United States

Department of

Agriculture

Forest Service

Pacific Northwest

Research Station

United States

Department of the

Interior

Bureau of Land

Management

General Technical

Report

PNW-GTR-477

October 2000

Rural Communities in the Inland

Northwest: An Assessment of

Small Rural Communities in the

Interior and Upper Columbia

River Basins

Charles Harris, William McLaughlin, Greg Brown, and

Dennis R. Becker
Authors

Charles Harris and William McLaughlin are professors, and Dennis R. Becker is a graduate student,

College of Forestry, Wildlife and Range Sciences, University of Idaho, Moscow, ID 83844; and Greg

Brown is an assistant professor of Environmental Studies, Alaska Pacific University, Anchorage, AK

99508. Final report on cooperative agreement PNW 40-04H1-8-1078 between the University of Idaho

and the Pacific Northwest Research Station.
Rural Communities in the Inland Northwest: An

Assessment of Small Rural Communities in the

Interior and Upper Columbia River Basins

Charles Harris, William McLaughlin, Greg Brown, and Dennis

R. Becker

Interior Columbia Basin Ecosystem Management

Project: Scientific Assessment

Thomas M. Quigley, Editor

U.S. Department of Agriculture

Forest Service

Pacific Northwest Research Station

Portland, Oregon

General Technical Report PNW-GTR-477

October 2000
Preface

The Interior Columbia Basin Ecosystem Management Project was initiated by the Forest Service

and the Bureau of Land Management to respond to several critical issues including, but not limited

to, forest and rangeland health, anadromous fish concerns, terrestrial species viability concerns, and

the recent decline in traditional commodity flows. The charter given to the project was to develop a

scientifically sound, ecosystem-based strategy for managing the lands of the interior Columbia River

basin administered by the Forest Service and the Bureau of Land Management. The Science Integration Team was organized to develop a framework for ecosystem management, an assessment of the

socioeconomic and biophysical systems in the basin, and an evaluation of alternative management

strategies. This paper is one in a series of papers developed as background material for the framework,

assessment, or evaluation of alternatives. It provides more detail than was possible to disclose directly

in the primary documents.

The Science Integration Team, although organized functionally, worked hard at integrating the approaches, analyses, and conclusions. It is the collective effort of team members that provides depth

and understanding to the work of the project. The Science Integration Team leadership included deputy

team leaders Russel Graham and Sylvia Arbelbide; landscape ecology—Wendel Hann, Paul Hessburg,

and Mark Jensen; aquatic—Jim Sedell, Kris Lee, Danny Lee, Jack Williams, Lynn Decker; economic—

Richard Haynes, Amy Horne, and Nick Reyna; social science—Jim Burchfield, Steve McCool, Jon

Bumstead, and Stewart Allen; terrestrial—Bruce Marcot, Kurt Nelson, John Lehmkuhl, Richard

Holthausen, and Randy Hickenbottom; spatial analysis—Becky Gravenmier, John Steffenson, and

Andy Wilson.

Thomas M. Quigley

Editor

United States

Department of

Agriculture

Forest Service

United States

Department of

the Interior

Bureau of Land

Management

Interior Columbia

Basin Ecosystem

Management Project
Abstract

Harris, Charles C.; McLaughlin, William; Brown, Greg; Becker, Dennis R. 2000. Rural communities in the inland Northwest: an assessment of small communities in the interior and upper

Columbia River basins. Gen. Tech. Rep. PNW-GTR-477. Portland, OR: U.S. Department of

Agriculture, Forest Service, Pacific Northwest Research Station. 120 p. (Quigley, Thomas M., ed.;

Interior Columbia Basin Ecosystem Management Project: scientific assessment).

An assessment of small rural communities in the interior and upper Columbia River basin was conducted for the Interior Columbia Basin Ecosystem Management Project (ICBEMP). The characteristics

and conditions of the rural communities in this region, which are complex and constantly changing,

were examined. The research also assessed the resilience of the region’s communities, which was defined as a community’s ability to respond and adapt to change in the most positive, constructive ways

possible for mitigating the impacts of change on the community. The study found that a town’s population size, autonomy, economic diversity, quality of life, and experience with change were all factors

related to the town’s resiliency and the extent to which it was changing and preparing for change.

Keywords: Rural communities, forest communities, resource dependence, community assessment,

ecosystem assessment, social impact assessment, resiliency, Columbia basin.
Research Goal and Premises

The goal of the research described here was to assess the characteristics and conditions of small, rural

communities in the interior and upper Columbia River basin (henceforth, the basin); the basin includes

a lower basin in eastern Washington and Oregon and an upper basin that spans all of Idaho, western

Montana, and western Wyoming. This research was based on several premises:

• The small, rural community is an important scale for social assessment. For most residents of rural

regions such as the study area—even those people living outside the borders of incorporated towns

and cities—the community where they socialize, shop, and perhaps work or go to church becomes

the focus of their social lives.

Social sciences recognize the significance of this scale of social organization. Those sciences include

sociology, which focuses on social groups, organizations, and communities as primary units of analysis, and for which conflict and cohesion are key forces underlying social change; and anthropology,

which is centered on social groups, communities, subcultures, and sometimes entire cultures, with a

focus on tradition (Machlis and Force 1988; Machlis et al., n.d.). Rural towns are too small to have

neighborhoods, and the only other definable social grouping between individuals and communities is

the sociocultural groups and organizations that often exist within communities. These groups can be

influential in making things happen where they are located, but most of the governmental, civic,

social, and in-frastructure mechanisms function at the community level.

The next highest level of social organization is one of polity: county government. Most data collected

by Federal and state agencies are reported at the county level. Unfortunately, in many places, conditions and changes in conditions at this broader level mask the differences across communities and thus

the impacts of change on their residents; this aggregation problem reflects the reality of the county as

a political entity that, for many residents, may not be a meaningful social grouping and thus a relevant

unit of analysis.

• The characteristics and conditions of small, rural communities in the region are complex and constantly changing. The present study examined the characteristics and conditions of the 387 small,

rural, incorporated communities in the study area, in part with 1990 U.S. census data on those communities and, in part, with indepth, detailed data from a first-quarter 1995 survey of a systematic

random sample of 198 communities. The data from the community self-assessments provide only a

snapshot in time, and the indepth case studies of communities experiencing significant change since

1980 provide information on communities in transition.

• The research also examined the resilience of the region’s communities, which was defined as a

community’s ability to respond and adapt to change in the most positive, constructive ways possible

for mitigating the impacts of change on the community. This concept was developed by the Science

Integration Team of the Interior Columbia Basin Ecosystem Management Project (ICBEMP).

The resilience of a community is relative, so the study focused on degrees of resilience—the communities can be thought of as representing a continuum from low to high resilience. Also, a

community’s resilience can change over time, depending on changing community conditions. Communities can undergo different stages in their development, and different stages of development can

reoccur, as reflected by the ongoing boom-and-bust cycles of the American West, which results in

different economic mixes and shifts in dominant industries at different times.
• The results on resilience presented here represent two kinds of information: residents’ perceptions

of their communities in 1995, and factual, documented information about community characteristics, such as their population size, actual response to change, and their actual economic structure in

the first quarter of 1995. Both kinds of information are important: both the ways people see and

know their community and believe it to be, and the ways the community actually is, can be important factors underlying a community’s development and its responses to change.

Research Methods

Several sets of data were collected for assessing community characteristics and conditions: First, empirical data were gathered for all 387 small, rural, incorporated communities in the interior and upper

Columbia River basin (data available from the Bureau of the Census). Secondly, a random sample of 198

communities was selected (about half of all small rural communities in the region), and 1,350 residents

of these communities completed a community self-assessment workbook. These residents then participated in community self-assessment workshops to provide data on their community’s current characteristics and conditions (i.e., community character and attractiveness, social cohesion, civic leadership,

quality of life, business attractiveness, economic diversity and resource dependence, and the community’s preparedness for the future). Second, community officials were contacted to provide other documented or recorded details about each community’s character and conditions (e.g., rate of population

growth, economic changes, school and utility capacities, distance from major transportation routes or

nodes).

A third set of data consisted of profiles of the economic structure of each of the 476 communities

(towns and cities) and census designated places (CDPs) in the basin, based on estimates of the proportion of a town’s total employment attributable to each industrial sector contributing to that town’s

economy. These data, which were developed in collaboration with University of Idaho researchers

specializing in community economics, provided a profile of each community’s economy in terms of

employment and earnings for industries, businesses, and agencies, which were aggregated into 21 major

industrial sectors.

A component of the research also assessed and analyzed the characteristics and experiences of 145

communities in the regions identified as “significant change communities.” These communities were

indicated as undergoing major change by (1) state economic development officials, agricultural extension experts, USDA Forest Service forest planners, or economic development coordinators; or (2) U.S.

census population estimates indicating changes of +20 percent since 1980. Collection of data on these

communities focused on identifying the kinds of changes occurring in them, the kinds of community

responses resulting, and the effects or characteristics of all these factors in terms of community conditions, activities, and lifestyles. A sample of 80 of these 145 communities was surveyed, with initial

contacts made with city clerks, who were asked to suggest the name of the person who would have the

greatest knowledge of the changes the town had experienced and its response to them. A structured

telephone interview was then conducted with this representative of the town.

Of these communities, 10 were identified as having already undergone major changes of the kinds most

prevalent in the study area since 1980, and indepth case studies of these communities were conducted to

better understand the major changes influencing them, the impacts of these changes and the community

responses to them.
Finally, a representative survey of residents of Chelan County, in central Washington, was conducted

to assess the opinions and attitudes of one county’s residents about growth and resource management

issues in a rapidly growing county. From 700 questionnaires sent to a sample of residents randomly

selected from local phone books, 222 completed questionnaires were returned, for a response rate of

32 percent. These data provided useful insights into the preferences and concerns of residents in a

county typical of those in the interior West experiencing significant population growth and community

development.

Major Findings

Initial analysis of these data indicated that:

• Small rural communities represent an important scale for gathering and analyzing social data on

human populations. Analysis of the data collected from Chelan County residents confirmed that,

although 38 percent of the county residents lived outside any community, most residents of the

county (79 percent) reported that the city or town where they collect their mail was somewhat or

very important in their lives (Harris 1996a).

• The economies of communities are complex, and citizen perceptions of them differ in accuracy.

When the importance of industries in rural communities were assessed in terms of proportion of

total employment, a complex picture of the economy of the Columbia River basin emerged: harvesting and processing (agriculture, timber) were important employers, especially in the smaller

rural towns across the region, with government (Federal and state or local) and travel and tourism

also among the region’s largest employers. The majority (62 percent) of jobs in the average rural

community is in service sectors: not counting government, which provides an average of 21 percent

of all the jobs, they account for 41 percent of all employment in rural towns. A difference is found,

however, between large and small towns in their employment in traditional economic base industries: the largest towns (over 3,000 in population) had a total of 18 percent of jobs, on average, in

those sectors, and in the smallest towns, those sectors accounted for an average 34 percent of all

jobs.

• The vast majority of rural communities are small (less than 1,500 in population), and a community’s population size is significant. Generally, the larger communities in the region tended to be more

resilient; not unexpectedly, those with larger populations tended to have a more developed, extensive infrastructure and workforce to build on. Also, the largest towns tended to have more diversified economies. These results support the 1993 analysis of the Forest Ecosystem Management

Assessment Team (FEMAT 1993) community assessment, which suggested that communities with

high capacity to adapt tend to be larger communities, and communities less able to adapt tend to

have more limited infrastructure, less economic diversity, less active leadership, more dependence

on nearby communities, and weaker links to centers of political and economic influence.

• The community resilience index indicates the ability of small rural towns to manage change. The

current study found that a small town’s population size is, in fact, the single best characteristic for

predicting its current conditions and likely response to change: larger towns tended to be more economically diverse and thus stable. The smaller and less developed a town is, the less vital, attractive, friendly, and attractive for business it is likely to be perceived to be by its residents. Overall,

the communities perceived to be more vital, attractive, and healthy generally were the larger ones.

The conclusion here is consistent with the basic premise of the plethora of community development handbooks and workshops provided in the 1970s and 1980s: if members of a small rural community want to “develop” their town, they should work to attract new industries and expand its

economic base (which will indirectly lead to an increase in population).
• Significantly, the findings of both the self-assessment study and the community economic profiles

suggested that the impacts of this improvement extend beyond the economic aspects of community

development, whose significance has long been recognized and is reaffirmed here, to its social elements as well. More autonomous rural communities and ones larger in population typically represent a more advanced stage of social and civic development than small ones. The importance for

community vitality of active social groups and civic organizations, increased educational infrastructure, availability of services, success in obtaining development grants, and greater preparedness for

the future—all of which increase with a town’s size—reflects the benefits that towns with a critical

mass of social capital and infrastructure were more likely to realize. An interesting question for

future research, however, is at what size and level of community development the net benefits of

growth are maximized, beyond which the social costs of further growth begin to exceed its benefits.

• Finally, our assessments of resilience and significant change in communities make clear that change

and resilience to it were found across the various economic types of communities. Interestingly,

towns perceived as timber dominant tended to be farther from an interstate highway and relatively

isolated, but they also tended to be relatively resilient compared to towns in which other industries

were perceived to be dominant. The least resilient communities were those in which farming and

ranching were perceived to be dominant. A complementary finding was that communities that have

changed the most in the last 5 years tended to be more resilient, which was likely due to their

greater experience in coping with change.

Also supporting these results were the findings on population changes in towns smaller than 10,000

where mills manufacturing wood or paper products have closed since 1980: although 52 percent of

these towns declined in population, the populations of an almost equally large proportion (48 percent)

increased. In total, the change in population of small towns in which mills closed was a net increase of

8 percent since 1980.

Communities, nonetheless, were unique in their characteristics and conditions, their experiences of

change, and thus their responses to it. Given that the details of their situations differed, they must be

assessed case by case to understand those details and their role in community change.

• The rates of growth of small rural communities differ across the region, and they are changing in

other diverse ways. The population in the region is continually changing, but with a clear trend

toward growth: U.S. census figures indicate that the population growth between 1988 and 1994 was

12 percent in Idaho, 7 percent in Montana, 8 percent in Oregon, and 9 percent in Washington; the

U.S. population grew only 4 percent during that period. A large majority (70 percent) of the communities across the region reported that they had experienced a moderate to high degree of change

since 1990. The kind of change reported by the largest proportion of Chelan County residents surveyed was growth and population increases, by a 2 to 1 margin (68 percent). Other important

changes included the conversion of agricultural lands to residential and commercial development

(32 percent), an increase in retail stores (26 percent), increased traffic (23 percent) and increased

crime (22 percent). A majority, over 55 percent, was somewhat to extremely concerned about the

overall changes in their community.

• Growth in employment in the region also far exceeded the national rate: employment increased

8 percent nationwide between 1988 and 1994, but it increased 28 percent in Idaho in that same

period and around 17 percent in the other states in the region. Recent changes in communities were

due to a variety of broader economic influences, such as global economic forces, economic diversification, plant modernization, and industrial downsizing (such as laying off company loggers and

hiring independent loggers, or “gypos,” to reduce the costs of benefit payments).
Some Preliminary Conclusions

• Small rural communities in the Columbia River basin have always been in a process of change and

will continue to be; the idea of community stability is a myth belying such influences as the volatility of markets for timber, mining, and other traditional extractive industries; the actions of private

companies in modernizing or closing plants and periodically laying off or terminating workers; the

decreased supply of timber from National Forests, sometimes due to past inaccuracies in estimates

of existing timber supply, current regeneration, and future sustainability; decreasing employment in

the industries as a result of all these changes; and the rapidly increasing in-migration of new kinds

of workers and residents (retirees, new ethnic groups, etc.) into many of these communities.

• Although closures of mills, mines, and other resource-processing plants can have significant impacts in some communities, past closures have had few effects on other communities. Many mills,

for example, have closed, been sold, been reopened, and been closed again in a series of changes

over past decades that have not always been related to public land management. Community

growth, as indicated by population increases, occurred in many communities that lost mills, but

not in others.

• Rural communities tend to be more resilient (i.e., adaptive to change) than commonly assumed.

Small towns in the Columbia River basin are unique and complex, though, and generalizing about

the kinds of towns that are resilient to change is always contingent on the situation of each. For example, many “timber communities” are fairly highly resilient and healthy, especially in comparison

to small ranching and farming communities. With their development of amenities, diversifying economies, and population growth, the face of these timber towns already is changing.

Importantly, even though a community’s resources, including its amenities and attractiveness, can be

factors influencing development, a decisive, major determinant of a community’s resilience clearly is its

residents—in particular, the willingness of residents to take leadership roles, organize, and realize their

community’s potential. Community residents are a central defining element in creating the future of

rural communities.

• New policy initiatives could help small communities cope with the changes facing them, and public

policy analysts could view the role of resilience in one of two alternative ways. The first view is

that, if government resources are to be expended on rural communities, those lowest in resilience—

ranching and farming communities, in particular—are the ones most needing support. An alternative view is that, in the name of economic efficiency and equity, America should “cut its losses” in

terms of communities that are “on the skids” and losing their human capital. According to this view,

expending any more societal resources on these communities would not be worth the benefits

derived. Rather, government resources would be more effectively used on communities at risk but

that have the potential to benefit from those resources.

• The history of Forest Service commitments and impacts on rural communities has been a continually evolving process. The nature of this process, changing societal values, the changing Agency

workforce reflecting those values, and the learning occurring within the Agency underscore the

importance of sound forest planning; information such as this research can be important for revising

Forest plans and planning individual projects. It also can be useful for the planning and management efforts of the towns themselves and those of the counties and states in which they are located.
Contents

1 Context for the Assessment

1 Introduction

2 Why the Community Assessment?

3 The Social Impact Assessment Process in FEMAT

4 Why the Focus on Smaller Communities?

6 Recent Research Relating to Communities Dependent on Forest and Rangeland

Resources

6 Research Goal and Premises

8 Review of the Literature

8 Introduction

8 Critical Dimensions of Community Characteristics and Conditions

8 Community Character

9 Community Cohesiveness

9 Community Services

10 Community Autonomy

11 Economic Diversity

12 Resource Dependence

12 Attractiveness For Business

13 Quality of Life

13 Community Leadership and Effectiveness of Community Government

14 Community Preparedness for the Future

15 Major Findings From the Literature on Social Impact Assessments

19 Methodology for Assessing Communities

19 Introduction

19 The Community Self-Assessment Study

19 Developing a Strategy for Sampling Communities

20 Development of the Community Self-Assessment Process: What and Why?

21 The Community Self-Assessment Process: How?

23 Assessment of Community Economies

23 Profiles of the Economic Structure of Rural Basin Communities

25 Travel and Tourism Employment

26 Surveys of Residents of Chelan County and Significant Change Communities

27 Analysis and Presentation

28 An Overview of the Status of Rural Communities in the Basin

28 Introduction
28 Gathering and Analyzing Community Scale Social Data

30 Most Rural Communities Are Small

30 The Geography of the Communities

31 Characteristics of Participants in Community Self-Assessments

32 Perceived Characteristics and Current Conditions

33 Current Characteristics and Conditions of Rural Communities

43 Economics: Perceptions and Reality

43 Perceptions of Workshop Participants

46 Profiles of the Economies by Actual Employment

68 Comparing Perceived and Actual Industry Dominance

84 A Community Resilience Index

84 Development and Validation

88 Resilience Classes

95 Other Findings

99 Rural Economies and Community Resilience

101 Other Findings on Rural Communities

101 Timber-Dependent Communities

104 Is Bigger Better—Or Is Being More Autonomous?

106 Quality of Life in Small Communities

107 Change in Small Rural Communities

111 Conclusions

113 Acknowledgments

114 References
1

Introduction

The management of natural resources in the

Pacific Northwest has been changing direction in

recent decades. Small rural communities in the

region, many significantly affected by changes in

resource management, also have been changing

dramatically.

Recent signs of change in resource management

have been most evident in renewed efforts to plan

for the region’s spotted owl forests west of the

Cascade Range—a planning process that began

with President Clinton convening a “forest summit” in 1993 and continued with the preparation

of a scientific assessment for the west-side forests

(Forest Ecosystem Management Assessment

Team [FEMAT] 1993). Based on this scientific

assessment, the USDA Forest Service selected

option 9 as the preferred alternative of its plan

for the Northwest’s forests, and the Agency presented an analysis of that option in the environmental impact statement (EIS) it conducted for

future forest management for the region’s forests

(USDA Forest Service 1994). A key objective of

the west-side forest plan was to establish and

implement ecosystem management as a dominant

strategy for future management of the region’s

forest resources.

Fundamental to an ecosystem approach are

several assumptions:

• Management priorities will include the protection and restoration of deteriorated ecosystems, as well as the traditional focus on providing multiple benefits for people (i.e., a

variety of resource values, products, and services) within the capabilities of ecosystems.

• Any analysis of the uses of natural resources,

the relation of these uses with local communities, and the effects of resource uses on the

natural environment will be conducted at a

landscape scale adequately accounting for the

broad geographic, place-based nature of these

relations and impacts.

• The management process will be an inclusive,

collaborative one having grass-roots support

and build on input from different communities

of place and interest, rather than be a process

that is exclusive and divisive.

• Management decisions will be based on

sound science to ensure that management

activities make use of the best available

scientific data.

Other changes also have had major impacts on

management of the Pacific Northwest’s natural

resources. Forest Service activities increasingly

have focused on noncommodity resource management (Farnham 1995, Farnham et al. 1995), at

the same time that the amount of timber offered,

sold, and harvested from National Forests has

significantly decreased since the late 1980s

(Farnham and Mohai 1995). Most recently, the

Forest Service has declared a moratorium on road

building in National Forests while it reconsiders

its forest harvesting and road-building policies.

At the same time, heated debate over necessary

steps to restore the region’s wild salmon runs

escalated in the late 1990s, mainly as a result of

an evaluation by the U.S. Army Corps of Engineers on the impacts of breaching four dams on

the Lower Snake River to aid in that restoration

effort. In addition, the in-migration of large numbers of people, many of whom are ex-urbanites

Context for the Assessment
2

building homes in forested areas, is increasing the

region’s population and the likelihood of conflicts. In light of these changes, a major concern

of affected Federal managers, as well as other

resource managers, industries, and local publics,

has been with the impacts of changing resource

outputs and management priorities on people

living in the region and the small rural communities located there.

The scope of these impacts and concerns about

them expanded scientifically as well as geographically when the Interior Columbia Basin

Ecosystem Management Project (ICBEMP), a

multiagency resource-management planning effort being led by the USDA Forest Service, began

in 1993. This project, unlike the abbreviated

FEMAT assessment, was more comprehensive

and thorough in its assessment of the natural resources and socioeconomic conditions in the

interior and upper Columbia River basin (or the

basin). This region of the interior West, which is

the size of Texas, is comprised of two basins: the

interior Columbia River basin, which extends east

from the Cascade crest across Washington and

Oregon, and the upper Columbia River basin,

which extends east from Idaho’s western border

to the Continental Divide and includes all of

Idaho plus western Montana and Wyoming.

The ICBEMP has produced a broad management

strategy for the region’s public lands, as prescribed in the EIS for that strategy (USDA-FS

and USDI-BLM 1997a, 1997b). That project was

designed to:

• Develop “big-picture” ecosystem management

strategies for restoring forest and rangeland

health while providing sustainable resources

and jobs for people.

• Address broad-scale problems crossing

jurisdictional lines, including providing for

species viability with an ecosystem approach

rather than a species-by-species approach.

• Better protect fish and other species and provide management needed to reduce ecological

risks in riparian and upland areas.

• Reduce polarization over concerns about

conservation and public land management by

providing a process that encourages interested

and affected parties to cooperate with one

another.

As part of the ICBEMP assessment, the project’s

Social Science Assessment Team recognized the

need to look beyond the characteristics and conditions of ecosystems and natural resources to

consider the situations of people and communities that are a part of those ecosystems and using

those resources. Accordingly, as part of the social

science assessment, research was started in 1994

to better understand the characteristics and conditions of small, rural communities in the region.

Why the Community

Assessment?

Throughout history, communities and

their residents have been shaped by the

interplay of the forces that cause social

change. The American West, for example, is sprinkled with ghost towns

standing as monuments to the power

such forces can exert on communities

and their residents. In the United States,

such changes have traditionally been

viewed as part of the natural course of

things, with the outcomes interpreted as

demonstrations of economic forces that

were beyond anyone’s responsibility to

control.

Branch et al. 1982:5

America’s interior West has experienced changes

that began with the region’s settlement by immigrants from the Nation’s coasts and continue

today with a diversity of economic, cultural, and

human migration trends unfolding in the Western

United States. Changes in Federal land management practices, such as those being proposed by

the ICBEMP, can affect the physical, cultural,

social, political, legal, economic, and psychological nature of the human environment (Gramling
3

and Freudenberg 1992). These effects are especially pronounced in a region—such as the

basin—having a large percentage of Federal land.

Practitioners of social assessment presume that

local, state, and Federal governments have a responsibility to help minimize the negative effects

of the changes set in motion by social forces and

shifts in land management policy, or at the least,

to assist their citizens in preparing for those

effects.

This report seeks to provide a better understanding of small rural communities in the basin faced

with current and future changes in natural resource management. The assessment research addressed a variety of questions: What is the current

character of communities in the inland Northwest

and northern Rocky Mountains? What are their

most notable attributes and characteristics, and

what are the interrelations among the attributes of

these towns? Do the region’s communities want

to remain largely as they are, or do they seek to

change? What is the capability of communities to

deal with change and prepare themselves for the

future? What makes a community more or less

resilient or adaptive to change? How might the

communities be impacted by changes in the policy direction of the Forest Service and Bureau of

Land Management (BLM)? How can government

entities ease the transition for these communities

and for the social, political, and cultural groups

that are important components of them?

The Social Impact Assessment

Process in FEMAT

The current assessment was built from the

research conducted for the social science

component of FEMAT (1993). The objectives

of the FEMAT social impact assessment were to:

1. Describe the nature and distribution of the

social values and uses found in the range of

the northern spotted owl (Strix caurina

occidentalis).

2. Describe how these values and uses would be

affected by various management options.

3. Identify how different constituents could be

affected by changes stemming from the

options.

4. Identify opportunities or strategies for dealing

with impacts of these consequences on people

and their communities (FEMAT 1993:5).

The social assessment process conducted by the

FEMAT team included the following components

(1993:6-8):

1. Commissioned papers to obtain expert opinions on various issues having to do with the

potential social impact of the range of Federal

options for the spotted owl forests.

2. An examination of Forest Service and BLM

public involvement records.

3. A survey of county extension agents throughout the region.

4. Two workshops with government employees

and extension agents from around the region

to assess the relative ability of communities to

deal with possible management options and

other changes in the region.

5. An assessment of the nature and value of

the region’s recreation, scenic, and subsistence values by conducting a number

of information-gathering efforts:

• A survey of BLM and Forest Service offices

to see what information was available on

these values. Recreational opportunities and

visual quality objectives also were assessed,

based on forest and district land-use

allocations.

• A case study of agency representatives from

selected areas, including 2 days spent by

BLM and Forest Service representatives to

map the location and extent of various social

values, with the purpose of assessing how

management options could affect these

values.

• A nominal group exercise, the purpose of

which was to identify barriers and impediments to integrated resource management

as well as opportunities to overcome those

impediments (FEMAT 1993).

Although the FEMAT team was severely constrained by time (only a few months were available to complete a full impact assessment of the

extensive spotted owl forests), the team later
4

wrote, “While acknowledging the limits imposed

by the above constraints, we also want to assert

that this social assessment represents one of the

most significant efforts ever undertaken to examine the social consequences of federal forest

management” (FEMAT 1993:5). The present research, which expands on the FEMAT study of

west-side communities, provides a start in gaining greater knowledge in the science and practice

of social assessment.

Why the Focus on Smaller

Communities?

A key premise of a regionwide, landscape-based

assessment, such as that conducted for the

ICBEMP, is that future resource management

should be based on an integrated, multiresource

analysis for promoting management of sustainable ecosystems. To realize sustainable management, the management region must be large

enough to account for species interdependence,

allow for long-term adaptation and catastrophic

change, and assure the healthy functioning of the

ecosystem at all levels. However, while nature

knows no borders, humans do.

Human activities are conducted at various levels

of scale: units of analysis can be based on different levels of social organization, and everyday

human activities can be based on collectivities,

geography, and political boundaries. Commonly

recognized levels of social organization based

on geography and human activities range from

households to neighborhoods, communities

(towns and cities), counties, multicounty regions,

and states. Historically, bureaucratic, administrative, and political boundaries have been a common hindrance to confronting the challenges

posed by achieving species conservation and

healthy ecosystems. Regionwide, cross-agency

coordination is critical for providing a consistent

overall direction to communities that reflects

changing priorities and approaches to resource

management.

Although larger scale areas such as watersheds,

ecological provinces, or whole regions may be

important as a basis for ecosystem management,

they may not be the most appropriate level for

conducting social assessment (Krannich et al.

1994). People experience the majority of their

ties to other people, their work, the services they

are provided, and their network of friends and

family at the level of community. Local communities are more than just a place where people

happen to live; they essentially and fundamentally “constitute the fabric of day-to-day life”

(Krannich et al. 1994:48-49). Some analysts suggest that, indeed, the slower pace in rural communities provides their residents with a fundamental

tie to social norms and traditions. As Branch and

associates write, “The linkages between community resources, social organization, and wellbeing and the important role communities play

as administrative and participatory units make it

essential that social assessments utilize an analytic framework that effectively focuses attention

on the community” (Branch et al. 1982:25-26).

The social sciences have long recognized the

significance of the community as a key scale of

social organization: included are sociology’s

focus on social groups, organizations, and communities as primary units of analysis, and on

conflict and cohesion as major forces of change;

and anthropology’s attention to social groups,

communities, subcultures, and sometimes entire

cultures, with a focus on tradition (Machlis and

Force 1988; Machlis et al., n.d.). Given that rural

towns are too small to have neighborhoods, the

only other definable social grouping between

individuals and communities is the sociocultural

groups and organizations that often exist within

communities. Although these groups can be

influential in making things happen where they

are located, most of the governmental, civic,

social, and infrastructure mechanisms function at

the community level. The next highest level of

social organization is one of polity: county government. But the concerns, activities, and impacts

at this level primarily have to do with land-use

regulation and provision of rural services that

lack a social or cultural dimension.

The guidelines and principles for social assessment (hereafter, “guidelines and principles;”

Interorganizational Committee on Guidelines and

Principles for Social Impact Assessment 1994)
5

make the point that, “just as the biological sections of EIS’s devote particular attention to

threatened or endangered plant and wildlife species, the socioeconomic sections of EIS’s must

devote particular attention to the impacts on

vulnerable segments of the human population”

(1994:4). In the case of analyzing the effects of

Federal land management actions and direction,

the most critical impacts may be to small, rural

communities.

In addition to the centrality of small communities

in the lives of people living in rural areas, rural

communities may be especially vulnerable when

they lack the leadership necessary to weather a

complex set of changes (Israel and Beaulieu

1990). As conditions worsen and resources become more limited, local governments often are

forced to transfer their decisionmaking and become more reliant on state and Federal governments (Weeks 1990). This may further limit local

initiative and creativity, especially in the face of

economic downturns. In addition, small towns

often lack the economic capacity to outlast downturns in a particular industry. They may not have

enough skilled labor available to attract new business and compete (Malecki 1988, Power 1994).

Rural communities also often lack adequate basic

infrastructure (e.g., water, sewage), much less the

communications and information infrastructure

important for economic growth (Dillman et al.

1989). As a consequence of all these factors,

the communities also may lack the financial

resources and economic diversity to withstand

changes impacting their economic base.

For all these reasons, rural communities are especially vulnerable to change. Consequently, the

community of place is an especially important

and relevant level for social assessment. Not all

social scientists agree, however, that the geographically based community, or community of

place, is always the appropriate level of analysis.

Carroll, for instance, makes the point in FEMAT

(1993) that a community is more than a municipality; when he refers to community, Carroll

focuses on communities of interest:

groups of people such as Tar Heel shake

and shingle workers, loggers, rural environmentalists, Native Americans and

ethnic/cultural groups who gather special forest products. In many ways, the

attachment these people have to each

other, the land, special places and their

life in common constitutes more of a

sociologically definable community than

the artificial boundaries of many towns.1

In the light of his and other similar concerns, the

FEMAT social scientists suggested a compromise

position (Clark and Stankey 1994:33):

A definition of community has long

troubled scholars, who recognize that

even in specific locations shifting constellations of people comprise different

communities with different purposes (for

example, occupational communities such

as loggers). However, geographic communities are important from an economic

and policy standpoint, especially for

isolated areas whose fortunes are linked

to their location. They also embrace occupational communities; thus, programs

directed at geographic communities

likely will reach members of occupational communities and their families

where they live.

In one sense, the present research deals with

some of Carroll’s concerns over the importance

of communities of interest by providing an inclusive process that sought to represent the diversity

of perspectives and subcultures that potentially

can exist within a community. It is not a complete

solution to the problem. Other analyses conducted for the basin assessment focused on the

level of stakeholders and special interest groups.

1

 Some early reviews of this process reflected a misunderstanding of it; they raised the concern that (1) by basing the

assessment on a small sample of a community’s most informed residents, its results would be biased by those who

believed participating would influence the results, and (2)

the snowball sample would result in people “inviting” likeminded associates to participate. These concerns were found

to be unfounded, as reflected in the diversity of participants

and their perceptions of their community in each town.
6

Nonetheless, the community focus detailed here

represents a wealth of information on the status,

vulnerability, needs, and aspirations of local

communities: the locus of everyday life and the

fabric of our society.

Recent Research Relating to

Communities Dependent on

Forest and Rangeland

Resources

Other studies assessing the social and economic

situations of human populations and addressing

their relation with forests and rangelands have

recently been conducted. They include the work

of Machlis and others (Force and Machlis, n.d.;

Machlis and Force 1988; Machlis et al. n.d.) on

resource-dependent towns and the use of countybased social indicators for mapping social conditions in the Pacific Northwest, Tarrant’s (1995,

1996) social assessment research for the Appalachian Ecosystem Management Project, and the

focus of Doak and Kusel (1996) on understanding

human populations for the Sierra Ecosystem

Management Project. Although some of these researchers suggest the need for an emphasis on human conditions at a community level, most have

followed the lead of the FEMAT process and

used secondary data, sometimes from the level of

community groupings and sometimes from the

county level: levels of scale that ironically do not

allow for the kind of indepth analysis of the community as the unit of analysis that their conceptual frameworks call for.

In contrast, Beckley (1998) notes the importance

of the community scale in research that provides

a conceptual framework for understanding the

components of forest dependence that need to be

considered in resource management. The ongoing

work of Carr et al. (1998a, 1998b) is building on

assessments such as Beckley’s and that presented

here, which focus on the importance of communities and networks among them, with projects that

seek to advance the substantive integration and

consideration of community conditions and

changes in resource management and planning.

Research Goal and Premises

The primary goal of our research was to assess the

characteristics and conditions of small, rural,

geographically based communities in the basin.

Specifically, the communities to be studied were

defined as incorporated towns in the region with

less than 10,000 in population.

The intent of the research was not to assess the

residents’ preferences and attitudes concerning

their community and issues affecting it. These

feelings could well differ among different groups

of residents, including those active, knowledgeable citizens who are highly involved in the activities of their community. The research did not

focus on personal feelings and values (as in the

case of preferences and attitudes about various

sides of various issues). Rather, the research

focused on residents’ beliefs about the characteristics and conditions of their community, and on

providing knowledgeable community residents

with an opportunity to share their information

and perspective on their communities. By obtaining a group assessment of the community’s current situation from people representing a variety

of perspectives (via sharing information in the

workshop as described in the chapter, “Methodology for Assessing Communities”), the research

process sought to provide a neutral, balanced, and

objective assessment of each community’s characteristics and conditions by those most qualified

to provide it.

The research was based on several other

premises, as well:

• The small, rural community is an important

scale for social assessment.

As noted above, for most residents of rural regions such as the study area (including those

people living well outside the borders of incorporated towns and cities), the community where

they pick up their mail, socialize, shop, and perhaps work or go to church is an important social

and economic focus of their lives. Despite this

reality, most data collected and reported at a local

level are from Federal and state agencies and for

entire counties. Consequently, in many places,
7

efforts at the county level to track changing conditions only mask the differences in those conditions in individual communities; thus, the kinds

and degrees of impacts that changing policies

have on local residents are muted. This aggregation problem reflects the reality of the county as a

political entity that, for many residents, may not

be a meaningful social grouping and thus a relevant unit of analysis.

• The characteristics and conditions of small,

rural communities in the region are complex

and constantly changing and need to be examined over time.

Our study examined the characteristics and conditions of the 387 small, rural, incorporated communities in the study area, in part with U.S. census data for all communities (U.S. Department of

Commerce, Bureau of the Census 1995a, 1995b),

and in part with indepth, 1995 first-quarter data

from a systematic random sample of 198 communities that was collected by the authors. The

census data from past decades provided a basis

for longitudinal analysis of community change

over time, while the community data we collected

provided a unique baseline for future efforts to

track the sociocultural and economic impacts of

the complex forces, both internal and external,

shaping the region’s communities.

• Along with objective, documented, and recorded data (e.g., as collected and reported by

the U.S. Bureau of the Census), a process of

community self-assessment is critical.

The assessment of rural communities designed

and conducted for ICBEMP, which collected

these data, followed the recommendations of the

west-side team (e.g., FEMAT 1993, Krannich et

al. 1994): first, that an on-the-ground assessment

of the situation of rural communities across the

region be conducted and, second, that these communities conduct their own self-assessments of

their conditions and characteristics. Accordingly,

the major primary-data collection effort for the

research was a community self-assessment study

that examined a random sample of half of the

region’s 387 rural communities. The results presented here thus represent two kinds of information: residents’ perceptions of their communities

in 1995, and factual, documented information

about community characteristics, such as their

population size, actual response to change, and

their actual economic structure in the first quarter

of 1995. Both kinds of information are important:

both the ways people see and know their community and believe it to be, and the ways the community actually is, are important factors underlying a community’s development and its responses

to change.

Because every community is unique, each needs

to be studied and understood on its own terms, in

light of its unique history and current situation.

The resulting data were a set of ratings of people’s perceptions based on the levels of attributes

in comparison with other small towns. Having

communities compare themselves to others on the

community dimensions assessed in the research

also provided a basis for exploring patterns in

these comparative data. The patterns, in turn, enabled researchers to generalize about different

kinds or categories of towns.

• In addition to describing community characteristics and conditions, the research also

assessed the resilience of the region’s

communities.

Resilience was defined as a community’s ability

to respond and adapt to change in the most positive, constructive ways possible for mitigating the

impacts of change on the community. The concept of resilience was developed by the Science

Integration Team of ICBEMP. A community’s

resilience is relative, so the study focused on

degrees of resilience; the communities can be

thought of as representing a continuum from low

to high resilience. Also, a community’s resilience

can change over time, depending on changing

community conditions. Communities undergo different stages in their development, and different

stages of development can reoccur, as reflected

by the ongoing boom-and-bust cycles of the

American West that have resulted in changes in

different economic mixes and shifts in dominant

industries at different times.
8

Introduction

Two surveys of recent research were completed

for the assessment. One was a telephone survey

of researchers currently studying topics relevant

to the assessment. Its purpose was to ascertain the

most current state of knowledge on rural communities, and particularly on resource-based communities. The second survey was a literature

search that reviewed published research on communities, their key characteristics, and their assessment. The results of the telephone survey

provided much of the background and discussion

found elsewhere in this report, and the results of

the literature review are the focus of this section,

which is divided into discussions of findings on

key community attributes and assessment research. The various articles, books, and other

research cited here provide recent findings on the

characteristics of communities in general and the

characteristics of rural communities in particular.

Although the literature review was not exhaustive, it provided a solid theoretical and empirical

basis for understanding the key community constructs (ones representing key dimensions of

community characteristics) assessed with the

research.

Critical Dimensions of

Community Characteristics

and Conditions

Community Character

Every community is unique: each community has

its own character and ambiance. A community’s

attractiveness, a key component of its character,

is a combination of many factors that are often

highly subjective, ranging from the community’s

visual appearance (or attractiveness) to the places

outside the community that contribute to its attractiveness. The attractiveness of a community

has generally been couched in terms of the areas

surrounding the community. The “appropriately

aesthetic setting” (Pulver 1989:6) or “environmental integrity, and physical beauty” (Johnson

1993:7) of the surrounding areas have been identified as an important draw for new residents and

businesses. Castle (1991:47) states that “an important part of rural development strategy is to

make the rural areas attractive as places to live.”

Power (1994:9) asserts that “attractive qualities

associated with the social and natural environments become both important determinants

of local economic well-being and important

source[s] of local economic vitality.” Thus, the

attractiveness of a community’s surroundings is

viewed as a potentially important factor in that

community’s economic well-being.

Another important aspect of community character

is the level of attachment that residents have for a

community. Attachment to place is an important

component in how people feel about the character

of their community and is generally characterized

as having several different components. Recent

literature on community attachment has emphasized its multidimensional nature (e.g., Stinner et

al. 1990). Indicators used by O’Brien et al. (1991)

to measure residents’ attachment to their community include their perceptions that a community is

an ideal place to live, satisfaction with the community as a place to live, having a lot in common

with other people living in the community, and

feelings that they fit in the community. Brown

(1993) distinguishes between community satisfaction and community attachment. Community

Review of the Literature
9

satisfaction can be measured by evaluation of a

community as an ideal place to live, the desirability of the community as a place to live, and

satisfaction with life in the community. Community attachment can be measured by social interaction, the degree to which residents feel they fit

in the community, and how much residents have

in common. Brown also includes length of residence and organizational involvement and membership as variables. Goudy (1990) uses local

bonds (i.e., friend and relative networks and

organizational memberships) and local sentiment,

which referred to feeling at home in the community, interest in knowing what’s going on in the

community, and response to the possibility of

moving away, as indicators of community

attachment.

Community Cohesiveness

The ability of a community to manage ongoing

changes in society can be greatly affected by the

capacity of its residents to work together to

accomplish projects and take action (Johnson

1993). This capacity to work together is referred

to as the “cohesiveness of a community” or, more

generally, as a “sense of community.” Communities with greater cohesiveness are more willing

and able to work together to achieve goals, complete projects, and particularly important today,

manage change.

The cohesiveness of a community, as defined

above, has been addressed by several authors in

the literature and consists of several components.

One component focuses on residents’ ability to

organize and cooperate to take action (Howell

and Bentley 1986, Johnson 1993, Lackey et al.

1987, Poplin 1979). A second component is the

capacity to actually move to completion, achieve

goals, or complete projects (Lackey et al. 1987,

Shaffer 1990). The availability and quality of

local leadership also are cited as important factors in the ability of communities to get things

done (Lackey et al. 1987, O’Brien et al. 1991).

Shaffer cites a “positive attitude toward experimentation” (1990:76) as being important, asserting that “the greatest asset communities have in

their struggle to maintain economic viability is

not distance, natural resource base, or current

economic structure but their own creativity and

insight” (p. 85). Thus, a willingness by community residents to take chances and try new things

also has been recognized as an important factor in

promoting community well-being.

An additional insight reported in the literature

merits special attention. Communities that have

successfully engaged in community action in the

past will be more likely (and more capable) to do

so in the future (O’Brien et al. 1991, Shaffer

1990). The idea here is that with community action, as with many other things, practice makes

perfect. This conclusion received support from

the findings of the rural community assessment

for the ICBEMP, as will be discussed later in this

paper.

Community Services

Community services are those things, whether

businesses, nonprofit or government institutions,

facilities, or programs, that are provided by either

the private or public sectors and that contribute to

the livability and desirability of a community by

helping meet people’s needs. Community services

include fire and police protection, schools, medical facilities and personnel, retail facilities, recreational facilities, and churches. The presence or

absence of these various kinds of services combine to make a community more or less livable

in the minds of current or potential community

residents.

A search of the literature showed that others did

not use the same comprehensive operational

definition of community services as we used in

our study. The majority of the literature referring

to community services focuses on medical services, in general, and on mental health services,

in particular.

There were exceptions to this focus, however,

including that of Pulver (1989:6), who describes

a “high-quality living environment [as including]

access to good schools, excellent health care,

physical security, recreational and cultural opportunities, [and] satisfactory housing and public
10

amenities.” Christenson (1976) includes libraries,

education, law enforcement, medical services,

state parks, cultural activities, public parks, recreation, childcare, food stamps, industry, apartments,

and family doctors in his research on the quality of

community services. In a study of satisfaction with

local services, Rojek et al. (1975) performed a

factor analysis yielding four clusters of service

types: medical services, including hospital-medical

facilities, medical doctors, and dentists; public

services, including streets and roads, water supply,

fire protection, and police protection; educational

services, including elementary and high schools;

and commercial services, including shopping

facilities, recreational facilities, job opportunities,

and educational services for the physically and

mentally handicapped.

Two important points about services appeared in

the literature. One was that the availability of

services can play an important role in attracting

retirees to an area, and retirees can have a significant, positive effect on economic stability (Cook

1990). It is likely that services play a role in attracting other types of individuals (“urban refugees,” for example) to an area, as well. The

second important point was that “the evaluation

of whether a service is adequate or not is clearly

a value judgment based upon the preferences and

expectations of the person making the evaluation” (Williams 1976:204). In our research, the

section of the workbook on community services

simply asked if each of a variety of kinds of services were available in the community, and if not,

the distance (in miles) to the service of each kind

most typically used by the community. A final

question in that section then asked respondents

to rate their community based on the overall adequacy of the services available in it.

Community Autonomy

A concept related to the availability of local services is that of community autonomy, which has

been defined as the extent to which a community

is economically, socially, and physically linked to

neighboring communities and to the region. The

more self-reliant and independent a community is

in relation to other communities, the more autonomous that community is. Community autonomy,

then, refers to the control that a community has

over “events and activities that occur within [its]

boundaries” (Poplin 1979:150). In the past, rural

communities had great control over their own

destinies, but now these communities—and

particularly their economies—are being affected

by forces “far broader than those that originate

within or can be controlled by the communities

themselves” (Freudenburg 1992:328). Today’s

small rural communities frequently can be at the

mercy of decisions made in boardrooms in distant

cities.

This situation means that the concept of community autonomy is not without a certain duality. On

the one hand, autonomy can be viewed as a positive and necessary community characteristic.

Warren states (1972:16) that a “barrier to effective community action is the loss of community

autonomy over specific institutions or organizations located within it and closely intermeshed

with the community’s welfare.” He asserts that

the increase in bureaucratic policymaking has

further eroded the ability of communities to

determine their own destinies and stresses the

importance of autonomy as a positive attribute.

On the other hand, a high degree of community

autonomy also has been portrayed negatively.

Castle states (1991:41) that the “rural areas that

are the most prosperous are those that have close

economic links with more densely populated

areas, frequently large urban centers.” Wilkinson

asserts (1986:8) that “what most small towns and

rural areas need is to become somewhat more

urban and less isolated from resources and institutions of our essentially urban society.” In each

of these cases, autonomy as the lack of connections to the larger, more urban society is conceived as being detrimental to the well being of

a community.

A recent Forest Service report (USDA 1998),

which used some of the data on communities in

the inland West described here, focuses on community autonomy in terms of the concept of the

isolation of a town. Forest Service analysts assert

that (p. 10):
11

Economic development specialists generally agree that smaller communities

geographically isolated from larger population centers have fewer economic

choices than more populated areas. They

are less likely to be economically diverse

and more likely to depend on a few

major industries for their economic

prosperity.

For its report, the Forest Service differentiates

among communities differing in their size and

degree of isolation (p. 10-11):

Rules were developed to determine...

[whether] a community is...geographically isolated. Distance from larger

cities, measured by a circle drawn around

each city, was the primary factor used.

The circle size was chosen to represent a

reasonable commuting distance. The

logic of the “city circle” approach is that

proximity to larger towns conveys some

advantages to social and economic opportunity. These advantages include job

choices, access to air and surface transportation, access to education opportunities, and access to cultural amenities and

higher order goods and services....

The Forest Service analysis designates towns not

in a circle of a certain number of miles as being

isolated, except where they had “a relatively larger population (above 1,900 people).” These

larger isolated towns were designated as “isolated

‘trade center’ towns” (p. 11):

The idea is that some larger isolated

“small towns” take the form of small

trade centers that serve many of the

shopping and business needs of rural

residents who live long distances from

larger cities. These towns may exhibit

different characteristics than other

isolated towns.

Our assessment examined the condition of geographic isolation in the larger context of community autonomy. Although geographic isolation

may be an important factor for some rural communities, many are being affected by other kinds

of forces not controllable by the communities

themselves. The definition of autonomy as applied here focused on those communities perceiving themselves to be less autonomous; i.e., more

closely linked socially, economically, and politically to other communities and the region as a

whole. Thus, they have less control over activities and events affecting them. Community autonomy therefore refers here to the control that a

community has over events and activities influencing its development and ability to respond

positively to societal and local changes. Some

isolated communities could be autonomous, vital,

and resilient communities, and the significance

of the issue of isolation was addressed in this

research.

Economic Diversity

The economic diversity of a community is the

mix of types of industries and businesses in a

community, the variety of those kinds of industries and businesses, and the number and variety

of employment opportunities that the mix represents (Belzer and Kroll 1986). In the past, rural

communities had economies dependent on a

particular industry (often an extractive one), with

the economic well-being of those communities

subject to local, national, and global changes in

that industry (Freudenburg 1992, Gramling and

Freudenburg 1992, Johnson 1993). Economic

diversity in small rural communities thus is

closely related to the concept of natural resource dependence, which is discussed as a key

construct in the following section.

Gramling and Freudenburg (1992) suggest the

concept of “economic overadaptation” as an indicator of a lack of economic diversity, where “a

straightforward measure of economic overadaptation involves the degree to which a region’s

economic fortunes have become tied to a single

industry” (1992:229). Many of the industries to

which communities have overadapted have been

subject to national and global policy and economic fluctuations, with these communities less

able to maintain control over their local economies. Freudenburg (1992) uses the metaphor of

an “addictive economy” to describe communities

unable to break the habit of dependence on industries that have been the traditional mainstays of
12

the local economy. As Johnson notes (1993:3), “in

recent years, rural communities have sought to

diversify their economies to avoid excessive

reliance on a single resource such as timber.”

All these researchers emphasize the value of a

variety of industries and employment opportunities in a community. Regardless of whether a

community’s economy is centered on a natural

resource, such as timber, or on a large industrial

plant, the lack of economic diversity is viewed as

problematic for the community. Diversifying its

economy can help a community minimize the

damage caused by a downturn in any particular

industry.

Resource Dependence

Many small rural communities depend on natural resources found on the land surrounding their

communities. The resources can include forest

products, mining and minerals, grazing and

ranching, farming and agriculture, outdoor recreation and tourism, and commercial fisheries and

aquaculture. Some communities depend on two

or more natural resources. As noted above, the

concept of resource dependence is closely related

to the concept of economic diversity. In many

communities dependent on a single industry, that

industry is natural resource related.

Most of the definitions used for resource dependency are presented in economic terms (Machlis

and Force 1988). The revised Economic Research

Service (ERS) county typology (Cook and Mizer

1994) places counties in categories of resource

dependency by percentages of total labor and

proprietor incomes in those counties. (Although

the ERS typology is a county typology, the definitions used are relevant for communities, as well.)

An emphasis on economic definitions of resource

dependency can result in the failure to adequately

recognize and consider the social and cultural implications of resource dependence, as well as the

noneconomic meanings that people attach to

natural resource occupations (Machlis and Force

1988). Dependence, on single industries in general and on natural resource industries in particular, has been linked with economic instability

(Power 1994, 1996), and resource-dependent

communities face the same problems as any community lacking economic diversity.

Many of the natural resources that communities

in the Western United States depend on are associated with Federal lands. Changes in Federal

natural resource policy therefore can have important implications for those resource-dependent

communities. Given that the values of the larger

society are changing, the public’s interest in how

the public lands are managed is increasing, and

concerns over ecological issues raised by resource production are growing. Changes in attitudes toward resource extraction, which is

increasingly perceived as being ecologically

undesirable, “is foreign to [the] traditions [of

rural communities]; their jobs and businesses

have depended on natural resources extraction

and use” (Castle 1991:49). As a result, along with

being subject to national and global economic

changes, resource-dependent communities are

now subject to the effects of significant changes

in how the public views the management of

public lands.

Attractiveness for Business

As the role of computers has increased and the

communications infrastructure has been improved and extended into rural areas, businesses

increasingly are relocating to areas where they

want to be, as opposed to where they have to be.

The physical beauty and other characteristics of

many rural locations are a large major draw for

businesses wishing to relocate, often from large

cities (Barkley et al. 1991, Johnson 1993, Pulver

1989). In addition to scenery and small-town

congeniality, these areas must provide the kinds

of other services that companies need to do

business and prosper. The greater the availability

of a variety of amenities and services, the greater

the attractiveness of rural communities.
13

The literature describes several needs and considerations important to firms wishing to relocate

or to people interested in starting new firms in

rural communities. Although access to transportation has long been deemed important for certain

kinds of businesses (Pulver 1989), new kinds of

business services, such as overnight delivery service (Malecki 1988), are increasingly considered

a necessity. The availability of capital, of bankers

willing to lend it to new ventures, and of tax

breaks provided to attract new business also are

key considerations (Fendley and Christenson

1989, Pulver 1989). Access to knowledge (Pulver

1989) and to technical personnel (Malecki 1988)

traditionally has been a draw for new businesses

wishing to relocate, and a well-developed communications and information infrastructure also

is now cited as a critical business need (Dillman

et al. 1989, Pulver 1989). Communications infrastructure, increasingly viewed as a necessity

in the computer age, is linking businesses to their

head offices, clients, and customers via fax, the

Internet, and email.

Quality of Life

Quality of life refers to those factors that make a

community either a safe, comfortable place to

live or a tense, dangerous place. Quality of life is

a catchall phrase of sorts that encompasses factors ranging from environmental features (clean

air and water) to social support networks (the

presence of friends and family) (Campbell and

Converse 1972, U.S. Environmental Protection

Agency 1973). For some time now, the concept of

quality of life has been viewed as having many

aspects that relate to a wide range of factors. As

Campbell and Converse noted (1972:441) several

decades ago,

The meaning of [quality of life]

obviously differs a good deal as it is

variously used but, in general, it is

intended to refer either to the conditions

in which people live or to some attribute

of people themselves. The first case

includes concern with pollution of the air

and water, overcrowding in the cities,

poor housing, the inadequacy of recreation areas, and similar aspects of

living. The second typically includes

references to health, family stability,

educational achievement, artistic and

cultural concerns, and other such

dimensions on which people differ.

Taking a similarly broad approach, the U.S. Environmental Protection Agency (1973) defined

quality of life with six categories of environmental qualities contributing to it. These include

a region’s economic, political, physical, social,

health, and natural environments (the last referring to pollution and toxic wastes). Included

under these main categories are 31 component

parts ranging from work satisfaction to toxicity

and noise.

Pulver more recently focused on quality of life

as it applied to local communities. He defined

a high-quality living environment as (1989:6)

“includ[ing] access to good schools, excellent

health care, physical security, recreational and

cultural opportunities, satisfactory housing and

public amenities, clean air and an appropriately

aesthetic setting.”

Community Leadership and

Effectiveness of Community

Government

The assessment research distinguished between

local government leadership and a more generic

concept of community leadership. Local government leadership focused on the ability of local

government to make plans and bring them to

completion, to act according to the community’s

wishes, and to have the trust of community residents. The more generic “community leadership”

referred to considerations of the effectiveness

and leadership of nongovernmental organizations

(e.g., the business community, service clubs, local

unions) and nonlocal governmental agencies

(e.g., the USDA Forest Service, Natural Resource

Conservation Service), as well as of local elected

officials. The availability and strength of local

leadership significantly influences a community’s

ability to meet the demands of a changing world

(Fendley and Christenson 1989, O’Brien et al.

1991). Effective leadership is more than simply

electing a mayor, however, and it is important to
14

look at both the quantity and the quality of local

leaders in determining how effective the leadership

is likely to be in a given situation.

One major feature of effective community leadership is that it is broad based, including a number

of different types of leaders (Lackey et al. 1987).

Lackey and others assert that “healthy communities are characterized by broad based leadership

in which many people have opportunities to

perform leadership roles” (1987:10). Likewise,

Poplin (1979) notes that leadership does not

come from a single source (although elected officials often are in key positions of information

gathering and decisionmaking). Rather, different

people often lead in different situations. Poplin

identified three types of leaders: institutional

leaders, who hold a formal leadership position

within the community (e.g., elected officials);

grassroots leaders, who rise up to lead in some

particular situations; and the power elite, who

lead based on positions of wealth and influence

(Poplin 1979).

A second important aspect of leadership concerns

its quality and effectiveness. Walzer (1991:113)

defines rural leaders as those who “attempt to

influence or motivate others, to build problemsolving capabilities, in order to bring about social

or economic change in a democratic environment.” It is important for people to feel as if their

leaders are paying attention to what they have to

say. Ayres and Potter (1984:14) state that “the

more residents felt that town leaders listened to

them, the more confidence they felt regarding the

ability of community decision makers to deal

with change effectively.” Effective leaders are

ones who involve the community’s residents,

listen to and respond to them, and work toward

meeting the needs of all residents, rather than the

needs of a powerful few.

Israel and Beaulieu (1990:182) also emphasize

the importance of placing the community’s well

being over that of the individual’s. They note that

communities that act effectively on matters of

local concern are graced with leadership that (1)

involves a diverse set of actors in local decisionmaking activities, (2) operates on the basis of

democratic principles, and (3) places the welfare

of the total community above the needs of any

given special interest. Interestingly, O’Brien and

others (1991) report that the experience of local

leaders is an important factor: leaders who have

successfully solved problems in the past are more

likely to be able to do so in the future.

Local community governments differ in the

degree to which they are effective. To the extent

that cities and towns depend on processes of

government for their maintenance and growth

(Penn 1993), the effectiveness of local government plays an important role in determining

whether a community grows or declines. In cases

where leaders are elected officials, the effectiveness of these leaders becomes representative of

the effectiveness of the local government. Warren

(1972:231) warns that “delegated governmental

authority...can become extremely insensitive to

the wishes of the electorate, even to the extent of

defeating or debilitating the efforts of newly

elected officials who presumably have a mandate

to change things.”

Community Preparedness for the

Future

Not only are communities changing from within,

but society also is constantly changing, and these

changes can have major effects at the community

level (Poplin 1979). Constant change necessarily

results in a certain amount of uncertainty for

communities trying to plan for their futures. By

assuming a proactive rather than reactive role in

looking at and shaping the future, communities

can better deal with changes taking place locally,

nationally, and internationally.

Most small, rural communities are fairly traditional socially and economically, reflecting a conservatism rooted in the small town way of life.

Inherently, change in this way of life often is not

viewed favorably, and some researchers have

posited that the leaders in rural communities are

generally more open to change than are other

community residents (Ayres and Potter 1989).

It also has been suggested that “those rural areas

that are prepared to evaluate the offering of

nontraditional goods and services are the most

likely to prosper” (Castle 1991:53). Castle notes
15

that “this does not mean that the traditional [extractive] industries will be abandoned” (1991:53),

but it does suggest that a willingness to take

chances and try new things is an important strategy for rural communities faced with change.

Shaffer (1990:76) states that “a positive attitude

toward experimentation” is an essential characteristic of an economically viable community. He

further asserts (p. 85) that “the greatest asset

communities have in their struggle to maintain

economic viability is not distance, natural resource base, or current economic structure but

their own creativity and insight.” In a key observation, Littrell and Littrell (1991:199-200)

point out that “through a process of envisioning

a future and asking what work needs to be performed or action taken, people can learn to anticipate the future and deal effectively with it.”

Communities need to be proactive in creating the

future they desire, rather than being at the mercy

of changes over which they have little or no

control.

Major Findings From the

Literature on Social Impact

Assessments

The literature review also identified some of the

more important, relevant conclusions found in the

literature on social impact assessments. The following points summarize these conclusions.

• Public participation in social impact

assessments is essential.

In addition to the aggregation of data on the critical variables from secondary sources, a face-toface exchange of information and ideas among

active, involved community members was

achieved with the workshop approach we used

(reasons for this approach are detailed in the

“Methodology for Assessing Communities” section, below). A wide body of research suggests

that public participation in social impact assessments is more effective in both the long and short

term than a hands-off, technocratic approach to

collecting data. Taylor and Bryan (1990: 43), for

instance, observed that “the most effective practitioners of SIA [social impact assessments] have

been those who have moved away from established work environments to undertake their

work.”

The practical ramifications of the research approach taken here is that the local population is

treated as truly being a source of expert opinion,

especially in the case of unrecorded, undocumented information for assessing communities

and their current situations. Residents can be lay

experts about their communities. Local perceptions and attitudes, the organization of the community, and how its citizens think, perceive, and

respond can sometimes be as important for understanding the potential impacts of a project as the

details of the project itself (Branch and others

1982, Interorganizational Committee on Guidelines and Principles for Social Impact Assessment

1994).

The Interorganizational Committee on Guidelines

and Principles for Social Impact Assessment

(1994) point out the tendency to dismiss concerns of the local population as being imagined

or perceived—as if they were irrelevant. Yet the

positions of various interests are all formed by

perceptions. How can officials and managers respond to them if perceptions are summarily dismissed? Dismissing a group or individual as

emotional or misinformed only increases the

resistance and conflict in a community over

proposed or unfolding change.

Nonetheless, we realized it would be costly and

of questionable value to sample all individuals in

each of the communities examined. Equally important, we sought, through the assessment, the

informed understanding of the particular structures and processes of small communities that

some community residents simply would not

have. Answers to many of the questions about

communities were clearly beyond the knowledge

of residents only superficially involved in their

communities.
16

This is not to say that the widely divergent views

of community residents are not valuable and

important for understanding potential impacts:

“Although individuals of different ideological

persuasions can be expected to differ greatly over

what they would prefer, such people can be expected to arrive at reliable estimates as to what

will happen, regardless of their preferences”

(Freeman and Frey 1986:236). This same point

can be made about perceptions of general recent

conditions in their community. The community

assessment workbook and workshops for our research took advantage of this wealth of knowledge of lay experts and involved a diversity of

community residents with a variety of experiences and perspectives on their communities.

• The extent to which rural economies are

dependent on natural resource extraction is

being questioned.

Changes that shifting demographics, evolving

technologies, clashing values, and conflicts over

resource uses have brought to the rural West are

closely tied to the region’s shifting economic base

and priorities. Traditionally, a common assertion

has been that resource extraction industries are

the economic linchpin of rural economies. Recently, however, some researchers have suggested

that, with changing rural economies and the

growing importance of retirement incomes, this

assertion is no longer true for many economies

(Power 1994, 1996; Rasker 1993, 1995).

Rasker (1993) examined what he calls the two

myths about the Greater Yellowstone Ecosystem:

(1) agriculture and the resource extractive industries are the region’s only basic industries; and

(2) promotion of the extractive sectors is often

deemed to be necessary and desirable, because

all that rural communities have available to them

is the timber, oil, gas, and minerals found on the

land. Rasker concludes that retirement income in

the Greater Yellowstone Ecosystem area is a

larger part of the regional economy than grazing,

mining, and timber combined. Furthermore, he

warns that continued emphasis on resourcedependent and export-oriented development

“places the local economy at the mercy of economic forces outside its control” (1993:117).

Johnson (1993) goes so far as to suggest that some

rural Northwest communities resemble “developing countries,” where resource-management

decisions are made by agencies or corporations

headquartered elsewhere, resources are exported

with little value-added processing, and much of

the generated income flows out of local

communities.

Power’s (1994) conclusions from his study of

the North Cascades Ecosystem were similar to

Rasker’s. He found that, in 1991, retirementrelated income was 11 times as large as income derived from lumber and mining in the

North Cascades. Power argues that healthy

environments result in healthy economies, and

environmental quality is anything but noneconomic: “The primary economic resource should be

seen as the high quality natural environment, and

extractive activities that threaten to degrade the

environment should be assumed to be incompatible with local economic stability” (1994:12).

Power expands on this argument in his 1996

book. Even though the common assertion in the

literature had been that resource-extraction industries are essential for rural economic survival, he

and other researchers (e.g., Power 1988, 1994;

Rasker 1993, 1995) assert another view: rural

economies have been changing in fundamental

ways, with traditional extractive industries decreasing in economic importance. This view postulates that much of the recent economic activity

in the inland Northwest has been stimulated by

environmental amenities (in particular, increasing

recreation and tourism and in-migration of people

relocating to areas with high amenity values) resulting in additional sales and jobs from outside

the region (Harris and Robison 1993). Taken to

the extreme, the view of some (e.g., Power 1988)

is that the West’s economic prosperity depends on

environmental quality: not only is the region’s

prosperity uncoupled from resource extraction

but it also may suffer when environmental amenities are reduced by commodity production. Recent analysis to support the validity of this thesis

has focused on a regionwide perspective on economic analysis, rather than on the importance of

traditional extractive industries in particular rural
17

communities. Importantly, analyses by researchers such as Power and Rasker typically

have characterized only part of the current situation in the region. By focusing on the region as a

whole and, in particular, on regional data from

the U.S. Bureau of the Census on income and

employment in the aggregate, their findings overlook significant differences in the varied and

unique economies of small rural communities,

focusing instead on broader trends in a larger region that includes rapidly growing population

centers such as the Puget Sound, Portland, Boise,

and Spokane.

The focus on this theory of regional economics

to the exclusion of any competing view has been

controversial (see, for example, Miller 1998).

Although past forecasts of economic disaster may

not be occurring on statewide levels,2

 the impacts of declining resource supplies in particular

communities and individual industries cannot be

disregarded. Also, some noneconomists have suggested that arguments of minimum impacts on

rural communities are highly questionable (Lee

1991, Lee et al. 1991), especially where social

and cultural disruptions are considered in addition to economic changes; e.g., some researchers

note that economic changes also bring lifestyle

changes that may be significant. As Krannich

et al. (1994:52) suggest: “In some cases...

alternative economic activities may be incongruent with the social meanings associated with

resource use and the lifeways of some cultural

groups.” A purely economic analysis overlooks

some impacts on certain occupational groups

and individuals less able to change and adapt

as their circumstances change (Carroll and Lee

1990:152).

• Much of the social impact assessment literature focuses on social responses to a specific

project and its consequences; FEMAT

focused on the capacity of communities to

adapt to an array of possible changes in forest

management activities.

The FEMAT social science team termed the

ability of a community to weather a change in

Federal land management “community capacity.”

The panel it convened from Washington, Oregon,

and California identified several factors that affected the capacity of a community to adapt to

change, including but not limited to economic

diversity (the most often mentioned), the degree

of timber dependence (including employment

and the availability of private timber), local

leadership, location, history of community-based

improvement efforts, community cohesion and

conflict, civic involvement, local control of resources, community attitude, cultural identity,

population size, and income levels (FEMAT

1993).

Unfortunately, the history of the literature on

risks to communities has focused mainly on economic analysis (FEMAT 1993). The current research on the basin presented here acknowledges

the importance of economic studies (this report

includes an economic analysis of the region), but

the community approach taken for this research

reflects the concerns of the FEMAT investigators

that economic analysis alone provides a narrow

definition of how communities depend on natural

resources. Timber dependence or any kind of

economic or industrial dominance in a town, although important for some communities within

the study region, was not the sole focus of the

current assessment. The west-side analysis, it

might be noted, concentrated on forest management issues and attempted to transcend simplified

polarizations such as “owls vs. jobs” to explore

how “communities are more than just bedrooms

for wood workers” (FEMAT 1993:66). That assessment stressed that the connection of a rural

community to natural resources is more than just

a paycheck; it often has been the basis of the

community’s customs and culture.

2

 Significantly for the thesis proposed by researchers like

these, the economic calamity forecast for the Northwest

following the imposition of option 9 has never occurred.

Egan (1994) reported in the New York Times that “three

years into the drastic curtailment of logging in federal forests, Oregon, the top timber-producing state, has posted

its lowest unemployment rate in a generation, just over 5 percent.” The newspaper article notes that, although Oregon had

lost 15,000 jobs in the forest industry in the previous five years,

the predicted number was 100,000 job losses, and the state

had gained 20,000 jobs in high technology, with workers being

retrained for some of those jobs.
18

Exploring these connections means that community assessment must move beyond easily measurable, objective data to subjective attitudes and

perceptions such as measures of quality of life.

Branch et al. (1982), for instance, recommend

an approach to measuring social well-being that

combines objective and subjective measures, including rates of the usual indicator behaviors, the

access to resources by various groups, and the

perceptions of community and individual wellbeing. In a similar vein, many of the factors that

the FEMAT panelists identified as affecting community capacity have to do with the hard-todefine concept of “quality of life.” Branch et al.

(1982:7) likewise suggest some factors affecting

quality of life:

Among other things, these factors can

include feeling a part of the community

where you live; knowing where you

stand in relationship to other people;

having a sense that you and people in

your community have control over the

decisions that affect your future; knowing that your government strives to act

in ways that benefit everyone equitably,

rather than benefiting just a privileged

few; living without undue fear of crime,

personal attack, or environmental hazard;

and feeling confident that your children

will get a fair start in life.

Researchers with limited time and money can go

only so far in measuring these factors within a

community, but our assessment reflects concerns

of Krannich et al. (1994) with the well-being and

quality of life experienced by affected individuals, groups, and populations. Our assessment

research shares their suspicion of reliance on

easily measured social indicators, such as employment and income levels, crime rates, and

divorce rates.

These kinds of concerns were integrated into our

focus on key dimensions of community conditions (key constructs), the indices developed to

measure them, and the approach used to assess

a community’s capacity to weather change (resilience of a community). Also, other kinds of

connection and dependence, such as how community members value the special places in and

around their communities, were examined, and

the ways they form other components of community capacity or cohesiveness were considered.

Central to our approach was development of an

assessment process that extended beyond simplistic indicators to measuring key community

constructs—one that reflected and incorporated

the results of the literature review summarized

above.

• Social impact assessments need a temporal

component.

A shortcoming of many social impact assessments is that they are conducted before the start

of a project but not throughout the life of the

project (Geisler 1993, Gramling and Freudenberg

1992). Variables, including Federal policy, changing regional developments, human populations,

land ownership, land value, and human values

(Geisler 1993), can change over the life of a project and in the long term. A change in any one of

the variables can significantly alter the impact of

a project or policy change. Also, the impacts of a

project or policy can begin at the time a project

or policy is initially proposed and at any time

during the actual implementation of the project

or policy (Gramling and Freudenberg 1992).

Our assessment examined a variety of communities and provides a snapshot of them at one point

in time. It does not forecast the potential impacts

of any particular policy or project on the communities, but rather provides an overview of the

current situation for those communities. Nonetheless, by examining many communities in different stages of development, the temporal issue

raised here could be addressed. Such an analysis,

however, was beyond the scope of this research.
19

Introduction

Our assessment of communities, as initially conceived, focused on the sociodemographic, cultural, and civic aspects of the rural towns in the

region. The research also analyzed secondary

data on the total population of 387 rural communities in the region; these data included

estimates of population characteristics from

the 1990 census, as well as 1992-94 state population projections.

Economic conditions in these communities were

approximated through resident perceptions of the

economic diversity of their communities and their

dependence on various resource-based industries.

Other kinds of information on the economies of

the individual communities were not initially

available (such as employment or income estimates for particular economic sectors), although

the value and significance of obtaining this information at a community level for the entire region

were recognized partway through the research

process.

Details of the methods used for the assessment

can be found in a companion publication.3

 It provides examples of the materials developed for

the research (i.e., workbooks, other forms, instructions), and it describes the procedures developed and applied in the use of those materials.

The Community SelfAssessment Study

Developing a Strategy for Sampling

Communities

Originally, the scope of work outlined for the

rural community assessment was to identify

counties in the region whose populations are

growing the most, along with counties declining

the most. To keep the number of assessed communities to a reasonably small number, the research was to focus on two communities from

each county for study, for a total of 40

communities.

The specified reliance on counties eventually

was abandoned as a frame of analysis with the

recognition that political boundaries have little

to do with sociocultural, economic, or resourcemanagement factors. The focus was shifted to

the communities themselves, and a list of the 40

fastest growing and fastest declining communities was generated. This change, however, led to

another consideration: What about communities

whose population remained constant? A third

category of communities having this kind of minimal population change was added. Three categories of communities would be sampled, 20 communities from each category, for a total of 60

communities.

This approach led to a lopsided selection of communities that could not represent or allow generalizations about the region. For instance, the

Methodology for Assessing

Communities

3

 Harris, C.C.; McLaughlin, M.J.; Brown, G. A detailed

methodology for assessing characteristics and conditions

of small rural communities. Manuscript in preparation.
20

declining communities consisted mainly of communities with populations less than 100 people,

where a relatively minor loss of population can

have a significant effect on any percentage of

change in population.

The sampling strategy based on population

change therefore was abandoned. Instead, a research design based on a simple random sample,

without considering population change, was developed that would yield a representative sample

of communities from across the region, with random variation in populations and other characteristics. This research design required a sufficiently

large sample of communities from which inferences could be drawn and generalizations made

about all communities in the study area.

A final issue considered was the inclusion of

census designated places (or CDPs), which are

unincorporated communities comprising densely settled concentrations of population that are

identifiable by name but are not legally incorporated places; examples are suburbs of cities

or towns within American Indian reservations.

To qualify as a CDP for the 1990 census, an

unincorporated area must have met the following

criteria (in all states except Alaska and Hawaii):

1,000 or more persons if the CDP is outside the

boundaries of an urbanized area delineated for

the 1980 census or a subsequent special census;

2,500 or more persons if it is inside the boundaries of an urbanized area; and 250 or more persons if it is outside the boundaries of a urbanized

area delineated for the 1980 census and within

the official boundaries of an American Indian

reservation recognized for the 1990 census. Although the Bureau of the Census has identified

and delineated boundaries for CDPs since 1950,

these boundaries have no legal status, and they

also do not have officials elected to serve traditional municipal functions. It was decided that

CDPs that were suburbs of cities would not be

sampled in the present assessment, based on the

assumption that the fate of a suburb of a city—

Spokane, for instance—would rise and fall

largely with its city and not, as with a smaller,

isolated community, on its own. Given that CDPs

are unincorporated areas, the only ones included

in the present study were those associated with

towns on reservations.

The sampling element that was finalized for the

study, then, was the community. Thus, the present

research focused on the 387 small rural communities in the interior and upper Columbia basin

that were incorporated towns with populations

estimated to be less than 10,000 residents in

1995. To ensure statistical significance and an

adequate number of cases to conduct multivariate

analyses, a sample was needed of as many of

those communities in the region as possible. Half,

or about 194 communities, were targeted as a reasonable number given potential budgeting and

logistical constraints. These communities were

selected randomly.

Development of the Community

Self-Assessment Process: What and

Why?

We recommend that further regionwide assessment should include a community self-assessment component.

Self assessment is a logical part of any

mitigation measure as it will reflect the

values of people living in the communities; provide a vehicle for integrating

local knowledge in policy decisions;

and contribute to a sense of communitylevel ownership in the resulting

recommendations...self-assessment may

prove beneficial by stimulating dialogue

about local conditions among locals that

can lead to community self-development.

FEMAT (1993:75)

Because of time constraints, the FEMAT social

assessment team was limited in its assessment of

communities to a survey of extension agents to

gather information about the communities they

worked in and around. For our assessment, we

visited the study communities and learned directly from opinion leaders about their communities: these opinion leaders were lay experts active

and involved in their communities and possessing
21

a good knowledge of the workings of their communities and attributes, such as politics, history,

businesses, and social cohesiveness. As an analogy, it likely would be difficult to hold a meeting

of citizens to obtain specific expert medical or

legal information, as opposed to a meeting with

a group of doctors or lawyers. In our approach,

insider insights and informed judgment counted

for something.

We decided, then, that the most effective and

efficient way to involve the local public was to

organize focus groups comprised of an optimum

number of these community opinion leaders representing various backgrounds and viewpoints.

This approach would enable the opinion leaders

to express their views of themselves and where

their communities were going from a broad range

of backgrounds and viewpoints within that community. The information sought from these

groups, then, was not observable, recorded data,

but the perceptions of community members and

their beliefs about their community’s situation.

As Branch et al. (1982:36) note,

Residents’ perceptions often do not correspond exactly to objective changes, but

perceptions can have a powerful influence on individual and social action. If

people perceive that they do not have access to resources, for example, they can

be as closed off from the resources as if a

formal system blocked their availability.

A community self-assessment workbook was designed to enable community members to dispassionately describe the characteristics of their

communities and the changing conditions in them

in a careful, thoughtful, balanced way. There are

a number of sound reasons for seeking this insider perspective. Common sense suggests that

active, involved community members will know

their community best and are the best source of

information. The researchers, moreover, can have

their own set of outsider’s assumptions and biases

about the functioning of different communities.

As Palinkas et al. (1985:15) caution,

Unless the investigator can take into account his own culturally constituted set

of theoretical and methodological limitations, he can never hope to understand

the present pattern of social relations

or make projections concerning future

changes in the social, cultural, economic,

and institutional life of the communities.

In order to secure this understanding and

make projections with any confidence, an

insider’s perspective is necessary.

Neutral investigators can play an important role

in gathering a variety of opinions about a community, facilitating the sharing of information,

and filtering through the various viewpoints

within a community. Although community members may, of course, have their own viewpoints

and perceptions, they are the views and understandings of insiders, of actively involved community members who are the most knowledgeable about their communities. As Branch et al.

(1982:8) observe, in addition to knowing what

changes will be occurring, “it is also necessary to

know what those changes will mean to the people

who will be affected by them.”

It is unclear why anyone from the communities

would accept results about local communities if

at least some of the local lay experts weren’t consulted. “People will not support what they don’t

understand,” Clark and Stankey (1994:35) observe, “and they cannot understand that in which

they are not involved.” Who could blame a community for being suspicious about a study conducted from afar that treats them as little more

than demographic data, or where outsiders discuss with outsiders what must be occurring in

that community?

The Community Self-Assessment

Process: How?

In sum, the community self-assessment study was

the major primary-data collection effort for the

assessment research. For each of the 198 small

towns sampled, we organized a focus group targeted for composition of eight different kinds of

residents. The residents asked to participate were
22

knowledgeable, active opinion leaders identified

by fellow residents in each community as best

representing eight specified categories of interests, specialties, and perspectives, including local

government, education, health and human services, and business.

A modified snowball sampling design was developed and applied, whereby five people in each

community (including the city or town clerk, an

elected official, the Chamber of Commerce executive or administrative secretary, an officer in

a major civic group, and the superintendent of

schools or a principal of a school in town) were

asked to provide a list of people to fit the specified categories (some provided more than one

name for each role, and others provided names

for certain roles only). The people whose names

were provided also were contacted and asked to

provide a list of eight, until five names for each

category were identified. The person mentioned

most often for each role was asked to participate

in the assessment.

Although it was not always possible to find someone for each category in some of the smaller communities, we included as many of them as possible. This factor of finding willing participants

was an important one. It was not always easy

to find people identified as being active and involved who were willing to donate the effort and

time needed to participate in the workshop process, much less uninvolved, or even apathetic or

otherwise occupied, people. Occasionally, even

some of these involved, committed people would

agree to attend a workshop but then would not do

so. It thus is likely that residents already less involved than others in their community would be

even less likely to agree to participate in a timeconsuming process or even a community workshop. Indeed, people less knowledgeable about

their town would likely be much less motivated

to participate in a workshop.

Each workshop was facilitated to gather and synthesize information about a sample community on

the basis of responses to the community selfassessment workbooks completed before the

workshop. The workbook responses provided

information on the perceptions and insights of

1,350 active and involved community members

across the 198 communities.

Each participant in the assessment was asked,

first, to fill out the community self-assessment

workbook (which took about an hour to complete). The purpose of the workbook was to help

community members describe the characteristics

of their communities and their aspirations for

their towns, providing indepth information on 13

key constructs depicting their town’s situation in

terms of various dimensions of characteristics

and conditions. The key constructs were:

• Attractiveness of the community

• Attractiveness and amenities of the region

surrounding the community

• Community attachment (personal attachment

to the community)

• Community cohesiveness (sense of

community)

• Adequacy of community services

• Community autonomy

• Economic diversity

• Resource dependence

• Ability to attract business

• Quality of life

• Strength of the community’s civic leadership

• Effectiveness of the community’s government

• The community’s preparedness for the future

(regardless of whether residents wanted their

community to change or remain the same)

The workbook was an instrument to obtain ratings for the key constructs. Its format was a series

of questions for each construct. Each section was

organized in the same general way: Most sections

began by asking an open-ended question related

to the central dimension of a particular construct

to help the respondent start thinking in broad

terms about that dimension of their community.

Then, a series of more specific questions were

asked by using seven-point, bipolar scales to
23

elicit quantitative ratings of the community on

specific aspects of that dimension. A more general, multiple choice question with descriptions

of alternative options was then asked whose purpose was to help respondents think about how

they would describe their community on that construct in general terms. Finally, a standard sevenpoint scale to measure the overall key construct

was presented to obtain an overall rating for it. In

most cases, the question set alluded to the questions just answered: “Keeping in mind the answers you have given above, how would you

rate the ___ in your community?”

After completing the workbook, the community

participants attended a 2-hour community workshop to discuss the answers given individually in

their workbooks. The purpose of the workshops

was to bring together a focus group representing the diversity of knowledge and perspectives

within each community and explore the depth and

complexity of conditions within the community.

In the workshop, community residents met with

one another to share and discuss the answers.

Comparisons of their comments and the results

were used to aid the group to better describe their

community. After discussing their ideas and information, they were asked to rate the 13 key

constructs a second time.

Thus, rather than simply aggregating the individual ratings of community members on the key

constructs for each town, the workshop was conducted so that the members themselves could provide a group rating after sharing ideas and information. Some members of the workshop might

have more information on a variable or know

more about factors affecting it (for instance,

an economic development official might have

greater knowledge about the community’s economic diversity). In other cases, a participant

might remind others of something they had not

considered in rating a variable. The role of the

workshop facilitator was to clarify the questions

in the workbook, ask participants to discuss their

individual rating for each construct variable, and

conduct the group rating. The intention in this

process was not to compel the group to reach

consensus, although this sometimes happened.

The goal was to facilitate the sharing of information and ideas that might affect an individual’s

rating of a construct variable and ensure a group

rating that was as reliable and valid as possible.

Assessment of Community

Economies

The economics group for the ICBEMP social

assessment team decided early in the assessment

process that regional information on the area’s

economy would be sufficient for its analysis. Although the value of data on the economies of

each of the communities was recognized, the collection of these data was incorporated into the

study only later in the research process. Consequently, the assessment of community economies

that eventually was conducted was somewhat

constrained by time and resources available.

Profiles of the Economic Structure

of Rural Basin Communities

The economic assessment of the region’s communities (cities and towns) provided profiles of

the economic structure of each of the 476 communities and CDPs in the region. These profiles

consisted of estimates of the proportion of total

employment in a community attributable to each

industrial sector contributing to that community’s

economy. The profiles were based on an inventory of all firms, businesses, and agencies in or

otherwise affiliated with each community. For the

purposes of the profiles, all employment in these

job-producing organizations was attributed to a

community if the firm or agency had its address

in the community.

Trade, service and professional businesses, and

government offices typically are located physically in a given community, and their employees

are likely to reside in that community. Primary

producers, secondary processors, and other manufacturers, however, may have their address in

one town but have a plant located between it and

one or more other towns and employ residents

from all of them. Likewise, farmers and ranchers

may have farms and ranches located some distance from the town where they get their mail and
24

socialize, and most of their economic activity

(i.e., their purchasing of goods and services for

both business and household and their selling

of their produce) takes place in trade centers or

“central places” further up the trade hierarchy

from these “home towns.”

The data in these profiles, therefore, do not represent the results of economic base or economic impact models. They represent the economic base of

a community only in a very rough way, in that a

town’s economic base depends to varying degrees

on primary producers and secondary processors

located beyond city limits (one could theorize

that the closer a mill or plant is to a town, the

greater its likely contribution to that community’s

economic base, although this was not investigated

here). Given the interconnectedness of industrial

links across communities, and the important role

of central places in trade hierarchies that are

especially relevant in rural regions such as the

study area, the economic importance of primary

producers and secondary processors for a given

town cannot be surmised from our data. The data

also do not indicate what the impact on the town

would be if a plant or mill closed. Different small

towns located in farming country, for example,

might be impacted to various degrees and in various ways if, say, the multitude of small family

farms and ranches in the area were consolidated

into one or two large ones, as has been the trend

in recent decades. Nonetheless, the data compiled

and reported here provide a rough indicator of

importance of various industries for specific

towns and thus provide a starting point for further economic analysis.

The profile of employment for each of the 476

communities (cities and towns) and CDPs in the

region provided a representation of the economic

structure of these communities. These data, (estimates of the proportion of a town’s total employment attributable to each industrial sector contributing to that town’s economy) were developed

in collaboration with University of Idaho economists (see, for example, Robison 1998, Robison

and Peterson 1995). These data provide a profile

of each community’s economy in terms of 22

categories of industrial sectors: agriculture,

agricultural services, wood and paper products

manufacturing, food processing, miscellaneous

manufacturing, sand and gravel mining, other

mining, construction, public utilities, communication, business and personal services, transportation, wholesale trade, retail trade, food and beverage, lodging, amusement and recreation, medical

and social services, Federal Government, state

and local government, and finance, insurance, and

real estate.

These major categories represent an aggregation

of all industrial activities included under the subcategories for each standard industrial category

(SIC); e.g, the major category of wood and paper

products manufacturing includes lumber milling,

paper milling, and logging activities among the

various subcategories of industrial activity that

the main category represents.

This data set represents an updating and disaggregation of 1992 employment and earnings

data from the U.S. Bureau of Economic Analysis

REIS (Regional Economic Information System;

1994) and the Forest Service’s IMPLAN data

(REIS data updated and estimated at the county

level for all counties in the study area; see

Robison 1998). These data were resolved and

allocated to all communities (towns, cities, and

CDPs) in the region. This disaggregation was

completed by using local sources such as phone

listings for businesses (InfoUSA, Inc. 1995) and

recent directories of businesses for the relevant

states. (For a discussion of the methods used and

their theoretical basis, see Robison and Peterson

1995.)

The only addition in the current research to the

methodology described by Robison and Peterson

(1995) was the ground-truthing of the employment estimates through interviews conducted by

telephone with city clerks, U.S. Postal Service

employees, county extension agents, and representatives of major businesses for each town.

This ground-truthing updated the employment

data to the extent possible to the first quarter of

1995, so that it would be temporally consistent

with the period when the community assessments

were conducted. This consistency ensured that

valid comparisons between the results of the two

databases could be made.
25

Travel and Tourism Employment

The sectors above did not include estimates of

employment for the travel and tourism industry.

Consequently, although estimates of employment

attributable to other resource-related industries

were available for comparison, this was not the

case for employment related to outdoor recreation, travel, and tourism. Yet much of the employment in travel and tourism is directly resource

linked, as in the case of economic activity resulting from recreation trips to the natural resources

in the region. Part of this travel and tourism employment also is indirectly resource linked, as in

the case of business travel by firms in various

resource-linked industries.

In the present analysis, a rough estimate of employment attributable to travel and tourism was

obtained from an indirect measure based on an

economic base technique applied at the level of

the small community: the minimum requirements

approach (Tiebout 1962; Ullman and Dacey

1960, as cited in Tiebout 1962). This approach

to estimating the economic base of a community

focused on redistributing the proportion of employment in the communities initially attributed

to other sectors that could be attributed to the

travel and tourism sector.

As Tiebout (1962) explains, this approach assumes that a minimum amount of employment in

a given sector of a community’s economy can

be attributed to local requirements (nonbasic employment), with the remaining employment attributable to the production of exported goods or

services (i.e., basic employment). In the case of

travel, tourism, and recreation, for which key

sectors include lodging, food and beverage, retail

trade, and amusements, an estimate of the contribution of this industry to a community’s economy can be derived from estimates of the basic

portion of employment in these travel and tourism subsectors. All lodging employment could be

assumed to be basic (scarcely ever do residents

of a town stay in the motels, hotels, etc., in their

own town), while the contributions of residents

to the other sectors needed to be estimated. Once

the local requirements for consumption of food

and beverages, retail trade, and amusements were

determined in proportion to each town’s total employment, these proportions for each sector were

subtracted from the proportion of employment for

that sector in each town. The remainder could

then be attributed to either basic employment or

the travel and tourism sector. Thus, the proportion of employment in the travel and tourism sector was the sum of the proportions attributable to

basic employment for all the travel and tourism

subsectors.

For our research it was assumed, following

Tiebout (1962), that the smallest communities

of the basin might not be typical of either the

location or the economies of the target population of rural communities. Accordingly, the

smallest 5 percent of the basin’s communities

were excluded from the analysis of minimum requirements for employment in the sectors under

consideration. Next, to calculate a town’s minimum requirements, the mean proportion of employment in each of the travel and tourism subsectors for the remaining towns was calculated,

along with a display of these proportions by

quartiles. After the lowest quartile proportions

were compared with the mean proportions for the

subsectors, it was decided that the former proportion (i.e., the cutoff proportion for the 25 percent

of the towns with the smallest proportions of employment in a given subsector) would provide the

more sound and conservative estimate of nonbasic employment needed to meet local service

needs.

A constant proportion of employment representing the local requirement for consumption in each

of the food and beverage, retail trade, and amusement subsectors was determined, and each of

these constants was subtracted from employment

for each sector in each town. As table 1 shows,

these constants included 2 percent for the food

and beverage subsector, 6 percent for the retail

trade subsector, and 1 percent for the amusements

subsector.
26

The remainder then could be attributed to basic

employment, or the travel and tourism sector.

The average “excess” proportion of total employment, or basic employment, across the small rural

towns in the basin included 2 percent for lodging,

4 percent for food and beverage, 5 percent for retail trade, and 1 percent for amusement and recreation (table 1). When these percentages for

basic employment in these subsectors were

totaled, they provided an estimate of an average

of 12.2 percent of total employment attributable

to travel and tourism across all rural communities

in the region.

Because the estimation of travel and tourism employment lumps both recreational travel (outdoor

recreation, tourism, etc.) and business travel together, the comparability of the travel and tourism sector with, say, the outdoor recreation and

tourism industry assessed in the community selfassessment workbooks was somewhat limited.

The travel and tourism sector actually was most

closely related to the workbook question asking

the extent to which an economy centered primarily around retail stores or tourism services.

Surveys of Residents of

Chelan County and Significant

Change Communities

A representative survey of residents of Chelan

County, located in central Washington, was conducted to assess the opinions and attitudes of one

county’s residents about growth and resource

management issues in a rapidly growing county.

Following Dillman’s (1978) survey procedures, a

mail questionnaire was used to collect data from

a population defined as adult representatives of

all households in the county. A total of 700 questionnaires was sent to a sample of residents randomly selected from local phone books. Eight

were undeliverable. After two questionnaire mailings and a postcard-reminder mailing, 222 completed questionnaires were returned, for a response rate of 32 percent. These data provided

useful insights into the preferences and concerns

of residents in a county typical of those in the

interior West experiencing significant population

growth and community development.

A fourth component of the assessment research

was to examine and analyze the characteristics

and experiences of 145 communities in the regions identified as “significant change communities.” These communities were indicated as

undergoing major change by (1) state economic

development officials, agricultural extension

experts, and Forest Service forest planners or

economic development coordinators; or (2) U.S.

census population estimates of changes of + 20

percent since 1980. Data collection on these

communities focused on identifying the kinds of

changes occurring, the kinds of community

responses, and the effects or characteristics of all

these factors in terms of community conditions,

activities, and lifestyles. A random sample of 80

of the 145 communities indicated them to be

Table 1—Estimated mean proportion of total employment in rural Columbia basin communities

that is attributable to the travel and tourism sector, with mean proportions of total employment

attributable to basic and nonbasic employment for subsectors

Sectors of travel

and tourism Total employment Nonbasic employment Basic employment

Percent of jobs

Lodging 2 0 2

Food beverage 6 2 4

Retail trade 11 6 5

Amusement recreation 2 1 1

Total 12
27

significant change communities, which were

surveyed about the major changes affecting them

and the impacts of these changes and their response to them. Initial contacts were made with

city clerks, who were asked to suggest the name

of the person who would have the greatest knowledge of the changes the town had experienced

and its response to them. The survey was conducted with a structured telephone interview of

this representative of the town.

The primary purpose of collecting these initial

data was to better identify communities to study

as part of the indepth case studies of 10 towns

also conducted as part of the assessment, as well

as to better understand factors or variables to consider in those case studies. The findings for the

10 communities identified as having undergone

major changes in recent decades of the kinds

most prevalent in the study area are reported elsewhere (Harris 1996). Harris describes the indepth

case studies of these communities, which focused

on gaining greater understanding of the major

changes influencing a diversity of communities,

the impacts of those changes, and responses by

the community.

Analysis and Presentation

Quantitative data were gathered for the assessment and were analyzed statistically with the

“Statistical Package for the Social Sciences”

software (SPSS, Inc. 1989). Univariate analyses

were performed on the census data, economic

profiles, and data obtained at the community

workshops. Mean values for relevant variables

are presented in the following sections of this

paper.

Where the data analyses presented here were

from the community self-assessment, they represent a community’s overall response; i.e., the

community is the unit of analysis. In the case of

continuous data collected with numerical scales,

the data reported in the following sections are the

mean values of the workshop responses. Frequencies for the nominal-level data were obtained

from the workbook results for all 198 sampled

communities. Where only one value is reported

for these kinds of data, it represents the mode for

responses from the workshop participants. The

initial results of the survey of significant change

communities also are presented in tabular form.

Multivariate analyses were performed on continuous data with one-way analysis of variance

(with appropriate posthoc tests of difference),

stepwise regression, and cross-tabulations (with

appropriate tests of strength of relationship). The

level of statistical significance used was p<0.05.
28

Introduction

This chapter describes some of the initial major

findings of the rural community assessment.

Given the extensiveness of the data collected, this

paper focuses on providing an overview of the

status of small rural communities in the basin and

the influences of public land management on the

region’s rural communities. Further analysis and

reporting will explore the full breadth and depth

of these data and their implications for understanding the region’s rural communities.

Gathering and Analyzing

Community Scale Social Data

Various possible levels of scale for studying human beings and their sociocultural and economic

organizations and processes were considered in

the assessment research. Units for such an analysis can be based on levels of social organization

and everyday human activities—individuals as

well as collectivities, geography, and political

boundaries. Levels of social collectivities include

groups of individuals in service clubs, civic

groups, and special interest groups, and the locus

for each of these groups and their activities can

range from the local level to state, regional, and

national levels. Commonly recognized levels of

social organization based on geography and human activities range from households to neighborhoods, communities (towns and cities), counties, multicounty regions, and states.

Communities were selected as the most appropriate unit of analysis primarily because towns

and cities typically are the center of daily life for

most people living in rural America. Rural communities are the places where individuals and

groups of individuals carry on much of their

work, play, and civic activities as well as the

places where they go for services important in

their lives (school, church, shopping, health,

sports, and recreation). Because of these important factors, social scientists studying social

groups (for instance, sociologists and anthropologists) most often focus on the community as the

primary unit of analysis.

Results of the Chelan County survey are instructive here. The analysis of that survey confirms

that, although 38 percent of county residents

lived outside a recognized town or city, most

(79 percent) felt that the town or city where they

collect their mail was a somewhat to very important aspect of their lives. Only 18 percent of

those surveyed rated the community where they

collect their mail as being only slightly important

in their lives. These results affirm that, although

many residents of a county live outside the city

limits of any town or cities, nearby communities

are important elements of rural living for all but a

small segment.

An Overview of the Status

of the Rural Communities

in the Basin
29

A secondary reason for choosing the community

as a scale of analysis for an assessment is that

higher levels of scale can always be examined by

aggregation of community data, which themselves represent the aggregation of individual and

household data. The primary locus for the relationship between residents of rural areas and

place is the community. Rural towns are sufficiently small that neighborhoods are not the

meaningful unit of analysis that they are in larger

cities. County-level activities and responses can

be examined by aggregating community-level

data, but county-level aggregation cannot depict

the differences in characteristics of different communities within a given county or the impacts of

Federal, state, and county policies on them. Further, communities are composed of both the individual residents and the social groups they join or

become a part of, and an accurate understanding

and description of communities requires data on

these elements.

The results of our research confirm that, in many

places, social conditions and key changes in those

conditions, when depicted at the broader level

of counties, mask important differences in those

conditions and changes across communities. For

instance, the population of a county and its

growth may not represent the situation for towns

within that county, as in the example of three

rural communities in Wallowa County, Oregon

(table 2). As table 2 shows, changes in past and

future trends in, say, population clearly differ by

community and are not reflected in county-level

data.

The economic links among communities in

different counties and even different states may

be equally significant. An initial analysis of the

social networks linking communities confirmed

that these networks are as important as political

and economic ties. Thus, the issue of scale underscores that the county as a sociocultural reality

may not be meaningful for many residents and

thus not as relevant a unit of analysis.

A final reason for the focus on small towns relates to policy development and its real-world

consequences. Many people are concerned about

the impacts of resource planning on their communities, as well as on individuals, families,

and a region’s customs and cultures. During the

1980s, when significant impacts of changes in

Federal resource management began to be felt in

communities, the focus of resource management

reflected the concerns of communities in transition and the concept of community stability. Although many people may not want to return to the

kinds of conditions that resulted in the boom-andbust cycles once characterizing many communities in the American West, the reality is that rural

communities will continue to evolve and change.

Accordingly, it is important to remember that any

description of a community’s characteristics and

conditions is a snapshot of its situation at one

point in time and the context in which that particular situation unfolded. Looking at the recent past

and current conditions surrounding a particular

situation can provide a better understanding of

where a community has been and where it seems

to be heading.

Table 2—Population changes in 3 communities in Wallowa County, Oregon

County and 1980 1990 Population change 1994 Population change

communities population population (1980-1990)a population (1990-2000) a

Percent change Percent change

Wallowa County: 7,273 6,911 -5 7,200 10

Enterprise 2,003 1,905 -5 1,935 5

Joseph 999 1,073 7 1,165 21

Wallowa 847 762 -10 755 -2

a Straight-line projections based on 1990 and 1994 population estimates obtained from the Oregon Center for Population

Research and Census (1995).
30

Most Rural Communities Are

Small

At the time the research was being conducted in

the region, the 387 small rural communities

ranged from 26 to 9,760 in population (1992-94

population estimates). Differences in the communities based on their population size were analyzed for a subsample of 198 towns by categorizing communities into one of four population size

classes: towns with fewer than 1,500 people;

1,501 to 3,000 people; 3,001 to 5,000 people;

and more than 5,001 people (fig. 1).

As figure 1 displays, the majority of the towns

had fewer than 1,500 residents (68 percent) and

were classified as rural villages by Johansen and

Fuguitt (1984). Within this class, communities

ranged from 22 to 1,500 people with an average

of 520. The second class of towns ranged from

1,501 to 3,000 residents. These communities

comprised 19 percent of all communities in the

basin, with an average size of 2,162. The third

largest class, which ranged from 3,001 to 5,000

people, accounted for 7 percent of the communities and were on average 3,974 in population. The

remaining 6 percent of the communities were in

the largest class, ranging from 5,001 to 10,000

people, with an average of 7,087.

The Geography of the

Communities

The role of geographic location in characterizing

communities and assessing community resilience

was considered in the analysis of the community

data. The selection and study of a large random

sample of towns across the region ensured an assessment representative of the entire basin. When

analyzed by state boundaries, the survey of 198

communities indicated that the largest proportion

of small rural towns were in Idaho (41 percent,

or 81 towns), with major proportions in eastern

Washington (28 percent, or 55 towns) and Oregon

as well (23 percent, or 46 towns). A much smaller

proportion of small towns were in western

Montana (7 percent, or 14 towns) and Wyoming

(1 percent, or 2 towns).

The Forest Service denotes major geographical

regions based on the ecology of the landscape by

classifying regions into ecological reporting units

(ERUs). These ERUs are aggregations of individual watersheds within major ecosystem types.

Thirteen ERUs, some spanning parts of two or

more states, were identified for the study area

(fig. 2).

Figure 1—Population size of rural communities across the Columbia basin with size class

proportions.
31

In terms of the largest number of rural communities within different ERUs, the most significant

unit was the Columbia Plateau ERU, where more

than 32 percent of all the communities were located. Another 15 percent of the region’s rural

communities were in the Northern Glaciated

Mountains ERU, 9 percent were in the Owyhee

Uplands ERU, and another 9 percent were in the

Blue Mountains ERU. The Central Idaho Mountains ERU followed closely with 8 percent of rural

communities, and the Upper Snake, Snake Headwaters, and Lower Clark Fork ERUs had the next

highest numbers of rural communities, with

between 5.1 and 6.1 percent. The Northern and

Southern Cascades, the Upper Clark Fork, and

the Upper Klamath ERUs accounted for the remaining communities in the region with between

2.0 and 3.5 percent. Only a few communities

(0.5 percent) were located in the Northern Great

Basin ERU.

Characteristics of

Participants in Community

Self-Assessments

The characteristics of residents who participated

in the community assessment workshops were

analyzed and the results compared with those

from the survey of all Chelan County residents.

This comparison was based on the assumption

that similarities between the characteristics of the

general populace of a randomly selected county

and those of the workshop participants would

minimize concerns about how representative

“opinion leaders” were of other citizens in their

communities. As discussed previously, a concern

of people who reviewed and commented on the

assessment methodology was that the participants

selected may not have adequately represented the

residents of their communities or those residents

Figure 2—Distribution of rural communities across ecological reporting units (ERUs) in the Columbia basin.
32

living outside the city limits (many residents live

outside an incorporated town, and they can comprise the majority of people living in a county). A

major objective of the survey of Chelan County

residents was to address these concerns and, to

the extent possible, assess their validity (see

Krull 1995).

Data collected on characteristics of the workshop

participants showed that 43 percent of participants were female and 57 percent were male. The

average age was 51, with ages ranging from 23 to

94 years and a median age of 49 years. Individuals aged 40 to 60 years old constituted the largest

age class, with nearly 60 percent of all participants. In addition, a greater percentage of older

individuals (over 60 years of age) participated in

the study than younger ones (less than 30 years of

age). Similarly, the mean age of Chelan County

respondents was 53 years old, with the proportion

of males and females found to be 53 and 47 percent, respectively.

Consistent with the age of the workshop participants, about 37 percent of the participants had

lived in their community for 25 years or more.

We targeted relative newcomers to elicit their

perspectives as well. About 21 percent of the

workshop participants had lived in their communities 5 years or less. In addition, workshop participants also represented a number of practical

and philosophical perspectives that differed with

occupation and civic activity within their community. To identify their ideological perspectives,

workshop participants were asked to rate themselves on a scale from 1 (liberal) to 7 (conservative) that allowed them to define these concepts

themselves. The resulting distribution was

skewed toward the conservative end of the scale

with a median rating of 5 and a mode of 6 (see

fig. 3). Similarly, the same mean and median

were obtained for the Chelan County residents,

affirming the ideological representation by the

workshop participants of other residents.

Workshop participants also were asked to select

the one category that best reflected the role or

position in their community or the perspective

they brought to the workshop. As figure 4 shows,

the two largest segments were elected officials

(272, or 20 percent of all participants) and business leaders (271, or 20 percent). Other roles

represented by the participants included educational leaders (171, or 13 percent), civic group

leaders (117, or 9 percent), retired individuals

(44, or 3 percent), self-identified environmentalists (40, or 3 percent), and individuals involved

in the community health services (38, or 3 percent). The remaining 29 percent of workshop

participants were “other leaders” and people of

miscellaneous backgrounds and perspectives,

including farmers, ranchers, firemen, policemen,

appointed city officials, community volunteers,

and individuals active in church affairs.

The gross household income of workshop participants ranged from less than $5,000 (0.4 percent)

to more than $100,000 (6.7 percent). Most participants’ household incomes fell within the

$25,000 to $34,999 range (21.5 percent), the

$35,000 to $49,999 range (22.0 percent), and

the $50,000 to $74,999 range (22.8 percent).

Perceived Characteristics and

Current Conditions

The geography and ecology of the landscape are

important for describing communities in the basin

and understanding differences and similarities in

their characteristics and experiences. The geography of these communities in large part predetermines their economic base and thus their economic structure. This condition along with location

and interrelations with other communities underlies a community’s way of life and subsequently

its social condition. In many cases, this geographic basis for community characteristics and conditions transcends political boundaries (e.g.,

counties and states) and, in some cases, several

ERUs. Based on geography alone and the attendant uniqueness of each community, the community becomes the scale for understanding the varied

characteristics and conditions in the region.
33

Current Characteristics and

Conditions of Rural Communities

Primary data were provided on the community’s

current characteristics and conditions by the participants in the community assessment workshops. These data included responses on the key

community constructs described in “Methodology

for Assessing Communities,” above. The results

discussed below reflect the end points, or anchoring descriptors, used in the seven-point scales for

the overall construct ratings, which helped workshop participants rate their community on each

construct. These responses represent the result of

a cumulative assessment for each dimension of

community.

Community attractiveness—A community’s

character was defined as a combination of attributes ranging from a town’s visual appearance

to special places in the region where the town is

located. One key dimension of community character is a town’s physical attractiveness, as perceived by its residents. As figure 5 shows, the

Figure 3—Ideological perspectives of workshop participants.

Figure 4—Percentage of workshop participants by position held within the community.

0 

10 

20 

30 

Very liberal Moderate Very conservative 
34

distribution of ratings for community attractiveness tended to be on the high end (extremely attractive) of the scale (above the midpoint of 4),

with a mean rating of 4.8 on a seven-point scale

(ranging from 1, extremely unattractive, to 7, extremely attractive). The distribution of attractiveness scores ranged from values of 2.0 to 7.0 with

a bimodal distribution of towns concentrated just

below and above the mean value. These results

confirm that many communities in the region perceived themselves to be as attractive as other

communities, and in some cases, more so.

Regional attractiveness—Another characteristic

contributing to a community’s character was its

regional attractiveness. This characteristic refers

Figure 5—Distribution of mean

ratings for community

attractiveness across rural

Columbia basin communities.

Figure 6—Distribution of mean

ratings for regional attractiveness

across rural Columbia basin

communities. Regional attractiveness 

4.50 5.00 5.50 6.00 6.50 7.00 

Frequency 

60

50

40

30

20

10

0 

Std. Dev = 0.50 

Mean = 6.11 

N = 198 

Community attractiveness 

2.00 2.50 3.00 3.50 4.00 4.50 5.00 5.50 6.00 6.50 7.00 

Frequency 

30 

20 

10 

0 

Std. Dev. = .093 

Mean = 4.75 

N = 198 
35

to the attractiveness of the area beyond the community’s city limits for a distance of 100 miles.

Attractiveness in this case referred to a variety of

attributes, including the importance of the scenery and outdoor recreational opportunities in the

region where a community is located.

As figure 6 shows, the distribution of ratings for

community attractiveness tended be very high

(close to the extremely attractive end of the

scale), with a mean rating of 6.1 on a sevenpoint scale (ranging from 1, extremely unattractive, to 7, extremely attractive). This mean was

the highest of any key construct, indicating the

relative abundance of scenery and amenities that

was perceived across the region, regardless of

specific location. Likewise, the distribution of the

regional attractiveness scores ranged from values

of 4.4 to 7.0, with a normal distribution of towns,

except for a concentration of communities around

the mean value, and a standard deviation of 0.76.

These results confirmed that most communities in

the study area perceived the region in which they

were located to be as attractive as that for other

communities, if not more so.

Community cohesion—A community’s social

cohesiveness was defined as “the degree to which

the residents of a community work together to

get things done” and their “sense of community.”

The distribution of mean values for the region’s

social cohesion scores was relatively small

(standard deviation of 0.78), with ratings ranging

from 2.3 to 6.6 and a mean of 4.9 on a sevenpoint scale (ranging from 1, an extremely weak

sense of community, to 7, an extremely strong

sense of community) (fig. 7).

Table 3 shows that, in response to a categorical

question on the extent of a strong sense of community, a very small segment of communities was

so diverse with respect to the values of the communities’ residents that there was no agreement

among those values. Alternatively, in about half

of the remaining communities, residents were not

only in agreement but also held similar values.

Community services—Community services included those provided by either the government

or the private sector. The mean rating of the adequacy of services in the sampled communities

was 4.7 on a seven-point scale (ranging from 1,

extremely adequate services and facilities, to 7,

extremely inadequate services and facilities),

with values ranging from 1.7 to 6.4 (fig. 8). The

distribution of responses for satisfaction with

community services was skewed with a disproportionate share of towns between 4.7 and 5.8.

Community autonomy—The autonomy of a

community was defined as “the degree to which a

Figure 7—Distribution of mean ratings for

community cohesiveness across 198

rural Columbia basin communities. Community cohesiveness 

2.25 2.75 3.25 3.75 4.25 4.75 5.25 5.75 6.25 

Frequency 

30 

20 

10 

0 

Std. Dev = 0.80 

Mean = 4.88 

N = 198 
36

community is linked economically, socially, and

physically to neighboring communities and to the

region as a whole.” For instance, a community

that perceived itself as lacking autonomy considered itself highly linked and dependent on

surrounding towns for its economic and social

well-being. On the other hand, a community perceiving itself as being highly autonomous was

one that considered itself to be very independent,

economically, socially, and physically, of other

communities.

On a seven-point scale (ranging from 1, not at

all autonomous, to 7, extremely autonomous),

the community autonomy construct was comparatively low with a mean rating of 3.4. This finding

underscores the relative dependence of small rural

communities on other towns. As figure 9 shows,

however, a comparatively large standard deviation

(1.14) and the rectangular distribution of the

ratings, with a wide range of values from 1.1 to

6.3, indicate a wide spread of means across the

scale for the community autonomy construct. This

result suggests that autonomy may not have been

conceived of strictly in terms of economics or the

supply of goods and services, but in the broader

social context the concept was meant to represent.

In comparison with constructs for other key

dimensions of community characteristics and

conditions, the mean rating for community

autonomy was found to be the lowest: 3.4 on a

Table 3—Extent of a sense of community in 198 Columbia basin study communities

Cumulative

Sense of community Frequency Percentage percentage

Most residents hold similar values and are in

agreement 90 45.5 45.5

The community has diverse values, but

residents have learned to work together 96 48.5 94.0

The community is very diverse and there is

no real agreement in the community 12 6.0 100.0

Total 198 100.0 100.0

Figure 8—Distribution of mean ratings for

community services across 198 rural

Columbia basin communities. Community services 

1.75 2.25 2.75 3.25 3.75 4.25 4.75 5.25 5.75 6.25 

Frequency 

30 

20 

10 

0 

Std. Dev = 0.96 

Mean = 4.72 

N = 198 
37

seven-point scale, in comparison with means

ranging from the next lowest mean of 3.9 for

attractiveness for business to a high of 6.1 for regional attractiveness. This low mean rating

indicates that the towns in the region have a

relatively low level of autonomy.

Table 4 confirms that the region’s communities

are split between those very dependent on other

towns and those dependent on other towns for

some things but not for others. When workshop

participants were asked to categorize their towns,

those sampled were split between those saying

their community was very dependent on other

towns (43.9 percent), and those saying their community was dependent on other towns for some

things but independent in terms of other things

(54.5 percent). Only three towns, or 1.5 percent,

reported themselves to be highly autonomous

(i.e., the community stands alone and functions

pretty independently of other communities).

These three towns—St. John, Washington;

Stanley, Idaho; and Sandpoint, Idaho—range in

population from 70 to 5,725 (1992-94 estimates).

Table 5 indicates that a community’s autonomy

was significantly related to a variety of other

characteristics and conditions. It confirms that

various aspects of community life, such as social

Figure 9—Distribution of mean ratings for

community autonomy across rural ICRB

communities.

Table 4—Levels of community autonomy in 198 Columbia basin study communities

Cumulative

Level of community autonomy Frequency Percentage percentage

The community is very dependent on other

communities 87 43.9 43.9

The community depends on other towns for some

things but is independent on other things 108 54.5 98.5

The community stands alone and functions

relatively independently of other communities 3 1.5 100.0

Total 198 100.0 100.0

Community autonomy 

1.25 1.75 2.25 2.75 3.25 3.75 4.25 4.75 5.25 5.75 6.25 

Frequency 

30 

20 

10 

0 

Std. Dev. = 1.14 

Mean = 3.42 

N = 198 
38

cohesion, community attractiveness, and population size were as important as economic constructs (e.g., economic diversity and attractiveness for business) for understanding a community’s sense of self-reliance and independence,

and consequently that autonomy is moderately

related to a community’s quality of life.

Quality of life—The quality of life of a community refers to a range of physical and social

aspects reflecting how good the good life is within a community and includes air and water quality, traffic congestion, perceived safety, social

problems, overall friendliness, and the abundance

of stimulating social activities. Most communities

rated themselves as having a high quality of life,

with a mean rating of 5.7 on a seven-point scale

(ranging from 1, extremely poor quality of life, to

7, extremely high quality of life). A small standard deviation of 0.56 for the mean (half of that

for the community autonomy construct) and the

concentration of mean ratings between 4.0 and

6.5 confirmed this construct’s narrow distribution

and the high quality of life perceived by residents

of most towns in the region (see fig. 10). Additionally, when asked if their communities were

safe, friendly, and good places to live, more than

80 percent of the respondents felt that few other

rural communities could match their quality of life

(table 6). Together, these results indicate that the

vast majority of towns surveyed perceived their

quality of life to be quite high.

Community leadership—Community leadership

referred to leadership from a variety of sources,

including the business community, government

agencies, other organizations, active individuals,

and elected officials. A relatively normal distribution was obtained from the mean ratings of the

study communities on the construct effectiveness

of community leaders. On a seven-point scale

(ranging from 1, extremely ineffective leadership,

to 7, extremely effective leadership), the mean

response was 4.8 with a range of 2.4 to 6.4 and a

standard deviation of 0.76 (fig. 11).

Community government—Workshop participants also rated the effectiveness of their community government, which referred to their perceptions of their local government’s ability to

make and carry out plans and projects, as well as

its performance in acting in accordance with the

will of the citizens and in ways that earned trust

in the government. The resulting distribution was

characterized by a mean rating of 4.8 on a sevenpoint scale (ranging from 1, extremely ineffective, to 7, extremely effective), with a range of

1.8 to 6.4 and a standard deviation of 0.85

(fig. 12). These results are consistent with the

findings shown in table 7, which indicate that less

than 5 percent of the communities felt that their

government did not know what to do, or that it

did only what influential people wanted it to do.

Not surprisingly, ratings of the effectiveness of

the community’s government were highly correlated with ratings of the effectiveness of the

community’s leadership (Pearson correlation

coefficient, r = 0.72). It also was significant that

the perceptions of elected officials about their

performance differed statistically from other participants’ perception of the effectiveness of the

community’s government. Analyses comparing

mean values for the two groups revealed a statistically significant difference (p<0.05) of about

0.5 in the scale means for effectiveness of community government and the community’s leadership. This difference may be explained by the

Table 5—Pearson correlation coefficients of

characteristics of 198 Columbia basin study

communities significantly correlated with

community autonomy

Community

Variable autonomy

Availability of services 0.52**

Attractiveness .52**

Attractiveness for business .40**

Social cohesion .40**

Economic diversity .45**

1992-94 population .35**

Quality of life .32**

** = correlation is significant at the 0.01 level (2-tailed).
39

Figure 10—Distribution of mean ratings for quality of life across rural Columbia basin

communities.

Table 6—Levels of quality of life in 198 Columbia basin study communities

Cumulative

Level of quality of life Frequency Percentage percentage

The community is safe, friendly, and a good

place to live; few rural communities can match

its quality of life 159 80.3 80.3

The community is not the best place to live for

health, safety, or social reasons, but it offers a

reasonable quality of life 38 19.2 99.5

The community has serious social problems; most

other communities offer a better quality of life 1 .5 100.0

Total 198 100.0 100.0
40

Figure 11—Distribution of mean ratings for the effectiveness of community leaders

across rural Columbia basin communities.

Figure 12—Distribution of

mean ratings of government

effectiveness across rural

Columbia basin communities.

Effectiveness of community leadership

2.50 3.00 3.50 4.00 4.50 5.00 5.50 6.00 6.50 

Frequency 

30 

20 

10 

0 

Std. Dev. = 0.76 

Mean = 4.81 

N = 198 

Government effectiveness 

1.75 2.25 2.75 3.25 3.75 4.25 4.75 5.25 5.75 6.25 

Frequency

30

20

10

0 

Std. Dev. = 0.85 

Mean = 4.80 

N = 198 
41

fact that the elected officials have a different, but

valid, perspective on the effectiveness of their

leadership than do other workshop participants.

An alternative explanation is that the systematic

differences in ratings on leadership effectiveness

represent a self-interested bias in the perceptions

of elected officials in evaluating their performance and the extent to which they represent the

views and desires of their constituents.

Preparedness for the future—Community preparedness for the future is defined in the selfassessment workbook as the “degree to which a

community is looking towards the future and preparing for its future.” The section devoted to this

key construct focused on questions about the

ways community members perceived their communities were already changing, the extent of

those changes, and how much residents were

discussing whether or how they wanted their

community to change.

The mean rating of the extent to which communities perceived being prepared for the future was

relatively low (4.1) on a seven-point scale (ranging from 1, or totally unprepared, to 7, totally

prepared). In fact, only the autonomy construct

had a lower mean rating. The distribution of the

preparedness for the future construct across rural

communities was relatively normal (fig. 13).

More communities fell at the upper end of the

scale, however, and perceived themselves as

being more prepared for the future than others.

Table 8, which looks at the results for a fixedresponse question about a community’s preparedness for the future, indicates that about a third of

the communities (3.5 + 30.8 = 34.3 percent) were

ones where citizens had plans and projects for

realizing some desired future. An additional

39.4 percent of the towns were ones where citizens had begun identifying future directions for

their community, but they were yet to identify any

actions, much less take any. In contrast, more

than one-fourth of the towns (18.2 + 8.1 = 26.3

percent) had little or no discussion about their

future, or about whether or how they wanted to

change. These data reveal that, while nearly 22

percent of the communities in the region had

decided they wanted to stay the same (3.5 + 18.2

= 21.7 percent), more than 38 percent wanted to

change (30.8 + 8.1 = 38.9 percent). Of those

communities already actively making plans and

taking action (34.3 percent), 90 percent had done

so to allow them to change to achieve a desired

future. Conversely, of the little more than a quarter (26.3 percent) of the towns whose citizens had

not as yet made any plans or taken any action,

only 31 percent were open to changing to achieve

a desired future, and 69 percent wanted to stay

the same.

A conclusion from these results is that the

proactive communities were the ones that had

realized change was coming and were readily

moving forward in dealing with that change and

trying to manage it. Communities desiring not to

change tended to be ones that were ignoring their

situation, or at least were not discussing or

considering change.

Table 7—Levels of government effectiveness in 198 Columbia basin study communities

How the community’s Cumulative

government operates Frequency Percentage percentage

Does pretty much what the citizens want 63 31.8 31.8

Does what some influential people want 15 7.6 39.4

Does what it thinks is best for the citizens 117 59.1 98.5

Does not know what to do 3 1.5 100.0

Total 198 100.0 100.0
42

Figure 13—Distribution of mean ratings for community preparedness for the future

across the Columbia basin.

Table 8—Extent of community preparedness for the future in 198 study communities

How prepared for the future Cumulative

is your community? Frequency Percentage percentage

Citizens have plans and projects identified that

will allow them to stay the same 7 3.5 3.5

Citizens have plans and projects identified that will

allow them to change to achieve a desired future 61 30.8 34.3

Citizens have discussed and identified future

directions for the community, but no actions

identified 78 39.4 73.7

Citizens have not had much discussion about the

town’s future, but they want to stay the same 36 18.2 91.9

Citizens have not had much discussion, about the

town’s future, but they are willing to change 16 8.1 100.0

Total 198 100.0 100.0
43

Economics: Perceptions and

Reality

The perceptions of workshop participants about

the economy of their community were assessed

with a variety of questions. These perceptions

could then be compared to the actual economic

structure of the region’s communities.

Perceptions of Workshop

Participants

Perceptions of community’s dependence on

resource-based industries—In the section of the

workbook on the economy of a community, workshop participants first were asked to name the

major businesses and industries in their communities. The residents then were asked to rate the

extent to which their towns were dependent on

various industries for their economic stability, a

seven-point scale ranging from 1 (extremely independent) to 7 (extremely dependent). The results for the major resource-based industries,

which included farming and agriculture, wood

products manufacturing, grazing and ranching,

outdoor recreation and tourism, and minerals and

mining, are reported in table 9 across all 198

communities for each of these industries.

Overall, residents of the rural communities of the

region perceived farming and agriculture as the

most important natural resource industry in terms

of their economic dependence, followed by grazing and ranching and outdoor recreation and tourism. Also significant is the finding that, on average

across the total sample of workshop participants,

the outdoor recreation and tourism sector was

perceived as being more important than wood

products as a contributor to small rural economies.

The validity of these perceptions was assessed

by comparing the industries in terms of the actual

contribution of the different industrial sectors to

rural economies, which was based on

prossportions of total employment, as discussed in

a later section of this paper.

Figure 14 shows that perceptions of overall dependence of the region’s communities on natural

resource industries was rated very highly by the

workshop participants, with a comparatively high

mean rating of 5.8 on a seven-point scale (from

1, extremely independent, to 7, extremely dependent). The distribution of mean ratings was

skewed, with an overall mean of 6.0; only 25 percent of the informants indicated a rating of 5.4

or less.

Classification of communities by dominant

industry—Many people have promoted the idea

of classifying communities on the basis of their

economic structure (Branch et al. 1982, Gale and

Cordray 1991). Here, just one application of the

community typology idea is presented, with communities identified as resource dependent and

classified by the industrial sector that residents

perceived their community was most dependent

on. This sector was termed the “dominant

industry.”

To operationalize this classification, communities

that were highly resource dependent were first

identified. These were communities with a mean

rating of 5.0 or higher on the seven-point scale

rating their perceived resource dependence. Communities meeting this criterion then were classified in terms of the dominant industry as perceived by residents; this classification was based

on that industry receiving the highest rating on

the economic dependence scale. Table 10 shows

the number and proportion of all communities

that citizens indicated as having economies dominated by particular natural resource industries.

Table 9—Mean ratings of perceived

dependence of rural Columbia basin

communities on resource-based industries

Industry Mean ratinga

Farming and agriculture 5.1

Grazing and ranching 4.4

Outdoor recreation and tourism 4.3

Wood products 3.6

Mining and minerals 1.7

a

Mean ratings based on results from community selfassessment workshops in 198 communities in the basin.
44

Only 11 towns, or 5.5 percent of all towns in the

region, were perceived as not being significantly

dependent on natural resources. Of the other

communities, residents perceived them to be

most dependent on one of four types of natural

resource-based industries: farming, ranching, timber, and recreation and tourism. Only four communities were found to be mining dominant;

given this small sample size, any results of

statistical analysis must be viewed as tentative.

Based on residents’ perceptions of their communities, about 46 percent of all communities in

the region could be labeled as primarily farming dominated, and another 8 percent perceived

themselves to be primarily ranching communities. In addition to these farming- and ranchingdominated communities, another 10 percent of

the region’s communities reported that they were

moderately highly dependent to very highly dependent on agriculture. Many of these communities also were dependent on wood products, tourism and recreation, and mining.

Figure 14—Distribution of mean

ratings of dependence on natural

resources across rural Columbia

basin communities. Dependence on natural resources

1.00 2.00 3.00 4.00 5.00 6.00 7.00

Frequency

80 

60 

40 

20 

0 

Std. Dev. = 0.95 

Mean = 5.81

N = 198

Table 10—Classification of 198 Columbia basin study communities by perceived industry

dominance

Cumulative

Perceived dominant industrya Frequency Percent percentage

Farming 90 45.5 45.5

Ranching 16 8.1 53.6

Timber 47 23.7 77.3

Travel and tourism 34 17.2 94.5

Not resource dependent 11 5.5 100.0

Total 198 100.0 100.0

a Because of the small number of communities rated highly dependent on mining and minerals (1 community), it was

not broken out for this analysis.
45

Nearly 24 percent of the region’s communities

were perceived by participants as being timberdominant communities. Many of these, however,

also perceived themselves to be dependent on

mining and outdoor recreation and tourism. Fully

two-thirds of all communities in the region perceived themselves as being somewhat dependent

to highly dependent on wood products.

Communities perceiving themselves as having

economies dominated primarily by tourism and

recreational activities totaled 17 percent of all

towns in the region. Another 11 percent perceived

themselves as being moderately highly dependent

to very highly dependent on tourism and

recreation.

Yet another 11 percent of the communities described their economy as primarily based on

government jobs.

Although the region’s towns were classified in

this way, most had mixed economies that were

perceived as being at least somewhat dependent

on a number of resource-based industries. For

example, only 9 percent of the communities examined were reported to be highly independent

of farming and ranching, only 13 percent were

reported to be highly independent of outdoor recreation and tourism, and 37 percent of the communities were not dependent on wood products to

a significant extent. Almost one-quarter of all

communities in the region (22 percent) were perceived by workshop participants as having primarily a mixed economy with no particular

dominant industry.

The relation of the ratings of perceived dependence on resource industries to community population size also was examined. As described

above, the towns were classified by four size

classes (i.e., less than 1,500 people, 1,501 to

3,000 people, 3,001 to 5,000 people, and 5,001

to 10,000 people). Statistical differences (p<0.05)

in communities’ ratings of perceived dependence

on timber, mining, or farming in relation to population size classes were not found. An analysis of

towns based on their dominant industry classification, however, indicated that different kinds

of industry-dominated communities did indeed

differ in their population size.

The majority (58 percent) of communities in the

smallest size category (communities under 1,500

in population) were ones in which agriculture

(farming, ranching, and food processing) was

perceived to be the dominant industry. Moreover,

the perceived dominant industry in the largest

segment of towns in every size category also was

farming.

Towns perceived as timber dominant were well

represented across each size category, with proportions ranging from 20 to 38 percent of communities in each class. In contrast, most of the

ranching-dominant communities (87 percent)

were among the smallest towns (under 1,500 in

population); the other ranching-dominant communities fell under the next smallest size class

(1,500 to 3,000 in population).

Interestingly, outdoor recreation and tourism was

particularly dominant in the smallest (under 1,500

in population) and the midsized (3,000 to 5,000

in population) communities with 19 and 29 percent, respectively. Not surprisingly, the largest

communities in the region, which were among the

most economically diverse, were the ones most

likely to be perceived as not being highly natural

resource dependent.

In a related question, communities were asked

which best characterized their economic base:

(1) one centered mainly around the growing,

gathering, or harvesting of raw materials; (2) one

centered around adding value to or processing

raw materials; (3) one centered primarily around

retail stores or tourism services; (4) one centered

primarily around government jobs; or (5) one too

diverse to be described by the preceding categories of economic activity. As table 11 shows,

nearly 16 percent of the communities identified

themselves as too diverse to classify, and the majority (58 percent) perceived themselves to be

dominated by traditionally extractive or agriculturally based sectors. Only 8 percent reported

themselves to be dominated by manufacturing

industries or ones adding value to or processing

raw or harvested materials. Twelve percent reported themselves to be dominated by retail and

tourism services, much the same proportion as

the average employment in that sector, which is

reported in the next section.
46

Perceptions of the economic diversity of

communities—A related focus of the community

self-assessment was on perceptions by the workshop participants of their community’s economic

diversity. These perceptions were rated on a

seven-point scale (ranging from 1, extremely nondiverse, to 7, extremely diverse). As shown in

figure 15, responses were broadly distributed

with a comparatively low mean of 3.91 and a

range of 1.83 to 6.67.

Community’s attractiveness for business—

Perceptions by residents of the attractiveness of

their communities for business were rated on a

seven-point scale (ranging from 1, extremely unattractive, to 7, extremely attractive). Responses

were skewed toward the low end of the scale;

with a comparatively low mean of 3.85 and a

range of 1.57 to 6.0 (fig. 16) indicating that a

majority of the communities assessed themselves

as being more unattractive than attractive for

business.

Profiles of the Economies by Actual

Employment

A profile of each community’s actual economic

composition was developed for the assessment

research, based on the estimated proportion of a

town’s total employment in each industrial sector.

Table 11—Types of industries perceived to dominate the economies of 198 Columbia basin study

communities

Cumulative

Industry Frequency Percentage percentage

Growing, gathering, or harvesting of raw

materials 114 58.8 58.8

Adding value to or processing raw materials 15 7.7 66.5

Retail and tourism services 24 12.4 78.9

Government services 11 5.7 84.5

Too diverse to classify 30 15.5 100.0

Total 198 100.0 100.0

Figure 15—Distribution of mean ratings of the

diversity of the economies of rural Columbia

basin communities. Economic diversity 

1.75 2.25 2.75 3.25 4.75 5.25 5.75 6.25 6.75 

Frequency 

30 

20 

10 

0 

Std. Dev.= 1.11 

Mean = 3.89 

N = 198 

3.75 4.25 
47

Figure 16—Distribution of mean

ratings of community

attractiveness for business

across rural Columbia basin

communities.

Table 12—Percentage of total employment across rural Columbia basin communities by

industrial sectora b

Industrial sector Type of sector Total employment

Percent

Agriculture Basic industry and processing 23.1

State and local government Service 15.2

Travel and tourism Service 12.4

Retail trade (nontourism related) Service 11.1

Food and beverage (nontourism

related) Service 5.1

Federal Government Service 4.8

Medical and social services Service 4.7

Mining and minerals Basic industry and processing 3.3

Harvesting and manufacturing

of wood products Basic industry and processing 5.6

Other 14.4

Total 100.0

aRural communities with 1992-94 population between 20 and 10,000 people (N=387).

b Based on the 1995 disaggregated regional employment (REIS) database.

Community attractiveness for business

1.50 2.00 2.50 3.00 3.50 4.00 4.50 5.00 5.50 6.00

Frequency

30 

20 

10 

0 

Std. Dev. = 0.95 

Mean = 3.84

N = 198
48

These profile data were then used to assess differences among communities based on their size

and actual economic diversity. The actual economic structure of communities also could be compared with perceptions by the residents of their

community’s economy.

Employment profiles of the region’s

communities—Table 12 shows the extent to

which different industrial sectors directly contributed to rural economies in the first quarter

of 1995, as indicated by the average proportion

of total employment in each sector across the region’s rural communities. These results provide

support for the proposition that both resourcebased industries (i.e., farming, ranching, timber,

and travel and tourism) and various service sectors are important components of rural economies. Of the industries displayed, agriculture,

manufacturing of wood products, and mining and

minerals are the more traditional, extractive (or

basic) resource-related industries, and the others

listed are service industries.

Agriculture is the region’s top employer (at an

average of 23.1 percent of total employment in

rural communities), and state and local government (15.2 percent) is the second largest employer. An average of 12.4 percent for the travel and

tourism industry places it third: This includes

employment attributable to such sectors as lodging, nonlocal retail trade, and nonlocal food and

beverage. Other significant employers in the region’s rural communities are a number of service

sectors, including locally based (nontourism related) retail trade and food and beverage. The

wood manufacturing and timber harvesting sector, on average, accounts for 5.6 percent of rural

community employment, and the mining and minerals sector accounts for 3.3 percent.

In terms of jobs in the average rural community,

the majority (62.1 percent) are ones in the service

sector. Excluding government jobs, which provide 21.4 percent of all jobs, other service jobs

account for 40.7 percent of all employment in

rural communities. A difference is found, however, between large and small communities in the

proportion of total employment in traditional economic base industries. Those towns having more

than 3,000 people had, on average, 18.4 percent

of all jobs in those sectors; in smaller towns,

fewer than 3,000 people, those sectors accounted

for 34 percent of all jobs.

Economic profiles of communities based on

size class—Table 13 presents the results of the

economic profiles of the region’s towns, based on

an analysis of employment in different industrial

sectors in towns grouped by population size.

Mean proportions of employment in each industrial sector are reported for each of the four size

classes of towns. For this analysis, which would

be indicative of the extent to which towns of

different sizes (based on population) were economically diverse, the 198 communities were classified according to the population size classes

described earlier (i.e., less than 1,500 people;

1,501 to 3,000 people; 3,001 to 5,000 people; and

5,001 to 10,000 people). (The results presented

in table 13 are for only the 198 communities for

which complete community assessment data

exist, for the sake of comparability, and not for

all 387 small rural communities; nonetheless, the

large sample size ensures the representational

value of these results for all towns in the region.)

Table 13 suggests that larger communities are,

indeed, somewhat less dependent on one or a few

industrial sectors and thus are more economically

diverse. Smaller communities are more dependent

on a number of natural resource industries for

employment, including agriculture and wood

products, than are larger communities; a major

exception here is the mining and minerals sector.

Just as telling was the finding that the proportion

of employment in the other industries category

was much greater for the two larger size classes

of communities (20.0 and 21.2 percent) than the

smaller town classes (9.2 and 15.0 percent).

In the smallest size class of communities (less

than 1,500), employment in agriculture accounted

for the greatest percentage of jobs (26.0 percent),

followed by state and local government (16.2

percent) and the travel and tourism sector (13.7

percent). Agriculture accounted for only 9.1 percent of all jobs in communities having between

3,001 and 5,000 people and 6.2 percent in communities with 5,001 to 10,000 people. The harvesting of timber and manufacturing of wood

products accounted for similar proportions of
49

Table 13—Percentage of 1995 total employment by industrial sector and population size in 193 Columbia basin communities

State and local

Population size (N)a Agriculture Wood products Travel and tourism government Federal Government

Percent

Less than 1,500 (128) 26.0 6.3 13.7 16.2 5.61,501 to 3,000 (36) 14.4 6.8 11.7 17.7 4.43,001 to 5,000 (16) 9.1 4.8 12.5 15.9 4.85,001 to 10,000 (13) 6.2 5.0 17.4 11.9 6.0

Mean 21.3 6.2 13.5 16.2 5.3

Mining and Food and Medical and

minerals Retail tradeb beverageb social services Other industries

Percent

Less than 1,500 (128) 2.0 11.4 5.9 3.4 9.51,501 to 3,000 (36) 4.2 12.0 4.6 9.1 15.13,001 to 5,000 (16) 4.5 12.5 4.9 11.0 20.05,001 to 10,000 (13) 3.5 13.2 7.4 8.2 21.2

Mean 2.7 11.7 5.7 5.4 12.0

a Number of study communities with complete data on relevant variables (N=193).b Nontourism related.
50

employment: regardless of community size, this

sector accounted for 6.3 and 6.8 percent of all

jobs in communities with less than 1,500 people

and 1,501 to 3,000 people, respectively, and for

only 5.0 percent in large communities (5,001 to

10,000 people). Sectors providing the greatest

percentage of jobs in larger communities (3,001

to 5,000 and 5,001 to 10,000 people) were primarily service oriented. In the largest class of

communities (5,001 to 10,000 people), the travel

and tourism sector provided the largest proportion of jobs (17.4 percent), followed by the retail

sector (13.2 percent), state and local government

(11.9 percent), medical and social services (8.2

percent), and food and beverage establishments

(7.4 percent).

Development of an economic diversity index—

A rough-and-ready indicator of actual economic

diversity was developed from the above employment data to measure the degree to which the 387

small rural communities in the region actually

were economically diverse. This index is a summative one of relative economic diversity. It was

calculated with two measures of the extent to

which a community actually was dependent on

a wide variety of industries as opposed to only

a few.

One component of the index was a measure of the

extent to which a given community’s economy

was comprised of only a few or, alternatively,

many sectors. This measure was an average number of industrial sectors having some proportion

of employment in that community and ranged

from an average of 0.04 for communities with

one sector (one-twenty-third of all 23 sectors) to

1.0 for all 23 sectors. This average was recorded

as a standardized score.

The second component of the index was a measure of the preponderance of total employment in

any one sector: this measure was first set at zero

and then increased by one for each sector for

which the proportion of a community’s total employment exceeded one-third (33 percent). The

most sectors for which the proportion of employment for any community exceeded this amount

was two, so a community average was calculated

for this measure that included 0.0, 0.5, and 1.0.

The higher this average, the less diverse the economy, so its sign was changed to provide an indicator consistent with the first measure. Again,

the average was standardized. Then, both measurements were summed for a cumulative index

of economic diversity.

The towns were classified by their level of economic diversity, based on the index score calculated

for each. Levels of economic diversity based on

the index ranged from low (-1.00 to 0.01) and

medium-low (0.01 to 0.35) to medium-high (0.35

to 0.90) and high (0.90 to 1.00). These levels

were relative ones with ranges based on quartiles

of the towns’ economic diversity index scores,

and each class thus representing an equal proportion of the communities under study (25 percent

each). The one-quarter of the towns receiving the

lowest economic diversity index scores (less than

0.01) were labeled as “low,” and so forth.

For example, Conconully, Washington, was a

town in which employment was found for only

7 of the 23 industrial sectors estimated, and 2 of

those sectors accounted for over 33 percent of the

town’s employment. Accordingly, it was very low

in economic diversity, receiving a standardized

score of -0.65 that ranked it 15 of all the region’s

towns and cities in being economically diverse

(in the lowest quartile), so it was classified in the

“low” economic diversity class. At the other end

of the diversity scale, a highly diverse community

like Bend, Oregon, was found to have employment in 20 sectors, with no sectors having a

major predominance of employment (over 33 percent). It received a diversity index score of 1.00

that ranked it 442—in the highest quartile as one

of the region’s most diversified towns—and it

was included in the high economic diversity

class. A more moderate level of diversity was

found for Troy, Idaho, which had employment

in 15 sectors; but because one of those sectors

had a major predominance of employment, it

was ranked as 192 in economic diversity with an

index score of 0.25, placing it in the medium-low

category of being economically diverse.

Text continues on page 68.
51

Table 14—Economic diversity index scores and levels of direct employment by industrial sector (based on proportions ofemployment) by rural Columbia basin community, with 1992-94 population estimates (N=387)

Levels of direct employment by industrial sectora

1992-94 Economic Travel and State and local Federal Mining and

State and town population diversity index Agriculture Timber tourism government Government mineralsIdaho:

Aberdeen 1,548 Med. high Med. high Low Low Med. low Med. low LowAcequia 103 Low High Low Low High Low LowAlbion 293 Low High Low Low High Low LowAmerican Falls 4,008 Med. low Med. high Low Low Med. high Low HighAmmon 5,469 Low Med. high Low High Low Low LowArbon Valley 628 Low High Low Low High Low LowArco 1,029 Med. high Low Low Low Low Med. high HighArimo 314 Med. low Med. high Low Low High Low LowAshton 1,104 Med. high Med. high High Med. low Low Low LowAthol 409 High Low Med. high High Med. high Low LowAtomic City 26 Low High Low Low Low Low LowBancroft 417 Med. low High Low Med. high Med. high Low LowBanks 570 Low Low Low High Low Low LowBasalt 450 Low Low Low Low Low Med. high LowBellevue 1,433 Med. high Med. high Low High Med. low Low LowBlackfoot 10,628 High Low Low Med. high Med. high Low LowBliss 196 Med. high High Low High Med. high Low LowBloomington 184 Low High Low Low Low Low LowBoise 135,506 High Low Low Med. high Med. high Low Med. lowBonners Ferry 2,244 High Med. low Med. low Med. high Med. high Low LowBuhl 3,743 Med. high Med. high Low Med. low Low Low LowBurley 8,918 High Low Low Med. high Med. high Low LowButte City 65 Low High Low Low Low Low LowCaldwell 20,800 High Low Low Med. low Med. high Low Med. highCambridge 367 Med. high High Med. high Med. low Med. high Low LowCarmen 0 Low High Low Med. high High Low LowCascade 1,001 High Low Med. low Med. high High Med. high LowCastleford 176 Med. high High Low Low High Low Low
52Table 14—Economic diversity index scores and levels of direct employment by industrial sector (based on proportions of

employment) by rural Columbia basin community, with 1992-94 population estimates (N=387) (continued)

Levels of direct employment by industrial sectora

1992-94 Economic Travel and State and local Federal Mining and

State and town population diversity index Agriculture Timber tourism government Government minerals

Challis 995 High Med. high Low Med. low Med. high Med. low HighChatcolet 73 Low High Low Low Low Low Med. lowChubbuck 8,354 Med. high Med. high Low High Med. high Low LowClark Fork 471 Med. low Low Med. low Med. high High Low LowClayton 20 Low Med. low Low High Low High LowCoeur d’Alene 26,611 High Low Low Med. high Med. high Low Med. lowCottonwood 852 Med. high Med. high Low Med. high High Low LowCouncil 951 Med. high Med. high Low Med. high Med. low Med. high LowCraigmont 571 Med. low High Low Med. low Med. low Low LowCuldesac 289 Med. low High Low Med. high High Low LowDalton Gardens 2,170 NA NA NA NA NA NA NADayton 382 Low High Low Low High Low LowDeary 548 Med. high Low High Med. high High Low Med. lowDeclo 289 Low High Low High Low Low LowDietrich 129 Low High Low Low High Low LowDingle Low Low Low Low Low Low LowDonnelly 155 Med. low Low Low High Med. low Low LowDover 335 Low Low Low High Low Low HighDowney 672 Med. high Med. high Low High High Low LowDriggs 980 High Med. high Low Med. high Med. high Low LowDrummond 33 Low High Low Low Low Low LowDubois 480 Med. low High Low Low Med. high Med. low LowEagle 3,694 High Med. low Low Med. high Med. low Low LowEast Hope 231 Low Low Low Low Low Low HighEden 329 Med. low High Low Low Med. low Low LowElk City Med. high High High Med. high Low High LowElk River 153 Low High Low Med. low Low Med. low LowEllis Low Low Low Med. high Low High LowEmmett 4,888 High Med. high Med. high Med. low Med. high Low Low
53

Table 14—Economic diversity index scores and levels of direct employment by industrial sector (based on proportions ofemployment) by rural Columbia basin community, with 1992-94 population estimates (N=387) (continued)

Levels of direct employment by industrial sectora

1992-94 Economic Travel and State and local Federal Mining and

State and town population diversity index Agriculture Timber tourism government Government minerals

Fairfied 376 High High Low Low Med. high Low LowFerdinand 141 NA NA NA NA NA NA NAFernan Lake 186 Low Low High Low Low Low LowFiler 1,716 High Med. high Low Med. low Med. high Low LowFirth 456 Med. low High Low Low Low Low LowFishhaven Low Low Low High Low Low LowFort Hall 1,453 Med. low Med. low Low Med. low Med. low High LowFranklin Med. low Low Low High Low Low LowFruitland 2,668 High Med. high Med. high Med. low Med. low Low LowGarden City 7,034 Low High Low Low Low Low LowGarden Valley Med. low Low Low High Low Low LowGenesee 783 Med. low High Low Low High Low LowGeneva Low High Low Low Med. high Low LowGeorgetown 659 Low High Low Med. low Med. high Low LowGibbonville Low Low Low High Low Low LowGlenns Ferry 1,359 Med. low Med. high Low Low Med. high High LowGlifton Low Low Low Low Low Low LowGooding 3,066 High Med. low Low Low Med. high Med. low LowGrand View 355 Med. low High Low Med. low Med. high Med. low LowGrangeville 3,208 High Med. low Med. low Med. high Med. high Med. high LowGreenleaf 681 Low Low Low High High Low LowHagerman 669 High Med. high Low Low Med. low High LowHailey 4,252 High Low Low Med. low Med. high Low LowHamer 86 Med. low High Low Low Med. low Low LowHansen 946 Med. low Med. high Low Med. high High Low LowHarrison 232 Med. high High Low High Med. low Low LowHauser 427 Low Low Low High Low Low LowHayden 4,693 Med. low Low High Low Low Low LowHayden Lake 374 Med. high Low Low High Low Low Low
54Table 14—Economic diversity index scores and levels of direct employment by industrial sector (based on proportions of

employment) by rural Columbia basin community, with 1992-94 population estimates (N=387) (continued)

Levels of direct employment by industrial sectora

1992-94 Economic Travel and State and local Federal Mining and

State and town population diversity index Agriculture Timber tourism government Government minerals

Hazelton 426 Med. low High Low Low Med. high Low LowHeyburn 2,836 Med. low High Low Low Med. low Low LowHolbrook Low High Low Low Med. low Low LowHollister 151 Low High Low Low Low Low LowHomedale 2,097 High Med. high Low Low Med. high Low Med. highHope 116 Med. low Low High High Med. low Low LowHorseshoe Bend 726 Med. high Med. low High Low Med. low High LowHuetter 85 Low Low High Low Low Low LowIdaho City 373 Med. low Low Low High High Low LowIdaho Falls 48,226 High Low Low Med. high Med. high Low LowIndian Valley Low High Low Low Low Low LowInkom 753 Med. high Low Low Med. low High Low HighIona 1,107 Med. low Low Low high Low Low LowIrwin 116 Med. low Low Low High Low Low LowIsland Park 163 Med. low Low Low Med. low High High LowJerome 7,077 High Med. high Low Med. low Med. low Low Med. lowJuliaetta 514 Med. low Med. high High Low High Low LowKamiah 1,190 Med. high Med. low High Med. high Med. high Low Low

Kellogg 2,495 High Low Low Med. high Med. low Low Med. highKetchum 2,685 Med. high Low Low High Low Low Low

Kimberly 2,656 Med. high High Low Med. high Low Low Low

Kooskia 708 Med. high High High Med. low Med. high Low LowKootenai 317 Med. low High Low Med. high Low Low Med. lowKuna 2,238 High Med. high Low Med. low High Low LowLakefork Low Med. high Low Low High Low LowLapwai 1,006 Med. low Med. high Low Low Med. high High Low

Lava Hot Springs 464 Med. low Med. low Low High Low Low LowLeadore 85 Med. low High Low Med. low High Low LowLemhi Low High Low Low Med. high Low Low
55

Table 14—Economic diversity index scores and levels of direct employment by industrial sector (based on proportions ofemployment) by rural Columbia basin community, with 1992-94 population estimates (N=387) (continued)

Levels of direct employment by industrial sectora

1992-94 Economic Travel and State and local Federal Mining and

State and town population diversity index Agriculture Timber tourism government Government minerals

Lenore Low High Low Low Low Med. high LowLetha Low High Low Low Low Low LowLewiston 29,119 High Low Med. high Med. high Med. high Low Med. low

Lewisville 549 Low High Low Low Low Low LowMackay 592 Med. high High Low High Low Med. high LowMalad City High High Low Med. low High Low Med. lowMalta 180 Med. low Med. high Low High Low Low LowMansfield 365 Med. high High Low Med. high High Low LowMarsing 809 Med. high High Low Low Med. high Med. low LowMay Low High Low Low High Low LowMcCall 2,329 High Low Low High Med. low Low LowMcCammon 763 Med. low High Low Med. high Low Low LowMelba 272 Med. low Med. high Low Med. low High Low LowMenan 768 Med. low High Low Low Low Low LowMeridian 11,181 High Low Low Med. low Med. low Low Med. lowMiddleton 2,081 Med. high High Low Med. low Med. low Low LowMidvale 116 Low High Low Low Med. low Low HighMinidoka 64 Low High Low Low High Low Med. LowMontour Low High High Low Low Low LowMontpelier High Med. low Low High Med. high Med. low Med. lowMoore 196 Low Low Low Low Low Low LowMoscow 19,122 Med. high Low Low High High Low Low

Mountain Home 8,107 Med. high Med. low Low Med. low Med. low High LowMoyie Springs 435 Med. low Med. low High Med. low Med. low Low LowMud Lake 182 Med. high Med. high Low Low High Med. low LowMullan 815 Low Low Low Low Low High HighMurtaugh 141 Med. low High Low Low Med. high Low LowNampa 31,416 High Low Low Med. high Low Low Med. lowNew Meadows 620 Med. low Low High Low High Med. low Low
56Table 14—Economic diversity index scores and levels of direct employment by industrial sector (based on proportions of

employment) by rural Columbia basin community, with 1992-94 population estimates (N=387) (continued)

Levels of direct employment by industrial sectora

1992-94 Economic Travel and State and local Federal Mining and

State and town population diversity index Agriculture Timber tourism government Government minerals

New Plymouth 1,465 Med. low High Low Low Low Low Med. lowNewdale 361 Low High Low Low High Low LowNez Perce 471 Med. low High Low Med. low Med. low Low LowNorth Powder 515 Low High High Low Med. high Med. low LowNotus 411 Med. high High Low Med. high High Low LowOakley 607 Med. high High Low High Low Low Med. lowOla 0 Low High Low Med. high High Low LowOldtown 166 High Med. high Med. high Med. high Low Med. high LowOnaway 208 NA NA NA NA NA NA NAOrofino 3,010 High Med. low Med. high Med. low High Med. low LowOsburn 1,507 Med. high Low Med. low Med. high High Low Med. lowOvid Low Low High Low Low Low LowParker 314 Low High Low Low High Low LowParma 1,702 High High Low Low Med. low Low LowPaul 1,000 Med. high High Low Low Med. high Low LowPayette 6,170 High Low Med. high Low Med. high Low LowPeck 166 Med. high Med. high Low Med. high Med. high Low Med. highPierce 755 Med. low Low High Low Med. low Low LowPilot Rock 1,540 Med. high Med. high High Low High Low LowPinehurst 1,785 High Low Med. high High Med. high Low LowPlummer 763 Med. high Med. high High Med. high Med. low High LowPocatello 47,914 High Low Low Med. high High Low Med. lowPonderay 491 Med. low High Med. low Low Med. low Low HighPost Falls 8,494 High Low Low High Low Low Med. lowPotlatch 743 Med. high Med. low High Med. low Med. high Low Med. highPreston High Med. high Low Med. high Med. high Low LowPriest River 1,679 High Low High High Med. low Low LowRathdrum 2,382 High Low Med. low High Med. high Low Med. lowRexburg 14,497 High Low Low Med. high Med. high Low Med. low
57

Table 14—Economic diversity index scores and levels of direct employment by industrial sector (based on proportions of employment)by rural Columbia basin community, with 1992-94 population estimates (N=387) (continued)

Levels of direct employment by industrial sectora

1992-94 Economic Travel and State and local Federal Mining and

State and town population diversity index Agriculture Timber tourism government Government minerals

Richfield 380 Med. low High Low Low Med. low Low LowRigby 2,950 High Med. low Low Med. high Med. high Low LowRiggins 460 Med. high Med. high Low High Med. high Med. high LowRirie 665 Med. low Med. high Low Low Med. high Low LowRoberts 647 Med. high High Low Med. low High Low LowRockland 305 Low High Low Low High Low LowRupert 5,636 High Med. low Low Med. low Med. high Low LowSalmon 3,093 High Med. low Med. low Med. high Med. high Med. low LowSandpoint 5,725 High Low Med. low High Med. high Low LowShelley 3,744 High Med. high Low Med. low Med. high Low LowShoshone 1,273 High High Low Low High Med. low LowSmelterville 453 Med. low Low Low High High Low Med. highSmiths Ferry Low High Low High Low Low LowSoda Springs 3,182 High Med. high Low Low Med. high Low HighSpalding Low Low Low Low Low High LowSpirit Lake 883 Med. high Low Low High High Med. low LowSt. Anthony 3,393 Med. high High Med. low Med. high Med. high Low LowSt. Charles 205 Low High Low Low Low Low LowSt. Marie 2,669 High Low High Med. Low Med. high Low LowStanley 70 Med. low Low Low High Med. high Low LowSugar City 1,410 Med. low High Low Med. low Med. low Low LowSun Valley 997 Med. high Low Low High Med. low Med. high LowSwan Valley 139 Low High Low High Low Low LowSweet Low High Med. low Low Low Low LowTendoy Low Low Low High Low Low LowTensed 91 Low High Low Med. low Med. low Med. low LowTeton 563 Low High Low Low Med. low Low LowTetonia 153 Med. low High Med. low Med. low Med. low Low LowTroy 782 Med. low High Med. low Low High Low Low
58Table 14—Economic diversity index scores and levels of direct employment by industrial sector (based on proportions of employment)

by rural Columbia basin community, with 1992-94 population estimates (N=387) (continued)

Levels of direct employment by industrial sectora

1992-94 Economic Travel and State and local Federal Mining and

State and town population diversity index Agriculture Timber tourism government Government minerals

Twin Falls 29,684 High Low Low Med. high Med. high Low LowUcon 932 Low High Low Low Low Low LowVictor 341 Med. low High Low Med. high Med. low Low LowWallace 994 Med. low Low Low Med. high High Med. low Med. highWardner 247 Low High Low Low Low Low LowWeippe 523 Med. low Med. low High Low High Low LowWeiser 4,891 High Med. high Low Med. low Med. high Low Med. lowWendell 2,179 Med. high High Low Low Med. low Low LowWeston 426 Low High Low Low Low Low LowWhite Bird 109 Med. low High Med. low Med. high Low High LowWidler 1,426 High High Low Low Med. high Med. low LowWinchester 272 NA NA NA NA NA NA NAWorley 194 Med. low High Low Med. high Med. high Med. high Low

Montana:

Alberton 358 Med. low Low Med. low High High Low LowAnaconda 10,037 High Low Low High High Low LowArlee 486 Med. high Low Low Med. high Med. high Med. low LowBonner-W. Riverside 1,654 Med. low Low High Med. low Med. high Low LowButte 33,555 High Low Low High Med. high Low Med. lowCharlo 406 Med. low High Low Low High Low LowColumbia Falls 3,044 High Low Med. high Med. high Med. low Low Med. highDarby 679 High Med. low High Med. low Med. low Med. high LowDeer Lodge 3,494 High Low Med. low Med. low High Low Med. highDrummond 270 Med. high Med. high High Med. low High Low LowEureka 1,039 High Low High Med. low Med. high Med. high Med. highFinley Point 376 Med. low High Low Low Low Low HighHamilton 3,023 High Med. low Low Med. high Med. high Low LowHot Springs 413 Med. low Med. low Low High Med. high Low LowKalispell 12,456 High Low Med. low Med. low Med. low Low Low
59

Table 14—Economic diversity index scores and levels of direct employment by industrial sector (based on proportions of employment)by rural Columbia basin community, with 1992-94 population estimates (N=387) (continued)

Levels of direct employment by industrial sectora

1992-94 Economic Travel and State and local Federal Mining and

State andtown population diversity index Agriculture Timber tourism government Government minerals

Kicking Horse 288 Low Low Low Low Low Low LowLibby 2,541 High Low Med. low Med. high Med. high Med. low LowLolo 2,746 High Low Low High Med. high Low LowMissoula 44,522 High Low Low Med. high Med. high Low LowOrchard Homes 10,317 Low High Low Low Low Low Med. lowPablo 1,264 Med. high Low High Low High Low Med. lowPhilipsburg 902 High Med. high Med. high Med. high Med. low Med. low LowPinesdale 665 Low Low Low High High Low Med. highPlains 1,014 High High Low Med. high Med. low Med. high LowPolson 3,621 High Med. low Low High Med. low Low LowRexford 134 Med. low Med. low High Low Low Low LowRonan 1,630 High Med. high Low Med. high Med. high Low LowSt. Ignatius 849 High High Low Low Low Med. low LowStevensville 1,340 High Med. high Low Med. high Low Med. low LowSuperior 879 High Low High High Med. high Med. low LowThompson Falls 1,313 High Low High Med. high Med. high Low LowTroy 1,054 Med. high Low Med. low High Med. high Med. high LowWalkerville 573 Low Low Low High Low Low LowWhitefish 4,551 High Low Low High Med. low Low Low

Oregon:

Adams 245 Low High Low Low Low Med. low LowAdrian 135 Low High Low Low Med. high Low LowAltamont 18,591 Low High Low Low Low Low Low

Antelope 35 Low High Low Low Low Low LowArlington 460 Med. high High Med. low Med. high High Low LowAthena 1,050 Med. high High Low Med. low Med. high Low Low

Baker City 9,585 High Med. low Med. low Med. high Med. low Med. low LowBend 29,425 High Low Med. low High Med. low Low LowBoardman 2,145 Med. high Med. high Low Med. low Med. high Med. low Low
60Table 14—Economic diversity index scores and levels of direct employment by industrial sector (based on proportions of employment)

by rural Columbia basin community, with 1992-94 population estimates (N=387) (continued)

Levels of direct employment by industrial sectora

1992-94 Economic Travel and State and local Federal Mining and

State and town population diversity index Agriculture Timber tourism government Government minerals

Bonanza 355 Med. low High Low Med. low Med. low Med. high LowBurns 2,870 High Med. high Med. low Med. high Med. high Low LowCanyon City 660 Med. low High Low Low High Low LowChiloquin 700 Med. high Med. low Low High Med. low Med. low LowCondon 725 High High Low Low Med. high Low LowCove 570 Med. low High Low Low Low Med. low LowCulver 660 Med. high Med. high Low High Med. low Low Med. highDayville 145 Med. low Med. high Med. low Med. low High High LowDufur 580 Med. low High Low Low Med. high Low LowEcho 515 Med. high High Low Med. high High Med. low LowElgin 1,655 Med. high Med. high High Med. high Med. high Low Low

Enterprise 1,935 High Med. high Low Med. high High Med. high Med. lowFossil 470 Med. low High Low Low High Low LowGranite 10 Low Low Low Low Low Low LowGrass Valley 160 Med. low High Low Med. high Med. low Low LowGreenhorn 3 Low High Low Low Low Low LowHaines 410 Med. low High Low Low Med. high Low LowHalfway 340 High Med. high Low High Med. high Low LowHelix 155 Low Low Low Med. low High Low LowHeppner 1,465 Med. high High Med. low Low High Med. low LowHermiston 10,330 High Med. low Low Med. high Med. low Low LowHines 1,445 Med. high High High Low Med. low High LowHood River 4,875 NA NA NA NA NA NA NAHuntington 560 Low Med. high Low Med. high High Low HighImbler 310 Med. high Med. low Med. high Med. high Med. high Med. high LowIone 250 Low Med. high Low High High Low LowIrrigon 890 Med. low Med. low Low Med. low High Low LowIsland City 825 Low Low Low Low Low Low LowJohn Day 1,900 High Med. low High Med. high Med. low Med. high Low
61

Table 14—Economic diversity index scores and levels of direct employment by industrial sector (based on proportions of employment)by rural Columbia basin community, with 1992-94 population estimates (N=387) (continued)

Levels of direct employment by industrial sectora

1992-94 Economic Travel and State and local Federal Mining and

State and town population diversity index Agriculture Timber tourism government Government minerals

Jordan Valley 400 Med. high Med. high Low Med. high High Med. high Med. lowJoseph 1,165 Med. low High High Med. high Med. high Low LowKlamath Falls 18,405 High Low Low High Med. high Low LowLa Grande 12,195 High Low Med. low Med. high Med. high Low LowLakeview 2,575 High Med. low Med. high Med. high Med. high Med. high LowLexington 285 Med. low High Low Low Low Low LowLonerock 20 Low High Low Low Low Low LowLong Creek 240 Med. high High Med. high High High Low LowLostine 230 Med. high Low High Med. high Low High Med. lowMadras 4,290 High Med. low Med. high Med. high Med. high Low LowMalin 740 Med. low Med. high High Low Low Low LowMaupin 485 Med. high High Low High Med. low Med. high LowMerrill 835 High Med. high Med. high Med. low Med. high Low LowMetolius 545 Low Low Low Low Low Low LowMilton-Freewater 5,865 High Med. low Low High Med. high Low LowMitchell 165 Med. low High Med. low Med. high High Low LowMonument 170 Low High Low Med. low Med. high Low LowMoro 295 Med. high High Low Med. high High Med. high LowMosier 275 Med. high Low Low High High Low LowMount Vernon 625 Med. low Low High Med. low High Low LowNorthfork Med. low Low Low High Low Low LowNyssa 2,675 High Med. high Low Med. high Med. high Low LowOntario 9,760 High Med. high Low Med. high Med. low High LowPaisley 345 Med. low High Med. low Low Low Low LowParis Med. low High Low Low High Low LowPendleton 15,715 High Low Low Med. high Med. high Med. low Med. lowPrairie City 1,160 Med. high Med. high Med. high Med. low Med. low High LowPrineville 5,945 High Med. low High Med. low Med. low Med. low LowRedmond 9,650 High Med. low Med. low High Med. low Low Low
62Table 14—Economic diversity index scores and levels of direct employment by industrial sector (based on proportions of employment)

by rural Columbia basin community, with 1992-94 population estimates (N=387) (continued)

Levels of direct employment by industrial sectora

1992-94 Economic Travel and State and local Federal Mining and

State and town population diversity index Agriculture Timber tourism government Government minerals

Richland 180 Med. high High Low High Med. high Low LowRufus 290 Med. low High Low High Med. low High LowSeneca 190 Med. low Low Low Low Med. low Low LowShaniko 30 Low High Med. low Low Low Low HighSisters 765 Med. high Med. low Low High Med. low Low LowSpray 155 Med. low High Med. low Med. low Med. high Low LowStanfield 1,620 Med. low High Low Med. low High Low LowSummerville 150 Med. low Low High Low Low Low Med. highSumpter 165 Med. high Med. high Med. low High Med. high Low Med. highTerrebonne 1,083 Med. low Low Low High Low Low LowThe Dalles 11,325 High Low Low High Med. high Low Med. lowThree Rivers 1,230 Low High Low Low Low Low LowUkiah 260 Low High Low High Med. low Low LowUmatilla 3,155 Med. high High Low Med. high High Med. low LowUnion 1,915 High High Low Med. high High Low LowUnity 110 Med. high Med. high Med. low Low Med. low High LowVale 1,495 Med. high High Low Low High Low LowWallowa 755 Med. low High Med. high Med. high Med. high Med. low Med. lowWarm Springs 2,287 Med. low Med. high High Low Low Med. low Med. lowWeston 640 Med. low Low Low Low Med. high Low Low

Washington:

Afton 1,534 High Med. high Low Med. high High Low LowAirway Heights 2,520 Med. high Low Low High High Low LowAlbion 655 Low High Low Low Low Low LowAlmira 315 Low High Low Low High Low LowAlpine 222 Med. low Low Low High Med. low Low LowAsotin 1,108 Low High Low High High Low LowBenton City 2,090 Med. high High Low Med. high Med. high Med. low LowBingen 660 High Med. high Med. high Med. low Med. high Med. low Low
63

Table 14—Economic diversity index scores and levels of direct employment by industrial sector (based on proportions of employment)by rural Columbia basin community, with 1992-94 population estimates (N=387) (continued)

Levels of direct employment by industrial sectora

1992-94 Economic Travel and State and local Federal Mining and

State and town population diversity index Agriculture Timber tourism government Government minerals

Brewster 1,645 Med. high Med. low Low Med. high Low Low LowBridgeport 1,705 Med. high High Low Low Med. high Low LowBurbank 1,695 Med. low Med. high Low Low High High LowCashmere 2,660 High High Low Med. low Med. low Low Med. highChelan 3,200 High Med. high Low Med. high High Low LowCheney 8,220 High Low Low Med. high High Low LowChewelah 2,243 High Med. high Med. low Med. high Med. low Low Med. lowClarkston 6,750 High Low Low High Med. high Low LowCle Elum 1,785 High Med. low Low High Med. high Low LowColfax 2,810 Med. high Low Low Med. low High Low LowCollege Place 6,710 Med. low Med. high Low Low Med. high Low Med. lowColton 350 Low High Low Low High Low Med. lowColville 4,440 High Low Med. low Med. high High Low LowConconully 180 Low High Low High Med. high Low LowConnell 2,640 High High Low Med. low Med. low Low LowCoulee City 612 Med. high High Low Med. low High Low LowCoulee Dam 206 Med. low Low Low Med. high High Med. high LowCountry Homes 5,126 Low Low Low High Low High LowCreston 239 Low High Low Low High Low LowCusick 256 Med. high Low Med. low Med. high High Med. high LowDavenport 1,550 High Med. high Low Low High Low LowDayton 2,505 High Med. high Low Med. low High Low LowDeerpark 2,570 High Med. low Low High Med. low Low LowEast Wenatchee 4,010 High Med. high Low Med. high Med. low Low LowElectric City 945 Med. low Low Low Med. high High Low LowEllensburg 12,860 High Low Low High High Low LowElmer City 310 Low Low Low High Med. high Low LowEndicott 360 Med. low Med. high Low Med. low High Low LowEntiat 545 Med. low High Low Low High Med. high LowEphrata 5,585 High Med. high Low Med. low High Low Low
64Table 14—Economic diversity index scores and levels of direct employment by industrial sector (based on proportions of

employment) by rural Columbia basin community, with 1992-94 population estimates (N=387) (continued)

Levels of direct employment by industrial sectora

1992-94 Economic Travel and State and local Federal Mining and

State and town population diversity index Agriculture Timber tourism government Government minerals

Fairchild 4,854 Med. low Low Low Low Low High LowFairfield 599 Med. high High Low Low High Low LowFarmington 130 Med. low High Low Low High Low LowGarfield 594 Med. low Med. high Low Low High Low Med. lowGeorge 365 Med. low High Low Low Low Low LowGoldendale 3,425 High Med. high Med. low Low Med. high Low Med. highGrand Coulee 1,045 Med. high Med. low Low High High Low LowGrandview 7,690 Med. high High Low Med. low Med. high Low LowGranger 2,085 Med. high Med. high Low Low Med. high Low Med. highGreenacres 4,626 High Low Low Low Med. low Low LowHarrah 453 Med. low High Low Med. low High Low LowHarrington 492 Med. low High Low Low Med. high Low LowHartline 185 Low High Low Low Low Low LowInchelium 392 Med. low Low Med. high Med. low Med. low High LowIone 501 Med. high Low High High Med. high Low LowKahlotus 200 Med. high High Low Low High Med. low LowKennewick 46,960 High Low Low Med. high Med. low Low Low

Kettle Falls 1,435 Med. high Med. low High Med. high Med. low Med. low Low

Kittitas 1,060 Med. low High Low Med. high High Low LowKrupp 65 Low High Low Low Low Low LowLa Crosse 390 Med. low High Low High Low Low LowLamont 93 Low High Low Low High Low LowLatah 211 Med. low High Low Low Low Low LowLeavenworth 2,020 High Med. high Low High Med. low Low LowLiberty Lake 2,036 Low Low Low Med. low Low Low High

Lind 470 Med. high High Low Low Med. high Low LowMabton 1,615 Med. low High Low Low High Low LowMalden 215 Low High Low Low Low Low LowMarcus 154 Med. low Low Low High Med. low Low LowMattawa 1,535 Med. low High Low Low Med. high Low LowMedical Lake 3,660 Med. high Low Low Med. high High Med. low Med. high
65

Table 14—Economic diversity index scores and levels of direct employment by industrial sector (based on proportions ofemployment) by rural Columbia basin community, with 1992-94 population estimates (N=387) (continued)

Levels of direct employment by industrial sectora

1992-94 Economic Travel and State and local Federal Mining and

State and town population diversity index Agriculture Timber tourism government Government minerals

Mesa 315 Med. low High Low Med. high Med. high Low LowMetaline 193 Low Low Low High High Low LowMetaline Falls 227 Med. low Low Low Low High High LowMoses Lake 12,190 High Low Low Low Med. low Low LowMoxee 925 Med. high High Med. low Low Low Low Med. highNaches 689 High Med. low Med. high Med. high High Low LowNespelem 225 Med. low Low Low Low Low High LowNewport 1,780 High Med. high Low Med. low High Low Med. highNorthport 342 Med. high Med. high High Low Med. high High Med. lowOakesdale 433 Med. high High Low Low High Low LowOdessa 957 High High Low Low Med. high Low LowOkanogan 2,400 Med. low Med. high Low Med. low High Med. low LowOmak 4,220 High High Med. low Low Med. low Med. low LowOpportunity 22,326 Med. low High Low Low Med. high High Med. highOroville 1,520 High High Med. low Med. low Med. high Low LowOthello 4,780 High Med. high Low Low Med. high Low LowOtis Orchard 5,790 High Low Low High Low Low Med. lowPalouse 960 Med. low High Low Low High Low LowPasco 22,170 High Med. low Low Med. high Med. high Low LowPateros 585 High Med. low High Med. high Med. low Low LowPomeroy 1,460 High High Low Low High Med. high LowPrescott 305 Med. high High Low Med. low High Low LowProsser 4,630 High High Low Low High Low LowPullman 23,770 Med. high Low Low Med. high High Low LowQuincy 3,860 High High Low Med. low Med. low Low LowReardan 497 Med. high Med. high Low Med. high High Med. low LowRepublic 1,080 High Med. high Med. high Med. low High Low Med. highRichland 35,430 Med. high Low Low Med. low Med. high Low LowRitzville 1,750 Med. low High Low Med. high Med. high Low LowRiverside 250 Med. high High Low High Med. low Low LowRock Island 555 Med. high High Low Med. low High Med. low Low
66Table 14—Economic diversity index scores and levels of direct employment by industrial sector (based on proportions of employment)

by rural Columbia basin community, with 1992-94 population estimates (N=387) (continued)

Levels of direct employment by industrial sectora

1992-94 Economic Travel and State and local Federal Mining and

State and town population diversity index Agriculture Timber tourism government Government minerals

Rockford 505 Med. high High Low Med. low High Low LowRosalia 620 High Med. low Low High Med. high Low LowRoslyn 885 Med. high Med. high Low High High Low LowRoyal City 1,200 Med. high High Low Med. low High Low LowSelah 5,170 Med. high Med. low Low Med. low High Low Med. lowSoap Lake 1,300 Med. high Low Low High High Low LowSouth Broadway 2,843 Low Low Low Low Low Low LowSpangle 245 Med. high High Low Low High Low Med. lowSpokane 185,600 High Low Low Low Low Low LowSprague 465 Med. low High Low Low Med. high Low LowSpringdale 355 Med. low High Med. low Low Low Low HighSt. John 508 Med. high Med. low Low Med. low High Low LowStarbuck 165 Low High Low Low Med. high High LowSunnyside 11,660 High Med. high Low Med. low Med. high Low Low

Tekoa 870 Med. low Med. high Low Med. low High Low LowTerrace Heights 4,223 Low Low Low Low Low Low LowTieton 891 Med. high Med. high Low Low Med. high Low LowTonasket 1,020 Med. low High Low Med. low Med. high Low LowToppenish 7,734 High Low Low Med. low High Med. high LowTwisp 910 High Med. high Low High Med. low Low LowUnion Gap 3,220 High Med. low Med. low High Med. high Low LowUniontown 305 Med. low High Low Low High Low LowVeradale 7,836 High Low Low High Med. low Low Med. lowWaitsburg 1,130 Med. high High FP High Med. low Low LowWalla Walla 28,730 High Low Low Med. high Med. high Low Med. lowWapato 3,790 High High Low Med. low Med. high Low LowWarden 1,765 Med. low High Low Med. low Med. high Low LowWasco 385 Med. high High Low High Med. low Low LowWashtucna 270 Med. low High Low Low High Low Low
67

Table 14—Economic diversity index scores and levels of direct employment by industrial sector (based on proportions of employment)by rural Columbia basin community, with 1992-94 population estimates (N=387) (continued)

Levels of direct employment by industrial sectora

1992-94 Economic Travel and State and local Federal Mining and

State and town population diversity index Agriculture Timber tourism government Government minerals

Waterville 1,065 Med. low High Low Low Low Low LowWaverly 111 Low High Low Low Low Low HighWenatchee 23,460 High Med. low Low Med. high Med. low Low Med. lowWhite Salmon 1,915 High Low Med. low Med. low High Med. low LowWhite Swan 2,755 Med. low High Med. low Med. low Med. high Med. high LowWilbur 875 Med. high Med. high Low Med. high Med. high Low LowWilson Creek 224 Med. low High Low Med. low Med. low Low LowWinthrop 345 High Med. high Med. low High Low Med. low LowYakima 59,740 High Med. low Low Med. high Low Low Low

Zillah 2,190 Med. high High Low Low Med. low Low Med. Low

Wyoming:

Jackson 5,605 Med. high Low Low High Med. low Low LowThayne 288 Med. high Low Low Med. low Low Low High

NA = not available.a A “low” level of direct employment represents 5 percent of total employment in a given industry, or less; “medium-low,” 6 to 10 percent; “medium high,” 11 to 19 percent;

and “high” 20 percent or more of total employment in a given industry.
68

Table 14 displays all the region’s cities and towns

and gives their 1992-94 population estimates,

scores on the economic diversity index, and

levels of employment per industrial sector. For

this analysis, the levels of actual employment

developed were low (5 percent of total employment in a given industry, or less), medium low

(6 to 10 percent), medium high (11 to 19 percent),

and high (20 percent, or more, of total employment in a given industry). These levels, which

were based on a comparison of quartiles for all

industries, provided absolute—as opposed to

relative—indicators of the level of community

dependence on the various industries for employment. That is, few communities would be rated

highly in construction employment, which represented a low proportion of employment across

most communities, but the levels would be much

more revealing for sectors representing higher

proportions and greater variation, such as agriculture or wood products.

Overall, as table 14 shows, although larger towns

and cities tended to have a comparatively higher

degree of economic diversity, many of the region’s smaller towns also were found to be comparatively diverse economically. For instance,

Bonners Ferry, Idaho, was found to have a highly

diverse economy, yet had only 2,244 residents in

1992-94.

Comparing Perceived and Actual

Industry Dominance

An analysis of industry employment by

dominant industry classifications—Economicprofile data for the 198 communities were analyzed to identify differences in actual employment in various sectors in those towns classified

as dominated by the various natural resource

industries (i.e., farming, ranching, timber, and

outdoor recreation and tourism; see the earlier

discussion on classification of communities by

dominant industry). As table 15 shows, the percentage of actual employment within each of the

major industrial sectors is reported for each type

of town classified as dominated by a particular

industry.

As one would expect, the 93 communities that

perceived themselves to be farming and ranching dependent had higher percentages of employment in the agricultural sector (28.5 percent).

Similarly, the 45 communities perceiving themselves to be timber dependent were primarily

represented by the higher levels of employment

(16.7 percent) in the harvesting of timber and

manufacturing of wood products. Additionally,

within the 33 communities that perceived themselves to be primarily dependent on travel and

tourism, over one-quarter of all jobs, on average,

were directly related to that industry. Higher proportions of employment in travel-related serviceoriented sectors, such as food and beverage

and retail trade, that were attributed to local

(nontourism related) trade, also were found for

travel and tourism communities as well. This

finding suggests that the attribution of nonlocal

(or tourism) jobs in these travel-related service

sectors based on the minimum requirements approach may be conservative; that is, the travel

and tourism sector may account for higher levels

of employment than indicated by the minimum

requirements approach applied here. In addition,

the travel and tourism sector was an important

provider of jobs in both timber- and ranchingdependent communities (13.0 and 14.3 percent,

respectively, of total employment), reflecting the

important role that resource amenities are playing

in many rural areas in the basin. The Federal

Government was also a comparatively important

sector in these towns, with nearly double the

mean estimate of employment as other kinds of

industrial-dominant towns.

Alternatively, industries such as wood products

and Federal Government are relatively unimportant to travel and tourism communities. Likewise,

mining and minerals was a relatively unimportant

economic sector, in terms of direct jobs provided,

in farming-, ranching-, and timber- dependent

communities. However, wood products manufacturing had the second-highest proportion of total

employment, next to timber-dependent towns, in

ranching communities. Aside from these timberdependent communities having the largest percentage of manufacturing of wood and paper

products, they also had the most diverse economies based on relatively high proportions of jobs

in each of the major industries.
69

Table 15—Percentage of total employment in selected economic sectors, by industry dominance classification, Columbia basinPerceived dominant State and local Federalindustry (N)a Agriculture Wood products Travel and tourism government Government

Percent

Farming (93) 28.5 2.0 9.1 17.9 4.0Ranching (18) 26.4 7.7 14.3 15.6 8.4Timber (45) 12.6 16.7 13.0 14.9 8.5Travel and tourism (33) 11.6 2.6 26.5 13.3 3.6Mining (4) 7.0 5.8 10.0 17.7 2.1

Mean 21.3 6.2 13.5 16.2 5.3

Mining and Food and Medical and social

minerals Retail tradeb beverageb services Other industries

Percent

Farming (93) 1.5 9.8 4.7 4.4 18.1Ranching (18) 1.2 13.5 6.1 5.3 1.5Timber (45) 3.2 12.5 4.8 7.1 6.7Travel and tourism (33) 5.1 15.0 9.5 5.2 7.6Mining (4) 13.5 12.4 3.6 10.0 17.9

Mean 2.7 11.7 5.7 5.4 12.0

aTotal number of rural communities in each class based on a sample of 193 communities that participated in a self-assessment workshop.b Nontourism related.
70

How accurately do perceptions of residents

match the reality of a community’s employment

profile?—The preceding discussion of residents’

perceptions of their community’s economy and its

actual composition in terms of employment provides insight on the question, How well and how

accurately do these perceptions reflect the reality

of any given community’s actual employment

base? One indication can be found in comparing

perceived levels of dependence on natural resource industries in the 198 sample communities

with levels based on proportions of actual employment for those communities (see table 16).

For this comparison, the levels of actual employment applied again ranged were low (5 percent of

total employment in a given industry or less),

medium low (6 to 10 percent), medium high (11

to 19 percent), and high (20 percent or more, of

total employment).

Table 16 indicates that the accuracy of perceived

dependence with actual dependence in terms of

jobs differed greatly, depending on community.

The perceptions by some communities (e.g.,

Genessee or Bellevue, Idaho) were consistent

with levels of employment, and those for other

communities (e.g., Clayton or Lava Hot Springs,

Idaho) were not.

An analysis of the match between perception

and reality on two traditionally important

resource-related industries—One difficulty

with assessing the accuracy of resident perceptions of their community’s economy and its diversity is that of determining an acceptable standard

for declaring a community “resource dependent.”

As a Forest Service (1977) policy statement

notes, “The definition of dependency has long

been debated...[with] no clear-cut definition of

dependency.” The criterion in the 1977 memorandum on dependent communities establishes

that, “if mills and/or communities utilize at least

50 percent of the annual capacity from National

Forest timber sales and have at least 10 percent of

their total employment in this industry, then the

mills and/or communities are dependent upon

National Forest timber sales.” Another approach

is presented by Bender et al. (1985), whose study

of mining-dependent counties classified all counties with 20 percent or more of total county income attributable to the mining industry as mining dependent. Our analysis used the broader,

more inclusive criterion used by the Forest

Service of 10 percent.

Our analysis focused in particular on data on two

key resource-based industrial sectors, wood products and agriculture, as well as on the relation of

perceptions to reality for all sectors of a community’s economy. The economic profile data were

analyzed by using this benchmark of 10 percent

or more of employment in an industry as an indicator that the industry was a major one in a

town’s economy. The analysis indicated that a

much higher percentage (about 70 percent) of the

towns were ones in which farming and ranching

were major industries than were perceived by

community residents (58 percent) to be agriculture dominant. (In the economic profile data,

ranching is combined with farming as part of the

agricultural sector for comparison with the employment data for that sector.) When the average

proportion of employment in agriculture (20 percent) was applied as the benchmark across all

communities, the percentage of towns in which

agriculture was the major industry (58 percent)

was much closer to the proportion based on perceptions. In contrast, a lower percentage of the

towns (17 percent) were found to be ones in

which timber was a major industry than were perceived by workshop participants (23 percent) to

be timber dominant. Communities in which timber played a significant role, as indicated by

having more than 10 percent in manufacturing

of wood products, included 71 communities representing 15 percent of all towns and cities in the

region (see table 17).

Of the 198 sample communities for which data on

resident perceptions of resource dependence were

collected, 37 (18.7 percent) had high employment

(10 percent or more of all jobs) in manufacturing

of wood products. As table 18 shows, workshop

participants in 3 (8 percent) of these 37 communities that actually had high dependence on wood

products manufacturing for employment (greater

than 10 percent of all jobs) perceived them to

have fairly low dependence on this sector.

Text continues on page 80.
71

Table 16—Comparisons of perceived and actual levels of dependence on natural resource industries (based on proportions of actualemployment) in the 198 study communities, Columbia basin

Timber and Travel and Mining and

Agriculture wood products tourism minerals

Perceived Total Perceived Total Perceived Total Perceived Total

State and town dependencea employmentb dependence employment dependence employment dependence employment

Idaho:

Ammon Low Med. high Low Low Low High Low LowAshton High Med. high High High High Med. low Low LowAthol Low Low High Med. high High High Low LowBancroft High High Low Low High Med. high High LowBellevue High Med. high Low Low High High Low LowBlackfoot High Low Low Low High Med. high Low LowBliss High High Low Low High High Low LowBonners Ferry High Med. low High Med. low Low Med. high Low LowCascade Low Low High Med. low High Med. high Low LowChallis High Med. high Low Low High Med. low High HighChubbuck High Med. high Low Low Low High High LowClark Fork Low Low High Med. low High Med. high Low LowClayton High Med. low High Low Low High High LowCraigmont High High High Low Low Med. low Low LowCuldesac High High High Low Low Med. high Low LowDalton Gardens Low NA Low NA High NA Low NADeclo High High Low Low Low High Low LowDonnelly High Low High Low High High Low LowDriggs High Med. high High Low High Med. high Low LowDubois High High Low Low Low Low Low LowElk River Low High High Low High Med. low Low LowEmmett High Med. high High Med. high High Med. low High LowFerdinand High NA Low NA Low NA Low NAFiler High Med. high Low Low Low Med. low Low LowFirth High High Low Low Low Low Low LowFort Hall High Med. low Low Low High Med. low Low LowFruitland High Med. high High Med. high Low Med. low Low LowGenesee High High Low Low Low Low Low LowGrangeville High Med. low High Med. low High Med. high Low Low
72Table 16—Comparisons of perceived and actual levels of dependence on natural resource industries (based on proportions of actual

employment) in the 198 study communities, Columbia basin (continued)

Timber and Travel and Mining and

Agriculture wood products tourism minerals

Perceived Total Perceived Total Perceived Total Perceived Total

State and town dependencea employmentb dependence employment dependence employment dependence employment

Hagerman High Med. high Low Low High Low Low LowHailey Low Low Low Low High Med. low Low LowHarrison High High High Low High High Low LowHazelton High High Low Low Low Low Low LowHomedale High Med. high Low Low Low Low Low Med. highIdaho City Low Low High Low High High Low LowIrwin High Low Low Low High High Low LowIsland Park Low Low High Low High Med. low Low LowKamiah High Med. low High High High Med. high Low LowKellogg Low Low High Low High Med. high High Med. highKetchum Low Low Low Low High High Low LowKooskia High High High High High Med. low Low LowKootenai Low High High Low Low Med. high Low Med. lowLapwai High Med. high High Low Low Low Low LowLava Hot Springs High Med. low Low Low High High High LowLeadore High NA High NA High NA Low NAMcCammon Low High Low Low Low Med. high Low LowMelba High Med. high Low Low Low Med. low Low LowMeridian Low Low Low Low Low Med. low Low Med. lowMountain Home High Med. low Low Low High Med. low Low LowMoyie Springs High Med. low High High Low Med. low Low LowMurtaugh High High Low Low Low Low Low LowNew Meadows High Low High High High Low Low LowOldtown Low Med. high High Med. high High Med. high Low LowOnaway Low NA High NA Low NA Low NAOrofino Low Med. low High Med. high High Med. low Low LowOsburn Low Low High Med. low High Med. high High Med. lowParker High High Low Low High Low Low LowParma High High Low Low High Low Low Low
73

Table 16—Comparisons of perceived and actual levels of dependence on natural resource industries (based on proportions of actualemployment) in the 198 study communities, Columbia basin (continued)

Timber and Travel and Mining and

Agriculture wood products tourism minerals

Perceived Total Perceived Total Perceived Total Perceived Total

State and town dependencea employmentb dependence employment dependence employment dependence employment

Payette Low Low Low Med. high Low Low Low LowPierce Low Low High High High Low Low LowPilot Rock High Med. high High High Low Low Low LowPriest River Low Low High High High High Low LowRathdrum Low Low High Med. low Low High Low Med. lowRichfield High High Low Low Low Low Low LowRiggins High Med. high High Low High High Low LowRirie High Med. high Low Low Low Low Low LowRoberts High High Low Low Low Med. low Low LowSalmon High Med. low High Med. low High Med. high High LowSandpoint Low Low High Med. low High High Low LowShoshone High High Low Low Low Low Low LowSmelterville Low Low High Low High High High Med. highStanley Low Low High Low High High High LowSugar City High High Low Low Low Med. low Low LowSwan Valley Low High Low Low High High Low LowTensed High High Low Low Low Med. low Low LowTeton High High Low Low High Low Low LowTetonia High High Low Med. low Low Med. low Low LowWallace Low Low High Low High Med. high High Med. highWeippe High Med. low High High High Low Low LowWeiser High Med. high High Low High Med. low Low Med. lowWinchester High NA Low NA High NA Low NAWorley High High High Low High Med. high Low Low

Montana:

Alberton Low Low High Med. low High High Low LowColumbia Falls Low Low High Med. high High Med. high High Med. highDarby High Med. low High High High Med. low Low LowDeer Lodge High Low High Med. low High Med. low Low Med. high
74Table 16—Comparisons of perceived and actual levels of dependence on natural resource industries (based on proportions of actual

employment) in the 198 study communities, Columbia basin (continued)

Timber and Travel and Mining and

Agriculture wood products tourism minerals

Perceived Total Perceived Total Perceived Total Perceived Total

State and town dependencea employmentb dependence employment dependence employment dependence employment

Drummond High Med. high High High High Med. low Low LowEureka High Low High High High Med. low Low Med. highLibby Low Low High Med. low High Med. high High LowPlains High High High Low Low Med. high Low LowPolson Low Med. low High Low High High Low LowRonan High Med. high High Low High Med. high Low LowSt. Ignatius High High High Low High Low Low LowStevensville High Med. high High Low High Med. high Low LowSuperior Low Low High High High High Low LowWhitefish Low Low High Low High High Low Low

Oregon:

Adams High High Low Low Low Low Low LowAdrian High High Low Low Low Low Low LowAntelope High High Low Low Low Low Low LowArlington High High Low Med. low Low Med. high Low LowBaker City High Med. low High Med. low High Med. high High LowBurns High Med. high High Med. low High Med. high Low LowChiloquin High Med. low High Low High High Low LowDayville High Med. high High Med. low High Med. low Low LowDufur High High Low Low High Low Low LowEcho High High Low Low Low Med. high Low LowEnterprise High Med. high High Low High Med. high Low Med. lowGrass Valley High High Low Low High Med. high Low LowHalfway High Med. high Low Low High High Low LowHelix High Low Low Low Low Med. low Low LowHeppner High High High Med. low High Low Low LowHood River High NA High NA High NA Low NAImbler High Med. low High Med. high Low Med. high Low LowIrrigon High Med. low Low Low High Med. low Low Low
75

Table 16—Comparisons of perceived and actual levels of dependence on natural resource industries (based on proportions of actualemployment) in the 198 study communities, Columbia basin (continued)

Timber and Travel and Mining and

Agriculture wood products tourism minerals

Perceived Total Perceived Total Perceived Total Perceived Total

State and town dependencea employmentb dependence employment dependence employment dependence employment

John Day High Med. low High High Low Med. high Low LowJordan Valley High Med. high Low Low High Med. high High Med. lowJoseph High High High High High Med. high Low LowLakeview High Med. low High Med. high Low Med. high Low LowLong Creek High High High Med. high Low High Low LowMalin High Med. high Low High Low Low Low LowMaupin High High Low Low High High Low LowMerrill High Med. high Low Med. high Low Med. low Low LowMosier Low Low Low Low High High Low LowNyssa High Med. high Low Low High Med. high Low LowPaisley High High High Med. low High Low Low LowPrairie City High Med. high High Med. high Low Med. low Low LowPrineville High Med. low High High High Med. low Low LowRedmond High Med. low High Med. low High High Low LowRichland High High High Low High High Low LowShaniko High High Low Med. low High Low Low HighSisters Low Med. low Low Low High High Low LowSpray High High High Med. low Low Med. low Low LowStanfield High High Low Low Low Med. low Low LowSumpter Low Med. high High Med. Low High High High Med. highUmatilla High High Low Low High Med. high Low LowUnion High High High Low High Med. high Low LowUnity High Med. high High Med. low High Low High LowWallowa High High High Med. high High Med. high Low Med. lowWarm Springs Low Med. high High High High Low Low Med. lowWeston High Low Low Low High Low Low Low

Washington:

Airway Heights Low Low Low Low Low High Low LowAlmira High High Low Low Low Low Low LowBenton City High High Low Low Low Med. high Low Low
76Table 16—Comparisons of perceived and actual levels of dependence on natural resource industries (based on proportions of actual

employment) in the 198 study communities, Columbia basin (continued)

Timber and Travel and Mining and

Agriculture wood products tourism minerals

Perceived Total Perceived Total Perceived Total Perceived Total

State and town dependencea employmentb dependence employment dependence employment dependence employment

Burbank High Med. high High Low High Low Low LowCashmere High High Low Low High Med. low Low Med. highChelan High Med. high Low Low High Med. high Low LowChewelah High Med. high High Med. low High Med. high High Med. lowCle Elum High Med. low High Low High High Low LowColfax High Low Low Low Low Med. low Low LowColville High Low High Med. low High Med. high Low LowConconully High High Low Low High High Low LowCreston High High Low Low High Low Low LowDayton High Med. high High Low High Med. low Low LowElmer City Low Low Low Low Low High Low LowEndicott High Med. high Low Low Low Med. low Low LowEntiat High High High Low High Low Low LowGeorge High High Low Low High Low Low LowGrand Coulee Low Med. low Low Low High High Low LowGranger High NA Low NA Low NA Low NAHarrah High NA Low NA Low NA Low NAHarrington High High Low Low High Low Low LowHartline High High Low Low Low Low Low LowInchelium High Low High Med. high High Med. low Low LowIone Low Low High High High High Low LowKettle Falls High Med. low High High High Med. high Low LowKittitas High High Low Low Low Med. high Low LowKrupp High High Low Low Low Low Low LowMabton High NA Low NA Low NA Low NAMattawa High High Low Low Low Low Low LowMedical Lake Low Low Low Low High Med. high Low Med. highMesa High High Low Low Low Med. high Low LowNewport Low Med. high High Low High Med. low Low Med. highOdessa High High Low Low Low Low Low Low
77

Table 16—Comparisons of perceived and actual levels of dependence on natural resource industries (based on proportions of actualemployment) in the 198 study communities, Columbia basin (continued)

Timber and Travel and Mining and

Agriculture wood products tourism minerals

Perceived Total Perceived Total Perceived Total Perceived Total

State and town dependencea employmentb dependence employment dependence employment dependence employment

Okanogan High Med. high High Low High Med. low Low LowOthello High Med. high Low Low High Low Low LowPalouse High High Low Low Low Low Low LowPomeroy High High High Low Low Low Low LowPrescott High High Low Low High Med. low Low LowQuincy High High Low Low High Med. low Low LowRepublic Low Med. high High Med. high Low Med. low High Med. highRitzville High High Low Low Low Med. high Low LowRock Island High High Low Low Low Med. low High LowRosalia High Med. low Low Low Low High Low LowSelah High Med. low High Low Low Med. low Low Med. lowSprague High High Low Low Low Low Low LowSt. John High Med. low Low Low Low Med. low Low LowTekoa High Med. high Low Low Low Med. low Low LowTieton High Med. high Low Low Low Low Low LowTonasket High High High Low High Med. low Low LowToppenish High Low Low Low High Med. low Low LowTwisp High Med. high High Low High High Low LowWasco High High Low Low Low High Low LowWashtucna High High Low Low Low Low Low LowWhite Salmon High Low High Med. low High Med. low Low LowWilson Creek High High Low Low High Med. low Low LowWinthrop Low Med. high High Med. low High High Low Low

Wyoming:

Jackson Low Low Low Low High High Low LowThayne High Low Low Low High Med. low Low High

NA = no estimates of employment available.a A rating of “low” represented a numerical rating of 4 or less on a 7-point scale (from 1, extremely independent, to 7, extremely dependent), and a rating of “high”

represented a numerical rating of more than 4 on that 7-point scale.

b A “low” level of direct employment represents 5 percent of total employment in a given industry, or less; “medium low,” 6 to 10 percent; “medium high,” 11 to 19 percent;

and “high,” 20 percent or more of total employment in a given industry.
78

Table 17—Rural Columbia basin communities with high percentages of total employment in

wood products with levels based on economic diversity index scores (N=71)

State and town Employment in wood products Level of economic diversitya

Percent

Idaho:

Ashton 20 Med. high

Athol 12 High

Cambridge 17 Med. high

Deary 30 Med. high

Elk City 27 Med. high

Emmett 14 High

Fernan Lake 89 Low

Fruitland 18 High

Hayden 21 Med. low

Hope 21 Med. low

Horseshoe Bend 32 Med. high

Huetter 100 Low

Juliaetta 33 Med. low

Kamiah 22 Med. high

Kooskia 30 Med. high

Lewiston 11 High

Montour 63 Low

Moyie Springs 64 Med. low

New Meadows 37 Med. low

North Powder 44 Low

Oldtown 16 High

Orofino 12 High

Ovid 86 Low

Payette 11 High

Pierce 64 Med. low

Pilot Rock 33 Med. high

Pinehurst 12 High

Plummer 20 Med. high

Potlatch 25 Med. high

Priest River 29 High

St. Marie 30 High

Weippe 42 Med. low

Montana:

Bonner-W. Riverside 47 Med. low

Columbia Falls 11 High

Darby 30 High

Drummond 26 Low

Eureka 22 High

Pablo 22 Med. high

Philipsburg 11 High

Rexford 55 Med. low

Superior 21 High

Thompson Falls 21 High
79

Table 17—Rural Columbia basin communities with high percentages of total employment in

wood products with levels based on economic diversity index scores (N=71) (continued)

State and town Employment in wood products Level of economic diversitya

Percent

Oregon:

Elgin 31 Med. high

Hines 20 Med. high

Imbler 12 Med. high

John Day 20 High

Joseph 34 Med. low

Lakeview 11 High

Long Creek 12 Med. high

Lostine 31 Med. high

Madras 11 High

Malin 66 Med. low

Merrill 16 High

Mount Vernon 38 Med. low

Prairie City 16 Med. high

Prineville 27 High

Summerville 33 Med. low

Wallowa 19 Med. low

Warm Springs 51 Med. low

Washington:

Bingen 17 High

Inchelium 12 Med. low

Ione 27 Low

Kettle Falls 22 Med. high

Naches 11 High

Northport 31 Med. high

Pateros 21 High

Republic 12 High

a Levels of economic diversity based on the index ranged from low (-1.00 to 0.01) and medium low (0.01 to 0.35) to

medium high (0.35 to 0.90) and high (0.90 to 1.00).

Table 18—Number and percentage of rural Columbia basin communities

with a high actual degree of dependence on wood products (based on

10 percent or more employment in wood products), by perception of

dependence (N=37)

High employment dependence

Perceived dependence Number of cases Percentage

High 34 92

Low 3 8

Total 37 100
80

In contrast, as table 19 shows, 162 communities

had less than 10 percent of their total employment in the wood products manufacturing sector

and could not be deemed timber dependent by

this measure. Of these, 58 (36.6 percent) were

perceived by key informants to have fairly high

dependence on wood products manufacturing.

Table 20 shows the names and statistics of towns

perceived to be independent of timber harvest and

wood products manufacturing but that actually

did have a significant proportion of employment

in wood products manufacturing. Table 21 shows

the names and statistics for towns perceived to be

dependent on timber, but that were found to have

no significant proportion of employment in wood

products manufacturing.

The correlation between perception of community dependence on timber harvesting and processing and empirical data on actual amount of

employment in manufacturing of wood products

as opposed to other industrial sectors (i.e., relative proportion) was measured with a Pearson

correlation coefficient, which produced a moderately strong correlation of 0.50. Although this

result suggests some degree of consistency between resident perceptions of the wood products

industry’s importance and its actual significance,

over a third of the region’s communities perceived that they were dependent on the timber

industry to an extent that they really were not.

In the case of agriculture, the 321 communities in

the region with high employment in agriculture

(10 percent or more of all jobs) represented 68.0

percent of the total of 476 communities. Of the

198 communities sampled and for which data

were gathered on resident perceptions of resource

dependence, 60 communities (25 percent) were

characterized by a moderately small proportion

(less than 10 percent) of employment in agriculture. Of the 60, the citizens of 35 of them (58.3

percent) indicated that they perceived that their

towns had a fairly high dependence on agriculture

(table 22).

Table 23 shows that, of the 138 sample communities characterized by a high proportion (more than

10 percent) of employment in agriculture, only 12

(8.6 percent) had residents who perceived them to

have a low rating of dependence on agriculture.

Pearson correlation coefficients were calculated

to indicate the strength of the relation of citizen

perceptions of community dependence on farming and ranching to actual results of empirical

data on agricultural employment. These coefficients were calculated to be 0.36 for farming

and 0.24 for ranching, which were statistically

significant (p<0.05) but indicated only a moderately weak relation between perceived and

actual economic dependence.

These results suggest that residents in some of the

region’s communities misperceived the extent to

which they were dependent on farming and

ranching. An alternative explanation is that this

question focused workshop participants’ attention

on dependence on particular industries, and that

this represented a different, more comparative

measure that focused on resource-related industries, which could be expected to provide different results from the employment profiles.

In summary, the empirical data suggest that most

workshop participants perceive their communities

to be dependent on traditional resource industries.

However, 37 percent of all communities in the

case of timber and 58 percent for agriculture perceived themselves to be moderately to highly dependent on timber and agriculture, yet these industries employed less than 10 percent of total

employees in those towns. The discrepancies

between perceptions and realities in these communities suggest that their economies may

actually be more diverse than first perceived.

Text continues on page 84.
81

Table 19—Number and percentage of rural ICRB communities with a low actual

degree of dependence on wood products (based on 10 percent or more employment

in wood products), by perception of dependence (N=162)

Low employment dependence

Perceived dependence Number of cases Percentage

High 59 37

Low 103 63

Total 162 100

Table 20—Rural Columbia basin communities perceived to be independent of

timber but with a significant proportion of employment in wood products (N=3)

Perceived timber Wood products

Town dependencea employment

Percent

Malin, Oregon 3.29 0.66

Merrill, Oregon 3.38 .16

Payette, Idaho .83 .11

aPerceived dependence based on a 7-point scale that ranged from 1 (not dependent) to 7 (very dependent).

Table 21—Rural Columbia basin communities perceived to be

dependent on timber but with no significant proportion of

employment in wood products (N=64)

Perceived timber Employment in

State and town dependencea wood products

Percent

Idaho:

Bonners Ferry 6.50 10

Cascade 6.43 9

Clark Fork 5.57 10

Clayton 5.80 0

Craigmont 5.13 3

Culdesac 4.86 0

Donnelly 5.25 0

Driggs 4.20 3

Elk River 5.00 4

Grangeville 5.14 8

Harrison 5.43 2

Idaho City 5.33 0

Island Park 4.43 0

Kellogg 5.67 2

Kootenai 5.00 0

Lapwai 5.00 0

Leadore 6.13 0

Osburn 5.67 8
82

Table 21—Rural Columbia basin communities perceived to be

dependent on timber but with no significant proportion of

employment in wood products (N=64) (continued)

Perceived timber Employment in

State and town dependencea wood products

Percent

Rathdrum 5.00 7

Riggins 5.63 3

Salmon 6.00 7

Sandpoint 6.00 8

Smelterville 5.17 0

Stanley 5.29 0

Wallace 5.63 0

Weiser 4.17 0

Worley 4.33 0

Montana:

Alberton 4.75 9

Deer Lodge 5.63 7

Libby 6.50 9

Plains 5.40 0

Polson 5.33 4

Ronan 5.14 2

St. Ignatius 4.38 0

Stevensville 4.57 4

Whitefish 4.86 5

Oregon

Baker City 6.20 7

Burns 5.75 10

Chiloquin 6.33 2

Dayville 4.29 6

Enterprise 6.38 2

Heppner 6.63 7

Paisley 7.00 6

Redmond 5.00 8

Richland 4.50 2

Spray 5.86 6

Sumpter 4.67 8

Union 5.71 2

Unity 6.57 6

Washington:

Burbank 6.17 0

Chewelah 6.00 8

Cle Elum 4.57 3

Colville 6.00 8

Dayton 4.71 1

Entiat 4.17 0
83

Table 21—Rural Columbia basin communities perceived to be

dependent on timber but with no significant proportion of

employment in wood products (N=64) (continued)

Perceived timber Employment in

State and town dependencea wood products

Percent

Harrah 4.43 0

Newport 5.17 2

Okanogan 6.40 0

Pomeroy 4.63 0

Selah 4.29 0

Tonasket 5.00 0

Twisp 4.00 3

White Salmon 6.29 7

Winthrop 4.43 7

a Perceived dependence based on a 7-point scale that ranged from 1 (not dependent)

to 7 (very dependent).

Table 22—Number and percentage of rural Columbia basin communities

with a low actual degree of dependence on agriculture (based on 10 percent

or more employment in agriculture), by perception of dependence (N=60)

Low employment dependence

Perceived dependence Number of cases Percentage

High 35 58

Low 25 42

Total 60 100

Table 23—Number and percentage of rural Columbia basin communities

with a high actual degree of dependence on agriculture (based on 10 percent

or more employment in agriculture), by perception of dependence (N=138)

High employment dependence

Perceived dependence Number of cases Percentage

High 126 91

Low 12 9

Total 138 100
84

A Community Resilience Index

The concept of community resilience refers to a

town’s ability to manage change and adapt to it in

positive, constructive ways relative to other communities. A measure of this construct, termed the

“community resilience index” (CRI), was developed to indicate a town’s likely response to

change. The higher the index, the greater the

community’s resilience in comparison to that of

other communities, and the more vital, attractive,

and healthy the community was compared to

other communities in the region. The index was

based on community characteristics critical to a

town’s capacity to adapt to future changes, including strong civic leadership, a highly cohesive

social organization, local amenities and attractiveness, and a diversified or stable economy, all

reflecting or contributing to civic pride, excitement, and typically proactive responses to

changes facing a community.

Development and Validation

The CRI was developed in the course of the research as a relative indicator of the degree of a

community’s resilience, based on patterns in the

perceptions of residents of their community’s

characteristics and current conditions. Community resilience emerged as a function of a number

of major dimensions of the attributes and characteristics of communities. Specifically, a high degree of resilience reflects:

• Strong civic leadership. A high commitment

of individual leaders and groups to community and active involvement in creating or responding to change; a strong sense of local

control regardless of external events or

influences.

• Positive, proactive attitude toward change.

Either residents promote change and thus

vitality in community development, or if

change is occurring on its own, residents

respond positively and create a desirable

alternative future.

• Strong social cohesion. A high degree of consensus in values and goals for desired future;

working together to achieve goals.

• Strong economic structure. A high continuity

or endurance in a few major industries, or a

high degree of diversity in economic base, or

some combination that provides a stable economy in the community.

• High degree of physical amenities. The historic character of a community’s downtown;

the attractiveness of its downtown, surrounding scenery, and region.

• Larger population. The larger the population

in rural towns (all other things being equal),

the more developed the infrastructure is and

the greater the resilience.

The CRI was an additive function of scales based

on the dimensions above. The relative importance

placed on the various scales, applied in the index

through the weighting of these scales, was based

on the results of empirical analysis—specifically,

factor analysis—as detailed in the next section.

The most important construct was civic leadership, which was weighted by a factor of 4, relative to the least important factor, physical amenities and attractiveness; also important were social

organization (weighted by a factor of 3.3 over

physical amenities) and economic structure

(weighted by a factor of 2.7).

Significantly, the weightings applied to these

scales were mirrored by their overall importance

for a community’s response to change, as rated

by workshop participants in the 10 significant

change communities examined in depth with case

studies. As part of the case studies, retrospective

workshops were held at which residents involved

in their community when it underwent recent
85

major changes were asked to assess the importance of various community characteristics for

managing those changes. During the workshops,

participants were asked to list responses of their

community to the changes they had experienced.

These responses were then broadly categorized,

and participants were asked to indicate the three

most important for responding to change. Across

all 10 communities, the results of this process

were highly consistent with results of the factor

analysis: the economic diversity construct was

indicated to be more important by a magnitude of

2+ than the attractiveness construct, the cohesiveness construct was more important by a magnitude of 3+, and the leadership construct was

more important by a magnitude of about 4. This

cross-validation of the weightings used for the

CRI lent important additional support for the

validity of this index.

Developing an index for measuring resiliency—

The specifics in developing an index for measuring community resilience were as follows: An

initial analysis was conducted to assess the

validity of the particular dimensions theorized to

contribute to the community resilience concept.

All the workbook items for the key constructs

were analyzed through factor analysis (principal

components, varimax rotation). The first four

factors reflected the findings of earlier research:

Construct Variance

 Percent

Civic leadership 32.7

Economic structure 12.3

Social organization 7.4

Physical amenities 5.5

These factors, whose component items are detailed in table 24, became the basis for constructing the four scales comprising the CRI and that

roughly corresponded to several key constructs

(amenities, economic structure, social organization, and civic leadership) measured in the community workshops:

Starting construct

Scale from workshop

Civic leadership Community leadership

Social organization Community cohesiveness

Economic structure Economic diversity

Regional amenities Regional attractiveness

The four scales developed are displayed in

table 25.

Each scale was developed with a scale reliability

analysis, which ensured that its component items

achieved the maximum Cronbach’s alpha possible. (Items that did not contribute to the greatest

alpha value were dropped from the scales.) As

a final check, factor analysis (principal components, varimax rotation) was run against the complete set of workbook and workshop variables.

Again, the four scales emerged as the most important factors, although the percentage of variance explained by each scale was slightly

reduced:

Construct Variance

Percent

Civic leadership 26.9

Social organization 7.1

Economic structure 11.4

Physical amenities 5.2

Finally, from results of the full factor analysis,

loadings were examined to see if any variables

should be included that did not appear in the previous steps. An adjustment was made to the

economic structure scale by adding two items:

business attractiveness and economic diversity.

When the scale reliability analysis was performed

a second time, the two additional items (business

attractiveness and economic diversity) adjusted

Cronbach’s alpha upward slightly for the economic structure scale. Table 25 shows the final

scales and the items comprising them.

Mean values for the items in each scale were

averaged for each community, to produce a scale

score for each community. Then the scores were

weighted and totaled to provide a resilience score

for each community.
86

Table 24—Results of the factor analysis of workshop ratings in 198 Columbia basin study

communities

Factor Factor items Factor loadings

Government effectiveness

q10 2 Extent of competence of community government 0.86

q10 3 Level of trust in community government .82

q10 4 Extent to which government’s positions reflect those of community .85

q9 2a Contribution of elected officials to leadership .82

q9 3 How visionary community leaders are .76

q9 4 How flexible and creative community leaders are .78

q9 5 Consistency of opinions and values of community leaders with

your own .81

Eigenvalue = 13.75 Percent of variance = 26.4

Economic structure

q4 3 Extent that people shop inside the community .79

q4 4 Extent that people work inside the community .72

q5 2 Extent that the community’s economy is comprised of different

types of businesses .78

q8 8 Abundance of social activities in community .63

q1A 2 Attractiveness of community’s downtown area .53

q9 2b Contribution of business community to leadership in the community .70

q9 2c Contribution of government agency to community leadership .57

q9 2d Contribution of nongovernment organizations to community leadership .66

q9 2e Contribution of other active individuals to community leadership .52

Economic diversity index .66

Eigenvalue = 6.44 Percent of variance = 12.4

Social organization

q2 2 Extent to which people work together to get things done .70

q2 3 Extent to which people are supportive of one another .74

q2 4 Extent to which people are committed to the community .74

q2 5 Extent that people’s beliefs and values are similar .69

q2 6 Extent to which people identify with community .64

q8 10 Social problems .51

q1A 3 Attractiveness of community’s residential neighborhoods .53

Eigenvalue = 3.49 Percent of variance = 6.7

Regional amenities

q1B 2 Importance of scenery outside the community .43

q1B 4 Importance of nearby recreation areas to community’s character .46

q1B 5 Importance of wilderness, parks, etc., to community’s character .69

q1B 6 Importance of history, customs, and cultures to community’s character .58

q1B 7 Uniqueness of region in special qualities and travel attractions .80

q6 1e Community’s dependence on recreation and tourism .49

Eigenvalue = 2.81 Percent of variance = 5.4
87

Tables 25—Scales comprising the community resilience index, with component scale items,

alphas (index of scale reliability), and item-total scale correlations

Item-scale

Scale item correlation

Civic leadership scale (alpha = 0.95)

q9 4 How flexible and creative community leaders are 0.84

q9 5 Consistency of opinions and values of community leaders with

your own .83

q10 2 Extent of competence of community government .80

q10 3 Level of trust in community government .79

q10 4 Extent to which government’s positions reflect those of community .79

q9 3 How visionary community leaders are .79

Construct Government effectiveness .73

Construct Community leadership .68

q9 2a Contribution of elected officials to leadership .62

Social organization scale (alpha = 0.92)

Construct Community cohesion .80

q2 3 Extent to which people are supportive of one another .74

q2 4 Extent to which people are committed to the community .74

q2 2 Extent to which people work together to get things done .70

q2 6 Extent to which people identify with community .64

Economic structure scale (alpha = 0.90)

q5 2 Extent that the community’s economy is comprised of

different types of businesses .82

q4 3 Extent that people shop inside the community .77

Construct Community autonomy .76

q9 2b Contribution of business community to leadership in the community .68

q4 4 Extent that people work inside the community .66

q9 2d Contribution of nongovernment organizations to community

leadership .63

q9 2c Contribution of government agency to community leadership .57

Construct Attractiveness for business .57

Construct Economic diversity .57

Regional amenities scale (alpha = 0.82)

q1B 7 Uniqueness of region in special qualities and travel attractions .80

q1B 4 Importance of nearby recreation areas .73

q1B 2 Importance of scenery outside the community .67

Construct Attractiveness of region .59

q1B 3 Abundance of special places .51

q1B 5 Importance of wilderness and parks to community’s character .50
88

Resilience Classes

For ease in interpreting and displaying the results

of the community resilience index, the 198 study

communities were classified with a continuum

of levels of different community resiliences: low,

medium low, medium high, and high. The towns

were classified by their level of resilience, based

on the CRI score calculated for each; these resilience classes helped to clarify a community’s

comparative resilience and its implications. These

levels were relative ones with ranges based on

quartiles of the towns’ index scores, and each

class thus representing an equal proportion of

the communities under study (25 percent each).

The one-quarter of the towns receiving the lowest

CRI scores were labeled low, and so forth. The

range of index scores for each class were low,

240.1 to 348.61; medium low, 348.69 to 374.54;

medium high, 375.64 to 402.51; and high, 402.8

to 466.98.

Statistical analysis of the CRI revealed that,

although population size was significantly correlated to the resilience index (Pearson correlation coefficient of 0.325; p<0.05), the relation is a

moderate one. There is evidence, then, that the

smaller a community, the less resilient it tends to

be, as might be theorized, although this evidence

is not strong. The proposition that there may be

some critical mass in terms of a population

threshold that is related to community growth

and development needs to be further examined.

Unquestionably, several small communities were

rated as highly resilient, again affirming the

qualification underlying the assessment that all

communities are unique and all generalizations

about them have their exceptions. Further, the

results suggest that several large towns were less

healthy and resilient than some of the smaller

towns characterized by a greater degree of social

organization and civic leadership. For example,

several small “timber communities” in the Blue

Mountains region of northeast Oregon were

rated as highly resilient (e.g., Joseph, pop. 1165;

Wallowa, pop. 755; and Weston, pop. 640), while

other larger towns in the “scablands” of southeastern Washington and north-central Oregon

were rated as being less so (Umatilla, Oregon,

pop. 3155; Benton City, Washington, pop. 2090;

and Othello, Washington, pop. 4730). The CRI

scores for all 198 communities, along with the

component scale ratings and their resilience class,

are displayed in table 26; figure 17 shows geographic locations and the levels of resilience classifying the 198 communities in the basin.

The spatial mapping of resilience ratings indicates that communities in particular types of

geographic areas tended to be higher or lower in

resilience, depending on physical characteristics

of the surrounding area (fig. 17). Analysis of

community resilience by ecoregion suggests that

different communities in the same basic type of

ecosystem may differ in their resilience. In the

ecosystems of the Blue Mountains of northeast

Oregon, for example, several “timber communities” are rated as highly resilient (John Day,

Joseph, Enterprise), while others are judged to be

less so (Long Creek, Prairie City, Unity). Patterns

of a greater prevalence of lower resiliency are

apparent, however, for communities in the agricultural and ranching regions of the Snake River

Plain in southern Idaho and the Columbia Plateau

in north-central Idaho and eastern Washington

and Oregon. Results of the spatial mapping of

resilience scores are discussed further later in this

section.

Of the 198 surveyed communities, 10 communities were examined in depth (table 27). Of the 10

communities, 6 were among those rated as being

highly resilient, 2 were classified as moderately

high in resilience, and the remaining 2 communities were rated as moderately low. Interestingly,

one inference from the case studies is that communities experiencing major changes in the past

(for instance population growth or decline, timber

mill closures, or major business closures) are

more prepared for the future and better able to

adopt to change. Results of an analysis of change

in these communities since 1990 affirms this

conclusion, as do the results of the indepth case

studies (see Harris 1996 for additional details

concerning the results of the community case

studies). Table 27 summarizes the resiliency

index results for the case-study communities as

well as results from the retrospective workshops.

Text continues on page 95.
89

Table 26—Level of community resilience for 198 Columbia basin study communities, with levels

of scores on scales comprising the community resilience index

Levels of scores on scales comprising

community resilience indexa

Levels of scores

on community Social Economic Physical

State and town resilience indexa Civic leadership cohesion structure amenities

Idaho:

Ammon Med. high High High Low Med. low

Ashton High High Med. high Med. high High

Athol Low Low Low Low High

Bancroft Med. high Med. high Med. high Med. low Med. low

Bellevue Med. low Med. high Low Med. low Med. high

Blackfoot Med. high Med. low Low High Med. low

Bliss Med. low High Low Low Low

Bonners Ferry High High High High Low

Cascade High High Med. high High High

Challis Med. low Low Med. high High Med. high

Chubbuck Med. high High Med. high Med. high Med. low

Clark Fork Low Med. low Low Med. low Med. low

Clayton High Med. high High High Med. low

Craigmont Med. high Med. high Med. low Med. high Low

Culdesac Med. low High Med. high Low Low

Dalton Gardens Low Med. low High Low Med. low

Declo High High High Low High

Donnelly Med. low Med. high Med. high Med. low Med. low

Driggs Med. low Low Med. low Med. high Med. high

Dubois Med. high Med. low Med. low High Low

Elk River Low Low Low Low Med. low

Emmett High High Med. high High Med. high

Ferdinand Med. low Med. high Low Med. low Med. high

Filer Low Low Low Low Med. high

Firth Med. high High Med. low Med. low Med. low

Fort Hall Low Low Low Med. low Med. high

Fruitland Med. high High Med. high Med. high Low

Genesee Med. low Med. low Med. high Low Low

Grangeville Med. high Med. low Med. high High High

Hagerman Low Low Low Med. high High

Hailey High High High High High

Harrison Med. low Low Med. high Med. low Med. high

Hazelton Low Med. low Med. high Low Low

Homedale Med. low Med. low Med. low Med. low Low

Idaho City Low Low Low Med. low High

Irwin Med. low Med. high Med. low Low High

Island Park Med. low Low Med. low Med. low High

Kamiah High Med. high High High High

Kellogg High High High High High

Ketchum High Med. high High High High
90

Table 26—Level of community resilience for 198 Columbia basin study communities, with levels

of scores on scales comprising the community resilience index (continued)

Levels of scores on scales comprising

community resilience indexa

Levels of scores

on community Social Economic Physical

State and town resilience indexa Civic leadership cohesion structure amenities

Kooskia High High Med. high Med. high Med. high

Kootenai Med. high High Med. low Med. Low High

Lapwai Med. low Low Med. low Med. Low Med. low

Lava Hot Springs Low Low Low Low Low

Leadore Med. low Med. low High Med. low Low

McCammon Med. low High Med. low Low Med. low

Melba Med. low Med. low Med. high Med. low Med. low

Meridian High High High High High

Mountain Home High High Med. low High Med. low

Moyie Springs Med. low Med. high Low Low Med. high

Murtaugh Med. low Med. low High Low Med. high

New Meadows Med. low Low Med. low Med. high Med. high

Oldtown Med. high High Low Med. low Med. high

Onaway Low Med. low Med. low Low Med. low

Orofino Med. high Med. low Med. low High Med. high

Osburn High High High Med. high High

Parker Low Med. high Med. low Low Med. high

Parma Med. high Med. low Med. high Med. high Med. low

Payette Med. low Low Low Med. high Med. high

Pierce Med. low Low Low Med. high Med. high

Pilot Rock Med. low Med. high Med. low Med. low Med. low

Priest River High High Med. high High High

Rathdrum Med. low Med. high Low Med. low High

Richfield Med. high High High Med. low Low

Riggins High High High Med. high High

Ririe Med. low Med. low Med. high Med. low Med. high

Roberts Low Low Low Low Med. low

Salmon High Med. high High High High

Sandpoint Med. low Low Med. low High Med. high

Shoshone Low Low Low Med. low Low

Smelterville Med. high Med. high Med. low Med. high Med. low

Stanley High Med. high High High High

Sugar City Med. high High High Low Med. high

Swan Valley Low Low Low Med. low High

Tensed Low Low Low Low Low

Teton Med. high High High Low Med. high

Tetonia Low Low Low Low Med. low

Wallace High High High High High

Weippe High High Med. low Med. low High

Weiser Med. high Med. low Med. high Med. high Med. high
91

Table 26—Level of community resilience for 198 Columbia basin study communities, with levels

of scores on scales comprising the community resilience index (continued)

Levels of scores on scales comprising

community resilience indexa

Levels of scores

on community Social Economic Physical

State and town resilience indexa Civic leadership cohesion structure amenities

Winchester High High Med. high Med. low High

Worley Low Med. low Low Low Med. low

Montana:

Alberton Med. low Med. low Med. low Low Med. high

Columbia Falls Med. high Med. low Med. high High High

Darby Med. high Med. low High Med. high High

Deer Lodge High High Med. high High Med. high

Drummond Low Low Med. low Med. high Med. high

Eureka Med. high Med. low High Med. high High

Libby High Med. high High Med. high High

Plains Med. high Med. low Med. high High Med. high

Polson Med. high Med. low Low High High

Ronan High Med. low Med. high High High

St. Ignatius Med. low Med. low Med. low Med. low High

Stevensville Med. high Med. low High Med. high High

Superior Med. high Med. low Med. high Med. high Med. high

Whitefish Med. low Low Med. low Med. high High

Oregon:

Adams Low Med. low Low Low Low

Adrian Low Low Med. high Med. low Low

Antelope Low Low Low Low Low

Arlington Med. low Med. low Low Med. low Low

Baker City High High High High High

Burns Med. high Med. high Med. low Med. high Med. low

Chiloquin Low Low Low Med. low High

Dayville Low Low Med. high Low Low

Dufur High Med. high Med. high High Med. low

Echo Med. low Med. high Low Low Low

Enterprise Med. high Med. low Med. low High Med. high

Grass Valley Med. high Med. high High Med. low Med. low

Halfway High High High Med. high Med. high

Helix Med. low High High Low Low

Heppner High High High High Low

Hood River Med. high Med. low Med. low High High

Imbler Med. low Med. low High Med. low Med. low

Irrigon Low Low Low Low Low

John Day High Med. high Med. high Med. high Med. high

Jordan Valley Med. low Med. low High Low Low

Joseph High Med. high Med. high High High

Lakeview High High High High Low

Long Creek Low Med. high Low Low Low
92

Table 26—Level of community resilience for 198 Columbia basin study communities, with levels

of scores on scales comprising the community resilience index (continued)

Levels of scores on scales comprising

community resilience indexa

Levels of scores

on community Social Economic Physical

State and town resilience indexa Civic leadership cohesion structure amenities

Malin Med. high Med. high High Med. high Low

Maupin Med. high Med. high Med. low Med. high Med. high

Merrill Med. low Low Med. high Med. high Med. low

Mosier Low Low Med. low Low Med. low

Nyssa Med. low Med. high Med. low Med. low Med. low

Paisley Med. high Med. low High Med. high Med. low

Prairie City Med. low Low Med. high Med. high Med. high

Prineville Med. high Low Med. high High High

Redmond High High High High High

Richland Med. high Med. high High Med. high Med. high

Shaniko High High High Med. high High

Sisters Med. high Med. low Med. high Med. high High

Spray Low Low Med. low Low Low

Stanfield Low Low Low Low Med. low

Sumpter Med. low Med. high Med. high Low High

Umatilla Low Med. low Low Med. low Low

Union Med. low Med. high Low Med. low Low

Unity Med. low Med. low Med. high Med. low Med. low

Wallowa High High High Med. high High

Warm Springs Low Low Low Med. low Med. low

Weston High High High Med. high Med. low

Washington:

Airway Heights Low Low Low Med. low High

Almira Med. low Med. high Med. low Med. low Low

Benton City Low Med. high Med. low Low Low

Burbank Low Low Low Low Low

Cashmere High Med. high High High High

Chelan Med. high Med. low Med. low High Med. high

Chewelah High Med. high High High Med. low

Cle Elum Low Low Low Med. high Med. high

Colfax High High Med. high High Med. low

Colville Med. high Med. low Med. low High Med. low

Conconully Low Low Low Low Med. low

Creston Low Med. low Med. high Low Med. low

Dayton High Med. high High High High

Elmer City Med. low Med. high Med. low Low Med. low

Endicott Med. high High Med. low Med. low Low

Entiat Med. low Med. low Med. low Med. low Low

George Med. low Med. high High Low Low

Grand Coulee High Med. high Med. high Med. high High

Granger Low Med. low Low Med. low Med. low
93

Table 26—Level of community resilience for 198 Columbia basin study communities, with levels

of scores on scales comprising the community resilience index (continued)

Levels of scores on scales comprising

community resilience indexa

Levels of scores

on community Social Economic Physical

State and town resilience indexa Civic leadership cohesion structure amenities

Harrah Low Med. high Low Low Low

Harrington Low Med. low Med. low Med. low Low

Hartline High High High Med. low Med. high

Inchelium Low Med. low Med. low Med. low Med. low

Ione Low Low Med. low Med. low Med. high

Kettle Falls Low Low Low Med. high Med. low

Kittitas Low Low Low Low Med. high

Krupp Low High Med. high Low Low

Mabton Low Med. high Low Low Low

Mattawa High Med. high Med. high High Low

Medical Lake Med. high High Med. low Med. low Med. low

Mesa Med. low Med. high Low Med. low Low

Newport Med. high Med. low Med. low High Med. high

Odessa High Med. high High Med. high Med. low

Okanogan Low Low Med. high Med. high Low

Othello Med. low Med. low Low Med. high Med. low

Palouse Med. low Med. high Med. low Med. low Med. high

Pomeroy Med. high Med. low High Med. high Low

Prescott Low Med. low Low Low Low

Quincy High Med. high Med. low High Med. high

Republic Med. low Low Med. high High Med. low

Ritzville Med. high Med. low Med. high High Med. low

Rock Island Low Med. high Low Low Med. low

Rosalia Med. high High High Med. low Med. high

Selah High High Med. high Med. high Low

Sprague Low Med. low Low Low Low

St. John High High High High Low

Tekoa Low Low Med. low Med. high Med. high

Tieton Med. low High Low Low Med. low

Tonasket Med. high Med. high Med. high Med. high Med. low

Toppenish High High Med. low High Med. low

Twisp High Med. high High High Med. high

Wasco Low Low Low Low Med. low

Washtucna Med. high Med. high Med. high Med. high Low

White Salmon Low Low Low Med. low High

Wilson Creek Med. high Med. high High Med. low Low

Winthrop Med. high Med. low High Med. high Med. high

Wyoming:

Jackson Med. high Med. low Med. high High High

Thayne Med. low Med. high Med. low Med. low Med. high

a

 Levels of community resilience scores and scores on component scales based on quartiles across 198 communities.
94

Figure 17—Location and resilience levels of the 198 study communities in the Columbia basin, with ERU boundaries.
95

Table 27—Community resilience scores, ranks, and resilience classes for 10 case study

communities, Columbia basin

1992-94 Resilience

Towna population Scoreb Rankc Classd

Baker City, Oregon 9,585 457 4 High

Salmon, Idaho 3,093 438 9 High

Joseph, Oregon 1,165 433 14 High

Riggins, Idaho 460 429 17 High

Kellogg, Idaho 2,495 425 20 High

Mattawa, Washington 1,535 404 46 High

Pomeroy, Washington 1,460 399 57 Med. high

Burns, Oregon 2,870 396 64 Med. high

Whitefish, Montana 4,551 354 132 Med. low

Driggs, Idaho 980 351 139 Med. low

a

 Towns are listed in order of magnitude of resilience, from highest to lowest.

bResilience scores based on summation of social cohesion, civic leadership, economic structure, and physical amenities

scales of rural communities.

cResilience rankings based on 198 communities.

d

 Levels of community resilience scores based on quartiles across 198 communities.

The CRI (i.e., a community’s ability to manage

change and mitigate its impacts) was used to assess rural communities and their likely responses

to change, as well as the nature and possible extent of impacts. Tables 28 and 29 provide

examples of communities that differ in population size, the dominant industry characterizing

them, and resilience ratings. Examples include a

comparative listing of the sampled communities

and their dominant industries from highest to

lowest in terms of resilience scores. They affirm

that different kinds of communities, regardless of

different economic bases and sizes, can have resilience levels from low to high, depending on the

unique situation within each town.

Other Findings

An analysis of the resilience of communities with

different industry dominance classifications

found that a community’s economy was related

to its resilience (table 30). Larger proportions of

communities in which timber and outdoor recreation and tourism were perceived dominant were

rated as moderately high and high in resilience,

and ranching communities were rated as lower in

resilience.

Communities also were classified by actual industry dependence, where employment proportion greater than 10 percent for a given sector

meant that it was important to the community’s

economy. Table 31 shows that economically diverse communities were perceived to have

changed the most since 1990 and to have had the

highest resilience scores; farming and ranching

changed the least and had the lowest resilience

scores. Interestingly, timber towns also were perceived to be changing while resilient; the rapid

population growth of tourism and recreation

towns had also caused them to undergo significant change but resulted in lower resilience. An

important complementary finding was that communities that had changed the most since 1990

tended to be more resilient, which likely was due

to their greater experience in coping with change.

Analysis of the variance in communities’ ratings

of the amount they had changed since 1990 indicated that the most resilient towns were rated

with a mean of 4.7, and the least resilient towns

were rated with a mean of 3.5 (statistically significant, p<0.05).
96

Table 28—A sample of communities having different dominant industries, by extent of resilience

and population size, Columbia basin

Community Community

1992–94 Perceived resilience resilience

Town population dominant industry score class

Stanfield, Oregon 1,620 Farming 284 Low

Chiloquin, Oregon 700 Timber 300 Low

Spray, Oregon 155 Ranching 310 Low

Lava Hot Springs, Idaho 464 Travel and tourism 320 Low

Whitefish, Montana 4,551 Travel and tourism 354 Med. low

Republic, Washington 1,080 Timber 365 Med. low

Challis, Idaho 995 Ranching 373 Med. low

Almira, Washington 315 Farming 366 Med. low

Sisters, Oregon 765 Travel and tourism 385 Med. high

Paisley, Oregon 345 Timber 390 Med. high

Burns, Oregon 2,870 Ranching 396 Med. high

Pomeroy, Washington 1,460 Farming 399 Med. high

Halfway, Oregon 340 Ranching 415 High

Baker City, Oregon 9,585 Timber 457 High

St. John, Washington 508 Farming 459 High

Wallace, Idaho 994 Travel and tourism 467 High

Table 29—A sample of Columbia basin communities typed by industries perceived as dominant,

by community resilience class and ratings on community construct scales

Community construct scales

Community

Towns by type of resilience Economic Social Civic Preparedness

dominant industry class diversity cohesion leadership for future

Towns where ranching

is perceived as dominant:

Spray, Oreogn Low 3- 5- 3+ 2+

Challis, Idaho Med. low 3+ 6- 4- 2+

Burns, Oregon Med. high 4- 5- 5+ 3+

Halfway, Oregon High 4- 5+ 5+ 4+

Towns where farming

is perceived as dominant:

Stanfield, Oregon Low 4- 4- 3+ 3-

Almira, Washington Med. low 2+ 5- 4+ 4+

Pomeroy, Washington Med. high 3+ 6- 5- 4-

St. John, Washington High 5+ 6+ 6- 5-
97

Table 29—A sample of Columbia basin communities typed by industries perceived as dominant,

by community resilience class and ratings on community construct scales (continued)

Community construct scales

Community

Towns by type of resilience Economic Social Civic Preparedness

dominant industry class diversity cohesion leadership for future

Towns where travel

and tourism is

perceived as dominant:

Lava Hot Springs, Idaho Low 3+ 4- 4+ 4-

Whitefish, Montana Med. low 4+ 4+ 4+ 4+

Sisters, Oregon Med. high 5- 5- 4+ 4-

Wallace, Idaho High 4+ 6- 6- 5+

Towns where timber

is perceived as dominant:

Chiloquin, Oregon Low 3- 6- 3+ 4-

Republic, Washington Med. low 6- 6+ 4- 4-

Paisley, Oregon Med. high 3- 7- 3+ 2+

Baker City, Oregon High 6- 6- 6+ 5+

Table 30—Percentage of 198 Columbia basin study

communities by perceived industry dominance

classification and level of community resilience

Level of community

resilience

Towns by industry dominance

classification High Low

Percent

Timber (N=44) 62 38

Travel and tourism (N=34) 53 47

Farming (N=90) 48 52

Ranching (N=16) 37 63

Not resource dependent (N=11) 27 73
98

Several relations between the community resilience scores and community characteristics were

statistically significant (p<0.05). The higher a

community’s resilience rating, the more autonomous the town was, the larger its population, the

higher the towns’ perceived quality of life, and

the more likely its economy was perceived to be

diverse. Other factors related to resilience were

that, in the more resilient communities, governments were more likely to be rated as doing what

the public wants and that the town had developed

plans involving future change.

Of the factors related to community resiliency, respondents’ perceived community autonomy was

found to be the strongest predictor (Pearson correlation coefficient of 0.63; p<0.05). Autonomy

was defined in the self-assessment workbooks

and community workshops as the degree to which

a community is linked, economically, socially,

and physically, to neighboring communities and

to the region as a whole. Not surprisingly, given

that correlational analyses suggest that larger

towns are somewhat more autonomous than small

ones, the larger communities in the region were

generally more resilient. Analysis of variance

conducted on the 1992-94 population estimates

based on CRI class indicated a statistically significant difference (p<0.05) between the average

size of communities in the medium-low and low

resilience classes (764 and 1,131 people, respectively) and the medium-high and high resilience

classes (2,028 and 2,420 people, respectively).

The largest towns in the region also tended to have

more diversified economies, as did the more

resilient communities, which had a mean economic diversity rating of 1.4 in comparison with a

mean of -0.30 for the least resilient towns (statistically significant difference, p<0.05). In FEMAT

(1993), the community assessment suggested that

communities with high capacity to adapt tended

to be larger communities; as indicated above,

those with larger populations tended to have a

more developed, extensive infrastructure and

workforce to build on. It is noted in FEMAT

(1993) that communities less able to adapt tend to

have less developed infrastructure, less economic

diversity and active leadership, and fewer links to

centers of political and economic influence, with

greater dependence on nearby communities. In

contrast to these findings, our research documented that autonomous communities were more

resilient, with spatial factors (e.g., transportation

corridors, isolation) being relatively insignificant

in their adaptive capacity; in fact, a statistically

significant, positive, though weak, relation (0.19,

p<0.05) between distance from an interstate highway and community resilience was found. These

findings were consistent with the relation between resilience and industry dominance indicated by table 31. Interestingly, towns perceived

as timber dominant tended to be farther from an

interstate highway and relatively isolated, and

they also tended to be relatively resilient compared to towns in which other industries were

perceived to be dominant.

Table 31—Average community resilience scores of Columbia basin study communities and

ratings of perceived change since 1990, by perceived industry dominance classification (N=198)

Average community

Dominant industrial sector a resilience score Perceived change rating

Farming and ranching (N=81) 361 3.8

Timber (N=20) 380 4.3

Travel and tourism (N=49) 386 4.6

Economically diverse (N=36)b 392 4.6

a Only 3 communities reported mining as a dominant industry and were not included here.

b

 Communities with 2 or more dominant industries with similar employment totals.
99

No statistically significant relation (p<0.05) was

found between the CRI score and community

growth in the 1980s (0.09); the strength of the

relation between the CRI score and perceived

degree of change in community in the 1990s was

a moderate 0.37 (as indicated by a Pearson correlation coefficient). The former result clarifies

that resilience is not simply a matter of a community’s growth in population, and the latter suggests it may be coupled with change, but of a

more complex kind than simply population increases. Supporting these results were the findings on population changes in towns smaller than

10,000 where mills manufacturing wood or paper

products have closed since 1980: 52 percent of

these towns have suffered population declines,

although the populations of 48 percent have increased. In total, the change in population of

small towns in which mills have closed has been

a net increase of 8 percent since 1980.

Rural Economies and Community

Resilience

The match between the perceived and actual

importance of particular industries in rural

communities—The results described earlier for

matching workshop participants’ perceptions

with the actual economic profiles for those communities suggested some inconsistency between

the two. In spite of this, however, a strong positive relation was found between the scores calculated to measure perceptions of the economic

structure of communities and the economic diversity index based on actual employment figures: a

Pearson correlation coefficient of 0.62 (p<0.05)

was found across the 198 study communities.

This finding suggests that most participants in the

community workshops were reasonably accurate

in their assessment of the relative extent to which

their towns’ economies were diverse. This finding lends support to the suggestion made earlier

that the measure of perceived dependence on

resource-related industries focused the attention

of workshop participants on their perceptions of

the absolute (noncomparative) importance of

these industries. In contrast, percentages of total

employment and the economic diversity data reflected the relative importance of these industries

vis a vis all the other manufacturing, service, and

industrial sectors. Nonetheless, the lack of an

even stronger relation between the mean scores

on the economic structure scale and the scores on

the economic diversity index could be due to inaccurate perceptions by workshop participants in

those towns perceived to be dependent on industries such as farming and timber harveting and

milling.

The past prominence of these industries or their

high visibility in a community may be the basis

for residents’ assumptions that these industries

are more important than they actually are. To test

this hypothesis, Pearson correlation coefficients

between perceived diversity and actual diversity

were calculated for those communities found

to have high agriculture and timber employment

totals. These coefficients were only 0.32 and

0.44; and although statistically significant

(p<0.05), they indicated only a weak to moderate strength of relation. Further, when the 48 agricultural and 46 timber communities were omitted

from the analyses owing to overestimates or

underestimates of workshop participants’ perceived dependence on the respective industries,

the resulting correlation coefficients increased to

0.65 (p>0.05) for agriculture communities and

0.70 (p>0.05) for timber communities. Thus, this

analysis indicated possible merit in the above

hypothesis.

Actual employment and community

resilience—Proportions of total employment in

natural resource industries such as agriculture,

manufacturing of wood and paper products, travel

and tourism, mining, Federal and state government, and others did not differ significantly by

degree of community resilience, with a few exceptions (table 32). In general, towns rated higher

in resilience tended to be more dependent, in

terms of total employment, on a mix of service

sectors (e.g., medical and social services, retail

trade, and travel and tourism) and less dependent

on some basic industries, such as agriculture and

mining. This relation did not exist, however, for

the wood products manufacturing sector: consistent with previous findings, communities with
100Table 32—Percentages of total employment in Columbia basin communities in industrial sectors, by community resilience class (N=198)

Level of community Wood and paper State and local Federalresilience (N) Agriculture products Travel and tourism government GovernmentHigh (47) 14.9 7.4 14.5 15.2 5.4Medium High (48) 22.7 6.0 13.8 13.5 4.5Medium Low (51) 20.2 6.6 12.6 17.8 4.7Low (47) 27.5 4.5 13.2 18.2 6.7

Mean 21.3 6.2 13.5 16.2 5.3

Mining and Eating and Medical and social

minerals Retail tradea drinkinga services Other industries

High (47) 3.6 13.8 5.1 8.2 11.9Medium High (48) 2.8 12.8 4.9 5.5 13.5Medium Low (51) 3.3 10.1 6.9 4.6 13.2Low (47) 1.3 10.4 5.7 3.2 9.3

Mean 2.7 11.7 5.7 5.4 12.0

a Nontourism related.
101

higher resilience ratings also had a higher percentage of wood products manufacturing employment. Conversely, towns with lower degrees of

resilience tended to be more dependent on state,

local, and Federal governments for employment,

as well as on the mining and mineral industry.

Less resilient towns typically had less employment in the travel and tourism industry, as well

as in the wood products manufacturing industry,

compared to communities that were more

resilient.

The picture provided by these findings is not as

clear and definitive as that provided by people’s

perceptions, which suggest that the more resilient

towns are the ones perceived as timber dominant

and less resilient ones are those in which farming,

especially ranching, is dominant. Perhaps it is the

case that, regardless of the actual employment

structure of the communities, those towns perceiving themselves as timber towns have been

undergoing change and increasing in resilience,

and the towns perceived as agriculture dominant

have not. The situation for timber-dependent

communities is examined in greater depth in the

next section.

Other Findings on Rural

Communities

Timber-Dependent Communities

Of the 198 rural communities sampled for the

community assessment in the basin, 34 were

identified by Forest Service policy analysts as

timber dependent (USDA Forest Service 1996).

Workshop participants from 20 of these perceived

their communities as timber dominant, 3 as diverse and extractive, 7 as nonresource dependent,

2 as travel and tourism, and 1 each as agriculture

and government dominant. Analysis of direct employment in the wood products manufacturing

industry indicated that only 40 percent of the Forest Service-designated towns were actually dependent on wood products manufacturing for a

significant amount of their employment (employment in the wood products manufacturing industry greater than 10 percent of total community

employment). Equally significant, 40 percent of

the 198 communities in the study region that had

more than 10 percent of their employment in the

wood products industry were not designated by

the Forest Service as timber dependent. Therefore, an important use of these data is to clarify

the situation for communities designated by the

Forest Service as not timber dependent but

having a large percentage of their total employment in the wood products industry.

In general, communities designated as timber

dependent by the Forest Service were rated

higher in resilience than other, nondesignated

communities, although the difference from other

communities was not statistically significant on

the economic structure scale discussed as part of

the CRI. Higher proportions of timber-dependent

communities were moderately small (1,500 to

3,000 people) to moderately large (3,000 to 5,000

people) in size. But these communities generally

did not differ significantly from other communities in their economic structure, or in average

population size.

Communities in the study region designated by

the Forest Service as timber dependent differed

from other communities in the proportions of

employment across several key industries, such

as agriculture, wood products and Federal Government. They did not differ from towns perceived by their residents to be timber dependent

(table 33). Interestingly, these Forest Servicedesignated communities were rated as actually

being more economically diverse (in their average score on the economic diversity index) than

towns perceived to be timber dependent. In addition, the average index scores for both kinds of

towns were much greater than the mean score for

other kinds of communities. Given these comparable diversity ratings, mean levels of employment

in the travel and tourism sector were about as

high in both the Forest Service-designated timber

communities and towns perceived to be timber

dependent as they were in other kinds of

communities.
102Table 33—Percentage of total employment in Columbia basin communities, by industrial sectors and type of community with mean economic diversity scores

Economic

diversity Travel and Federal Medical and

Community type index Agriculture Wood products tourism Government social services

Score – – – – – – – – – – – – – – – – – – – – Percent – – – – – – – – – – – – – – – – – – – – –

Designated by USDA

Forest Service, as“timber dependent” 1.20 14.0 16.6 12.2 7.6 6.5

Perceived timber dependent,

but nondesignated 1.01 12.6 16.7 13.0 8.5 7.1

Other communities .34 21.0 6.4 14.0 5.5 5.4

Not surprisingly, communities across the basin

were found to have significant differences in their

characteristics associated with their biophysical

geography and their location relative to specific

ERUs (table 34). In examining communities in

each of the 13 ERUs by level of resilience, it was

found that a high proportion of communities in

the Southern Cascades, the Upper Clark Fork,

and the Central Idaho Mountains ERUs had a

high level of resilience (fig. 17). Many of the

communities in these ERUs are in mountainous

regions possessing a high degree of physical

amenities, and these communities are responding

constructively and proactively to a changing economic structure and growing population.

Other ERUs having communities with high levels

of resilience include the Blue Mountains of

northeastern Oregon, the Lower Clark Fork, and

the Upper Snake River ERUs. These ERUs also

are well endowed with amenity resources and increasingly diversified economies. In contrast, the

Columbia Plateau, Snake River Headwaters, and

the Owyhee Uplands ERUs generally had lower

resilience ratings and were dominated by farming

and ranching industries. These ERUs are characterized by high plains deserts and “scablands”

that are perceived as comparatively lacking in

physical amenities.

Communities in these 13 ERUs show consistent

patterns in characteristics and conditions for perceived and empirical data that are most easily displayed by combining the units into four major

regions: a Coastal Mountains ERU (comprised of

the Northern and Southern Cascades ERUs), a

High Plains Desert and Prairie ERU (Columbia

Plateau and Owyhee Uplands ERUs), a Northern

Rocky Mountains ERU (the Northern Glaciated

Mountains, the Blue Mountains, the Central

Idaho Mountains, the Lower Clark Fork, and the

Snake Headwaters ERUs), and the Upper Snake

ERU. The Upper Klamath and Northern Great

Basin ERUs, which accounted for only 2.5 percent of all communities, were not included in the

analysis.
103

Table 34—Percentage of Columbia basin study communities in ecological reporting units, with

majority proportions in high or low community resilience classes

Percentage of all Majority proportions of communities

Ecological reporting unit communities (N=387)a by resilience class

Percent

Columbia Plateau 32 60 – low

Northern Glaciated Mountains 15 NA

Owyhee Uplands 9 56 – low

Blue Mountains 9 60 – high

Central Idaho Mountains 8 60 – high

Upper Snake 6 58 – high

Snake Headwaters 6 63 – low

Lower Clark Fork 5 60 – high

Northern Cascades 4 NA

Southern Cascades 3 67 – high

Upper Clark Fork 2 75 – high

Upper Klamath 2 NA

Northern Great Basin 1 NA

NA = not applicable: a large majority of communities in the ecological reporting unit was classified as neither low nor high in

resilience.

a Number of rural communities with less than 10,000 population, based on 1992-94 estimates (Oregon Center for Population

Research and Census 1995 ).

An analysis of the trends in responses on perceived community characteristics revealed the

following differences in absolute scale ratings

or in the number of communities across different

ERUs:

• Statistically significant increases in community attractiveness from towns of the High

Plains Desert and Prairie and the Upper Snake

ERUs to the Northern Rocky Mountain and

Coastal Mountain ERUs.

• Statistically significant increases in community autonomy from the Upper Snake and

High Plains Desert and Prairie ERUs to the

Coastal Mountain and Northern Rocky

Mountain ERUs.

• Increases in regional attractiveness, uniqueness of community, and community resilience

from the High Plains Desert and Prairie and

the Upper Snake ERUs to the Coastal Mountain and Northern Rocky Mountain ERUs.

• Increases from the Upper Snake and the High

Plains Desert and Prairie ERUs to the Northern Rocky Mountain and Coastal Mountain

ERUs in perceived levels of economic diversity, dependence on travel and tourism and

timber, degree of perceived change in communities between 1990 and 1995, and population migration patterns as indicated by the

percentage of households living in a different

house but in the same state (perhaps indicating a migration within the state to more residentially attractive areas).

• Increases from the Upper Snake and High

Plains Desert and Prairie ERUs to the

Coastal Mountain and Northern Rocky

Mountain ERUs in distance in miles to an

interstate highway.

• Increases from the Coastal Mountain and

Northern Rocky Mountain ERUs to the High

Plains Desert and Prairie and the Upper

Snake ERUs in percentage of households

with farm income and dependence on

ranching.
104

• Increases from the Coastal Mountain and the

Northern Rocky Mountain ERUs to the Upper

Snake and High Plains Desert and Prairie

ERUs in traffic congestion.

• Increases from the Northern Rocky Mountain

and Coastal Mountain ERUs to the Upper

Snake and High Plains Desert and Prairie

ERUs in percentage of people employed in

agriculture, forestry, fisheries.

• No statistical differences were found in perceived characteristics such as social cohesiveness, services, business attractiveness, dependence on natural resources, government

and civic leadership, preparedness for the

future, or quality of life.

Is Bigger Better—Or Is Being More

Autonomous?

Analyses of both the documented data obtained

from town officials and perceptions gathered

from the community self-assessment workshops

indicated that population size is among the variables that are related to a community’s current

condition and likely responses to change. As previously indicated, however, even though population size is statistically related to a community’s

level of resilience, this relation is not a strong

one. Therefore, any strategic decision by a community to grow in size should not necessarily be

made to increase its resilience, which depends on

a variety of interrelated factors.

Nonetheless, as the variety of statistically significant findings on a community’s population size

suggests, the size of a community is statistically

related to a number of different community characteristics and conditions.

Documented community data:

• The larger the community, the more churches

(r = 0.65) and civic groups (r = 0.28) it has,

and the more economically diverse its economy, as indicated by the relation between a

community’s population size and its score on

the economic diversity index (r = 0.45).

• Statistically significant but weak correlations

were found for several characteristics: The

larger the community, the more successful it

was in obtaining grant funding for community

improvement (r = 0.28), the more it grew in

the 1980s (r = 0.24), the lower the proportion

of households receiving social security income (r = -0.22), and the higher the cost of

housing (r = 0.15).

• The economies of rural communities, in terms

of proportions of employment in various sectors, were not significantly different based on

their population size: the only statistically

significant correlations were those between

population size and proportions of employment in agriculture and medical services. The

strongest of these correlations were found

for communities with high proportions of employment in farm and ranching, with those

towns showing some tendency to be smaller

in population, but even that correlation was

relatively weak (r = -0.18).

• Characteristics for which population size

made no statistically significant difference

included geographic isolation and the proportion of households receiving public assistance income or retirement income.

Community perception data:

• The larger the community, the lower its

rating of traffic congestion (r = -0.53), and

the greater its attractiveness for business

(r = 0.49), social and economic changes since

1990 (r = 0.42), and autonomy (r = 0.35).

• Statistically significant but weak correlations

were found for several perceived characteristics of rural communities: the larger the community, the higher the rating of its overall

resilience (r = 0.29), its preparedness for

the future (r = 0.26), the adequacy of its services (r = 0. 25), its attractiveness (r = 0.22),

and how interesting it is as a community

(r = 0.22).

• Characteristics for which population size

made no statistically significant difference

included the extent of a community’s social

cohesion, friendliness, leadership, quality of

life, regional attractiveness, and how safe the

community is perceived to be.
105

In general, then, rural communities perceived

to have greater suitability and capability for economic development tended to be larger ones; that

is, larger towns were somewhat more likely to

have a more developed infrastructure, increased

availability of services, especially education, and

greater preparedness for the future. A rural

town’s population size is related to its current

conditions and likely response to change: statistical analyses indicated that larger towns tend to be

more economically diverse, autonomous, and attractive for business. The conclusion here is consistent with the basic premise of the plethora of

community development handbooks and workshops provided in the 1970s and 1980s: if members of a small rural community want to “develop” their town, they should work to attract

new industries and expand the economic base,

which would result in population increase.

Significantly, the findings of both the selfassessment study and the community economic

profiles suggest that the impacts of this improvement extend beyond the economic aspects of

community development, whose significance has

long been recognized and is reaffirmed here, to

its social elements as well. Larger rural communities tend to represent a more advanced stage of

social and civic development than small ones.

The importance for community vitality of active

social groups and civic organizations, increased

educational infrastructure, availability of services, success in obtaining development grants,

and greater preparedness for the future—all of

which increase with a town’s size—reflects the

benefits that towns with a critical mass of social

capital and infrastructure are more likely to realize. An interesting question for future research,

however, is at what size and level of community

development the net benefits of growth are maximized, beyond which the social costs of further

growth begin to exceed the benefits.

A factor as important as a community’s size for

its future development is its autonomy, which has

been defined in terms of the extent to which communities are linked—economically, socially, and

politically—to neighboring communities and the

region as a whole. (The following discussion is

excerpted from the work of Harris and Russell,4

which focused on “timber communities,” those

having some employment in the wood products

sector.)

The more autonomous a community is, the less it

is linked to the “outside.” Implicitly, then, use of

the term “autonomy” underscores a community’s

independence, self-reliance and ability to function as a cohesive and functional unit. Community autonomy is a complex, multidimensional

construct, as reflected in the duality of its definition. The autonomy of a town can reflect a state

of isolation and remoteness, which can be viewed

negatively, as well as a condition of independence and self-reliance that are positive

attributes—and this duality can suggest ambiguous implications for change in rural communities.

Many researchers (e.g., Lackey et al. 1987,

Warren 1971), nonetheless, emphasize the positive connotation of autonomy as a community

attribute characteristic of strong, healthy communities, and our research results support this assessment. The autonomy construct was most

strongly correlated with residents’ perceptions of

the availability of services in their communities

(r = 0.52), their communities’ overall attractiveness (r = 0.52), their attractiveness for business

(r = 0.40), and the social cohesion and quality

of life in their communities (r = 0.40, 0.32). The

autonomy construct also was strongly correlated

with documented data, such as economic diversity (r = 0.45) and, as noted above, population

size (r = 0.35). It is not significantly related to

employment in most key industries, such as tourism or wood products, and it is negatively related,

although fairly weakly, to agriculture (r = -0.28).

There is some tendency, then, for more agriculturally dominated towns to be less autonomous, but even this is not the case for all such

towns; autonomy is not as strongly tied to any

particular kind of economy as it is to economies

that have a more diverse mix of sectors on which

they are dependent.

4

 Russell, K.; Harris, C.C. [In press]. Economic, social and

political dimensions of community autonomy in timber towns.

Society and Natural Resources.
106

These results suggest that autonomous communities, whether isolated or not, are those that have

developed the service and economic infrastructure to provide residents with goods, services, and

jobs. Our results also indicate that community residents perceive that the most autonomous towns

are those that not only have a diversity of services

and economy but also have a social resiliency

that enables them to manage the changes they experience. This result challenges the notion that

autonomous communities are either higher order

communities with an ability to provide for citizens internally or isolated towns; rather, towns

that are autonomous are likely to be isolated,

have a strong sense of community, and have

healthy, diversified economies.

Quality of Life in Small

Communities

Most communities in the region, whether large or

small, rated themselves as having a high level of

quality of life. As previously shown, fully 80 percent of the communities rated their quality of life

as very high, and another 19 percent indicated it

was moderately high; only one of the 198 communities indicated that their quality of life was

low (see table 6). Part of a community’s quality

of life is due to the presence of scenic and recreational amenities in the surrounding area that

are related to the natural resources. The results

of regression analysis, as shown below, confirm

that a town’s quality of life is partially dependent

on the attractiveness of the region where it is located. Even more important, however, are social

factors, such as how interesting a community is,

the extent a community is plagued with social

problems, how safe its residents feel, and the

social cohesiveness. Significantly, the extent of

a town’s quality of life is strongly related to its

resilience (r = 0.50):

Multiple R 0.7727 Analysis of variance

R-square 0.5970 Df Sum of squares Mean square

Adjusted R-square 0.5863 Regression 5 35.9624 7.1925

Standard error 0.3593 Residual 188 2.2737 0.1291

F = 55.7058 Significant F = 0.000

Standard

Variable B error Beta t Significant t

(Constant) 0.7679 0.4057 1.893 0.0599

How interesting the

community is .2815 .0417 0.3651 6.751 .0000

Social problems .2058 .0357 .3339 5.769 .0000

Community safety .2216 .0619 .2131 3.578 .0004

Social cohesion .1004 .0396 .1400 2.533 .0121

Regional attractiveness .1420 .0551 .1279 2.579 .0107
107

Population changes—U.S. census data for the

communities in the study area indicate that, on

average, the populations of these towns increased

by 7 percent between 1980 and the early 1990s.

(The most recent population estimates available

from the states at the time of this analysis were

from 1992 or 1994, depending on the state; see

citations in next paragraph). Population-change

proportions range from a minimum of a 60 percent decline to a maximum increase of 413 percent, but the distribution of these proportions is

skewed toward population growth: 60 percent of

all towns in the region increased in population

between 1980 and 1992-94, with the bottom 20

percent of all towns in the region decreasing in

population by -9.6 percent and the top 20 percent

increasing by over two times as much, or 19.9

percent.

In the 1990s, this trend accelerated. The average

populations of rural communities in all five states

in the Columbia River basin are estimated to

have increased since 1990, although in different

amounts: these increases ranged from an average

of about 3 percent in communities in Montana

(Montana Department of Commerce 1995) and

4 percent in Idaho communities (Idaho Division

of Financial Management 1995), to a high of an

average 12 percent in communities in Wyoming

(Wyoming Department of Administration and

Information 1995). Likewise, as table 35 shows,

the vast majority (86 percent) of all towns in the

region have been growing since 1990—a significant change from the ingrowth in the 1980s.

Recent estimates also indicate that, statewide, the

population growth between 1988 and 1994 has

been 12 percent in Idaho, 7 percent in Montana,

8 percent in Oregon, and 9 percent in Washington

(Idaho Division of Financial Management 1995,

Montana Department of Commerce 1995, Oregon

Center for Population Research and Census

1995a, Washington Office of Financial Management 1994, Wyoming Department of Administration and Information 1995). In contrast, the U.S.

population grew only 4 percent during this same

period. Significantly, even for as short a period as

Also significant is the finding that a town’s size

is unrelated to its quality of life, which begs the

question of the goal or desired future for towns

seeking to become more viable, healthy, vital, and

thus resilient in the face of change. But it also

suggests that, just because a town grows, this

change does not mean that a community’s quality

of life is necessarily compromised.

Change in Small Rural

Communities

Workshop participants in a large majority of communities (70 percent) in the basin reported that

they had experienced a moderate to high degree

of change since 1990. Similarly, the indepth case

study of Chelan County, Washington, residents

found that a majority (68 percent) perceived that

their communities had experienced a moderate

to high degree of change in the 1980s. When

asked about the kinds of change that had occurred, 68 percent of Chelan County residents

reported growth and population increases as one

of the major changes. Other important changes

included the conversion of agricultural lands to

residential and commercial development (32 percent), increase in retail stores (26 percent), increased traffic (23 percent), and increased crime

(22 percent). More than half of the residents

(55 percent) were somewhat to extremely concerned about the overall changes taking place in

their community (for instance, population growth

and economic structure).

Table 35—Percentage of Columbia basin

communities with increasing populations

Communities with

State increasing populations

Percent

Idaho 85

Montana 73

Washington 86

Wyoming 100

Average, all communities 86

Sources: State departments of administration, finance, and

information.
108

1990 to 1994, the present study indicates that

residents of larger towns are more likely to report

that their town has changed (Pearson correlation

coefficient of 0.44; p<0.05).

In addition to population growth, a multiplicity of

changes and influences are affecting the character

of rural communities in the study area. They include not only changing natural resource supplies

and resource-management policies, but also social changes due to aging populations and the inmigration of commuters, welfare recipients, retirees, and new ethnic groups. In many instances,

these new types of residents are changing the social makeup and character of communities, along

with their traditions, customs, and cultures.

Economic changes—The assessment of the 145

communities identified as having changed significantly in recent years affirmed that the economies of the region’s small communities have

changed throughout their history and continue to

change (see previous section, “Surveys of Residents of Chelan County and Significant Change

Communities”). Significantly, our assessments

of community resilience and significant change

communities made clear that change and resilience to it are found across the various economic

types of communities. Government policies on

public lands clearly have affected the economies

of some rural communities in significant ways,

thereby influencing their resilience. Other influences, including the decisions and actions of

small business owners and large corporations,

and the methods with which the public sector has

subsidized these industries (e.g., crop payment

programs, logging road construction, biddingpreference systems for small sawmills), also have

long affected the development of small rural communities in the region.

For towns with wood products mills, mining and

minerals processing, and the like, concerns of residents and agency resource managers have traditionally focused on “community stability” in

terms of the economic stability assured by a

steady, dependable flow of resources from public

lands and the resulting stable employment base.

Some congressional acts (e.g., Organic Administration Act of 1897, Multiple Use-Sustained

Yield Act of 1960, Forest and Rangeland Renewable Resources Planning Act of 1974, and National Forest Management Act of 1976) reflect

this concern and, as a Forest Service policy memorandum notes, they mandate that the Forest Service provide a continuous supply of outputs for

the nation (USDA Forest Service 1977). This

document, while noting that “none of the language [in these acts] specifically addresses ‘community stability,’” also recognizes that “the basic

charge [of the agency] to provide the goods and

services is well ingrained” (USDA Forest Service

1977:1).

Recent changes in communities have resulted

from a variety of broader economic influences,

such as global economic forces, economic diversification, plant modernization, and industrial

downsizing (e.g., laying off company loggers and

hiring independents to reduce the costs of benefit payments). Significantly, growth in employment in the Pacific Northwest has far exceeded

the national rate: employment increased nationwide 7.7 percent between 1988 and 1994, but it

increased 27.7 percent in Idaho in that same period, and around 17 percent in the other states in

the region (Idaho Division of Financial Management 1995, Montana Department of Commerce

1995, Oregon Center for Population Research and

Census 1995a, Washington Office of Financial

Management 1994, Wyoming Department of

Administration and Information 1995).

As discussed in “Context for the Assessment,”

key characteristics of communities include economic ones, such as the levels of economic

development of a town, its economic diversity

(Belzer and Kroll 1986, Freudenburg 1992,

Gramling and Freudenburg 1992, Johnson 1993),

and its resource dependence (Castle 1991,

Machlis and Force 1988, Power 1994). Although

the literature has often asserted that resourceextraction industries are essential industries for
109

rural economic survival, some researchers (e.g.,

Power 1994; Rasker 1993, 1995) note that traditional extractive industries are decreasing and

service industries increasing in importance across

the Pacific Northwest; for example, The Wilderness Society (Rasker 1995), examines U.S. Bureau of the Census statistics on income and employment in the Columbia River basin since the

late 1960s. These statistics clearly document that,

across the region as a whole, traditional, extractive economic base industries (e.g., agriculture,

forestry, and mining) have remained at fixed

levels over the last two decades, while the major

increases in the region’s economy have occurred

in service sectors.

The analysis conducted by The Wilderness Society reflects only part of the current situation in

the region, however. By focusing on the region as

a whole, their analysis overlooks the significant

differences between the economic base of small

rural communities vs. that of large cities. When

our research assessed the importance of industrial

sectors in rural communities in 1995 as proportions of the total employment, a different picture

of the region’s economy emerges: harvesting and

processing (e.g., agriculture, timber) were among

the most important employers in small rural

towns across the region. So too are newer industries, such as travel and tourism, with retail trade

and food and beverage (mainstays of tourism as

well as important for meeting local needs) and

the Federal and local governments becoming increasingly important. Yet analyses like Power’s

(1994), which ostensibly focus on the region’s

rural communities, present few data representing

that scale.

In some cases, the total dependence of a town on

a particular industry may be less important than

the proportion of that industry controlled by one

entity, such as a government agency’s control of

timber supply or a company’s control of processing plants. Finally, economic sectors are often

complementary rather than substitutable or competitors for one another; consequently, economic

diversification began occurring long before public policy started restricting commodity supplies

on public lands and companies in extractive industries began plant improvements and employee

layoffs to increase company competitiveness. A

key point here is that the economies of these

communities are more complex and unique than

simplistic, ideologically driven analyses may

suggest.

Significant change communities—Another

component of the research focused on assessing

and analyzing the characteristics and experiences

of 145 towns in the region identified as significant change communities. These communities

were identified as undergoing major economic or

social change, or both, by state economic development officials, agricultural extension experts,

Forest Service forest planners or economic development coordinators, and population estimates

indicating changes of greater than 20 percent

since 1980, from the U.S. Bureau of the Census

(1995a, 1995b). Data collection focused on identifying the types of changes occurring in rural

communities, community responses to these

changes, and the effects or characteristics of all

these factors in terms of community conditions,

activities, and lifestyles. Of the 145 identified

communities having experienced significant

change, 80 communities (55 percent) were surveyed. The following is a brief summary of those

findings. (For more detailed results of significant

change communities, see Harris et al. 1996.)

• Of the 80 significant change communities

surveyed, 3 percent were perceived as nonresource dependent, 13 percent as having

predominately ranching economies, 20 percent as farming based, 29 percent as primarily

travel and tourism based, and 35 percent as

predominately timber based.

• Of the surveyed communities, 35 percent had

populations that were growing, and 39 percent

had populations that had decreased.

• In addition, 36 percent of the surveyed communities were not responding to change, and

the other 64 percent were much more proactive in responding to change.
110

Results from the surveyed communities suggest

that the impacts of population growth, and the

social and land-use changes that have come about

as a result of that growth, were as critical or more

so than any recent changes in resource management. Additionally, a community’s degree of

resilience was more related to how it responded

to changes than to its economic structure. More

resilient communities were more likely to take

proactive actions to respond to changes affecting

their quality of life. Of the sample of 80 communities, 34 percent were rated as being highly resilient, and 26 percent and 21 percent were rated

as being moderately high to moderately low in

resilience, respectively. Of those having a high

resilience rating, 44 percent were perceived as

predominantly timber dependent, 30 percent as

travel and tourism, 22 percent as agricultural, and

4 percent as not resource dependent. Conversely,

of the communities having a low level of resilience, 52 percent were perceived as predominantly agricultural based and only 20 percent as

timber dependent.

Among communities having undergone significant changes, higher proportions of communities

in the higher resilience classes were perceived as

timber dependent, and they reported activities

suggesting that they were proactive in responding

to change. The number of respondents perceiving

their communities as travel and tourism dependent were represented equally across the four resilience classes, as were trends in population

changes, thereby reaffirming that these characteristics do not fully predict resiliency.
111

The structures and functions of small rural communities in the region are more complex than

some analyses would suggest, especially those

conducted at a regional level or that used county

data to assess local conditions. The extent to

which communities depend on different industries differs, both in the perceptions of active and

involved residents and in actual employment

numbers. Generalizations about individual communities or industries must be made with care.

To develop constructive strategies for managing

changes, it is important to assess the current

characteristics and conditions of communities in

the region, changes affecting them, and the major

factors influencing responses to change. If communities are to develop in a coherent, managed,

and well-planned way, residents must deal with

the realities and potentialities of their towns,

including their social, economic and political

advantages and disadvantages, attractions and

drawbacks. Importantly, although a community’s

resources, including its amenities and attractiveness, can be a factor influencing development, a

decisive, major determinant of a community’s

resilience clearly is its residents, in particular, the

willingness of residents to take leadership roles,

organize, and realize their community’s potential.

Community residents are a central defining element in creating the future of rural communities.

Some major conclusions of the research are:

• Small, rural communities in the interior and

upper Columbia River basin have always been

changing and will continue to change. The

idea of community stability is a myth belying

various influences: the volatility of markets

for timber, mining, and other traditional extractive industries; the actions of private

companies in modernizing and closing plants

and periodically laying off or terminating

workers; the decreasing supply of timber from

National Forests; and the rapidly increasing

in-migration of new workers and residents

(e.g., retirees and new ethnic groups).

• Although closures of mills, mines, and other

resource-processing plants have had a significant impact on many communities, past

closures have had little social and economic

effects on other communities. Many mills, for

example, have been closed, sold, reopened,

and closed again as a result of a series of

changes over past decades not necessarily

related to public land management decisions.

Community growth, as indicated by population increase, has occurred in many communities that have lost mills.

• Rural communities tend to be more resilient

(able to adapt to change in positive, constructive ways) than is commonly assumed. Small

communities in the basin are unique and complex; generalizing from the types of towns

that are resilient thus should be done with

great care.

• Many of the region’s timber towns had a

relatively high level of resilience and were

perceived to be healthy when compared to

small ranching and farming towns. With their

amenities, diversified economies, and degree

of population growth, the face of many of

these communities is changing. New policy

initiatives are needed to help small

communities cope with the external forces

resulting in change.

Conclusions
112

• Public policy analysts could view the role of

resilience in one of two ways. One is that, if

government resources (i.e., funding, workforce, and other kinds of subsidies) are to be

expended on rural communities, those lowest

in resilience (ranching and farming communities, in particular) are the ones most needing

support.

• The second alternative view is that, in the

name of economic efficiency and equity,

America should “cut its losses” in terms of

communities that are “on the skids” and

losing their human capital. Expending any

more societal resources on these communities

would not be worth the benefits derived;

rather, government resources would be most

effectively used on communities that are at

risk but have the potential to benefit most

from those resources.

• The history of Forest Service commitments

and impacts on rural communities has continually evolved. The nature of this evolution, along with changing societal values, a

changing Agency workforce reflecting those

values, and the learning that occurs within the

Agency, underscores the importance of sound

forest planning (for example, see Blattner et

al., in press; Brown 1994; Clark and Stankey

1994; FEMAT 1993; Gale and Cordray 1991;

Grumbine 1994; Krannich et al. 1994; Lee et

al. 1990; Machlis and Force 1988; Rasker

1995; Waggener 1977). Information such as

that provided in this research may be an important tool for revising forest plans and planning individual projects. Additionally, it also

may be useful for planning and management

efforts of the communities themselves and

their counties and states.

Various approaches may help rural communities

adapt to their changing environments and conditions. The CRI suggests that different communities require different mixes of solutions or responses, depending on the nature of the changes

affecting them and their strengths and weaknesses

as indicated by the resilience index. From the

index, solutions and responses could be tailored

to the situations of individual communities. They

might include programs for rebuilding social networks and increasing a community’s social

cohesion; leadership training programs; growth

management strategies; investments in improving

physical infrastructure; and financial and infrastructure support for traditional industries to

maintain their role in local economies. Mitigation

programs could include a process for indepth

community self-assessment that further clarifies

and details community needs. This process could

help communities and their leaders assess their

current conditions, evaluate the challenges and

opportunities facing their community, and develop short- and long-range strategies to respond

to changes that make the most effective, efficient

use of outside funding.

As detailed in the research done on significant

change communities (see Harris 1996a, 1996b),

distrust of government, issues of self-reliance

versus dependence on public resources, concerns

with private property rights, and conflicts over

resource uses of Federal lands are commonplace

in the interior and upper Columbia basins. Accordingly, resource management agencies need to

take actions that advance a positive, proactive approach promoting consensus building and collaborative problem solving across the region,

rather than one that fans the flames of conflict,

confrontation, and divisiveness among the various publics in the inland Northwest. Recent

social changes are altering small rural communities across the basin as much as the changing

supplies of natural resources. Residents of these

rural communities need to focus their attention

and efforts on dealing with the many coming

changes constructively and resolving resulting

problems as expediently as possible.
113

Funding for the original assessment research was

provided by the Interior Columbia Basin Ecosystem Management Project, USDA Forest Service,

and U.S. Department of the Interior Bureau of

Land Management, Walla Walla, Washington.

Additional support was provided with funds from

the Pacific Northwest Research Station, USDA

Forest Service, Portland, Oregon, and the USDA

McIntire-Stennis research program at the Idaho

Forestry, Wildlife and Range Experiment Station,

Acknowledgments

College of Forestry, Wildlife and Range Sciences,

University of Idaho, Moscow, Idaho. The authors

express their appreciation for the assistance of

research assistants Chris Wall and Jean Haley in

planning, conducting, and reporting the assessment. The economic data reported in this paper

were developed in collaboration with regional

economists Hank Robison and Steve Peterson of

the University of Idaho.
114

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The Forest Service of the U.S. Department of Agriculture is

dedicated to the principle of multiple use management of the

Nation’s forest resources for sustained yields of wood, water,

forage, wildlife, and recreation. Through forestry research, cooperation with the States and private forest owners, and management of the National Forests and National Grasslands, it

strives—as directed by Congress—to provide increasingly

greater service to a growing Nation.

The U.S. Department of Agriculture (USDA) prohibits discrimination in all its programs and activities on the basis of race,

color, national origin, gender, religion, age, disability, political

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prohibited bases apply to all programs.) Persons with disabilities

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information (Braille, large print, audiotape, etc.) should contact

USDA’s TARGET Center at (202) 720-2600 (voice and TDD).

To file a complaint of discrimination, write USDA, Director,

Office of Civil Rights, Room 326-W, Whitten Building, 14th

and Independence Avenue, SW, Washington, DC 20250-9410

or call (202) 720-5964 (voice and TDD). USDA is an equal

opportunity provider and employer.

Pacific Northwest Research Station

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