:T392

A53

f -7 States

i-^ ment of

^ Agriculture

Economic

Research

Service

Rural Development

Research Report

Number 63

Choosing Among

Local Impact Models

Marlys Knutson Nelson

Lloyd D. Bender
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CHOOSING AMONG LOCAL IMPACT MODELS. By Marlys Knutson Nelson and Lloyd D.

Bender, Agriculture and Rural Economics Division, Economic Research Service,

U.S. Department of Agriculture. Rural Development Research Report No. 63.

ABSTRACT

Large-scale development projects attract many participants in education,

assessment, negotiation, planning, and impact mitigation processes. However,

each potential user of a model of a project's impacts has different goals and

incentives to participate in the process. Impact model users must be familiar

with the potentials and limitations of impact models in order to select one

and interpret the results for their purposes. This report summarizes the

features, capabilities, and limitations of large-scale impact models and

assesses the kinds of information produced and the differences in the

techniques used in the estimation process.

Keywords: Socieconomic impacts, rural growth models, local government

planning, regional models, rural economic growth.

Washington, DC 20005-4 788 November 1986
CONTENTS:

PREFACE.

Page

111

GLOSSARY: GENERAL TERMS.

INTRODUCTION. ...;..,

IV

1

BACKGROUND,

IMPACT MODEL DESIGNS......<

Glossary: Model Designs,

3

4

IMPACT MODEL USES .........

Equilibrium Planning. .^ .•••••..•.••....•..• «

Education. • • • • • • • • • •••••.••••...

Impact Analysis........••••••••••••.....••.

Impact Mitigation and Contingency Planning,

6

7

10

m

10

IMPACT MODEL CHARACTERTSTIGS,

Impact Area* •••...••.

Impact Submodels••.•••••..<

Glossary: Techniques......

12

12

12

18

IMPACT MODEL LINKING COEFFICIENTS, 24

UNRESOLVED ISSUES..................

Model Structures

Nature of Existing Data..........

Issues Beyond Current Technology.

25

25

26

27

REFERENCES. 29

APPENDIX: MODELS REVIEWE^^D, 32

11
PREFACE

Stringent environmental regulations of Federal and State Governments in the

seventies spawned a long and often complex process of education, impact

assessment and evaluation, negotiation for benefits, planning, and mitigation

of undesirable effects of new industrial plants and projects. Impact models

are tools for evaluating such new projects. Federal, State, and local

government officials, project developers, and numerous citizens groups enter

into the process, with different goals and incentives«

Impact models focus the attention of participants on the desirable and

undesirable effects of new developments, establishing a basis for negotiation

by each participant. To be most effective, impact models must provide

information useful to a wide array of people with different goals and concerns.

Users of local impact models must be familiar with the potentials and

limitations of impact models to select one and interpret the results for their

purposes. Project developers strive for a cooperative and satisfied labor

force, cutting down on labor turnover and inefficiencies. Housing and

amenities are important to them. Local planning officials want to anticipate

local government service requirements and identify revenues sufficient to cover

budgets. State officials may desire projects to pay all the costs of

development, including intergovernmental transfers to localities and costs

faced by other communities. Environmentalists have similar concerns in

addition to potential hazards specific to each project.

This report summarizes the features, capabilities, and limitations of impact

models. The authors reviewed 16 such models to determine the uses, strengths,

and weaknesses of these tools and assessed the kinds of information produced

and the differences in the techniques used in the estimation process. They

evaluated models according to the number of estimates made, the sequence in

which estimates are made, interrelationships among estimates in the model, and

the techniques used in the estimating process.

Current technology limits the usefulness of impact models for some purposes.

First, the technology of impact modeling has not yielded dynamic models that

can be applied to year-to-year planning. Second, the prediction of settlement

patterns is very inexact, yet settlement patterns are necessary to anticipate

expenditures and revenues of each local government. Third, impact models do

not reflect the uncertainty of some events.

Ill
GLOSSARY: GENERAL TERMS

Assassinent. Detailed estimates of eeonömic and social changes attributable to

a potential new facilityj contingeTít upon the development proceeding as

specified.

Contingency plan. Alternative course of action addressing potential problems

that could arise from uBpredictable future alterations in the authorized and

planned construction, ôperatioit, or closure of a faciLity.

Demographic impact. Change in births, deaths, migration, and population.

Economic impact. Change in employment, income, labor force participation,

resource productivity, demands for goods and services, or governmental

revenues.

Evaluation. Ititerpretation of the impact assessment for changes that are most

likely to bring prohlems, including overruns or shortfalls in local government

revenues and temporary lïulges in éemânds for housing or schools; a search for

ways to make the project more berneficialj such as judicious use of ravanue

overruns.

Fiscal evaluation. The difference between local government revenues and

expenditures.

Impact. Effect of a distinct and sudden economic change.

Impact analysis. All phases of impact assessment, planning, mitigation,

evaluation, and monitoring.

Impact area. The geographic area included in an impact analysis; one county or

a set of counties in most analyses.

Impact model. A logical step-by-step procedure for describing and analyzing

sudden economic changes.

Impact policymaker. Any organized public or private group taking direct action

concerning a new industrial facility that will affect an area, its goverhments,

or its people.

Local government jurisdiction. The geographic boundaries of one of many local

governments, including a county, town, school or irrigation district, or

several types of special improvement districts.

Management. All phases of impact analysis.

Mitigation. Actions preventing unwanted effects from a new facility, including

changes in plant design, operation, location, and construction and operation

schedules, usually prior to the authorization of construction and operation.

Monitoring. Day-to-day oversight of a project to gauge the accuracy and

adequacy of initial assessments, evaluations, and mitigation procedures, and to

trigger the use of contingency plans when necessary.

iv
Public service damand» Estimate of the levels of capital and operating

expenditures of local government functions, including schools, streets, and

sewage treatment«

Récx>very value> The market value of an asset at some future time.

Scoping, Preliminary description of plans and effects of locating, building,

operating, and closing a facility.

Sensitivity analysis. Variation in estimates produced by repeated applications

of a model to a given situation in which a value of a model, such as a linking

coefficient or project employment or scheduling, is varied.

Siting permit. A legal authorization to construct and operate a new facility

at a particular site.
Choosing Among Local Impact Models

Marlys Knutson Nelson

Lloyd D. Bender^

INTRODUCTION

For years, planners have analyzed the economic and social effects of economic

growth and decline to aid State and local government officials. Many

econometric models were designed to analyze growth and decline for States,

regions, and large cities. Within the last decade, large-scale models were

designed to assess a broad range of social and economic effects of growth and

decline in relatively small and isolated rural regions.

Impact models are simplified descriptions of an economic and social system.

Such models provide a systematic way of thinking through the steps of a

problem.

This report summarizes the uses, characteristics, and methods of alternative

local impact and planning models and describes their features, capabilities,

and limitations. A companion report annotates various models and approaches

(21).1/

BACKGROUND

Models became complex as computer technology and additional data became

available. Early regional planning models estimated changes for whole regions.

They could not easily estimate changes in each small area of the region or the

timing of those changes because of conceptual and computational difficulties.

Some models also estimated only a few items such as population and income.

They could not estimate important planning dimensions such as local government

service demands or the revenues available to local governments until they could

take account of the interplay of the various forces.

Models with those added capabilities proliferated by the midseventies.

Families of models evolved, becoming more and more complex with each

generation. One such family illustrates the influence of an initial set of

models on subsequent ones (fig. !)• The Susquehanna River Basin model, which

dates from 1966, linked together economic and demographic systems and

emphasized nonmetropolitan areas rather than a single urban area (8,11) . The

most prolific part of this family sprang from the work on the ATOM series—the

Arizona Tradeoff Model (3_) • ATOM 3 eventially led to the Bureau of Reclamation

Economic Assessment Model (BREAM) in 1977 (5) and several other models.

*The authors were economists with the Agriculture and Rural Economics

Division, Economic Research Service, U.S. Department of Agriculture, when they

conducted this research. Nelson was assigned to Oklahoma State University at

Stillwater, and Bender was assigned to Montana State University at Bozeman.

W Underscored numbers in parentheses refer to items in the references at the

end of the report.
Figurai

The Development of Economic and Demographic Projection IModeis

I960 -

\

Industrial dynamics

(1961)

1970 -

Susquehanna

River Basin

(1966,1969)

Uriaan Dynamics

(1969)

San Diego

IPEF

(1972)

1976

1980

ATOM-1

(1973)

ATOM-2

(1974)

—1 , ^ UPE&(1972)

\T

Upland

(1974) II

ATOM-3

(1975)

ADAS

(1976)

San Diego

IPÈF

(1977)

H CPE (1974) I I

I

TVA (1972) —,

NAVAHO

(1976)

Ll

CPEIO

(1977)

I

MULTIflEGION

(1975,1977)

North Platte

River Basin

(1976)

BOOM-1

(1976)

New Mexico

(1977)

RED-1

(1977)

L^JL J. ^__ZU

CUPS

(1979)

BREAIVI

(1978)

RED-2

(1979)

-TC

BOOM-H

(1978)

BOOIW-P

(1978)

__ TAMS

(1979)

"Direct descent

- Influenced development
Federal and State legislation fostered continued development of complex impact

assessment models. The National Environmental Policy Act of 1969 (NEPA)

requires analyses of the social, economic, and environmental impacts of major

developmental projects (29)• NEPA requires that an environmental impact

statement (EIS) including effects on the quality of the human environment be

prepared for each development.

Other Federal and State impact assessment requirements have since been imposed.

The Nuclear Regulatory Commission's (NRC) Regulatory Guide and the Council on

Environmental Quality's (CEQ) guidelines require a thorough assessment of the

social and economic consequences of major developmental projects (29)• State

industrial siting legislation, such as the Wyoming Industrial Development and

Siting Act of 1975 and similar laws in Montana, North Dakota, Minnesota, and

California (_8) , frequently requires social and economic assessments and impact

mitigation alternatives.

Developmental concerns are also represented in the many rural areas

participating in the population turnaround of the seventies (2_). The shift

from Federal programs to State initiatives to promote enterprise growth and

development has been dramatic (18) • Growth of population and employment,

especially in isolated rural areas, brings new problems. Local, county, and

State officials as well as private groups use assessment models to help them

anticipate and manage the impacts of many types of changes.

State and local officials frequently must decide which model to apply to a

situation from among the proliferation of increasingly complex and detailed

impact assessment models. In making such decisions, the officials must

determine the purpose of the estimates, the importance of each estimate as

ranked by the goals of the analysis, the methodologies behind the estimates,

the interpretation of the estimates, and the accuracy of the model estimates.

Despite the need to chose from among models, the advantages and disadvantages

of alternative models are not well understood. The massive literature ranges

from theoretical approaches to summaries of various impacts. Furthermore, many

models are not well documented. Documentation is often either proprietary and

not available, or it is complex and inappropriate.

IMPACT MODEL DESIGNS

Models may produce different estimates of the future because of differences in

structural design and the accuracy of the linking coefficients. These two

model features are equally important. They are the assumptions about the

directions of effect and the exact amount one estimate in a model causes others

to change.

The skeleton of any model is its structure, and the directions of effect form

the structural design of a model. That structure is first based upon which

estimates are assumed important enough to be included, and second upon which

estimate affects others. The structure is typically a network of interactions

in complex models, but the calculations may be relatively straightforward in

simple models. For example, population change may be a result of births and

deaths in one model. In others it may be a result of employment changes.

Each estimate a model produces is systematically calculated as the model

progresses through its structural network along prescribed routes. The

internal network is usually divided into distinct blocks, often called
dimensions or submodels. Each submodel produces at least one estimate.

Population, for example, may be estimated in one distinct block. That estimate

is used later to make other estimates as each block in turn is related to

others by the structural design. Thus, a population estimate will eventually

affect the demand for local government functions as well as the tax revenues of

local governments.

Tying together the structural design are linking coefficients giving the exact

amounts that one estimate causes others to change. Errors in one of these

linking coefficients early in a model would produce inaccurate estimates that

are spread and magnified many times over as they are used in later

calculations. An inaccurate population estimate used to calculate both local

government service demands and local government tax revenues would yield

erroneous estimates of the fiscal balances of local governments later in the

model.

Glossary; Model Design

Driving force. The beginning point in a model's structure; the force that

starts the analysis in a model; usually changes in an area*s economic base.

Economic base. Any economic activity that brings income from outside the area

into a local economy, including the export of goods to other areas, the sale of

services and goods to outside shoppers, and net retirement, dividend and

interest income, and subsidies, gifts, and other transfers of funds from

outside sources.

Extrapolation. An estimate of future conditions based on limited current data

that are known to be incomplete.

Forecast. A prediction with a known degree of accuracy»

Gross inmigration. The total number of people moving into an area over a

specified time period.

Gross outmigration. The total number of people moving from an area over a

specified time period.

Linking coefficient. Number within a model measuring the effect of one

estimate on each of the other estimates.

Model dimensions. The impact estimates produced by a model, including

employment, income, population, migration, local government service levels,

local and State revenues, and school, hospital, and housing and transportation

demands.

Model structure. A schematic outlining the way each estimate affects others,

including impact estimates made, directions of causation, and exact sequences

in which one estimate affects others*

Natural population increase. The excess of births over deaths in an area in a

specified time period.
Complex model structures increase the chances that errors will build up (22^, p.

361). The importance of a single linking coefficient's accuracy in a model may

be judged by the number of other estimates that it indirectly affects in the

structural network: would errors attributable to that coefficient spread

throughout the system?

Changes in the economic base are the driving force that starts a model and

makes it run. The economic base consists of any basic economic activities

bringing income into the area from the outside. Productive activities create

basic goods exported to other regions of the Nation or goods and services sold

to people who commute into an area to shop. These basic productive activities

use some locally produced goods and local labor, the foundation for all

personal income« Income coming into an area from sources not related to

employment and production, such as retirement income, is also basic to a local

economy, increasing the derived demands for goods and services in a local

Net migration. The difference between gross in- and outmigration over a

specified time period.

Projection. An estimate made by projecting past and present trends into the

future.

Submodel. A subset of a model designed to produce at least one principal

estimate; one dimension of a model.

Types of analyses. Different conceptual and procedural approaches to making

estimâtes that affect the usefulness of model estimates for impact analyses.

Before-after analysis. An equilibrium planning analysis (simple or complex),

in which the reason for change is known, but its effect is not stated

separately.

Equilibrium. The point at which no additional changes are made; the point at

which all forces are stable and balanced.

Equilibrium analysis. A picture of conditions at some future time at which a

balance among forces has been established, disregarding intermediate time

periods.

Interactive analysis. The user must interact with the model by supplying

information at certain points in the estimation process.

Now-then analysis. An equilibrium planning analysis, extrapolating a trend

to a point in the future, not explicitly taking into account any one cause

behind the changes.

With analysis. Estimates assuming a sudden economic change in addition to

continuous baseline changes over the period of the analysis.

Without (baseline) analysis. Benchmark estimates assuming no new and sudden

change; only contimous increases or decreases in current economic activities in

the area over the period of the analysis are considered.

With-without (impact) analysis. The difference between with and without

analyses, yielding impact estimates for each model dimension.
economy and, therefore, the total number of jobs. Thus, translating changes in

basic activities into changes in total economic activity isa fundamental part

of any impact model.

A simplified description of the estimates summarized by an impact model might be

as follows. An area's population changes because of more births than deaths

(natural increase), special migration to and from an area (such as for family

reasons or for retirement rather than for employment) , and employment^related

migration. Net inmigration is related to new jobs when the number of jobs

exceeds the capacity of the local labor force to supply sufficient labor to fill

them. The total number of jobs ultimately depends on the basic economic

activities in the local economy and on the unique characteristics of that

economy. New jobs in turn increase population, and the demands for services

provided by local governments. Local government revenues depend not only on

population but also on the industrial tax base of each local government.

Submodals describe a part of such activities. A submodel may be very

complicated operationally even though it has a fairly straightforward task.

Not only are the population and special subpopulations estimated, but labor

force participation and migrant characteristics play a part in those estimates,

for example. The number of inraigrants filling new jobs depends on the number

of current residents taking new jobs—that is, on the rate of local labor force

participation. The number of additional family members migrating with new

jobseekers also depends on their labor force participation, in turn affecting

demands for housing, schools, and other facilities» Each of these estimates

must be linked together in impact models.

A model may produce several other kinds of estimates. The spread of population

within the study area, determining which local governments are affected, is

important to the estimation process. The number of jobs or net inmigration

translates into housing demands. Estimates of various social characteristics

that interest some groups have not received the same degree of attention,

probably because of the priorities of polic3miakers using the models» Estimates

of both local government expenditures and the flow of local government revenues

are primary objectives of many impact models. These estimates in combination

signal potential shortfalls or overruns and the need to plan alternative

courses of action.

IMPACT MODEL USES

Models serve as aids to planners, policymakers, and the general public. Models

may be used for planning for a future time period without regard for

intermediate time periods—equilibrium planning; providing a learning

environment for policymakers, the public, or researchers—^an education process;

preparing an environmental impact statement—an Impact analysis; and estimating

the timing of changes that signal alternative problem situations to which local

government officials and citizens groups must respond—impact mitigation and

contingency planning (table 1).

This variety of uses suggests that the complexity of impact models, the

methodologies they use, and the kinds of estimates they produce will be

important in selecting an impact model. Models designed for one purpose may be

quite inappropriate for others, some model types can be used for planning but

not for impact analyses. TTie selection of a model must be based on the user's

objective—whether the model yields the kind of estimates desired and whether
impact analysis or planning is the application. In addition, the source and

magnitude of expected changes and each model's capabilities and limitations are

important selection criteria. These characteristics provide a general basis for

evaluating models.

Equilibrium Planning

Equilibrium planning analyses disregard intermediate time periods and the

processes at work as the system moves toward a new equilibrium. The objective

is to provide a picture of conditions at some future time. The usar has little

interest in the cause or timing of changes. Planning models are inappropriate

for impact analyses.

Equilibrium planning models and their techniques may be simple or complex. The

simple extrapolation of a trend is termed a "now-then" analysis. It might

effectively extrapolate the age distributions of a stable population and its

replacements. For example, plans for schools and medical services are closely

related to age. A "now-then" planning model can estimate the adequacy of local

government revenues to support those services. This type of planning model is

appropriately applied to stable farming communities or to small towns and

school districts with a stable economic base.

A simple "before-after" analysis is another type of equilibrium planning model.

In this case the primary reason for the change is known. However, the model

does not separate that part of the estimated results caused by any single

change from the amount caused by other continuing changes. The objective is

still a picture of the future without regard for intermediate periods or the

results of a single change. Simple averages from secondary data could be used

as the linking coefficients for such models when the anticipated changes are

relatively small. That approach may be appropriate for expansions and

contractions of small manufacturing plants in rural areas and for larger

changes in many towns and cities.

Planning models used for analysis when changes are large require careful

attention to the data and methods of estimation.2^/ Planning estimates are very

sensitive to'employment and income changes, local labor force participation,

and population settlement patterns. Employment and income changes in

rapid-growth communities may not average the same as those in slow-growth

places. Very small changes in labor force participation can also materially

affect estimates of population change.3^/ Working people who commute from

outlying areas can affect population estimates in the same way as labor force

participation. Thus, the values of the linking coefficients may be vastly

different before and after such changes.

2^/ Analysts evaluating large changes frequently adopt estimating coefficients

from "similar" places that underwent "similar" changes. Econometric

evaluations of such coefficients from cross-section data show that those

coefficients are far from accurate, however, when applied to another area or

region (4^, p. 6) .

3_/ Some attention also should be given to the time period allowed for a

return to equilibrium. Recent unpublished analyses from the Economic Research

Service indicate that 5 to 8 years may be necessary for reestablishing an

equilibrium.
Table 1—Characteristics of interest for impact model evaluation by user objectives

: Type of : General model products : Appropriate situation : Special capabilities

User objective : analysis : of interest to client : for the analysis : desirable in model

Equilibrium planning Estimating conditions at

one future period

Specific trends : "Now-then" Community facilities Stable local economic base Method of trend analysis.

affected by trend; revenues and a minimum of change relationships among populaat future time in external forces ; for

example, the age distribution affects demand for

medical services

tion characteristics and

local government service

levels and relationships

among local government

revenue sources

Economic base "Before-after" Community facilities

changes requirements after change;

local government revenues

after change

Small changes : Small relative changes;

for example, expansions or

contractions of employment

in an existing plant

Capacities in local government services sector to

absorb changes and direct

local government revenue

changes

Large changes ; Very large relative

changes; for example,

natural resources exploitation, resources-based

processing, Federal and

State construction

projects

Change in average multipliers, labor force

participation, population

changes from net migration,

population settlement

patterns, local government

service demands, revenues

sources and flows

Education : Interactive Effects of one variable on Any change Components Included in

with audience others and amount model, interactions among

components, accessibility

of model to participation,

and understandabillty of

model structure

Continued—
Table 1—Characteristics of interest for impact model evaluation by user objectives—Continued

Type of : General model products : Appropriate situation : Special capabilities

User objective : analysis : of interest to client : for the analysis : desirable in model

Impact analysis : "With-without" One project's effects on Large relative changes in

local government expendi- economic base , for example.

tures and revenues, popula- most Federal and many State

tion, employment, and projects

income in given political

jurisdictions

NEPA impacts : Estimating effects in one

future period attributable

to one project

Baseline : Baseline

"without" the

' project under

: consideration

Conditions with no new

development project

Changes in the economic

base that would have come

about any way without a new

project; includes all

capabilities of small

economic base changes in

Equilibrium planning above

Changes "With" analysis,

: including the

: project under

: consideration

Conditions after a project

is added to the baseline

Accuracy of estimates and

timing of external changes,

includes all capabilities of

Large economic base changes

in Equilibrium planning above

Impacts : "With-without" Results of one project Large changes in economic Includes all capabilities of

base Baseline and Changes

objectives above

Dynamic impacts : "Dynamic Estimates timing of changes Extremely large relative Timing of local government

: with-without" for each year or period changes ; for example, expenditures and revenues,

irrigation projects, population settlement

mines, power plants, patterns, stability and

hydroelectric, projects. accuracy of economic base

military bases changes, interactions among

model components, and

accuracy of estimates
Education

Interactive models are used as educational tools. These models can be skeleton

structures of cause-and-effeet relationships and show where the linking

coefficients logically belong. Model builders typically supply values for the

linking coefficients of such models. Nevertheless users are encouraged to

change the values of the linking coefficients. As each linking coefficient is

altered, users can evaluate whether the changes are important to them.

Successive trials permit users to evaluate the sensitivity of the results to

each coefficient. They can eventually concentrate on those estimates that are

affected the most and that are important. The fundamental feature of such

models is the series of cause-and-effeet relationships.

Impact Analysis

The most common application of impact models is to fulfill NEPA requirements.

NEPA impact analyses require a "with-without" approach but may use either a

dynamic or equilibrium model. The objective is to find the changes due to one

project alone. The changes that would have come about anyway are the baseline

estimates. The difference between estimates "with" and "without" a project are

the impacts attributable to the project.

Baseline estimates simulate the outcome with no new development project—the

"without" case. The baseline estimates account for any national and regional

trends. Of special importance are the projections of expected increases or

decreases in the existing economic base, that is, the levels of manufacturing,

agriculture, mining. Social Security, and other sources of income from outside

the area. The baseline case is the benchmark for impact estimates and is

extremely important.

A new project included with the baseline is the "with" part of the analysis.

Accurate estimates of a new project's employment are essential to the model.

Sources of large errors in impact analyses are underrepresentations of a

project's employment and misspecifications of the timing of those emplo3mient

changes. The NEPA impact estimates are the differences between estimates

"with" and "without" a new project.

Impact Mitigation and Contingency Planning

Contingency planning goes beyond the assessment and evaluation phases of impact

management. Evaluations of the results of impact assessment models anticipate

problems at each phase of a project. Early in the project, planners may use

mitigation measures to address problems that are almost certain to emerge.

Planners may alter the project's design or construction and operation schedules

or negotiate with the company and State and Federal officials for funds.4^/

Other potential problems are less certain. In such cases, alternative courses

of action—contingency plans—should be prepared.

The objective of contingency planning is to be flexible enough to maintain some

balance between the demands of growth and the capacity and capability to meet

those demands when the future is uncertain (8^). Impact models can be used to

kj Barrows and Charlier stress the use of impact analyses for purposes of

negotiating for funds (2,? P« 197) .

10
narrow the array of possible problems to those that would be serious• By

slightly changing construction and operation schedules or some of the important

linking coefficients in the model, the potential magnitude of those problems

can be gauged. Alternative plans addressing those problems could involve

either mitigating the impacts early in the project or preparing alternative

plans of action in case a problem appeared at some future time—that is,

preparing contingency plans.

Two groups have a high stake in impact mitigation and contingency

planning—local governments and project owners. Project owners can minimize

costs if a stable and productive labor force is available and if the project

remains on schedule. Their incentive is to ensure adequate housing and

transportation and living conditions that are conducive to productivity (6^) •

Local governments have the responsibility for balancing tax revenues and the

additional demands for local government services, most notably schools, sewers,

health services, water systems, streets, and police protection. Impact

management must involve anticipating problems and negotiating funds to address

those problems. Specifying mitigation measures and preparing contingency plans

are initial steps in that process (20^, pp. 97-113).

Planning for contingencies places stringent standards on models. The

requirements for data and accurate linking coefficients are formidable.

Contingency planning requires a dynamic model, that is, one that accurately

forecasts year-to-year changes and also takes into account the interplay of

estimates within the model.

Two examples illustrate model uses. In the first, an unexpected delay in a

development project's first phase might cause it to overlap the beginning of

construction of a second project. Pressures are put on an already tight labor

market, driving wages and housing values up and increasing labor inmigration

further than initially planned.

Planners and officials can develop alternative plans for addressing this

situation. The potential problem might be averted by direct mitigation actions

before the project started. A complete rescheduling of the construction and

operation phases of both projects could have preceded State and local approval

of the project.

A contingency plan—held in reserve in case the problem appeared—might call

for the developer to construct housing for temporary workers. Families of some

workers would be discouraged from living in the immediate community by that

action. The temporary demand for school facilities would also be lessened.

A second example is very common for large projects. Local government revenues

typically begin to flow after a project's construction, sometimes several years

after operation has begun.^/ In cases where revenues or the magnitude of the

changes are uncertain, contingency planning can be valuable. A local

government should not contruct a new school before there are students to use it

or funds to support it. The uncertain timing of such facilities may require a

set of conditional plans, put into action only when the needs become certain.

5_l The lag in tax revenues from a new project will vary greatly from State to

State.

11
Contingency planning requires a dynamic model to anticipate many of the

problems that might arise because of the timing of changes and potential sudden

alterations in the magnitude of impacts. Plans that are conditional on

uncertain events in the future can be developed by using impact models.

IMPACT MODEL CHARACTERISTICS

Each impact assessment model has its own structural design and set of linking

coefficients. Similarities among models usually result from the use of similar

data or techniques or from similar perceptions of users' desires. The analysis

in this report characterizes models according to impact area, kinds of

estimates produced by a modelj and the techniques used to arrive at the values

of linking coefficients (table 2).

Of the 16 models we describe, 15 handle estimates for cases "with" and

"without" a project. Only these types are appropriate for impact analyses.

Impact Area

The géographie area of interest—the impact area— for most impact models is

the community in which new projects are to be located and its immediate

coiranuting area. While the distribution of benefits and costs is in^ortant to

policymakers in State and local industrial development programs, the

geographic areas involved often are small, limiting the fundamental objectives

to the anticipation and amelioration of only major problems. Impact models

are not designed as benefit-^cost models; they assess only major benefits and

costs in a limited geographic area.

The choice of geographic boundaries and units of analysis is severely

constrained by data availability. County-level income, employment, and

population estimates are available from the Bureau of Economic Analysis, U.S.

Department of Commerce (28) and are the most commonly used secondary data.

Only one model explicitly addressed the issue of the appropriate boundaries of

an impact area (30). A gravity technique defined points of equal influence

for a given city or town. Those points defined the impact area under

consideration. Others used gravity techniques to allocate aggregate

population, income, and other estimates among small geographic areas within a

region or State, however.6/

Impact Submodels

Impact models typically are divided into submodels, each of which produces one

or a set of estimates. Submodels include employment and income; population and

its characteristics; interactions among employment, income, and population; and

other effects such as demands for housing and local government services, local

government revenue flows, and various social and demographic characteristics

that may signal potential problems.

Each submodel is usually built around one of several different techniques.

Those techniques invariably influence the structure and overall design of the

hj Reluctance to use past geographic patterns of population distribution as

predictors of future patterns may reflect modelers* suspicions that large

projects may completely change settlement patterns in a region.

12
model. The submodels and the techniques most commonly used are identified and

explained briefly below (table 3).

The Employment and Income Submodel

The primary purpose of this submodel is to estimate either emplojonent or

income* When income is the choice, some average wage coefficient translates

income into an employment equivalent. Other practitioners favor employment as

the primary objective. They hypothesize that wages may vary greatly,

depending upon the growth rate of the local economy, its industrial mix, and

the skill mix of the labor force; income is of secondary interest. These

practitioners want to factor employment into population by using a varying

labor force participation rate that reflects the demand for labor. Several

regional economic estimating approaches commonly are used—economic base,

input-output (I/O), econometric, or a blend of these (23).

A simple economic base approach measures economic activity in terms of either

employment or income. This approach is inexpensive and requires minimal data

and calculation. Economic base models usually deal in aggregate changes in the

whole system and rarely in individual industry sectors. The economic base

model is a very special case of an input-output model with only two sectors.

One sector is composed of all of the industries that are "basic" to the local

economy, generating a flow of income from outside the region into the local

economy. The other sector is "nonbasic," composed of the remaining activities

whose demand and sales depend on the basic sectors' activity. The coefficient

linking the nonbasic and the basic sectors is a multiplier.

The economic base multiplier, calculated beforehand and incorporated into the

model, translates a new level of basic activity into an estimate of total

economic activity (n_, pp. 11-22). The difference between basic and total

activity is nonbasic activity, consisting of a variety of services and goods

produced for local use. The multiplier is the ratio of total economic activity

to the basic activity, in terms of either income or employment. A multiplier

of 1.5 would mean that each unit (dollar or employee) of basic activity on

average translates into 1.5 units of total activity—that is, the unit of basic

activity plus another 0.5 unit of nonbasic activity.7_/

The multiplier is applied to expected basic activities without a new project to

estimate baseline totals. Applying the multiplier to basic activities plus a

new project results in estimates of total economic activity including the new

project under evaluation.

The simple economic base multiplier is an average relationship that assumes

equilibrium. Such a multiplier does not estimate the time needed to arrive at

equilibrium nor does it identify which nonbasic sectors increase. Furthermore,

the average relationship may change as the regional economy changes, making

7/ An important consideration, but one that is abused, is that the same

measures be used throughout each complete analysis. The method chosen to

calculate multipliers used in the model must be the method used to define

baseline activities, for instance. If employment or income is reported in

standardized full employment terms, the multipliers in the model must be

calibrated to those units. Models are misused when coefficients in the model

are built using one method and applied to data that are constructed using

another method.
Table 2—Socioeconomic impact models reviewed by characteristics and

techniques employed

Characteristic/technique '' Models j_/

Type of analysis:

Impacts from project—no baseline : SIA

Project impacts ("with-•without") : ATOM, BATTELLE II,

: BREAM, CLIPS,

: COÂLTOWN, DEISM, DRI,

: MRMI, NED, NEDAM,

: OCDSM, PURDUE, REAP,

: SEAM II, TAMS

Impact area:

Community : BREAM, CLIPS, DRI,

: NEDAM, OCDSM, REAP,

: SEAM II, TAMS

County ATOM, BATTELLE,

COALTOWN, DEISM,

MRMI, SIA

State or regions NED

Nation PURDUE

Dimensions of models and techniques: :

Employment and income:

Economic base BATTELLE II, BREAM,

CLIPS, DEISM, DRI,

SEAM II, SIA

Input-output ATOM, NEDAM, OCDSM,

REAP, TAMS

Input-output and econometric : MRMI

Economic base and econometric : COALTOWN, NED

Linear programming PURDUE

Population:

Cohort-survival ATOM, BATTELLE,

BREAM, CLIPS, DEISM,

MRMI, NED, NEDAM,

OCDSM, PURDUE, REAP,

SEAM II, TAMS

Employment-population ratio : COALTOWN, DRI, SIA

See footnote at end of table. Continued—

14
Table 2—Socioeconomic impact models reviewed by characteristics and

techniques employed—Continued

Characteristic/technique ; Models l_l

Interface of dimension:

Employment and population : ATOM, BATTELLE II,

: CLIPS, DRI, NED,

: SEAM, SIA

Employment, income, and population : BREAM, COALTOWN,

: DEISM, MRMI, NEDAM,

: OCDSM, PURDUE,

: REAPS, TAMS

Spatial disaggregation of impacts:

None : COALTOWN, DEISM,

: MRMI, PURDUE, SIA

Subjective DRI

Existing or adjusted proportions . ATOM, BREAM, CLIPS,

. REAP, TAMS

Simple or adjusted gravity model BATTELLE, BREAM,

: CLIPS, NED, NEDAM,

OCDSM, REAP, TAMS

Linear programming model SEAM

Other dimensions:

None ATOM, DEISM, MRMI,

NED

Fiscal ; BREAM, CLIPS,

COALTOWN, DRI

NEDAM, OCDSM, PURDUE,

REAP, SEAM, SIA, TAMS

Public services : BATTELLE, BREAM,

CLIPS, DRI, OCDSM,

PURDUE, REAP, SEAM,

SIA, TAMS

Hous i ng ; BREAM, CLIPS, DRI

REAP, SEAM, SIA, TAMS

1_/ See the appendix to this report for a glossary of models and their

references. Additional references and discussion may be found in (8, 21).

15
Table 3—Techniques used in socioeconomic impact assessment by assessment

objective

Technique [ Assessment objective/

assumption or uses

Static approach : To project employment, population, and so

forth; assumes status quo will continue.

Economic base : To project basic (export-oriented)

employmentj estimates, future total

employment using an average multiplier«

Location quotient : To estimate the ratio of local service (or

induced) employment to basic employment.

Minimum requirements : To estimate the minimum percentage of

emplo3rment across regions for each industry

required to satisfy local needs; assumed any

excess is export emplo)mient.

Input-output analysis ; To estimate how impacts originating in one

sector are transmitted throughout other

sectors in the economy.

Gravity model ; To estimate the geographic distribution of

; population; uses the initial distribution to

; distribute the expected population at a

: future time.

Linear programming : To estimate the geographic distribution of

; population by minimizing the cost of housing

Î and commuting of workers in the area.

Cohort-survival : To estimate population changes based upon

: birth and death rates by age and sex.

Targeted service

requirements

: To estimate changes in public service

: requirements (for example, water, sewer, and

: fire protection) that may result from the

: change in population in the study area.

Fiscal projects : To project public sector revenues and

: expenditures, baseline and incremental,

: resulting from a development project.

16
estimation at some future date unreliable. When used in dynamic situations,

the average multiplier probably overstates estimates«

The critical operation in calculating a multiplier coefficient from secondary

data is to determine which economic activities are basic and which are nonbasic

(14)• Mistakes in that operation result in an economic base multiplier that is

biased ij) »S^/ In the simplest case, all activity in each industrial sector is

merely assumed to be either basic or nonbasic• For example, manufacturing is

assumed to be basic and business services nonbasic«

We can use other techniques to calculate the basic and nonbasic components of

economic activity of each industry sector and to find their totals (table 3).

The location quotient technique assumes that the basic part of each sector is

the amount each sector's activity is above the average proportion for the

region. The minimum requirement technique assumes that the basic part of each

sector is the amount each sector's activity is above some minimum proportion,

typically a lower proportion than that of the region.

The economic base method describes the overall economy at one point in time.

This method assumes that the pattern of purchases, productivity, and all other

relationships among industry sectors remain the same over the period of

analysis, whether economic activity is going up or down. For these reasons,

the economic base method is best applied to small changes in an economy with a

relatively stable economic base mix.

Input-output models have a high level of industrial disaggregation with linking

coefficients which are averages. These coefficients specify the amounts one

industrial sector buys from and sells to each other sector at some given point

in time. Because data actually gathered in the field are very expensive, many

input-output submodels now use readily available secondary data (24).

The advantage of an input-output model is its ability to trace changes in each

sector's activity that are caused by some initial change in any one of the

others. A change in one sector will affect all other sectors from which it

buys supplies or to which it markets its output. Economies with highly

interdependent sectors will produce large changes in total regional output as

the economic base increases.

Input-output models can produce several different types of multipliers. These

multipliers are not equal to economic base multipliers. Multipliers from

different input-output models also may have different values and applications

(25, pp. 55-66). Input-output multipliers are averages and are used for

equilibrium analyses (15).

Input-output relationships also may change over time as the economy of a region

changes, although the coefficients of the model usually are not altered during

the analysis. Thus, input-output multipliers have some of the same limitations

as economic base multipliers.

8^/ The degree of sector disaggregation, the level of geographic coverage, and

the exact method used to calculate relationships all affect the values of the

multipliers and the accuracy of the estimates, whether the technique is

economic base, input-output, or econometric.

17
Econometric models usually estimate aggregate changes in the whole system, even

though some estimate changes in each industry» Their comparative advantage is

in evaluating the accuracy of the model's linking coefficients. In some cases,

they produce dynamic estimates tracing the timing of changes toward a final

equilibrium. Secondary data are used to generate the linlcing coefficients,

although the statistical procedures are time consuming and expensive.

Econometric techniques typically include regression analyses of time-series and

cross-section data. Models applicable to different geographic settings for

equilibrium planning usually use cross^-section data. Dynamic models allow the

linking coefficients within the model to change as the economy changes.

Time-series data permit the dynamic to be described and included, although the

applicability of such models to different regions may be questioned.

Pooled time-series data from across several different regions through time can

account for both geoçraphic differences and the dynamics of change.

Econometric techniques can allow modelers to build dynamics tnta models, but

the characteristics of each model will be different, and the accuracy of each

model must be evaluated. Econometric tnodels can be very complex. The logic

of changes in the estimates can be so complex and obscure that practitioners

cannot interpret them; hence, they lahel the model a "black box."

Approaches to modeling employment and income are often a mixture of different

methods sometimes combining elements of economic base, input-output, and

Glossary : Techniques

Cohort-survival technique. A procedure for calculating the natural population

increase from an intial population over a specified period of time; used in

calculating net migration as the difference between natural population

increases and a population level consistent with employment and labor force

participation.

Cross-section data. Observations of an event at one time across several

geographic locations.

Dynamic. Characteristic of a model, procedure, or technique in which

year-to-year changes toward a final equilibrium are estimated, taking into

account changes in linking coefficients of the model during growth or decline.

Econometric technique. Statistical procedures used to derive a mathematical

description of an area's economic system; technique used to derive the linking

coefficients and interrelationships of an economic model and to test the

accuracy of the linking coefficients and the complete model's forecast

accuracy.

Economic base submodel. An employment- and income-estimating procedure that is

driven by an aggregate of all basic economic activities, disregarding changes

in the composition of the economic base.

Gravity technique, A procedure for distributing population, income, or other

model estimates among the local government jurisdictions within an area,

usually in proportion to past settlement patterns but with adjustments for

other influences such as the cost or time of commuting to and from work.

18
econometric techniques. Three models we reviewed--COALTOWN, MRMI, and NED—

used at least two of these techniques (table 2), Finally, modelers often

arbitrarily tailor the coefficients of a model to a particular situation even

though data are not available, especially when a model applies to very larga

developments for which comparable historical observations are not available*

The Population Submodel

Population estimates typically use either cohort-survival or employmentspopulation ratio techniques. The cohort-survival technique starts by applying

birth and death rates to an initial population in order to estimate the

potential population from indigenous sources at some future period as though

migration had not taken place.9^/ That process yields estimates of the maximum

amount of labor that could possibly come from the initial population, assuming

various rates of labor force participation. The difference between that

potential labor supply and the estimated future employment is an estimate of

net worker migration. Inmigration would occur if demand exceeds supply, or net

outmigration if the potential supply exceeds the employment estimate. This

9j In (26) see page 778 for a brief review of the cohort-component method,

and pages 735-42 and 746-50 for technical discussions of age, sex, and

geographic estimates•

Input-output submodel. An employment or income submodel driven by changes in

each economic sector's demand from outside the area; a detailed description of

goods and services bought from and sold to each other sector within the area;

traces the effects on each sector in an area of changes in purchases and sales

by each sector.

Interface. Interactions among estimates in a model.

Linear programming. A mathematical procedure for estimating the geographic

distribution of new residents (as it is applied to impact modeling), yielding

the expected distribution of new residents, minimizing the combined cost of

housing and commuting, and explicitly taking into account the location of new

plants, the availability and cost of new and vacant housing, and the cost of

commuting to work.

Location quotient. A procedure for estimating the economic base, yielding the

amount of activity in each industry, above its average regional or national

proportion of total activity, that is available to handle demands from other

regions.

Minimum requirements. A procedure for estimating the economic base, yielding

the amount of activity in each industry, above the proportion of total activity

a county or area of some assumed minimum critical size, that is available to

handle demands from other regions.

Static. Characteristic of a model, procedure, or technique in which time is

disregarded and the model's linking coefficients are constant and unchanging,

regardless of the rate of economic growth or decline.

Time-series data. Observations of an event in one location through time.

19
procedure can be amended to include recent migrants as part of the indigenous

population in each successive period.

Refinements of the cohort-survival technique have been introduced since its

early use in modeling (II)# For example, subpopulatipns with different

demographic characteristics, such as university students, military personnel,

or racial or ethnic subpopulations, can be analyzed separately. The advantages

are that birth and death rates and rates of labor force participation and

migration specific to each group can be used to estimate potential future labor

force levels. Some models even calculate migration that is unrelated to labor

demand •

The cohort-survival approach estimates migration as potential population (with

rates of labor force participation applied to it) less the estimates of

employment. The rate of labor force participation in this approach frequently

takes the form of an assumed average number of family members and an assimied

average number of working persons per family. These data are available only

from the decennial census. Changes in those averages must be assumed in an

attempt to calibrate them to very large economic developnrents. Because the

cohort-survival method estimates the potential age and gender mixtures of the

population at some future time period in a "now-then" equilibrium analysis, the

method is most appropriately applied to areas with stable economies.

The method using a ratio of employment to population (Ê/P) recognizes that

labor force participation depends upon the demand for labor which may vary

substantially during growth. The ratio of emplojrment to population is a rálte

of labor force participation. This ratio may be based on historical regional

data, national ratios adjusted by the user to reflect rapid or slow growth, or

an econometric forecast of the ratio based upon prior employment changes (labor

demand) and certain regional characteristics.

Population is obtained by dividing estimated future employment by an estimated

ratio of employment to population. An estimated population lacrease has to

come from net ininigration or an excess of births over deaths. The excess of

births over deaths can be estimated by using the cohort survival method from

the preceding year's population. This natural increase is a minor part of

population increase in periods of rapidly changing economic activity; migration

is the major source. Furthermore, population estimated by this method can be

disaggregated into school-age or other groups by the same relative weights used

in the traditional cohort-survival system in order to gauge the damands for

schools and medical services.

Differences between the cohort-survival and the ratio of employment to

population methods can be more apparent than real. However, the starting

points and the methods of getting the linking coefficients are different and

important. In the cohort-survival method, very small changes in assumptions of

either the average number of members in each family or in the number employed

in each family will affect population and migration estimates greatly.

The similarity between the two approaches is that family size and the number

working in each family (used in the cohort-survival approach) translate into an

20
implied ratio of employment to population«10/ A difference of 0.05 in the rate

of labor force participation applied to a county population of only 10,000

would result in a 500-person difference in the estimated local labor force

response. That, in turn, implies differences in net worker migration,

population, housing demand, and local government expenditures.

The population submodel is an extremely vital component of any impact

assessment model. Because numerous other estimates are dependent directly on

it, one should closely evaluate this submodel.

The Interface Dimension

Estimates produced by impact models interact with each other in the model

design. These interactions are the interface dimension. Because a model is a

network of relationships, every estimate is probably related at least

indirectly with every other estimate. Indeed, under some methods of solving

models, each estimate can directly influence the values of all others.

The interrelationships between the employment and population estimates are

examples of an interface. The interaction may be a simple balancing of the two

with no prior interaction—that is, potential population may be estimated

independently of emplojrment. Estimated emplo3niient and the population

supporting it would be balanced through migration; employment growth which is

more rapid than potential indigenous population growth would bring inmigration,

but population growth which is more rapid than employment growth would bring

outmigrátion.

A more complex interaction involves employment estimates in calculating

population estimates. Employment changes affect labor force participation (the

ratio of employment to population) in a succeeding year. Labor force

participation then translates into a population level consistent with it.

The most complex interface is the use of one estimate to calculate two other

estimates that later are balanced. For example, population affects both

government expenditures and revenues. Then the difference between revenues and

expenditures is the estimated fiscal balance at a given tax rate.

Interactions among model estimates are required for dynamic or equilibrium

analyses. Yet, the complexity of the interrelationships implies that erroneous

estimates early in the calculating network magnify other errors introduced at

later points in the system (22^, p. 361).

The Geographic Distribution of Effects

Expenditures in each local government depend on its population. One can

estimate population for a region and then apportion that estimate among

10/ The following example illustrates how the two methodologies might be

identical and points out potential sources of errors in population and other

impact estimates. Assume an average of 3.5 members per family and an average

of 1.5 of the family members working. The implied ratio of employment to population is 0.428—that is, 1.5 workers divided by 3.5 people. In this cohortsurvival example, an increase of 100 workers would require 66 new families if

1.5 people in each family of 3.5 members worked, and a population increase of

233 people, the same value produced by dividing new employment of 100 by 0.428t

21
localities, or one can calculate total local government: expenditures associated

with population and then allocate the expenditures among local governments•

Population may be distributed on thé hasis of a gravity technique, a method

based on linear programming^ or the best judgment of cotmmumty leaders and

planners. In all cases, one must consider commuting time, vacancy rates,

availability of private land for housings and herusing costs.

The simplest gravity techni<pie distributes the future population in proportion

to the current population.11/ More complex techniques take into account the

distance from a specific developmental project as a proxy for the cost of

commuting to work. But even with refinemetits, the gravity technique is less

accurate in predicting settlement patterns in rural areas than in urban areas

(19f p. 470). Large developmental projects in relation to the local economy can

change commuting and settlement patterns from what they were.

Another approach, based on linear programming, attempts to minimize the cost of

housing and commuting for workers (27). The linear programming technique

constrains the availability and cost of suitable private land for housing,

temporary quarters, and rental properties. This technique also considers the

c o St 0 f c ommut i ng.

A special problem with the linear programming approach highlights a fundamental

tîifficulty hidden in all other approaches—the "recovery value" of housing.

The initial cost of housing minus its recovery value at some future time is the

realized market depreciation cost. Housing used during a temporary boom period

would become very expensive if its market value depreciates rapidly afterwards.

The recovery value of housing is the most important cost of housing for

temporary workers. Linear programming estimates of population distribution and

type of housing are particularly sensitive to recovery value.

Many practitioners use their best judgment to estimate population settlement

patterns. They maintain that communities have some degree of control over

settlement patterns--that is, the outcome depends on decisions made by a

community. The attitudes of community leaders and the incentives and

disincentives that they impose on housing developers can alter and direct the

type and location of new housing. For example, growth patterns can be

controlled by restricting or expanding services (9^, p. 78).

The location of new housing and public facilities may even be fixed prior to

construction and operation of projects. Authorities in some States authorize

projects on condition that developers provide new housing and other public

facilities beforehand, Coiranunity leaders may use contingency planning as a

part of the authorization process to anticipate the escpected amount and timing

of population clianges. By altering the scheduling of construction, one can

use assessment motlels to yield upper and lower bounds for housing estimates.

Authorities may also preselect the most advantageous taxing jurisdictions in

which to locate new projects, housing, and other facilities. These uses of

impact assessment models go beyond that necessary for NEPA.

11/ See (12) for applications of the gravity technique*

22
Other Dimensions

Population estimates are extremely important because they provide the basis for

many additional impact estimates. These estimates include housing and public

services, such as water and wastewater treatment and disposal, solid waste

management, education, health services, law enforcement, fire protection, and

libraries.

Planning coefficients imbedded in the model translate population estimates into

other estimates. For example, an average number of persons per family or the

number of workers per family may be used to estimate new households. An

average expenditure per person or household for each local government function

yields an estimate of total expenditures.

Practitioners also can use linking coefficients derived from regression

analyses to estimate local government expenditures. However, data limitations

force the use of a two-step process. The Census reports only expenditures by

function for county-level aggregates. Thus, county population is associated

only with county expenditures. The county totals are allocated to local

government jurisdictions using methods parallel to those for allocating

population.

Capital expenditure budgets are derived by using engineering and planning

coefficients in relation to the population. The square feet of school

buildings per pupil multiplied by the number of pupils and the cost per square

foot yields school-building costs, for example. The largest capital outlays

are for roads and streets, schools, water and sewage systems, and hospitals.

Communities are especially interested in the timing of capital outlays, because

local planners need reasonable certainty that new facilities will not go unused

and that revenues will be available.

Although one of the more important dimensions of impact assessment models is

local government revenues, revenue estimation is difficult. Tax codes change

frequently, and local government revenues vary greatly from State to State,

among local governments, and from industry to industry. The expense of

updating and maintaining a revenue estimating system frequently prevents its

use in models. Furthermore, the complexity of State and local revenue systems

results in errors when simplified calculations are made.

The most accurate method of revenue estimation involves tracing through every

source of potential revenue change. First, one must identify every potential

source of change in the tax base. Then, one can trace the exact distribution

of revenue from those sources through the system to the government that spends

the revenue.

Local governments in some States depend heavily upon property taxes as a source

of revenue. Other States share their revenues among local governments based

upon population in some cases and complex formulas in others. Many States fund

local governments in relation to local effort and other criteria. School

revenues often are a combination of local revenues plus revenues from the

State's school foundation program. Finally, some local revenues are from users

fees and local collections, such as hospitals and water and sewer fees.

The interrelationships among all of those sources can become very complex, with

spending from State sources decreasing as spending from local sources

increases, for example. Although some models use equations based upon assessed

23
values, population, income, and business receipts, the most accurate revenue

estimation system takes account of the exact detail of every tax code and

revenue distribution formula, tracing all changes from source to final

disposition—a revenue and expenditure algorithm.

IMPACT MODEL LINKING COEFFICIENTS

Prior to using a model, one must derive the values of linking coefficients in

one of several ways, the best judgment of the analyst or knowledgeable

coitffliunity leaders, linear extrapolations of current trends, national, regional,

or county averages, averages from other counties that are similar in regard to

the conditions being addressed, averages that are assumed to change as

development occurs, or econometric forecasts from a broad data base.

Linking coefficients describe the amount that one estimate affects others.

Linking coefficients are averages—that is, they describe what happens on

average. In deriving the value of linking coefficients, one must answer

several critical questions: Does the average represent the data from which it

is calculated? What is the chance that the impact area is well described?

Does economic growth or decline change the coefficients?

Too often shortcut methods are used to derive linking coefficients. Relying on

"best judgment" is to rely on the artistry of the analyst. Extrapolations of

current trends assume the same forces will be working in the future. Simple

averages of national, regional, or county data not only assume that

relationships will be the same, but also that the average describes the impact

area under study. Model designers must be sure that linking coefficients are

accurate and that users know the statistical properties and accuracy of the

coefficients.

Certain statistical methods can help. First, the linking coefficients can be

tailored to the unique conditions in an impact area. Second, the accuracy of

the coefficients can be gauged. Large banks of data are used for those

purposes. The data are observations of each measure through time (a time

series) or across a number of places (a cross section). From those data, one

then computes an average coefficient. In the best of cases, a very large

proportion of observations lies close to the overall arithmetic average rather

than being widely scattered. Chances are good that such an average effectively

describes a large proportion of the population from which it is computed.12/

When observations are widely scattered, the average would not be a good

description of a large portion of the whole population.

Other simple statistical methods produce a more effective deseriptor from a set

of observations. One method separates observations into different groups on

some preconceived basis. For illustration here, some observations may be

metropolitan areas and others nonmetropolitan. Each group has an average that

more effectively describes the observations in it than does th^e overall

i2/ Arithmetic averages should alwajrs be computed from normally distributed

observations.

24
average*13/ Chances are good that averages of population density, labor force

participation, age, and other measures would be significantly different«

Simple statistical tests measure the effectiveness of these averages compared

with the overall average—that is, whether the descriptive power of the

averages has been improved by separating the population into groups. This

methodology can be misleading, however, because it attributes all of the

differences in the averages to their metropolitan status in this example« We

know that much of the difference could be attributed to industry mix,

transportation systems, or communications, for instance«14/ Additional

refinements would involve several different classifications and cross

classifications of data into distinct subgroups.

Another step is similar to using simple averages but takes more influences into

account. Multiple regression techniques automatically separate observations

into groups on the basis of those influences thought to be important. These

analyses tell the amount and direction that the average changes when each

characteristic of an area is different. This technique tailors the linking

coefficient to important characteristics of the area under study, increasing

the chances that a particular linking coefficient describes the impact area«

Multiple regression analyses may also indieate changes in linking coefficients

with growth or decline. Dynamic changes are reflected in a model as an area

grows or declines. For exan^le, labor force participation increases as the

d^aand for labor increases; that is, rates of labor force participation in 1

year will be higher as a result of employment increases in the prior year. A

higher labor force participation in turn translates into fewer new people to

fill jobs. Instead, the residents supply additional labor. Thus, one model

using a fixed average rate of labor force participation would predict a larger

population change than a model with rates of labor force participation that

increased as a result of economic groinrth in the area.

Despite the accuracy of individual linking coefficients, a model may predict

inaccurately because of the way model coefficients interact with one another.

Thus, estimates produced by the complete model should be compared with the

historical record.

UNRESOLVED ISSUES

Certain issues remain unresolved in many large scale impact models. Because

there is a distinct pattern of one model evolving from predecessors, the same

issues and problems posed in early models tend to be passed along from one

generation of models to others.

Model Structures

The complexity of model structures is closely associated with the possibility

of forecast errors. Forecast errors begin when one linking coefficient early

13/ This simple method is reflected in attempts to use coefficients computed

from a group of observations from areas that are "similar" to the impact area.

However, that approach has proven to be untrustworthy.

14/ This situation is called a misspecification of the problem and is also a

serious concern with advanced methods.

25
in a model is ineffective in describing future outcomes>13/ It produces an

unreliable estimate. That estimate produces yet another estimate in the

calculation chain» The second estimate is often more unreliable than the

first-'-that is, the error is compounded, not simply carried forward or

cancelled. The longer the chain of estimates made from each prior one, the

greater is the chance that errors will be magnified (22, p* 361)• Thus, several

linking coefficients that are erroneous will multiply forecast errors unless

each neatly counterbalances the preceding errors.

This difficulty leads to some general guidelines about model structures.

First, the number of estimates in a model should be kept at a minimum,

minimizing the number of links in the chain where errors can be introduced,16/

Second, estimates made in a model should be aggregates when appropriate in

order to minimize unnecessary linkages. If each subclass of population, for

example, is estimated separately and then summed to the total population that

is to be used later, then needless links are used to arrive at a total. Third,

forecasts for early years are probably more accurate than for later years. In

this case, the forecasts for 1 year are used in making the next year's

estimates. Each additional time period perpetuates and magnifies forecast

errors.

Impact models are designed for many different uses, and they necessarily must

be complex to provide forecasts for a variety of purposes. In the final

analysis, users should know the accuracy of models in estimating events of the

past or for other regions. With notable exceptions, few models are evaluated

in this way.

Natures^ jof jSxj^sJfcijig Data

Certain data characteristics cause diffieulty in judging the accuracy of

linking coefficients, whether the eoef^f i elects are derived from simple averages

or by using other statistical tectiníqués,17/ Special econometric procedures

address such problems.

Three common diffi^cjalties--&erial correlation, tnulticollitteartty, and spatial

interdependence—relate to a series of data through time, the interrelatedness of an area's clmracteris^ic^, and the linkage of añ area to the

economies of its neighbors. In the first ease, a linking coefficient's

effectiveness can be overstated by the statistical analysis when usitvg time

series data.18/ In the second case, identifying a specific characteristic

15/ Errors also can be introduced ^t the start when incorrect information

about the local area is given to tlie model as a starting point,

16/ This statement does not mean that the model should be simplistic or

naive. Rather, we recognize that the sources of forecast errors within th^e

model must be minimized.

17/ In addition, the accuracy of data used in models is open to question.

Miernyk (17) concludes that "...oiar carpacity to develop highly sophisticatedr

regional models has far outrun our ability to implement them, given the

primitive nature of available data>.,."

18/ Serial correlation occurs when ah observation at one time period is not

independent of others at later time periods: the accuracy at one time is not

independent of the accuracy at later time periods. This situation biases the

calculated variances of the estimates and contributes to overconfidence in the

accuracy of a coefficient (16, p. 275).

26
causing differences in a linking coefficient's value becomes difficult when the

characteristics within the impact area are closely related>19/ Finally, when

the impact area is adjacent to a large town or city, some events in the impact

area are directly associated with events in the adjacent area. Yet, that

association is difficult to separate out and take into account in the model's

linking coefficients.

Those who wish to evaluate impact models should, at the very least, receive a

description of the models' linking coefficients, the purpose of each

coefficient, and an explanation of how they fit together. The statistical

properties of the models' coefficients, procedures for deriving the

coefficients, and each coefficient's accuracy should be available to users for

evaluation by independent authorities. The best evaluation of a model is to

compare its forecast with actual historical events in some prior use of the

model.

Issues Beyond Current Technology

Current technology does not adequately address specific issues that would be

helpful for purposes of using models for impact management and contingency

planning.20/ These issues are dynamics, prediction of settlement patterns, and

uncertainty. These topics pose challenges for future research.

Dynamics

Most of the impact models we reviewed are of the equilibrium type. Except for

pure planning models, equilibrium models are appropriate for preparing NEPA

environmental impact statements. These models are not appropriate for

estimating annual changes because a new equilibrium is not probable in the same

period as the initial change. Dynamic models do estimate year-to-year changes

by allowing only a partial movement each year toward the final equilibrium.

A year-by-year path of an economy is required for contingency planning as a

part of impact management. Population estimates that translate into

expenditures for community services, for example, have to be related to the

year-to-year flows of revenues that support those services. Thus, accurate

representations of the dynamics of change are important. At this point, the

technology of impact modeling has not yielded many dynamic models that can be

applied to contingency planning.

Settlement Patterns

Revenue flows will not match expenditures for local government services when

workers in a new industrial development choose to live in a nearby area whose

government does not benefit from the new industrial tax base. Impact models

cannot anticipate this common problem accurately. Such a model requires

accurate forecasts of setlement patterns to gauge the year-'to-year adequacy of

19/ Multiqollinearity is the correlation among two or more of the

independent variables. It produces larger variances of the estimators than is

true, leading to a possible lower reported level of confidence in coefficients

-than is valid (22, p. 66).

20/ Halstead and others (10) recommend close monitoring of the progress of

construction and operation of new facilities as a method of circumventing these

difficulties.

27
the revenues of local governments in advance of potential impacts and as a

prelude to contingency planning.

Current impact models are quite inexact in predicting settlement patterns.

That fact signals tliat careful planning and zoning and possibly the use of

preconditions for authorizing permits must be used in impact mitigation,

planning, and management.

Uncertainty

Impact models do not directly address questions relating to uncertainty, such

as: What is the chance that planned construction and operation schedules will

be changed? Will the price of the proxtuct change and affect the plant's

operations? Will the operation slow down significantly during recessions?

Will the industrial facility close early?

Sensitivity analysis can provide partial answers. One can vary scenarios of

the project in repeated runs of a model as an experiment to see the impact

estimates most sensitive to each change. Even more fundamental to this issue

is the probability that one of these problems will appear.

The problem of uncertainty âs it relates to impact management and contingency

planning can be stated as "... the essence of the planning problem.... The

public is not well served by a strategy that simply plans for the most likely

future.••" (5^)» Perhaps the best advice to prospective model users is to treat

impact models as "sophisticated calculating mechanisms" and not modern-day

crystal balls.

28
REFERENCES

!• Barrows, Richard, and Marj Charlier. "Local Government Options for Managing

Rapid Growth," Chap. 8, Coping with Rapid Growth in Rural Communities. Ed.

Bruce A. Weber and Robert E. Howell. Boulder, CO: Westview Press, 1982,

pp. 193-220.

2. Beale, Calvin L. "The Recent Shift of United States Population to

Nonmetropolitan Areas 1970-75," International Regional Science Review, Vol.

2, No. 2, Winter 1977, p. 113.

3. Beckhelm, Terrance L., James A. Chalmers, and William M. Hannigan. ATOM 3;

A Description of ATOM 3 and of the Research Related to Its Development.

Arizona Off. Econ. Plan, and Dev., Bur. of Bus. and Econ. Res.; and Arizona

State Univ., Dept. of Economics, July 1975.

4. Bender, Lloyd D. Differences in the Timepaths of Service Employment

Responses: Rapid Growth and Local Planning. ERS Staff Report AGES 841201.

U.S. Dept. Agr., Econ. Res. Serv., Dec. 1984.

5. Chalmers, J. A., and E. J. Anderson. Economic/Demographic Assessment

Manual. U.S. Dept. Int., Bur. Rec., Nov. 1977.

6. Cummings, Ronald G., and William D. Schulze. "Optimal Investment Strategy

for Boom Towns," ^erican Economic Review, Vol. 68, No. 3, 1978, pp.

374-85.

7. Gerking, Shelby D., and Andrew M. Isserman. "Bifurcation and the Time

Pattern of Impacts in the Economic Base Model," Journal of Regional

Science, Vol. 21, No. 4, 1981, pp. 451-67.

8. Gilmore, J. S., and others. Socioeconomic Impacts of Power Plants. EPRI

EA-2228. Palo Alto, CA: Electric Power Research Inst., Feb. 1982.

9. Goodwin, H. L., Jr., Gerald A. Doeksen, and Robert L. Oehrtman.

"Determination of Settlement Patterns in Rapidly Growing Rural Areas," The

Annals of Regional Science, Vol. 18, No. 3, Nov. 1984, pp. 67-80.

10. Halstead, John M., and others. Socioeconomic Impact Management. Boulder,

CO: Westview Press, 1984.

11. Hamilton, H. R., and others. System Simulation for Regional Analysis: An

Application to River-Basin Planning. Cambridge, MA: Massachusetts Inst. of

Technology Press, 1969.

12. Haynes, Kingsley E., and A. Stewart Fotheringham. Gravity and Spatial

Interaction Models. Sage Scientific Geography Series, Vol. 2. Beverly

Hills, CA: Sage Publications Inc., 1982.

13. Hustedde, Ron, Ron Shaffer, and Glenn Pulver. "Community Economic Analysis:

A How To Manual," North Central Regional Center for Rural Development, Iowa

State Univ., 1984.

14. Isserman, Andrew M. "Estimating Export Activity in a Regional Economy: A

Theoretical and Empirical Analysis of Alternative Methods," International

Regional Science Review, Vol. 5, No. 2, Winter 1980, pp. 155-84.

29
15. Jensen, R. C. "The Concept of Accuracy in Regional Input-Output Models,"

International Regional Science Review, Vol. 5, No. 2, Winter, 1980,

pp. 139-54.

16. Kmenta, Jan. Elements of Econometrics, New York, NY: Macmillan Pub1.ishing

Co., 1971.

17» Miernyk, W. H, "Comments on Recent Develppments in Regional Input-Output

Analysis," International Regional Science Review, Vol. 1, No« 2, 1976,

pp. 47-55.

18. Miller, James P. Interstate Competition for Business; Changing Roles of

Federal and State Initiatives> IRS Staff Report AGES 831012* U.S. Dept.

Agr., Econ. Res. Serv., Dec. 1983.

19. Murdock, S» H,, J. S. Wieland, and F. L. Leistritz* "An Assessment of the

Validity of the Gravity Model for Predicting Community Settlement Patterns

in Eiural Energy-Impacted Areas of the West," Land Economics, Vol. 54, No.

4, Nov. 1978, pp. 461-71.

20. Murray, James A., and Bruce A. Weber• "The Impacts of Rapid Growth on the

Provision and Financing of Local Public Services," Chap. 4, Coping with

Rapid Growth in Rural Communities. Ed. Bruce A. Weber and Robert Ê.

Howell. Boulder, GO:^ Westview Press, 1982, pp. 97-113.

21. Nelson, Marlys Knutson. Socioeconpmic Impact Models: An Annotated

Bibliography. ERS Staff Report AGES 850228. U.S. Dept. Agr., Econ. Res.

Serv., Feb. 1985.

22. Pindyck, Robert S., and Daniel L. Rubinfeld. Econometric Models and

Economic Forecasts. New Yorks McGraw-HiXl Book <k)., 1976.

23. Fleeter, Saul. Economic Impact Analysis: Methodology and Applicatiptys^.

Boston, MA: MartinusNijhoff Publishing Co., 1980.

24. Round, Jeffery I. "Nonsurvey Techniq^ues : A Critical Review of the Theory

and the Evidence," International Regional Science Review, Vol. 8, No. 3,

1983, pp. 189-212.

25. Shaffer, Ron. "Estimating Local Income Multipliers: A Review and Evaluation

of the Techniques for Ex Ante Use," Proceedings of the Ex Ante Growth

Impact Models Conference. Eá^ Leroy J. Husak and George Morse. Nóirth

Central Regional Center for Rural Development, Iowa State Univ., 1979, pp.

43-73

26. Shryock, Henry S., and Jacob S. Siegel and Associates. The Methods and

Materials of Demography, Volume 2. U.S. Dept. Comm*, Bur. of the Census,

June 1980.

27. Stenehjem, Erik. Svimmary Description of SEAM; The Social and Economic

Assessment Model. Ü.S. Dept. Energy, Argonne Nati. Lab., Apr. 1978«

28. U.S. Department of Commerce, Bureau of the Census. "Regional Economic

Information System." Annual computer files.

30
29. U.S. General Services Administration. Code of Federal Regulations. Title

40, Parts 1500-08.

30. Woods, Michael Dentón. "A Simulation Model for Rural Communities in

Oklahoma." Ph. D. dissertation. Oklahoma State Univ., 1981.

31
APPENDIX: MODELS REVIEWED

Acronym

ATOM

Do cutnen ta ti on

Battelle Columbus Laboratories. Final Report on

Development of the Arizona Trade-Off Model« Arizona

Dept, of Econ. Plan, and Dev., Mar. 1973.

Beckhelm, Terrance L., James A. Chalmers, and William

M. Hannigan. ATOM 3: A Description of ATOM 3 and the

Research Related to Its Development. Arizona Off.

Econ. Plan, and Dev., Bur. of Bus. and Econ. Res.;

and Arizona State Univ., Dept. of Economics, July

1975.

BATTELLE II

BREAM

CLIPS

COALTOWN

DEISM

Cluett, Christopher, and Jeffrey Jacobsen.

Socioeconomic and Demographic Forecasting Model.

Battelle Human Affairs Research Center and U.S.

Dept. Energy, May 1978.

Chalmers, J., and E. Anderson. Economic/Demographic

Assessment Manual. U.S. Dept. Int., Bur. Rec., Nov.

1977.

Monts, J. Kenneth, and E. Ray Bareiss.

Community-Level Impacts Projection System. Texas

Energy Advisory Council and Center for Energy

Studies, Univ. of Texas at Austin, Feb. 1979.

Bender, Lloyd D., George S. Temple, and Larry C.

Parcels. An Introduction to the COALTOWN Impact

Assessment Model. EPA-600/7-80-146, U.S. Env.

Protection Agcy. and U.S. Dept. Agr., Econ. Res.

Serv., Aug. 1980

Governor's Office of Planning Coordination. Nevada's

Demographic and Economic Impact Simulation Model,

Dec. 1978.

DRI

MRMI

NED

Denver Research Institute, Resource Planning

Associates. Socioeconomic Impacts of Western Energy

Resource Development. U.S. Dept. Energy, June 1979.

Harris, Curtis, Jr. "New Developments and Extensions

of the Multiregional, Multi-Industry Forecasting

Model," Journal of Regional Science, Vol. 20, No. 2,

May 1980.

Reeve, Ross, Rodger Weaver, and Eric Natwig. The

Navajo Economic-Demographic Model: A Method of

Forecasting and Evaluating Alternative Navajo

Economic Futures, Vol. 1. Navajo Tribe and Utah Off.

of State Planning Coordinator, Off. of Prog. Dev.

32
Acronym

NEDAM

OCDSM

PURDUE

REAP

SEAM

Documentation

Chase, R. A«, and others. Expansion and Adaptation

of the North Dakota Economic-Demographic Assessment

Model for Montana; Technical Description, Misc. Rpt.

No. 61. North Dakota State Univ., Dept. of Agr. Econ.

Woods, Michael Dentón. "A Simulation Model for Rural

Communities in Oklahoma." Ph. D. dissertation.

Oklahoma State Univ., 1981.

White, T. Kelley, and others. The Purdue Development

Model: A Systems Approach to Modeling

Demographic-Economic Interaction in Agricultural

Development. Agr. Exp. Sta. Res. Bui. No. 925.

Purdue Univ., Oct. 1975.

Johnson, A. William, and F. Larry Leistritz. The

REAP Economic-Demographic Model 1: User Manual.

North Dakota State Univ. and North Dakota Reg. Env.

Assess. Prog., Dec. 1976.

Hertsgaard, Thor, and others. REAP

Economic-Demographic Model; Technical Description.

North Dakota State Univ. and North Dakota Reg. Env.

Assess. Prog., Aug. 1978.

Stenehjem, Erik, and James Metzger. A Framework for

Projecting Employment and Population Changes

Accompanying Energy Development. Phase I and Phase

II. U.S. Dept. Energy, Argonne Nati. Lab., Aug. and

Oct. 1976.

SIA

TAMS

Stenehjem, Erik. Summary Description of SEAM: The

Social and Economic Assessment Model. U.S. Dept.

Energy, Argonne Nati. Lab., Apr. 1978.

Murphy/Williams Urban Planning and Housing

Consultants. Socioeconomic Impact Assessment; A

Methodology Applied to Synthetic Fuels. U.S. Dept,

Energy, Apr. 1978.

Murdock, S., and others. The Texas Assessment

Modeling System; User Manual. Texas Center for

Energy and Min. Res., Texas Rural Dev. Prog., and

Texas Agr. Exp. Sta., Texas A&M Univ., Sept. 1979.

^U.S. Government Printing Office : 1986 -180-917/40694

33
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