also con- A.R.A. Area Survey 33 plants (mixed) 1,262 43.0
siderable evidence that nonwhites were underrepresented in Mt. Airy, N. C. Appliances 435 8.0
rural factories. Jefferson, Ia. Stamping, athletic
There appeared to be two primary reasons why local poor, equipment 369 3.0
minorities and disadvantaged were infrequently hired: Orange City, Ia. 10 plants (mixed) 364 19.0
Creston, Ia. Appliance, chemicals,
First, the labor pool for a rural industry evends well (At filters 424 1.0
beyond the area of the host communitv. Long distance Grinnell. Ia. Farm machinery,
stadium bleachers,
commuters are not uncommon, and the new i'actory plastics 200 7.0
generates considerable in-migration and setti Decorah, Ia. Screws; undetermined 212 8.0
workers from the surrounding area (Table 2). From this Star City, Ark. Apparel (shirts) 336 9.5
widespread labor force, industry selects the better educated,
more highly skilled worker with the "right" racial heritage. Table ?
The local unskilled resident often has little hope of quali-
fying-* Proportion of Plant Workers Migrating
to Take New Employment
Second, many jobs are taken b 'v newcomers to the labor
force, primarily women. Many rural industries, particularly Census No. of A verage
texti les and electronics assembly, prefer female labor. Thus Region Studies Percent
previously nonworking women rill the factory jobs. This North Central 6 32
increases the number of people in the labor force but does South 4 32
not decrease the number of' unemployed workers in the
community. West 1 18
Ironically, it is possible for new industry to reduce tinem-
ployment and poverty in a community without providing a A -11 1@egions 11 30

An example of the encroachment of housing developments on agricultural land. Photo courtesy of Jim Larison. University
of Wisconsin.

single job to the disadvantaged who live there. Although lite
Table 3
labor force may expand faster than the ranks of the unem- Unemployment Rates Before and
ployed, the absolute number of persons in economic distress After Industrial Development
may be unchanged or slightly increased (Table 3). In general,
file case studies showed that the operations of' the local Dates Rates
labor market often work against the needs of' the people fOr Sandy Sites Before After Before After Change
whom rural industrial development has been allegedly pro-
moted. Jackson Co. la. 1950 1960 1.8 3.7 +1.9
Cross Co.. Ark. 1960 1970 5.2 4.6 -o.6
Washington Co..Miss. 1950 1963 10.1 4.2 -5.9
I Employment - Multiplier Effect
Besides hiring local workers for its factory, new industry Box fider Co.. Utah 1955 1965 6.7 7.o +0.3
is expected to generate secondary jobs in the retail, whole- Putnam, LaSalle and
Bureau Co., Ill. l966 1973 3.6 5.0 +1.4
sale and service trades ofthe host Community. This indirect Adair Co.. Okla. 1900 1971) 16.4 17.5 41.1
effect on employment is called a -multiplier.- A multiplier
Cherokee Co., Okla, 1960 1970 16.2 10.0 -6.2
of 1.0 means the industry brings no new jobs except those Muskogee Co.. Okla. 1960 1970 8.9 7.4 -1.5
by direct hiring. A multiplier of' 1.65 means that for ever
Hot Springs Co.. Ark. 1958 1971) 11.9 7.o -4.9
new job in the factory, another .65 jot) is created within tit
Ba\ ter Co., Ark. 1964 1971 8.2 4.7 -3.5
community. 1970 4.3 3.) 0.4
A significant finding of the case study review was that the . HowardCo.. Ark 1960
Logan Co.. Ark. 1958 1970 15.6 6.8 -8.8
majority of' industries in the rural COMMunity had a mul- Randolph co.. Ark 1964 1970 9.4 9.3 -0.1
tiplier effect of less than 1.2. Several reasons were given for Benton Co., A r k. 1901) 1970 5.5 4.5 1.0
these low multipliers:
White Co,, Ark. 1960) 1971) 12.1 12.1 0.0
First, the less diversified the existing manufacturing
Laurel Co.. Ky. I960 1963 12.6 7.1 -5.5
commercial and service industries (ire. the less impact the Lamar Co.. Texas 195 2 1902 0.0 5.2 -0.8
i new industry will have on local economy.
6

Second, commuters, who generally make up a substantial showed that average increases in individual income varied
part of the rural factory work force, often spend their frorn 5.3 to 183.0 percent, and average family income in-
salary in their place of residence rather than their place of creases ranged from 25.6 to 178.4 percent. However, in
work. Much of the factory income "leaks out" of the [lost most cases both family and individual income increases were
community. less than 50 percent.
Third, many small towns already, hvre exccess under- Three factors were largely responsible for the frequent
utilized business capacity. As a result, tile firm call handle cases of relatively small income growth:
ind ust ry -induced increases in sales without hiring additional Small income increases were usually associated with
workers or enlarging their capital stock. lower wage industries such as wood, textiles and
Fourth, many industries are linked by a national network apparel.
to outside suppliers and processors and have no need to
Industries importing raw materials into the area and
draw upon local services or products. exporting products out of the area created smaller
At worst, the local community may become little more I
than a labor source for the factory with virtually no indirect secondary income effects as discussed above,
or induced employment. A Substantial amount of commuting by nonresidents
Four often cited studies (nos. 15. 16, 17, and 18 in into an area for work, and by residents out of an area
Table 4) that depict nonmetropolitan industry with a mul. to shop, reduced the size of income growth.
tiplier of 1.5 or more were closely examined by the review Significantly, of the numerous case studies on industry's
team. In each of tile studies it was found that only those impact. very few had considered how income growth is dis-
rural counties had been selected that [lad relatively large tributed throughout the population. Of those studies which
manufacturing sectors (more than 15 percent of total em-
did examine this factor, all suggested that certain sectors of
ployment) and were undergoing rapid and substantial eco- tile population receive no benefits from industrial develop-
nomic growth. According to these criteria, only 30 counties ment. Indeed, for groups such as tile elderly and blacks,
in the entire U.S. qualified in 1970. industrialization often has negative effects. As the com-
munity's standard of living rises, prices go up and the pur-
Income chasing power of these disadvantaged groups decreases.
Industrialization of the rural area does bring an increase In addition. several of the impact studies showed that tile
in average income over a period of time. The case studies greatest gain in benefits went to newcomers in the coin-

Table 4
Employment Multipliers

Unit of Research Industrial Direct Employment
Study Site AnalYsis Time Period Product EmploYment Multiplier

I Linton, Ind. City 1964 Aluminum chairs 119 1.02-
2. Gassville, Ark. 8-County Area 1960-63 Shirt plant 750 1.11
3. Summerville, S. C. 4-County Area 1963 Brick Factorv 25 1.36
4. Pickens, Miss. 4-COUnty Area 1964-65 Tissue paper mill 57 1. 14
5. Braxton Co., W. Va. County 1963 Particle board plant 77 1.5O
6. Hart Co., Ky. County 1963 Bedding plant 111 .06-
7. Fleming Co., Ky. County 1958-63 Auto& appliance
trim. shoes 328 I'll
8. Laurel Co., Ky. County 1958-63 Yarn 107 1.18
9. Lincoln Co., Ky. County 1958-63 Apparel 380 1.00-
10. Marion Co., Ky. County 1958-63 Barrels, Communications
Nuipment, Apparel 496 1.11-
11. Russell Co., Ky. County 1958-63 Apparel 206 1.03
12. Howard Co., Ind. County 1949-60 All manul'acturing 4.006 44
13. Box Elder Co., Utah County 1955-61 Chernicals .5.688 .34
14. Lawrence Co., Tenn. County 1954-63 Bicycles 2,270 1.36
15. Select U.S. Counties I I Counties 1950-60 All manufacturing 17.116 -1.65-
16. Select U.S. Counties 10 Counties 1960-70 All manufacturing 25,677 1.68
17. Leflore Co., Miss. County 1959-64 All manufacturing 1,430
18. White Co., Ark. County 1951-59 All manfacturing 59o 1.71
77

munity rather than to the original residents. lJhis suggests 1popular notion that attracting more industry to the small
that the people who bear the cost of the development (by town will eliminate the need for the young to leave home in
increased taxes for land development, for example) may not search of work.
be the same people who will capture the b
enefits and in jBenefits to the Public Sector
fact they may find themselves in a worse relative position I
i Industry is actively sought by small communities in the
after development than before. i
.1 hopes of enlarging the community's tax base. An enlarged tax
The question arises as to whether industrial development
1base means an increase in public income and the expansion
is a desirable community goal simply because it inay mar- I
ginally increase average income. The basic issue boils down of community services. In general, industry's contributions
to whether growth in "community" well-being should be to the public income can be divided into two categories:
purchased at the expense of the disadvantaged. direct payments and induced (or indirect) payments.
Population Changes Direct PaYments
Properij., Tax. The actual size ofindustry's property tax
Does industrial development halt population decline in bill is largely determined by local and state tax structures
small towns or rural communities? The answer is une- and by negotiated agreements between local government
quivocally, yes. officials, development representatives and industrial man-
All case studies dealing with industry's impact on rural agement. Case studies show that frequently local govern-
ipopulation showed that the rate of population decline nient is willing to grant "tax holidays" exempting industrial
had been slowed, halted, or-as in the majority Of cases- property from taxation for 5, 10, or 15 years. This is a form
reversed after industry's arrival. However, the studies also of subsidization for industrial development and as such is a
made it clear that most population growth was based on an cost to local government.
Iincreased migration of workers into the area. FeesandService Oiarges. Communities with municipally
In eleven case studies, an average 30 percent of factory owned utilities can expect direct payments from industry
workers had moved into the host communities to take their Ifor services rendered. These utility fees should at least
jobs. The majority of these workers had griginally commuted equal the cost of extending service to the plant. The evi-
to the factory from neighboring areas within a radius of 50 dence suggests that in many communities costs are, in fact,
Imiles. Eventually, however, as the workers became more all that is recovered from fees and there are no net gains
settled and secure in their jobs, most of them moved into_ from utility payments.
the host community or nearby towns. Exceptions to tills Tile few studies which focused on industry's direct pay-
trend occurred when a county had well-developed trans- merits to local government suggest that most of the potential
portation and educational systems, as well as a surplus of for income gain by the host community is bargained away.
labor. In such instances, employees preferred to commut
Many local leaders are willing to trade direct revenues from
rather than move to town.
The population growth that accompanied industrializa new industrv for indirect funds on the apparent assumption
ithat the latter will outweigh the former.
tion was found to be centered in the factory town rath er'
than being spread throughout the country. In almost all In(firect Pa'vinents
cases, the population in the host town increased while the Indirect payments by industry to the public sector are
rural and farm population of the surrounding area decreased. more diverse and are based on industry's ability to boost
Thus, industrialization frequently caused more of the coun ty local average income and subsequently increase the value-
population to become "urbanized" or "suburbanized" and tax assessments-of local properties and businesses.
A without causing any overall increase in county population. Wages and salaries paid by the new industry are a stimulus
Industrial location is often promoted as a technique for to growth and add to local income only to the extent that
achieving urban-rural population balance. Our findings, the plant's payroll is spent in the host community. How-
however, suggest that what industry does achieve is a redis- ever, one case study revealed that through leakage of income
tribution of the local rural population rather than a inove- to noillocal recipients, ail average weekly plant payroll of
ment of people into the area from distant metropolitan areas. S6.000 shrunk to S4.779. The "leaked" money was spent
In a number of case studies, the age composition of the primarily oil food, services arid investments in neighboring
A population also showed slight change with the arrival of cornmuni ties'. was put into savings-, and was used to pay off
industry. The changes were primarily due to migration in old debts. In rural communities, gains in aggregate dispos-
one form or another. In some instances, age declined due to able income may be more apparent than real for the local
in-migiralion of young worker, will, young families, marke
A close look at twelve case studies revealed that most in- Increases in local public revenues result from industrial
dustries preferred to hire young adults who Could handle development only when growth in the private sector is con-
physically hard work. Yet, surprisingly, industrial develop- verted into public monies. These monies include increased
ment failed to stem the flow 0fV0Ullg People Migrating out properly taxes from file expansion or construction of new I
of rural communities. This is noteworthy in light of the homes and businesses, increased retail sales and sales tax,

growth without ever increasing
its commercial or residential tax
COST NEW INDUSTRY BENEFIT base.
STREAM STREAM

OUT PUT
TOTAL TOTAL
LOST TAX PAYROLL PROPERTY Retail Sales. Case studies
payME NTS EMPLOYMENT TAX PAYMENTS showed that retail sales In indus-

TAX
CONCESSIONS GROSS RECEIPS trially developing communities
TAX PAYMENTS increased substantially from pre-
SITE EMPLOY /PAYROLL industry levels. In those com-
SERVICES FEES AND
'\LEAKAGE LEAKAGE SERVICE CHARGES munities which have a local sales

ENVIRONMENT tax, or which receive a transfer of
COSTS IND! T a DIRECT INDIRECT state sales tax receipts, this
DIRECT INCOME -rowth in sales can mean in-
EMPLOY MENT INDIRECT PERSONAL INDUCED
EMPLOYMENT MULTIPLIER INDUCED MULTIPL IER
EMPLOYME NT INCOmE INCOME creased revenues.
Fees for Services. User fees
INCOME and charges such as licenses,
LEAKAGE building permits and rental fees
NET DISPOSABLE on publicly owned land generally
POPULATION INCOME
DIRECT INDIRECT increased as a result of increases
& IN DUCED
DIRECT: INDIRECTT in disposable income or a change
RESIDENTIAL
INDUCED PROPERTY in the consumption pattern of
GOVERNMENT LOCAL
NON-SCHOOL PROPERTY residents.
SERVICES DIRECT INDIRECT TAX
&INDUCED PAYMENTS
INDUSTRIAL Public utility income was seen
SCHOOL PROPERTY to rise in a number of case studies.
POPULATION
DIRECT & "INDUCED INTERGovernmental Transfer of
INDUCED
DIRECT, INDIRECT COMMERCIAL TAX PaYments. Because of legislative
INDUCED PROPERTY RECEIPTS
SCHOOL constraints placed upon their
SALES

SERVICES STATE
TRANSFER INDIRECT taxing authority, many munici-
PAYMENTS INDUCED palities appeal to state and federal
DIRECT INDIRECT FEES
&INDUCED governments for a transfer of
FEDERAL
ENVIRONMENT TRANSFER

COSTS PAYMENTS kinds back to the local level. The
ADDED case studies indicate that as in-
COSTS dustrial development increases
NET
AIN tile local average income and as
Figure 1: Flow chart for Public Sector Costs and Benefits of New Industrv industry's output grows, the
(Adapted from Hirsch, 1901) volume of these transfer pay-
inents also increases. Larger
amounT s of the taxes on personal
increased utility fees and an increase in the transfer of state INCOMe (gasoline, sales, and income tax are typical)
and federal revenues to the local community. I rind their way back to the local community. Similarly,
Residential and Commercial PropertY Tax. New Manu- a greater proportion of' corporate income taxes or
facturing jobs in a community generalLY mean that more gross receipt taxes on industrial output are turned
income will flow into home construction 'and improvements. back to the host community rather than being added
This in tum means an increase in property values and pro- to tile state's general fund.
portionately, property taxes. Likewise, as residents spend The case studies suggest, however, that industrialized
more disposable income and as industry draws upon the communities may come to depend on state and federal
services of local businesses, existing commercial establish- payments FOR larger share of' their total receipts. Fre-
ments will expand. In fact, all the case studies showed quently, this dependence on transfer payments is only
that industrial development did bring increases in assessed temporary and declines after a period of adjustment. For
valuation of property and subsequent increases in local example, since tile gasoline tax is more immediately re-
property tax revenues. sponsive to growth in economic activity than is assessed

However, the case studies also revealed that increases in valuation I property. local officials may temporarily rely
housing construction or business expansion cannot be pre- on gasoline tax transfer payments rather than on property
dicted with certainty. Many small towns have both under- tax to Meet immediate costs.
utilized housing and excess business capacity. This slack The case Studies are verv consistent in reporting increases
means that the town can accommodate a certain amount of in local revenue foLlowing' industrial location. Tile assessed

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

IWO
>

valuation of properly clearly is expanded and property tax Stances where new industry is given a tax "holiday"
receipts increased in every community. Retail sales con- or reduced rate,
sistently increased resulting in added revenue from sales tax
Intergovernmental transfer payments increased iN absolutE , iNCREASED police arid fire protection,
dollar amounts and communities appeared to shift tile ta provision ofwater arid sewerage. electricitY and/or gas,
often for fees that are less than cost.
burden from local toward nonlocal revenue sources. The
sum in the benefit column call add up to a Substantial As all example of tile large investments that some coin-
amount. munities havc in their efforts to attract industry, consider
tile CITY in Kentucky that issued S250,000 worth of indus-
Cost to the Public Sector
trial rEVENUE bonds to Fiance land acquisition arid building
If one considers only the benefit stream, tile Conclusion Construction for a shoe factory. Since tile land and building
must be that new industry produces added revenue for file Were city-owned. they were exempt( from real property tax.
local public sector. But an often overlooked fact is that the Ill addition, (lie City granted tile Company a five-year
added revenue brought to tile Community by indusTry may exemption from personal property taxes.

be equalled or even exceeded by added and often UnexpeCted In another case, a Kentucky city issued a S650,000
costs. For this reason it is extremely important to consider revenue bond and held title to the [arid, building arid part
flow new industry contributes to tile costs of, tile public Of the equipment of the plant making the in nontaxable.
sector. The city also extended a water line to tile plant at a cost of
Attracting New Industry. The initial costs of new in $10,000 to the City.
dustry arise when a community attempts to attract a plant' All these devel'opment efforts by the local COMMUnity
to its area. Tile most frequently incurred costs in the wooing are forms of' subsidy and must be regarded as costs to tile
of industry are as follows: community.In some instances, part of the subsidy cost is
* land acquisition costs. recovered, but in other instances only a partial recovery is
. site preparation (including extension and improve- achieved. Often local public officials underestimate a new
ment of access roads arid preliminary landscaping) industry's requirements for Community services above and
beyond the initial commitment to land. building and equip-
* loss of previously collectable property taxes in in. ment. These additional costs of government services. Plus
10

costs of school expansion and environmental degradation. I Lirvironmental Degradation. Industry brings long-term
also must be recovered by the public sector if it is to realize alterations of the environment: loss of open space and
a net gain from new industry. agricultural land, increased man-land density and changes in
Accommodating Growth. Besides the costs of attracting land use patterns. In addition. industry frequently brings
industry, the host community must also accommodate the problems of air. noise and water pollution. At the time
costs of a growing population. As mentioned above, industry most of the case studies were made, tile environment was
frequently brings an influx of new workers who are primarily not a major concern and one observer made this comment:
young adults with families. These in-migrants place increased I The most striking social cost to the town imposed by industry is
demands on the community for schools, health care. arid Water pollution. which in most of the towns studied has reached
serious Proportions. The concern for this problem shown by town
recreational and general services. governments is after the fact. Since industry is primarily respon-
Growth in the number of residential and business prop- slible, the weak position taken bv local government suggests that the
absence of water Pollution con'trol is one form of industrial in- I
erties also places greater demands oil local government to centive.2
p
ide improved police and fire protection, road main-
rov Net Gains
tenance and water and sewerage services. Eleven out of
twelve case studies showed substantial increases in costs of Tile net gain of new industry to the local public sector is
community services to residents with tile arrival of industry. tile difference between its direct and indirect cost and its
Water and sewerage services, particularly, were important direct and indirect benefits. While most case studies have
sources of increased cost. Rockdale, Texas, for example, stressed the benefits side of' the ledger, a few have also
was forced to drill a new city well and to issue a bond for looked at the cost side and found some interesting facts. In
sewerage line extension as a result of industrial development. one study live Kentucky towns with eight new plants were
The case studies suggest that while public officials often examined. It Was found that only two of the plants pro-
duced revenues in excess of that yielded by the property
overeitimate t eir 7-o-m-m-TUR'ities jr_5_w_tF-caFacities, they
underestimate t _e capacity of existing uti iii-e-s---an-i-d'-s-e'r'v-'ic-e--s prior to the plant location. Analysis of secondary impacts,
.1 to I- accommodate deve here one might expect net benefits due to operation of tire
Llopment. Tire result is a major outlay
of public funds that increase the per I multiplier effect, corroborated the negative impact of new
R@-ffices. industrv.
Expanding School Senices. The case studies provided Othe'r studies which compared estimated net gains of the
consistent evidence that new industry increases the popu- private sector with net gains by the public sector also
lation of school-age children. It is also clear that increased showed some sharp contrasts. One estimate, which closely
enrollment resulted in increased operating budgets for approximated actual conditions in twelve communities,
schools and sometimes in high capital outlay to acc()Illlll()- showed (lie private sector averaging a net gain of $152,981.
L
date new students. The public sector averaged only $521 and the school dis-
While some of these additional costs are recovered trict $401. This kind of evidence challenges the belief that
through increased taxes and intergovernmental transl -er new industry will substantially improve the fiscal burden of
payments, part of the burden must be carried by the host many nonnietropolitan coninitin i ties. The evidence also
munity. suggest that were local government more assertive in chan-
I ;ieling private sector gains into the public sector, industrial
com ocation Could contribute more positively to a community's
Left (page 10): Aerial photograph of the new Hennepin I
Works Diyision of Jones & Laughlin Steel Corporation, about fiscal well-being,
120 miles southwest of Chicago. The plant, which is now In summary, industrial location in tile rural community
nearing full production, is located on J&L's 6,000 acre site, call bring employment, population growth and economic
and has about 30 acres under roof. prosperity to the area, but as the studies have shown, these
Below: Jim Larison photo shows the effect of new sewage benefits do not come automatically nor do they appiv in all
lines on rural land. cases. In some instances the structure of the comillUnity
arid the character (if the particular industry inerge to tile
More often the industry clearly
benefit of both parties.
gains while having a negligible or even negative effect oil the
M.
host community over tile long run.

Gene F. Summers, Sharon Evans, Jon Minkoff, Frank Clemente,
--and Elwood NI. Beck, Jr., Industrial Invasion of Nonmetropolitan
Anurrica. New York: Praeger Publishers. 1976.
Xbt Associates, Inc.. "The Industrialization of Southern Rural
P
Areas: A Study or' Industry and I@ederal Assistance in smau'r )wns
with Recommendations for P uture Policy," Washington, D.C.: U.S.
Dept. of
Commerce, Fconoinic Development Administration, Office
of Economic Research. December. 1969.
F171

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