rural communities in the inland northwest: an assessment ofquality of life, business attractiveness,...

United StatesDepartment ofAgriculture

Forest Service

Pacific NorthwestResearch Station

United StatesDepartment of theInterior

Bureau of LandManagement

General TechnicalReportPNW-GTR-477October 2000

Rural Communities in the InlandNorthwest: An Assessment ofSmall Rural Communities in theInterior and Upper ColumbiaRiver BasinsCharles Harris, William McLaughlin, Greg Brown, andDennis R. Becker

AuthorsCharles 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 GregBrown is an assistant professor of Environmental Studies, Alaska Pacific University, Anchorage, AK99508. Final report on cooperative agreement PNW 40-04H1-8-1078 between the University of Idahoand the Pacific Northwest Research Station.

Rural Communities in the Inland Northwest: AnAssessment of Small Rural Communities in theInterior and Upper Columbia River Basins

Charles Harris, William McLaughlin, Greg Brown, and DennisR. Becker

Interior Columbia Basin Ecosystem ManagementProject: Scientific Assessment

Thomas M. Quigley, Editor

U.S. Department of AgricultureForest ServicePacific Northwest Research StationPortland, OregonGeneral Technical Report PNW-GTR-477October 2000

PrefaceThe Interior Columbia Basin Ecosystem Management Project was initiated by the Forest Serviceand the Bureau of Land Management to respond to several critical issues including, but not limitedto, forest and rangeland health, anadromous fish concerns, terrestrial species viability concerns, andthe recent decline in traditional commodity flows. The charter given to the project was to develop ascientifically sound, ecosystem-based strategy for managing the lands of the interior Columbia Riverbasin administered by the Forest Service and the Bureau of Land Management. The Science Integra-tion Team was organized to develop a framework for ecosystem management, an assessment of thesocioeconomic and biophysical systems in the basin, and an evaluation of alternative managementstrategies. 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 directlyin the primary documents.

The Science Integration Team, although organized functionally, worked hard at integrating the ap-proaches, analyses, and conclusions. It is the collective effort of team members that provides depthand understanding to the work of the project. The Science Integration Team leadership included deputyteam 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, JonBumstead, and Stewart Allen; terrestrial—Bruce Marcot, Kurt Nelson, John Lehmkuhl, RichardHolthausen, and Randy Hickenbottom; spatial analysis—Becky Gravenmier, John Steffenson, andAndy Wilson.

Thomas M. QuigleyEditor

United StatesDepartment ofAgriculture

Forest Service

United StatesDepartment ofthe Interior

Bureau of LandManagement

Interior ColumbiaBasin EcosystemManagement Project

AbstractHarris, Charles C.; McLaughlin, William; Brown, Greg; Becker, Dennis R. 2000. Rural com-

munities in the inland Northwest: an assessment of small communities in the interior and upperColumbia River basins. Gen. Tech. Rep. PNW-GTR-477. Portland, OR: U.S. Department ofAgriculture, 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 con-ducted for the Interior Columbia Basin Ecosystem Management Project (ICBEMP). The characteristicsand 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 de-fined as a community’s ability to respond and adapt to change in the most positive, constructive wayspossible for mitigating the impacts of change on the community. The study found that a town’s pop-ulation size, autonomy, economic diversity, quality of life, and experience with change were all factorsrelated 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 PremisesThe goal of the research described here was to assess the characteristics and conditions of small, ruralcommunities in the interior and upper Columbia River basin (henceforth, the basin); the basin includesa lower basin in eastern Washington and Oregon and an upper basin that spans all of Idaho, westernMontana, 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 ruralregions such as the study area—even those people living outside the borders of incorporated townsand cities—the community where they socialize, shop, and perhaps work or go to church becomesthe focus of their social lives.

Social sciences recognize the significance of this scale of social organization. Those sciences includesociology, which focuses on social groups, organizations, and communities as primary units of analy-sis, 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 afocus on tradition (Machlis and Force 1988; Machlis et al., n.d.). Rural towns are too small to haveneighborhoods, and the only other definable social grouping between individuals and communities isthe sociocultural groups and organizations that often exist within communities. These groups can beinfluential 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 collectedby Federal and state agencies are reported at the county level. Unfortunately, in many places, condi-tions and changes in conditions at this broader level mask the differences across communities and thusthe impacts of change on their residents; this aggregation problem reflects the reality of the county asa political entity that, for many residents, may not be a meaningful social grouping and thus a relevantunit of analysis.

• The characteristics and conditions of small, rural communities in the region are complex and con-stantly 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 com-munities and, in part, with indepth, detailed data from a first-quarter 1995 survey of a systematicrandom sample of 198 communities. The data from the community self-assessments provide only asnapshot in time, and the indepth case studies of communities experiencing significant change since1980 provide information on communities in transition.

• The research also examined the resilience of the region’s communities, which was defined as acommunity’s ability to respond and adapt to change in the most positive, constructive ways possiblefor mitigating the impacts of change on the community. This concept was developed by the ScienceIntegration 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 com-munities can be thought of as representing a continuum from low to high resilience. Also, acommunity’s resilience can change over time, depending on changing community conditions. Com-munities can undergo different stages in their development, and different stages of development canreoccur, as reflected by the ongoing boom-and-bust cycles of the American West, which results indifferent economic mixes and shifts in dominant industries at different times.

• The results on resilience presented here represent two kinds of information: residents’ perceptionsof their communities in 1995, and factual, documented information about community characteris-tics, such as their population size, actual response to change, and their actual economic structure inthe first quarter of 1995. Both kinds of information are important: both the ways people see andknow their community and believe it to be, and the ways the community actually is, can be impor-tant factors underlying a community’s development and its responses to change.

Research MethodsSeveral sets of data were collected for assessing community characteristics and conditions: First, em-pirical data were gathered for all 387 small, rural, incorporated communities in the interior and upperColumbia River basin (data available from the Bureau of the Census). Secondly, a random sample of 198communities was selected (about half of all small rural communities in the region), and 1,350 residentsof these communities completed a community self-assessment workbook. These residents then partici-pated in community self-assessment workshops to provide data on their community’s current character-istics and conditions (i.e., community character and attractiveness, social cohesion, civic leadership,quality of life, business attractiveness, economic diversity and resource dependence, and the commun-ity’s preparedness for the future). Second, community officials were contacted to provide other docu-mented or recorded details about each community’s character and conditions (e.g., rate of populationgrowth, economic changes, school and utility capacities, distance from major transportation routes ornodes).

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 pro-portion of a town’s total employment attributable to each industrial sector contributing to that town’seconomy. These data, which were developed in collaboration with University of Idaho researchersspecializing in community economics, provided a profile of each community’s economy in terms ofemployment and earnings for industries, businesses, and agencies, which were aggregated into 21 majorindustrial sectors.

A component of the research also assessed and analyzed the characteristics and experiences of 145communities in the regions identified as “significant change communities.” These communities wereindicated as undergoing major change by (1) state economic development officials, agricultural exten-sion 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 thesecommunities focused on identifying the kinds of changes occurring in them, the kinds of communityresponses resulting, and the effects or characteristics of all these factors in terms of community condi-tions, activities, and lifestyles. A sample of 80 of these 145 communities was surveyed, with initialcontacts made with city clerks, who were asked to suggest the name of the person who would have thegreatest knowledge of the changes the town had experienced and its response to them. A structuredtelephone 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 mostprevalent in the study area since 1980, and indepth case studies of these communities were conducted tobetter understand the major changes influencing them, the impacts of these changes and the communityresponses to them.

Finally, a representative survey of residents of Chelan County, in central Washington, was conductedto assess the opinions and attitudes of one county’s residents about growth and resource managementissues in a rapidly growing county. From 700 questionnaires sent to a sample of residents randomlyselected from local phone books, 222 completed questionnaires were returned, for a response rate of32 percent. These data provided useful insights into the preferences and concerns of residents in acounty typical of those in the interior West experiencing significant population growth and communitydevelopment.

Major FindingsInitial analysis of these data indicated that:

• Small rural communities represent an important scale for gathering and analyzing social data onhuman 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 thecounty (79 percent) reported that the city or town where they collect their mail was somewhat orvery 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 oftotal employment, a complex picture of the economy of the Columbia River basin emerged: har-vesting and processing (agriculture, timber) were important employers, especially in the smallerrural towns across the region, with government (Federal and state or local) and travel and tourismalso among the region’s largest employers. The majority (62 percent) of jobs in the average ruralcommunity is in service sectors: not counting government, which provides an average of 21 percentof 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 indus-tries: the largest towns (over 3,000 in population) had a total of 18 percent of jobs, on average, inthose sectors, and in the smallest towns, those sectors accounted for an average 34 percent of alljobs.

• The vast majority of rural communities are small (less than 1,500 in population), and a communi-ty’s population size is significant. Generally, the larger communities in the region tended to be moreresilient; not unexpectedly, those with larger populations tended to have a more developed, exten-sive infrastructure and workforce to build on. Also, the largest towns tended to have more diversi-fied economies. These results support the 1993 analysis of the Forest Ecosystem ManagementAssessment Team (FEMAT 1993) community assessment, which suggested that communities withhigh capacity to adapt tend to be larger communities, and communities less able to adapt tend tohave more limited infrastructure, less economic diversity, less active leadership, more dependenceon 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. Thecurrent study found that a small town’s population size is, in fact, the single best characteristic forpredicting its current conditions and likely response to change: larger towns tended to be more eco-nomically diverse and thus stable. The smaller and less developed a town is, the less vital, attrac-tive, 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 develop-ment handbooks and workshops provided in the 1970s and 1980s: if members of a small rural com-munity want to “develop” their town, they should work to attract new industries and expand itseconomic base (which will indirectly lead to an increase in population).

• Significantly, the findings of both the self-assessment study and the community economic profilessuggested that the impacts of this improvement extend beyond the economic aspects of communitydevelopment, whose significance has long been recognized and is reaffirmed here, to its social ele-ments as well. More autonomous rural communities and ones larger in population typically repre-sent a more advanced stage of social and civic development than small ones. The importance forcommunity vitality of active social groups and civic organizations, increased educational infrastruc-ture, availability of services, success in obtaining development grants, and greater preparedness forthe future—all of which increase with a town’s size—reflects the benefits that towns with a criticalmass of social capital and infrastructure were more likely to realize. An interesting question forfuture research, however, is at what size and level of community development the net benefits ofgrowth 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 changeand 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 relativelyisolated, but they also tended to be relatively resilient compared to towns in which other industrieswere perceived to be dominant. The least resilient communities were those in which farming andranching were perceived to be dominant. A complementary finding was that communities that havechanged the most in the last 5 years tended to be more resilient, which was likely due to theirgreater experience in coping with change.

Also supporting these results were the findings on population changes in towns smaller than 10,000where mills manufacturing wood or paper products have closed since 1980: although 52 percent ofthese 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 of8 percent since 1980.

Communities, nonetheless, were unique in their characteristics and conditions, their experiences ofchange, and thus their responses to it. Given that the details of their situations differed, they must beassessed 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 inother diverse ways. The population in the region is continually changing, but with a clear trendtoward growth: U.S. census figures indicate that the population growth between 1988 and 1994 was12 percent in Idaho, 7 percent in Montana, 8 percent in Oregon, and 9 percent in Washington; theU.S. population grew only 4 percent during that period. A large majority (70 percent) of the com-munities across the region reported that they had experienced a moderate to high degree of changesince 1990. The kind of change reported by the largest proportion of Chelan County residents sur-veyed was growth and population increases, by a 2 to 1 margin (68 percent). Other importantchanges 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 increasedcrime (22 percent). A majority, over 55 percent, was somewhat to extremely concerned about theoverall changes in their community.

• Growth in employment in the region also far exceeded the national rate: employment increased8 percent nationwide between 1988 and 1994, but it increased 28 percent in Idaho in that sameperiod and around 17 percent in the other states in the region. Recent changes in communities weredue to a variety of broader economic influences, such as global economic forces, economic diversi-fication, plant modernization, and industrial downsizing (such as laying off company loggers andhiring 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 vola-tility of markets for timber, mining, and other traditional extractive industries; the actions of privatecompanies in modernizing or closing plants and periodically laying off or terminating workers; thedecreased supply of timber from National Forests, sometimes due to past inaccuracies in estimatesof existing timber supply, current regeneration, and future sustainability; decreasing employment inthe industries as a result of all these changes; and the rapidly increasing in-migration of new kindsof 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 im-pacts 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 changesover past decades that have not always been related to public land management. Communitygrowth, as indicated by population increases, occurred in many communities that lost mills, butnot 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 aboutthe kinds of towns that are resilient to change is always contingent on the situation of each. For ex-ample, many “timber communities” are fairly highly resilient and healthy, especially in comparisonto small ranching and farming communities. With their development of amenities, diversifying eco-nomies, 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 befactors influencing development, a decisive, major determinant of a community’s resilience clearly is itsresidents—in particular, the willingness of residents to take leadership roles, organize, and realize theircommunity’s potential. Community residents are a central defining element in creating the future ofrural communities.

• New policy initiatives could help small communities cope with the changes facing them, and publicpolicy analysts could view the role of resilience in one of two alternative ways. The first view isthat, 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 alterna-tive view is that, in the name of economic efficiency and equity, America should “cut its losses” interms 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 benefitsderived. Rather, government resources would be more effectively used on communities at risk butthat have the potential to benefit from those resources.

• The history of Forest Service commitments and impacts on rural communities has been a contin-ually evolving process. The nature of this process, changing societal values, the changing Agencyworkforce reflecting those values, and the learning occurring within the Agency underscore theimportance of sound forest planning; information such as this research can be important for revisingForest plans and planning individual projects. It also can be useful for the planning and manage-ment efforts of the towns themselves and those of the counties and states in which they are located.

Contents1 Context for the Assessment1 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 Literature8 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 Communities19 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 Basin28 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 Conclusions113 Acknowledgments114 References

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IntroductionThe management of natural resources in thePacific Northwest has been changing direction inrecent decades. Small rural communities in theregion, many significantly affected by changes inresource management, also have been changingdramatically.

Recent signs of change in resource managementhave been most evident in renewed efforts to planfor the region’s spotted owl forests west of theCascade Range—a planning process that beganwith President Clinton convening a “forest sum-mit” in 1993 and continued with the preparationof a scientific assessment for the west-side forests(Forest Ecosystem Management AssessmentTeam [FEMAT] 1993). Based on this scientificassessment, the USDA Forest Service selectedoption 9 as the preferred alternative of its planfor the Northwest’s forests, and the Agency pre-sented an analysis of that option in the environ-mental impact statement (EIS) it conducted forfuture forest management for the region’s forests(USDA Forest Service 1994). A key objective ofthe west-side forest plan was to establish andimplement ecosystem management as a dominantstrategy for future management of the region’sforest resources.

Fundamental to an ecosystem approach areseveral assumptions:

• Management priorities will include the pro-tection and restoration of deteriorated ecosys-tems, as well as the traditional focus on pro-viding multiple benefits for people (i.e., avariety of resource values, products, and ser-vices) within the capabilities of ecosystems.

• Any analysis of the uses of natural resources,the relation of these uses with local communi-ties, and the effects of resource uses on thenatural environment will be conducted at alandscape scale adequately accounting for thebroad geographic, place-based nature of theserelations and impacts.

• The management process will be an inclusive,collaborative one having grass-roots supportand build on input from different communitiesof place and interest, rather than be a processthat is exclusive and divisive.

• Management decisions will be based onsound science to ensure that managementactivities make use of the best availablescientific data.

Other changes also have had major impacts onmanagement of the Pacific Northwest’s naturalresources. Forest Service activities increasinglyhave focused on noncommodity resource man-agement (Farnham 1995, Farnham et al. 1995), atthe same time that the amount of timber offered,sold, and harvested from National Forests hassignificantly decreased since the late 1980s(Farnham and Mohai 1995). Most recently, theForest Service has declared a moratorium on roadbuilding in National Forests while it reconsidersits forest harvesting and road-building policies.At the same time, heated debate over necessarysteps to restore the region’s wild salmon runsescalated in the late 1990s, mainly as a result ofan evaluation by the U.S. Army Corps of Engi-neers on the impacts of breaching four dams onthe Lower Snake River to aid in that restorationeffort. In addition, the in-migration of large num-bers of people, many of whom are ex-urbanites

Context for the Assessment

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building homes in forested areas, is increasing theregion’s population and the likelihood of con-flicts. In light of these changes, a major concernof affected Federal managers, as well as otherresource managers, industries, and local publics,has been with the impacts of changing resourceoutputs and management priorities on peopleliving in the region and the small rural communi-ties located there.

The scope of these impacts and concerns aboutthem expanded scientifically as well as geo-graphically when the Interior Columbia BasinEcosystem Management Project (ICBEMP), amultiagency resource-management planning ef-fort being led by the USDA Forest Service, beganin 1993. This project, unlike the abbreviatedFEMAT assessment, was more comprehensiveand thorough in its assessment of the natural re-sources and socioeconomic conditions in theinterior and upper Columbia River basin (or thebasin). This region of the interior West, which isthe size of Texas, is comprised of two basins: theinterior Columbia River basin, which extends eastfrom the Cascade crest across Washington andOregon, and the upper Columbia River basin,which extends east from Idaho’s western borderto the Continental Divide and includes all ofIdaho plus western Montana and Wyoming.

The ICBEMP has produced a broad managementstrategy for the region’s public lands, as pre-scribed in the EIS for that strategy (USDA-FSand USDI-BLM 1997a, 1997b). That project wasdesigned to:

• Develop “big-picture” ecosystem managementstrategies for restoring forest and rangelandhealth while providing sustainable resourcesand jobs for people.

• Address broad-scale problems crossingjurisdictional lines, including providing forspecies viability with an ecosystem approachrather than a species-by-species approach.

• Better protect fish and other species and pro-vide management needed to reduce ecologicalrisks in riparian and upland areas.

• Reduce polarization over concerns aboutconservation and public land management byproviding a process that encourages interestedand affected parties to cooperate with oneanother.

As part of the ICBEMP assessment, the project’sSocial Science Assessment Team recognized theneed to look beyond the characteristics and con-ditions of ecosystems and natural resources toconsider the situations of people and communi-ties that are a part of those ecosystems and usingthose resources. Accordingly, as part of the socialscience assessment, research was started in 1994to better understand the characteristics and con-ditions of small, rural communities in the region.

Why the CommunityAssessment?

Throughout history, communities andtheir residents have been shaped by theinterplay of the forces that cause socialchange. The American West, for ex-ample, is sprinkled with ghost townsstanding as monuments to the powersuch forces can exert on communitiesand their residents. In the United States,such changes have traditionally beenviewed as part of the natural course ofthings, with the outcomes interpreted asdemonstrations of economic forces thatwere beyond anyone’s responsibility tocontrol.

Branch et al. 1982:5

America’s interior West has experienced changesthat began with the region’s settlement by immi-grants from the Nation’s coasts and continuetoday with a diversity of economic, cultural, andhuman migration trends unfolding in the WesternUnited States. Changes in Federal land manage-ment practices, such as those being proposed bythe ICBEMP, can affect the physical, cultural,social, political, legal, economic, and psycholog-ical nature of the human environment (Gramling

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and Freudenberg 1992). These effects are espe-cially pronounced in a region—such as thebasin—having a large percentage of Federal land.Practitioners of social assessment presume thatlocal, state, and Federal governments have a re-sponsibility to help minimize the negative effectsof the changes set in motion by social forces andshifts in land management policy, or at the least,to assist their citizens in preparing for thoseeffects.

This report seeks to provide a better understand-ing of small rural communities in the basin facedwith current and future changes in natural re-source management. The assessment research ad-dressed a variety of questions: What is the currentcharacter of communities in the inland Northwestand northern Rocky Mountains? What are theirmost notable attributes and characteristics, andwhat are the interrelations among the attributes ofthese towns? Do the region’s communities wantto remain largely as they are, or do they seek tochange? What is the capability of communities todeal with change and prepare themselves for thefuture? What makes a community more or lessresilient or adaptive to change? How might thecommunities be impacted by changes in the pol-icy direction of the Forest Service and Bureau ofLand Management (BLM)? How can governmententities ease the transition for these communitiesand for the social, political, and cultural groupsthat are important components of them?

The Social Impact AssessmentProcess in FEMATThe current assessment was built from theresearch conducted for the social sciencecomponent of FEMAT (1993). The objectivesof the FEMAT social impact assessment were to:

1. Describe the nature and distribution of thesocial values and uses found in the range ofthe northern spotted owl (Strix caurinaoccidentalis).

2. Describe how these values and uses would beaffected by various management options.

3. Identify how different constituents could beaffected by changes stemming from theoptions.

4. Identify opportunities or strategies for dealingwith impacts of these consequences on peopleand their communities (FEMAT 1993:5).

The social assessment process conducted by theFEMAT team included the following components(1993:6-8):

1. Commissioned papers to obtain expert opin-ions on various issues having to do with thepotential social impact of the range of Federaloptions for the spotted owl forests.

2. An examination of Forest Service and BLMpublic involvement records.

3. A survey of county extension agents through-out the region.

4. Two workshops with government employeesand extension agents from around the regionto assess the relative ability of communities todeal with possible management options andother changes in the region.

5. An assessment of the nature and value ofthe region’s recreation, scenic, and sub-sistence values by conducting a numberof information-gathering efforts:

• A survey of BLM and Forest Service officesto see what information was available onthese values. Recreational opportunities andvisual quality objectives also were assessed,based on forest and district land-useallocations.

• A case study of agency representatives fromselected areas, including 2 days spent byBLM and Forest Service representatives tomap the location and extent of various socialvalues, with the purpose of assessing howmanagement options could affect thesevalues.

• A nominal group exercise, the purpose ofwhich was to identify barriers and impedi-ments to integrated resource managementas well as opportunities to overcome thoseimpediments (FEMAT 1993).

Although the FEMAT team was severely con-strained by time (only a few months were avail-able to complete a full impact assessment of theextensive spotted owl forests), the team later

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wrote, “While acknowledging the limits imposedby the above constraints, we also want to assertthat this social assessment represents one of themost significant efforts ever undertaken to ex-amine the social consequences of federal forestmanagement” (FEMAT 1993:5). The present re-search, which expands on the FEMAT study ofwest-side communities, provides a start in gain-ing greater knowledge in the science and practiceof social assessment.

Why the Focus on SmallerCommunities?A key premise of a regionwide, landscape-basedassessment, such as that conducted for theICBEMP, is that future resource managementshould be based on an integrated, multiresourceanalysis for promoting management of sustain-able ecosystems. To realize sustainable manage-ment, the management region must be largeenough to account for species interdependence,allow for long-term adaptation and catastrophicchange, and assure the healthy functioning of theecosystem at all levels. However, while natureknows no borders, humans do.

Human activities are conducted at various levelsof scale: units of analysis can be based on dif-ferent levels of social organization, and everydayhuman activities can be based on collectivities,geography, and political boundaries. Commonlyrecognized levels of social organization basedon geography and human activities range fromhouseholds to neighborhoods, communities(towns and cities), counties, multicounty regions,and states. Historically, bureaucratic, administra-tive, and political boundaries have been a com-mon hindrance to confronting the challengesposed by achieving species conservation andhealthy ecosystems. Regionwide, cross-agencycoordination is critical for providing a consistentoverall direction to communities that reflectschanging priorities and approaches to resourcemanagement.

Although larger scale areas such as watersheds,ecological provinces, or whole regions may beimportant 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 theirties to other people, their work, the services theyare provided, and their network of friends andfamily at the level of community. Local commu-nities are more than just a place where peoplehappen to live; they essentially and fundament-ally “constitute the fabric of day-to-day life”(Krannich et al. 1994:48-49). Some analysts sug-gest that, indeed, the slower pace in rural commu-nities provides their residents with a fundamentaltie to social norms and traditions. As Branch andassociates write, “The linkages between commu-nity resources, social organization, and well-being and the important role communities playas administrative and participatory units make itessential that social assessments utilize an ana-lytic framework that effectively focuses attentionon the community” (Branch et al. 1982:25-26).

The social sciences have long recognized thesignificance of the community as a key scale ofsocial organization: included are sociology’sfocus on social groups, organizations, and com-munities as primary units of analysis, and onconflict and cohesion as major forces of change;and anthropology’s attention to social groups,communities, subcultures, and sometimes entirecultures, with a focus on tradition (Machlis andForce 1988; Machlis et al., n.d.). Given that ruraltowns are too small to have neighborhoods, theonly other definable social grouping betweenindividuals and communities is the socioculturalgroups and organizations that often exist withincommunities. Although these groups can beinfluential in making things happen where theyare located, most of the governmental, civic,social, and infrastructure mechanisms function atthe community level. The next highest level ofsocial organization is one of polity: county gov-ernment. But the concerns, activities, and impactsat this level primarily have to do with land-useregulation and provision of rural services thatlack a social or cultural dimension.

The guidelines and principles for social assess-ment (hereafter, “guidelines and principles;”Interorganizational Committee on Guidelines andPrinciples for Social Impact Assessment 1994)

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make the point that, “just as the biological sec-tions of EIS’s devote particular attention tothreatened or endangered plant and wildlife spe-cies, the socioeconomic sections of EIS’s mustdevote particular attention to the impacts onvulnerable segments of the human population”(1994:4). In the case of analyzing the effects ofFederal land management actions and direction,the most critical impacts may be to small, ruralcommunities.

In addition to the centrality of small communitiesin the lives of people living in rural areas, ruralcommunities may be especially vulnerable whenthey lack the leadership necessary to weather acomplex set of changes (Israel and Beaulieu1990). As conditions worsen and resources be-come more limited, local governments often areforced to transfer their decisionmaking and be-come more reliant on state and Federal govern-ments (Weeks 1990). This may further limit localinitiative and creativity, especially in the face ofeconomic downturns. In addition, small townsoften lack the economic capacity to outlast down-turns in a particular industry. They may not haveenough skilled labor available to attract new busi-ness and compete (Malecki 1988, Power 1994).Rural communities also often lack adequate basicinfrastructure (e.g., water, sewage), much less thecommunications and information infrastructureimportant for economic growth (Dillman et al.1989). As a consequence of all these factors,the communities also may lack the financialresources and economic diversity to withstandchanges impacting their economic base.

For all these reasons, rural communities are espe-cially vulnerable to change. Consequently, thecommunity of place is an especially importantand relevant level for social assessment. Not allsocial scientists agree, however, that the geogra-phically based community, or community ofplace, is always the appropriate level of analysis.Carroll, for instance, makes the point in FEMAT(1993) that a community is more than a munic-ipality; when he refers to community, Carrollfocuses on communities of interest:

groups of people such as Tar Heel shakeand shingle workers, loggers, rural en-vironmentalists, Native Americans andethnic/cultural groups who gather spe-cial forest products. In many ways, theattachment these people have to eachother, the land, special places and theirlife in common constitutes more of asociologically definable community thanthe artificial boundaries of many towns.1

In the light of his and other similar concerns, theFEMAT social scientists suggested a compromiseposition (Clark and Stankey 1994:33):

A definition of community has longtroubled scholars, who recognize thateven in specific locations shifting con-stellations of people comprise differentcommunities with different purposes (forexample, occupational communities suchas loggers). However, geographic com-munities are important from an economicand policy standpoint, especially forisolated areas whose fortunes are linkedto their location. They also embrace oc-cupational communities; thus, programsdirected at geographic communitieslikely will reach members of occupa-tional communities and their familieswhere they live.

In one sense, the present research deals withsome of Carroll’s concerns over the importanceof communities of interest by providing an inclu-sive process that sought to represent the diversityof perspectives and subcultures that potentiallycan exist within a community. It is not a completesolution to the problem. Other analyses con-ducted for the basin assessment focused on thelevel of stakeholders and special interest groups.

1 Some early reviews of this process reflected a misunder-standing of it; they raised the concern that (1) by basing theassessment on a small sample of a community’s most in-formed residents, its results would be biased by those whobelieved participating would influence the results, and (2)the snowball sample would result in people “inviting” like-minded associates to participate. These concerns were foundto be unfounded, as reflected in the diversity of participantsand their perceptions of their community in each town.

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Nonetheless, the community focus detailed hererepresents a wealth of information on the status,vulnerability, needs, and aspirations of localcommunities: the locus of everyday life and thefabric of our society.

Recent Research Relating toCommunities Dependent onForest and RangelandResourcesOther studies assessing the social and economicsituations of human populations and addressingtheir relation with forests and rangelands haverecently been conducted. They include the workof Machlis and others (Force and Machlis, n.d.;Machlis and Force 1988; Machlis et al. n.d.) onresource-dependent towns and the use of county-based social indicators for mapping social condi-tions in the Pacific Northwest, Tarrant’s (1995,1996) social assessment research for the Appa-lachian Ecosystem Management Project, and thefocus of Doak and Kusel (1996) on understandinghuman populations for the Sierra EcosystemManagement Project. Although some of these re-searchers suggest the need for an emphasis on hu-man conditions at a community level, most havefollowed the lead of the FEMAT process andused secondary data, sometimes from the level ofcommunity groupings and sometimes from thecounty level: levels of scale that ironically do notallow for the kind of indepth analysis of the com-munity as the unit of analysis that their concep-tual frameworks call for.

In contrast, Beckley (1998) notes the importanceof the community scale in research that providesa conceptual framework for understanding thecomponents of forest dependence that need to beconsidered in resource management. The ongoingwork of Carr et al. (1998a, 1998b) is building onassessments such as Beckley’s and that presentedhere, which focus on the importance of communi-ties and networks among them, with projects thatseek to advance the substantive integration andconsideration of community conditions andchanges in resource management and planning.

Research Goal and PremisesThe primary goal of our research was to assess thecharacteristics and conditions of small, rural,geographically based communities in the basin.Specifically, the communities to be studied weredefined as incorporated towns in the region withless than 10,000 in population.

The intent of the research was not to assess theresidents’ preferences and attitudes concerningtheir community and issues affecting it. Thesefeelings could well differ among different groupsof residents, including those active, knowledge-able citizens who are highly involved in the activ-ities of their community. The research did notfocus on personal feelings and values (as in thecase of preferences and attitudes about varioussides of various issues). Rather, the researchfocused on residents’ beliefs about the character-istics and conditions of their community, and onproviding knowledgeable community residentswith an opportunity to share their informationand perspective on their communities. By obtain-ing a group assessment of the community’s cur-rent situation from people representing a varietyof perspectives (via sharing information in theworkshop as described in the chapter, “Method-ology for Assessing Communities”), the researchprocess sought to provide a neutral, balanced, andobjective assessment of each community’s char-acteristics and conditions by those most qualifiedto provide it.

The research was based on several otherpremises, as well:

• The small, rural community is an importantscale for social assessment.

As noted above, for most residents of rural re-gions such as the study area (including thosepeople living well outside the borders of incor-porated towns and cities), the community wherethey pick up their mail, socialize, shop, and per-haps work or go to church is an important socialand economic focus of their lives. Despite thisreality, most data collected and reported at a locallevel are from Federal and state agencies and forentire counties. Consequently, in many places,

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efforts at the county level to track changing con-ditions only mask the differences in those condi-tions in individual communities; thus, the kindsand degrees of impacts that changing policieshave on local residents are muted. This aggrega-tion problem reflects the reality of the county as apolitical entity that, for many residents, may notbe a meaningful social grouping and thus a rel-evant unit of analysis.

• The characteristics and conditions of small,rural communities in the region are complexand constantly changing and need to be ex-amined over time.

Our study examined the characteristics and con-ditions of the 387 small, rural, incorporated com-munities in the study area, in part with U.S. cen-sus data for all communities (U.S. Department ofCommerce, Bureau of the Census 1995a, 1995b),and in part with indepth, 1995 first-quarter datafrom a systematic random sample of 198 com-munities that was collected by the authors. Thecensus data from past decades provided a basisfor longitudinal analysis of community changeover time, while the community data we collectedprovided a unique baseline for future efforts totrack the sociocultural and economic impacts ofthe complex forces, both internal and external,shaping the region’s communities.

• Along with objective, documented, and re-corded data (e.g., as collected and reported bythe U.S. Bureau of the Census), a process ofcommunity self-assessment is critical.

The assessment of rural communities designedand conducted for ICBEMP, which collectedthese data, followed the recommendations of thewest-side team (e.g., FEMAT 1993, Krannich etal. 1994): first, that an on-the-ground assessmentof the situation of rural communities across theregion be conducted and, second, that these com-munities conduct their own self-assessments oftheir conditions and characteristics. Accordingly,the major primary-data collection effort for theresearch was a community self-assessment studythat examined a random sample of half of theregion’s 387 rural communities. The results pre-

sented here thus represent two kinds of informa-tion: residents’ perceptions of their communitiesin 1995, and factual, documented informationabout community characteristics, such as theirpopulation size, actual response to change, andtheir actual economic structure in the first quarterof 1995. Both kinds of information are important:both the ways people see and know their commu-nity and believe it to be, and the ways the com-munity actually is, are important factors underly-ing a community’s development and its responsesto change.

Because every community is unique, each needsto be studied and understood on its own terms, inlight of its unique history and current situation.The resulting data were a set of ratings of peo-ple’s perceptions based on the levels of attributesin comparison with other small towns. Havingcommunities compare themselves to others on thecommunity dimensions assessed in the researchalso provided a basis for exploring patterns inthese comparative data. The patterns, in turn, en-abled researchers to generalize about differentkinds or categories of towns.

• In addition to describing community char-acteristics and conditions, the research alsoassessed the resilience of the region’scommunities.

Resilience was defined as a community’s abilityto respond and adapt to change in the most posi-tive, constructive ways possible for mitigating theimpacts of change on the community. The con-cept of resilience was developed by the ScienceIntegration Team of ICBEMP. A community’sresilience is relative, so the study focused ondegrees of resilience; the communities can bethought of as representing a continuum from lowto high resilience. Also, a community’s resiliencecan change over time, depending on changingcommunity conditions. Communities undergo dif-ferent stages in their development, and differentstages of development can reoccur, as reflectedby the ongoing boom-and-bust cycles of theAmerican West that have resulted in changes indifferent economic mixes and shifts in dominantindustries at different times.

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IntroductionTwo surveys of recent research were completedfor the assessment. One was a telephone surveyof researchers currently studying topics relevantto the assessment. Its purpose was to ascertain themost current state of knowledge on rural commu-nities, and particularly on resource-based com-munities. The second survey was a literaturesearch that reviewed published research on com-munities, their key characteristics, and their as-sessment. The results of the telephone surveyprovided much of the background and discussionfound elsewhere in this report, and the results ofthe literature review are the focus of this section,which is divided into discussions of findings onkey community attributes and assessment re-search. The various articles, books, and otherresearch cited here provide recent findings on thecharacteristics of communities in general and thecharacteristics of rural communities in particular.Although the literature review was not exhaus-tive, it provided a solid theoretical and empiricalbasis for understanding the key community con-structs (ones representing key dimensions ofcommunity characteristics) assessed with theresearch.

Critical Dimensions ofCommunity Characteristicsand ConditionsCommunity CharacterEvery community is unique: each community hasits own character and ambiance. A community’sattractiveness, a key component of its character,is a combination of many factors that are oftenhighly subjective, ranging from the community’s

visual appearance (or attractiveness) to the placesoutside the community that contribute to its at-tractiveness. The attractiveness of a communityhas generally been couched in terms of the areassurrounding the community. The “appropriatelyaesthetic setting” (Pulver 1989:6) or “environ-mental integrity, and physical beauty” (Johnson1993:7) of the surrounding areas have been iden-tified as an important draw for new residents andbusinesses. Castle (1991:47) states that “an im-portant part of rural development strategy is tomake the rural areas attractive as places to live.”Power (1994:9) asserts that “attractive qualitiesassociated with the social and natural environ-ments become both important determinantsof local economic well-being and importantsource[s] of local economic vitality.” Thus, theattractiveness of a community’s surroundings isviewed as a potentially important factor in thatcommunity’s economic well-being.

Another important aspect of community characteris the level of attachment that residents have for acommunity. Attachment to place is an importantcomponent in how people feel about the characterof their community and is generally characterizedas having several different components. Recentliterature on community attachment has empha-sized its multidimensional nature (e.g., Stinner etal. 1990). Indicators used by O’Brien et al. (1991)to measure residents’ attachment to their commu-nity include their perceptions that a community isan ideal place to live, satisfaction with the com-munity as a place to live, having a lot in commonwith other people living in the community, andfeelings that they fit in the community. Brown(1993) distinguishes between community satis-faction and community attachment. Community

Review of the Literature

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satisfaction can be measured by evaluation of acommunity as an ideal place to live, the desir-ability of the community as a place to live, andsatisfaction with life in the community. Commu-nity attachment can be measured by social inter-action, the degree to which residents feel they fitin the community, and how much residents havein common. Brown also includes length of resi-dence and organizational involvement and mem-bership as variables. Goudy (1990) uses localbonds (i.e., friend and relative networks andorganizational memberships) and local sentiment,which referred to feeling at home in the commu-nity, interest in knowing what’s going on in thecommunity, and response to the possibility ofmoving away, as indicators of communityattachment.

Community CohesivenessThe ability of a community to manage ongoingchanges in society can be greatly affected by thecapacity of its residents to work together toaccomplish projects and take action (Johnson1993). This capacity to work together is referredto as the “cohesiveness of a community” or, moregenerally, as a “sense of community.” Communi-ties with greater cohesiveness are more willingand able to work together to achieve goals, com-plete projects, and particularly important today,manage change.

The cohesiveness of a community, as definedabove, has been addressed by several authors inthe literature and consists of several components.One component focuses on residents’ ability toorganize and cooperate to take action (Howelland Bentley 1986, Johnson 1993, Lackey et al.1987, Poplin 1979). A second component is thecapacity to actually move to completion, achievegoals, or complete projects (Lackey et al. 1987,Shaffer 1990). The availability and quality oflocal leadership also are cited as important fac-tors in the ability of communities to get thingsdone (Lackey et al. 1987, O’Brien et al. 1991).Shaffer cites a “positive attitude toward experi-mentation” (1990:76) as being important, assert-ing that “the greatest asset communities have intheir struggle to maintain economic viability is

not distance, natural resource base, or currenteconomic structure but their own creativity andinsight” (p. 85). Thus, a willingness by commun-ity residents to take chances and try new thingsalso has been recognized as an important factor inpromoting community well-being.

An additional insight reported in the literaturemerits special attention. Communities that havesuccessfully engaged in community action in thepast will be more likely (and more capable) to doso in the future (O’Brien et al. 1991, Shaffer1990). The idea here is that with community ac-tion, as with many other things, practice makesperfect. This conclusion received support fromthe findings of the rural community assessmentfor the ICBEMP, as will be discussed later in thispaper.

Community ServicesCommunity services are those things, whetherbusinesses, nonprofit or government institutions,facilities, or programs, that are provided by eitherthe private or public sectors and that contribute tothe livability and desirability of a community byhelping meet people’s needs. Community servicesinclude fire and police protection, schools, medi-cal facilities and personnel, retail facilities, rec-reational facilities, and churches. The presence orabsence of these various kinds of services com-bine to make a community more or less livablein the minds of current or potential communityresidents.

A search of the literature showed that others didnot use the same comprehensive operationaldefinition of community services as we used inour study. The majority of the literature referringto community services focuses on medical ser-vices, in general, and on mental health services,in particular.

There were exceptions to this focus, however,including that of Pulver (1989:6), who describesa “high-quality living environment [as including]access to good schools, excellent health care,physical security, recreational and cultural op-portunities, [and] satisfactory housing and public

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amenities.” Christenson (1976) includes libraries,education, law enforcement, medical services,state parks, cultural activities, public parks, recrea-tion, childcare, food stamps, industry, apartments,and family doctors in his research on the quality ofcommunity services. In a study of satisfaction withlocal services, Rojek et al. (1975) performed afactor analysis yielding four clusters of servicetypes: medical services, including hospital-medicalfacilities, medical doctors, and dentists; publicservices, including streets and roads, water supply,fire protection, and police protection; educationalservices, including elementary and high schools;and commercial services, including shoppingfacilities, recreational facilities, job opportunities,and educational services for the physically andmentally handicapped.

Two important points about services appeared inthe literature. One was that the availability ofservices can play an important role in attractingretirees to an area, and retirees can have a signifi-cant, positive effect on economic stability (Cook1990). It is likely that services play a role in at-tracting other types of individuals (“urban re-fugees,” for example) to an area, as well. Thesecond important point was that “the evaluationof whether a service is adequate or not is clearlya value judgment based upon the preferences andexpectations of the person making the evalua-tion” (Williams 1976:204). In our research, thesection of the workbook on community servicessimply asked if each of a variety of kinds of ser-vices were available in the community, and if not,the distance (in miles) to the service of each kindmost typically used by the community. A finalquestion in that section then asked respondentsto rate their community based on the overall ad-equacy of the services available in it.

Community AutonomyA concept related to the availability of local ser-vices is that of community autonomy, which hasbeen defined as the extent to which a communityis economically, socially, and physically linked toneighboring communities and to the region. Themore self-reliant and independent a community isin relation to other communities, the more auton-

omous that community is. Community autonomy,then, refers to the control that a community hasover “events and activities that occur within [its]boundaries” (Poplin 1979:150). In the past, ruralcommunities had great control over their owndestinies, but now these communities—andparticularly their economies—are being affectedby forces “far broader than those that originatewithin or can be controlled by the communitiesthemselves” (Freudenburg 1992:328). Today’ssmall rural communities frequently can be at themercy of decisions made in boardrooms in distantcities.

This situation means that the concept of commu-nity autonomy is not without a certain duality. Onthe one hand, autonomy can be viewed as a posi-tive and necessary community characteristic.Warren states (1972:16) that a “barrier to effec-tive community action is the loss of communityautonomy over specific institutions or organiza-tions located within it and closely intermeshedwith the community’s welfare.” He asserts thatthe increase in bureaucratic policymaking hasfurther eroded the ability of communities todetermine their own destinies and stresses theimportance of autonomy as a positive attribute.

On the other hand, a high degree of communityautonomy also has been portrayed negatively.Castle states (1991:41) that the “rural areas thatare the most prosperous are those that have closeeconomic links with more densely populatedareas, frequently large urban centers.” Wilkinsonasserts (1986:8) that “what most small towns andrural areas need is to become somewhat moreurban and less isolated from resources and insti-tutions of our essentially urban society.” In eachof these cases, autonomy as the lack of connec-tions to the larger, more urban society is con-ceived as being detrimental to the well being ofa community.

A recent Forest Service report (USDA 1998),which used some of the data on communities inthe inland West described here, focuses on com-munity autonomy in terms of the concept of theisolation of a town. Forest Service analysts assertthat (p. 10):

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Economic development specialists gen-erally agree that smaller communitiesgeographically isolated from larger pop-ulation centers have fewer economicchoices than more populated areas. Theyare less likely to be economically diverseand more likely to depend on a fewmajor industries for their economicprosperity.

For its report, the Forest Service differentiatesamong communities differing in their size anddegree of isolation (p. 10-11):

Rules were developed to determine…[whether] a community is…geographi-cally isolated. Distance from largercities, measured by a circle drawn aroundeach city, was the primary factor used.The circle size was chosen to represent areasonable commuting distance. Thelogic of the “city circle” approach is thatproximity to larger towns conveys someadvantages to social and economic op-portunity. These advantages include jobchoices, access to air and surface trans-portation, access to education opportuni-ties, and access to cultural amenities andhigher order goods and services....

The Forest Service analysis designates towns notin a circle of a certain number of miles as beingisolated, except where they had “a relatively lar-ger population (above 1,900 people).” Theselarger isolated towns were designated as “isolated‘trade center’ towns” (p. 11):

The idea is that some larger isolated“small towns” take the form of smalltrade centers that serve many of theshopping and business needs of ruralresidents who live long distances fromlarger cities. These towns may exhibitdifferent characteristics than otherisolated towns.

Our assessment examined the condition of geo-graphic isolation in the larger context of com-munity autonomy. Although geographic isolationmay be an important factor for some rural com-munities, many are being affected by other kindsof forces not controllable by the communitiesthemselves. The definition of autonomy as ap-

plied here focused on those communities perceiv-ing themselves to be less autonomous; i.e., moreclosely linked socially, economically, and polit-ically to other communities and the region as awhole. Thus, they have less control over activi-ties and events affecting them. Community auton-omy therefore refers here to the control that acommunity has over events and activities influ-encing its development and ability to respondpositively to societal and local changes. Someisolated communities could be autonomous, vital,and resilient communities, and the significanceof the issue of isolation was addressed in thisresearch.

Economic DiversityThe economic diversity of a community is themix of types of industries and businesses in acommunity, the variety of those kinds of indus-tries and businesses, and the number and varietyof employment opportunities that the mix repre-sents (Belzer and Kroll 1986). In the past, ruralcommunities had economies dependent on aparticular industry (often an extractive one), withthe economic well-being of those communitiessubject to local, national, and global changes inthat industry (Freudenburg 1992, Gramling andFreudenburg 1992, Johnson 1993). Economicdiversity in small rural communities thus isclosely related to the concept of natural re-source dependence, which is discussed as a keyconstruct in the following section.

Gramling and Freudenburg (1992) suggest theconcept of “economic overadaptation” as an in-dicator of a lack of economic diversity, where “astraightforward measure of economic overadapta-tion involves the degree to which a region’seconomic fortunes have become tied to a singleindustry” (1992:229). Many of the industries towhich communities have overadapted have beensubject to national and global policy and econo-mic fluctuations, with these communities lessable to maintain control over their local econo-mies. Freudenburg (1992) uses the metaphor ofan “addictive economy” to describe communitiesunable to break the habit of dependence on indus-tries that have been the traditional mainstays of

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the local economy. As Johnson notes (1993:3), “inrecent years, rural communities have sought todiversify their economies to avoid excessivereliance on a single resource such as timber.”

All these researchers emphasize the value of avariety of industries and employment opportuni-ties in a community. Regardless of whether acommunity’s economy is centered on a naturalresource, such as timber, or on a large industrialplant, the lack of economic diversity is viewed asproblematic for the community. Diversifying itseconomy can help a community minimize thedamage caused by a downturn in any particularindustry.

Resource DependenceMany small rural communities depend on natu-ral resources found on the land surrounding theircommunities. The resources can include forestproducts, mining and minerals, grazing andranching, farming and agriculture, outdoor rec-reation and tourism, and commercial fisheries andaquaculture. Some communities depend on twoor more natural resources. As noted above, theconcept of resource dependence is closely relatedto the concept of economic diversity. In manycommunities dependent on a single industry, thatindustry is natural resource related.

Most of the definitions used for resource depen-dency are presented in economic terms (Machlisand Force 1988). The revised Economic ResearchService (ERS) county typology (Cook and Mizer1994) places counties in categories of resourcedependency by percentages of total labor andproprietor incomes in those counties. (Althoughthe ERS typology is a county typology, the defini-tions used are relevant for communities, as well.)An emphasis on economic definitions of resourcedependency can result in the failure to adequatelyrecognize and consider the social and cultural im-plications of resource dependence, as well as thenoneconomic meanings that people attach tonatural resource occupations (Machlis and Force1988). Dependence, on single industries in gen-

eral and on natural resource industries in particu-lar, has been linked with economic instability(Power 1994, 1996), and resource-dependentcommunities face the same problems as any com-munity lacking economic diversity.

Many of the natural resources that communitiesin the Western United States depend on are as-sociated with Federal lands. Changes in Federalnatural resource policy therefore can have impor-tant implications for those resource-dependentcommunities. Given that the values of the largersociety are changing, the public’s interest in howthe public lands are managed is increasing, andconcerns over ecological issues raised by re-source production are growing. Changes in at-titudes toward resource extraction, which isincreasingly perceived as being ecologicallyundesirable, “is foreign to [the] traditions [ofrural communities]; their jobs and businesseshave depended on natural resources extractionand use” (Castle 1991:49). As a result, along withbeing subject to national and global economicchanges, resource-dependent communities arenow subject to the effects of significant changesin how the public views the management ofpublic lands.

Attractiveness for BusinessAs the role of computers has increased and thecommunications infrastructure has been im-proved and extended into rural areas, businessesincreasingly are relocating to areas where theywant to be, as opposed to where they have to be.The physical beauty and other characteristics ofmany rural locations are a large major draw forbusinesses wishing to relocate, often from largecities (Barkley et al. 1991, Johnson 1993, Pulver1989). In addition to scenery and small-towncongeniality, these areas must provide the kindsof other services that companies need to dobusiness and prosper. The greater the availabilityof a variety of amenities and services, the greaterthe attractiveness of rural communities.

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The literature describes several needs and con-siderations important to firms wishing to relocateor to people interested in starting new firms inrural communities. Although access to transporta-tion has long been deemed important for certainkinds of businesses (Pulver 1989), new kinds ofbusiness services, such as overnight delivery ser-vice (Malecki 1988), are increasingly considereda necessity. The availability of capital, of bankerswilling to lend it to new ventures, and of taxbreaks provided to attract new business also arekey considerations (Fendley and Christenson1989, Pulver 1989). Access to knowledge (Pulver1989) and to technical personnel (Malecki 1988)traditionally has been a draw for new businesseswishing to relocate, and a well-developed com-munications and information infrastructure alsois now cited as a critical business need (Dillmanet al. 1989, Pulver 1989). Communications in-frastructure, increasingly viewed as a necessityin the computer age, is linking businesses to theirhead offices, clients, and customers via fax, theInternet, and email.

Quality of LifeQuality of life refers to those factors that make acommunity either a safe, comfortable place tolive or a tense, dangerous place. Quality of life isa catchall phrase of sorts that encompasses fac-tors ranging from environmental features (cleanair and water) to social support networks (thepresence of friends and family) (Campbell andConverse 1972, U.S. Environmental ProtectionAgency 1973). For some time now, the concept ofquality of life has been viewed as having manyaspects that relate to a wide range of factors. AsCampbell and Converse noted (1972:441) severaldecades ago,

The meaning of [quality of life]obviously differs a good deal as it isvariously used but, in general, it isintended to refer either to the conditionsin which people live or to some attributeof people themselves. The first caseincludes concern with pollution of the airand water, overcrowding in the cities,poor housing, the inadequacy of rec-reation areas, and similar aspects of

living. The second typically includesreferences to health, family stability,educational achievement, artistic andcultural concerns, and other suchdimensions on which people differ.

Taking a similarly broad approach, the U.S. En-vironmental Protection Agency (1973) definedquality of life with six categories of environ-mental qualities contributing to it. These includea region’s economic, political, physical, social,health, and natural environments (the last re-ferring to pollution and toxic wastes). Includedunder these main categories are 31 componentparts ranging from work satisfaction to toxicityand noise.

Pulver more recently focused on quality of lifeas it applied to local communities. He defineda high-quality living environment as (1989:6)“includ[ing] access to good schools, excellenthealth care, physical security, recreational andcultural opportunities, satisfactory housing andpublic amenities, clean air and an appropriatelyaesthetic setting.”

Community Leadership andEffectiveness of CommunityGovernmentThe assessment research distinguished betweenlocal government leadership and a more genericconcept of community leadership. Local govern-ment leadership focused on the ability of localgovernment to make plans and bring them tocompletion, to act according to the community’swishes, and to have the trust of community resi-dents. The more generic “community leadership”referred to considerations of the effectivenessand leadership of nongovernmental organizations(e.g., the business community, service clubs, localunions) and nonlocal governmental agencies(e.g., the USDA Forest Service, Natural ResourceConservation Service), as well as of local electedofficials. The availability and strength of localleadership significantly influences a community’sability to meet the demands of a changing world(Fendley and Christenson 1989, O’Brien et al.1991). Effective leadership is more than simplyelecting a mayor, however, and it is important to

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look at both the quantity and the quality of localleaders in determining how effective the leadershipis likely to be in a given situation.

One major feature of effective community leader-ship is that it is broad based, including a numberof different types of leaders (Lackey et al. 1987).Lackey and others assert that “healthy communi-ties are characterized by broad based leadershipin which many people have opportunities toperform leadership roles” (1987:10). Likewise,Poplin (1979) notes that leadership does notcome from a single source (although elected of-ficials often are in key positions of informationgathering and decisionmaking). Rather, differentpeople often lead in different situations. Poplinidentified three types of leaders: institutionalleaders, who hold a formal leadership positionwithin the community (e.g., elected officials);grassroots leaders, who rise up to lead in someparticular situations; and the power elite, wholead based on positions of wealth and influence(Poplin 1979).

A second important aspect of leadership concernsits quality and effectiveness. Walzer (1991:113)defines rural leaders as those who “attempt toinfluence or motivate others, to build problem-solving capabilities, in order to bring about socialor economic change in a democratic environ-ment.” It is important for people to feel as if theirleaders are paying attention to what they have tosay. Ayres and Potter (1984:14) state that “themore residents felt that town leaders listened tothem, the more confidence they felt regarding theability of community decision makers to dealwith change effectively.” Effective leaders areones who involve the community’s residents,listen to and respond to them, and work towardmeeting the needs of all residents, rather than theneeds of a powerful few.

Israel and Beaulieu (1990:182) also emphasizethe importance of placing the community’s wellbeing over that of the individual’s. They note thatcommunities that act effectively on matters oflocal concern are graced with leadership that (1)involves a diverse set of actors in local decision-making activities, (2) operates on the basis ofdemocratic principles, and (3) places the welfare

of the total community above the needs of anygiven special interest. Interestingly, O’Brien andothers (1991) report that the experience of localleaders is an important factor: leaders who havesuccessfully solved problems in the past are morelikely to be able to do so in the future.

Local community governments differ in thedegree to which they are effective. To the extentthat cities and towns depend on processes ofgovernment for their maintenance and growth(Penn 1993), the effectiveness of local govern-ment plays an important role in determiningwhether a community grows or declines. In caseswhere leaders are elected officials, the effective-ness of these leaders becomes representative ofthe effectiveness of the local government. Warren(1972:231) warns that “delegated governmentalauthority...can become extremely insensitive tothe wishes of the electorate, even to the extent ofdefeating or debilitating the efforts of newlyelected officials who presumably have a mandateto change things.”

Community Preparedness for theFutureNot only are communities changing from within,but society also is constantly changing, and thesechanges can have major effects at the communitylevel (Poplin 1979). Constant change necessarilyresults in a certain amount of uncertainty forcommunities trying to plan for their futures. Byassuming a proactive rather than reactive role inlooking at and shaping the future, communitiescan better deal with changes taking place locally,nationally, and internationally.

Most small, rural communities are fairly tradi-tional socially and economically, reflecting a con-servatism rooted in the small town way of life.Inherently, change in this way of life often is notviewed favorably, and some researchers haveposited that the leaders in rural communities aregenerally more open to change than are othercommunity residents (Ayres and Potter 1989).

It also has been suggested that “those rural areasthat are prepared to evaluate the offering ofnontraditional goods and services are the mostlikely to prosper” (Castle 1991:53). Castle notes

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that “this does not mean that the traditional [ex-tractive] industries will be abandoned” (1991:53),but it does suggest that a willingness to takechances and try new things is an important strat-egy for rural communities faced with change.Shaffer (1990:76) states that “a positive attitudetoward experimentation” is an essential charac-teristic of an economically viable community. Hefurther asserts (p. 85) that “the greatest assetcommunities have in their struggle to maintaineconomic viability is not distance, natural re-source base, or current economic structure buttheir own creativity and insight.” In a key ob-servation, Littrell and Littrell (1991:199-200)point out that “through a process of envisioninga future and asking what work needs to be per-formed or action taken, people can learn to anti-cipate the future and deal effectively with it.”Communities need to be proactive in creating thefuture they desire, rather than being at the mercyof changes over which they have little or nocontrol.

Major Findings From theLiterature on Social ImpactAssessmentsThe literature review also identified some of themore important, relevant conclusions found in theliterature on social impact assessments. The fol-lowing points summarize these conclusions.

• Public participation in social impactassessments is essential.

In addition to the aggregation of data on the crit-ical variables from secondary sources, a face-to-face exchange of information and ideas amongactive, involved community members wasachieved with the workshop approach we used(reasons for this approach are detailed in the“Methodology for Assessing Communities” sec-tion, below). A wide body of research suggeststhat public participation in social impact assess-ments is more effective in both the long and shortterm than a hands-off, technocratic approach tocollecting data. Taylor and Bryan (1990: 43), for

instance, observed that “the most effective prac-titioners of SIA [social impact assessments] havebeen those who have moved away from estab-lished work environments to undertake theirwork.”

The practical ramifications of the research ap-proach taken here is that the local population istreated as truly being a source of expert opinion,especially in the case of unrecorded, undocu-mented information for assessing communitiesand their current situations. Residents can be layexperts about their communities. Local percep-tions and attitudes, the organization of the com-munity, and how its citizens think, perceive, andrespond can sometimes be as important for under-standing the potential impacts of a project as thedetails of the project itself (Branch and others1982, Interorganizational Committee on Guide-lines and Principles for Social Impact Assessment1994).

The Interorganizational Committee on Guidelinesand Principles for Social Impact Assessment(1994) point out the tendency to dismiss con-cerns of the local population as being imaginedor perceived—as if they were irrelevant. Yet thepositions of various interests are all formed byperceptions. How can officials and managers re-spond to them if perceptions are summarily dis-missed? Dismissing a group or individual asemotional or misinformed only increases theresistance and conflict in a community overproposed or unfolding change.

Nonetheless, we realized it would be costly andof questionable value to sample all individuals ineach of the communities examined. Equally im-portant, we sought, through the assessment, theinformed understanding of the particular struc-tures and processes of small communities thatsome community residents simply would nothave. Answers to many of the questions aboutcommunities were clearly beyond the knowledgeof residents only superficially involved in theircommunities.

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This is not to say that the widely divergent viewsof community residents are not valuable andimportant for understanding potential impacts:“Although individuals of different ideologicalpersuasions can be expected to differ greatly overwhat they would prefer, such people can be ex-pected to arrive at reliable estimates as to whatwill happen, regardless of their preferences”(Freeman and Frey 1986:236). This same pointcan be made about perceptions of general recentconditions in their community. The communityassessment workbook and workshops for our re-search took advantage of this wealth of knowl-edge of lay experts and involved a diversity ofcommunity residents with a variety of experi-ences and perspectives on their communities.

• The extent to which rural economies aredependent on natural resource extraction isbeing questioned.

Changes that shifting demographics, evolvingtechnologies, clashing values, and conflicts overresource uses have brought to the rural West areclosely tied to the region’s shifting economic baseand priorities. Traditionally, a common assertionhas been that resource extraction industries arethe economic linchpin of rural economies. Re-cently, however, some researchers have suggestedthat, with changing rural economies and thegrowing importance of retirement incomes, thisassertion is no longer true for many economies(Power 1994, 1996; Rasker 1993, 1995).

Rasker (1993) examined what he calls the twomyths about the Greater Yellowstone Ecosystem:(1) agriculture and the resource extractive indus-tries are the region’s only basic industries; and(2) promotion of the extractive sectors is oftendeemed to be necessary and desirable, becauseall that rural communities have available to themis the timber, oil, gas, and minerals found on theland. Rasker concludes that retirement income inthe Greater Yellowstone Ecosystem area is alarger part of the regional economy than grazing,mining, and timber combined. Furthermore, hewarns that continued emphasis on resource-dependent and export-oriented development“places the local economy at the mercy of eco-nomic forces outside its control” (1993:117).

Johnson (1993) goes so far as to suggest that somerural Northwest communities resemble “de-veloping countries,” where resource-managementdecisions are made by agencies or corporationsheadquartered elsewhere, resources are exportedwith little value-added processing, and much ofthe generated income flows out of localcommunities.

Power’s (1994) conclusions from his study ofthe North Cascades Ecosystem were similar toRasker’s. He found that, in 1991, retirement-related income was 11 times as large as in-come derived from lumber and mining in theNorth Cascades. Power argues that healthyenvironments result in healthy economies, andenvironmental quality is anything but nonecono-mic: “The primary economic resource should beseen as the high quality natural environment, andextractive activities that threaten to degrade theenvironment should be assumed to be incompa-tible with local economic stability” (1994:12).

Power expands on this argument in his 1996book. Even though the common assertion in theliterature had been that resource-extraction indus-tries are essential for rural economic survival, heand other researchers (e.g., Power 1988, 1994;Rasker 1993, 1995) assert another view: ruraleconomies have been changing in fundamentalways, with traditional extractive industries de-creasing in economic importance. This view pos-tulates that much of the recent economic activityin the inland Northwest has been stimulated byenvironmental amenities (in particular, increasingrecreation and tourism and in-migration of peoplerelocating to areas with high amenity values) re-sulting in additional sales and jobs from outsidethe region (Harris and Robison 1993). Taken tothe extreme, the view of some (e.g., Power 1988)is that the West’s economic prosperity depends onenvironmental quality: not only is the region’sprosperity uncoupled from resource extractionbut it also may suffer when environmental ameni-ties are reduced by commodity production. Re-cent analysis to support the validity of this thesishas focused on a regionwide perspective on eco-nomic analysis, rather than on the importance oftraditional extractive industries in particular rural

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communities. Importantly, analyses by re-searchers such as Power and Rasker typicallyhave characterized only part of the current situa-tion in the region. By focusing on the region as awhole and, in particular, on regional data fromthe U.S. Bureau of the Census on income andemployment in the aggregate, their findings over-look significant differences in the varied andunique economies of small rural communities,focusing instead on broader trends in a larger re-gion that includes rapidly growing populationcenters such as the Puget Sound, Portland, Boise,and Spokane.

The focus on this theory of regional economicsto the exclusion of any competing view has beencontroversial (see, for example, Miller 1998).Although past forecasts of economic disaster maynot be occurring on statewide levels,2 the im-pacts of declining resource supplies in particularcommunities and individual industries cannot bedisregarded. Also, some noneconomists have sug-gested that arguments of minimum impacts onrural communities are highly questionable (Lee1991, Lee et al. 1991), especially where socialand cultural disruptions are considered in addi-tion to economic changes; e.g., some researchersnote that economic changes also bring lifestylechanges that may be significant. As Krannichet al. (1994:52) suggest: “In some cases...alternative economic activities may be incon-gruent with the social meanings associated withresource use and the lifeways of some culturalgroups.” A purely economic analysis overlookssome impacts on certain occupational groupsand individuals less able to change and adaptas their circumstances change (Carroll and Lee1990:152).

• Much of the social impact assessment litera-ture focuses on social responses to a specificproject and its consequences; FEMATfocused on the capacity of communities toadapt to an array of possible changes in forestmanagement activities.

The FEMAT social science team termed theability of a community to weather a change inFederal land management “community capacity.”The panel it convened from Washington, Oregon,and California identified several factors that af-fected the capacity of a community to adapt tochange, including but not limited to economicdiversity (the most often mentioned), the degreeof timber dependence (including employmentand the availability of private timber), localleadership, location, history of community-basedimprovement efforts, community cohesion andconflict, civic involvement, local control of re-sources, community attitude, cultural identity,population size, and income levels (FEMAT1993).

Unfortunately, the history of the literature onrisks to communities has focused mainly on eco-nomic analysis (FEMAT 1993). The current re-search on the basin presented here acknowledgesthe importance of economic studies (this reportincludes an economic analysis of the region), butthe community approach taken for this researchreflects the concerns of the FEMAT investigatorsthat economic analysis alone provides a narrowdefinition of how communities depend on naturalresources. Timber dependence or any kind ofeconomic or industrial dominance in a town, al-though important for some communities withinthe study region, was not the sole focus of thecurrent assessment. The west-side analysis, itmight be noted, concentrated on forest manage-ment issues and attempted to transcend simplifiedpolarizations such as “owls vs. jobs” to explorehow “communities are more than just bedroomsfor wood workers” (FEMAT 1993:66). That as-sessment stressed that the connection of a ruralcommunity to natural resources is more than justa paycheck; it often has been the basis of thecommunity’s customs and culture.

2 Significantly for the thesis proposed by researchers likethese, the economic calamity forecast for the Northwestfollowing the imposition of option 9 has never occurred.Egan (1994) reported in the New York Times that “threeyears into the drastic curtailment of logging in federal for-ests, Oregon, the top timber-producing state, has postedits lowest unemployment rate in a generation, just over 5 per-cent.” The newspaper article notes that, although Oregon hadlost 15,000 jobs in the forest industry in the previous five years,the predicted number was 100,000 job losses, and the statehad gained 20,000 jobs in high technology, with workers beingretrained for some of those jobs.

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Exploring these connections means that commun-ity assessment must move beyond easily measur-able, objective data to subjective attitudes andperceptions such as measures of quality of life.Branch et al. (1982), for instance, recommendan approach to measuring social well-being thatcombines objective and subjective measures, in-cluding rates of the usual indicator behaviors, theaccess to resources by various groups, and theperceptions of community and individual well-being. In a similar vein, many of the factors thatthe FEMAT panelists identified as affecting com-munity capacity have to do with the hard-to-define concept of “quality of life.” Branch et al.(1982:7) likewise suggest some factors affectingquality of life:

Among other things, these factors caninclude feeling a part of the communitywhere you live; knowing where youstand in relationship to other people;having a sense that you and people inyour community have control over thedecisions that affect your future; know-ing that your government strives to actin ways that benefit everyone equitably,rather than benefiting just a privilegedfew; living without undue fear of crime,personal attack, or environmental hazard;and feeling confident that your childrenwill get a fair start in life.

Researchers with limited time and money can goonly so far in measuring these factors within acommunity, but our assessment reflects concernsof Krannich et al. (1994) with the well-being andquality of life experienced by affected indivi-duals, groups, and populations. Our assessmentresearch shares their suspicion of reliance oneasily measured social indicators, such as em-ployment and income levels, crime rates, anddivorce rates.

These kinds of concerns were integrated into ourfocus on key dimensions of community condi-tions (key constructs), the indices developed to

measure them, and the approach used to assessa community’s capacity to weather change (re-silience of a community). Also, other kinds ofconnection and dependence, such as how com-munity members value the special places in andaround their communities, were examined, andthe ways they form other components of com-munity capacity or cohesiveness were considered.Central to our approach was development of anassessment process that extended beyond sim-plistic indicators to measuring key communityconstructs—one that reflected and incorporatedthe results of the literature review summarizedabove.

• Social impact assessments need a temporalcomponent.

A shortcoming of many social impact assess-ments is that they are conducted before the startof a project but not throughout the life of theproject (Geisler 1993, Gramling and Freudenberg1992). Variables, including Federal policy, chang-ing regional developments, human populations,land ownership, land value, and human values(Geisler 1993), can change over the life of a pro-ject and in the long term. A change in any one ofthe variables can significantly alter the impact ofa project or policy change. Also, the impacts of aproject or policy can begin at the time a projector policy is initially proposed and at any timeduring the actual implementation of the projector policy (Gramling and Freudenberg 1992).

Our assessment examined a variety of communi-ties and provides a snapshot of them at one pointin time. It does not forecast the potential impactsof any particular policy or project on the com-munities, but rather provides an overview of thecurrent situation for those communities. None-theless, by examining many communities in dif-ferent stages of development, the temporal issueraised here could be addressed. Such an analysis,however, was beyond the scope of this research.

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IntroductionOur assessment of communities, as initially con-ceived, focused on the sociodemographic, cul-tural, and civic aspects of the rural towns in theregion. The research also analyzed secondarydata on the total population of 387 rural com-munities in the region; these data includedestimates of population characteristics fromthe 1990 census, as well as 1992-94 state popula-tion projections.

Economic conditions in these communities wereapproximated through resident perceptions of theeconomic diversity of their communities and theirdependence on various resource-based industries.Other kinds of information on the economies ofthe individual communities were not initiallyavailable (such as employment or income esti-mates for particular economic sectors), althoughthe value and significance of obtaining this infor-mation at a community level for the entire regionwere recognized partway through the researchprocess.

Details of the methods used for the assessmentcan be found in a companion publication.3 It pro-vides examples of the materials developed forthe research (i.e., workbooks, other forms, in-structions), and it describes the procedures de-veloped and applied in the use of those materials.

The Community Self-Assessment StudyDeveloping a Strategy for SamplingCommunitiesOriginally, the scope of work outlined for therural community assessment was to identifycounties in the region whose populations aregrowing the most, along with counties decliningthe most. To keep the number of assessed com-munities to a reasonably small number, the re-search was to focus on two communities fromeach county for study, for a total of 40communities.

The specified reliance on counties eventuallywas abandoned as a frame of analysis with therecognition that political boundaries have littleto do with sociocultural, economic, or resource-management factors. The focus was shifted tothe communities themselves, and a list of the 40fastest growing and fastest declining communi-ties was generated. This change, however, led toanother consideration: What about communitieswhose population remained constant? A thirdcategory of communities having this kind of min-imal population change was added. Three catego-ries of communities would be sampled, 20 com-munities from each category, for a total of 60communities.

This approach led to a lopsided selection of com-munities that could not represent or allow gen-eralizations about the region. For instance, the

Methodology for AssessingCommunities

3 Harris, C.C.; McLaughlin, M.J.; Brown, G. A detailedmethodology for assessing characteristics and conditionsof small rural communities. Manuscript in preparation.

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declining communities consisted mainly of com-munities with populations less than 100 people,where a relatively minor loss of population canhave a significant effect on any percentage ofchange in population.

The sampling strategy based on populationchange therefore was abandoned. Instead, a re-search design based on a simple random sample,without considering population change, was de-veloped that would yield a representative sampleof communities from across the region, with ran-dom variation in populations and other character-istics. This research design required a sufficientlylarge sample of communities from which infer-ences could be drawn and generalizations madeabout all communities in the study area.

A final issue considered was the inclusion ofcensus designated places (or CDPs), which areunincorporated communities comprising dense-ly settled concentrations of population that areidentifiable by name but are not legally incor-porated places; examples are suburbs of citiesor towns within American Indian reservations.To qualify as a CDP for the 1990 census, anunincorporated area must have met the followingcriteria (in all states except Alaska and Hawaii):1,000 or more persons if the CDP is outside theboundaries of an urbanized area delineated forthe 1980 census or a subsequent special census;2,500 or more persons if it is inside the bound-aries of an urbanized area; and 250 or more per-sons if it is outside the boundaries of a urbanizedarea delineated for the 1980 census and withinthe official boundaries of an American Indianreservation recognized for the 1990 census. Al-though the Bureau of the Census has identifiedand delineated boundaries for CDPs since 1950,these boundaries have no legal status, and theyalso do not have officials elected to serve tradi-tional municipal functions. It was decided thatCDPs that were suburbs of cities would not besampled in the present assessment, based on theassumption that the fate of a suburb of a city—Spokane, for instance—would rise and falllargely with its city and not, as with a smaller,

isolated community, on its own. Given that CDPsare unincorporated areas, the only ones includedin the present study were those associated withtowns on reservations.

The sampling element that was finalized for thestudy, then, was the community. Thus, the presentresearch focused on the 387 small rural commu-nities in the interior and upper Columbia basinthat were incorporated towns with populationsestimated to be less than 10,000 residents in1995. To ensure statistical significance and anadequate number of cases to conduct multivariateanalyses, a sample was needed of as many ofthose communities in the region as possible. Half,or about 194 communities, were targeted as a rea-sonable number given potential budgeting andlogistical constraints. These communities wereselected randomly.

Development of the CommunitySelf-Assessment Process: What andWhy?

We recommend that further region-wide assessment should include a com-munity self-assessment component.Self assessment is a logical part of anymitigation measure as it will reflect thevalues of people living in the communi-ties; provide a vehicle for integratinglocal knowledge in policy decisions;and contribute to a sense of community-level ownership in the resultingrecommendations…self-assessment mayprove beneficial by stimulating dialogueabout local conditions among locals thatcan lead to community self-development.

FEMAT (1993:75)

Because of time constraints, the FEMAT socialassessment team was limited in its assessment ofcommunities to a survey of extension agents togather information about the communities theyworked in and around. For our assessment, wevisited the study communities and learned dir-ectly from opinion leaders about their communi-ties: these opinion leaders were lay experts activeand involved in their communities and possessing

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a good knowledge of the workings of their com-munities and attributes, such as politics, history,businesses, and social cohesiveness. As an anal-ogy, it likely would be difficult to hold a meetingof citizens to obtain specific expert medical orlegal information, as opposed to a meeting witha group of doctors or lawyers. In our approach,insider insights and informed judgment countedfor something.

We decided, then, that the most effective andefficient way to involve the local public was toorganize focus groups comprised of an optimumnumber of these community opinion leaders rep-resenting various backgrounds and viewpoints.This approach would enable the opinion leadersto express their views of themselves and wheretheir communities were going from a broad rangeof backgrounds and viewpoints within that com-munity. The information sought from thesegroups, then, was not observable, recorded data,but the perceptions of community members andtheir beliefs about their community’s situation.As Branch et al. (1982:36) note,

Residents’ perceptions often do not cor-respond exactly to objective changes, butperceptions can have a powerful influ-ence on individual and social action. Ifpeople perceive that they do not have ac-cess to resources, for example, they canbe as closed off from the resources as if aformal system blocked their availability.

A community self-assessment workbook was de-signed to enable community members to dispas-sionately describe the characteristics of theircommunities and the changing conditions in themin a careful, thoughtful, balanced way. There area number of sound reasons for seeking this in-sider perspective. Common sense suggests thatactive, involved community members will knowtheir community best and are the best source ofinformation. The researchers, moreover, can havetheir own set of outsider’s assumptions and biasesabout the functioning of different communities.As Palinkas et al. (1985:15) caution,

Unless the investigator can take into ac-count his own culturally constituted setof theoretical and methodological limita-tions, he can never hope to understandthe present pattern of social relationsor make projections concerning futurechanges in the social, cultural, economic,and institutional life of the communities.In order to secure this understanding andmake projections with any confidence, aninsider’s perspective is necessary.

Neutral investigators can play an important rolein gathering a variety of opinions about a com-munity, facilitating the sharing of information,and filtering through the various viewpointswithin a community. Although community mem-bers may, of course, have their own viewpointsand perceptions, they are the views and under-standings of insiders, of actively involved com-munity members who are the most knowledgea-ble about their communities. As Branch et al.(1982:8) observe, in addition to knowing whatchanges will be occurring, “it is also necessary toknow what those changes will mean to the peoplewho will be affected by them.”

It is unclear why anyone from the communitieswould accept results about local communities ifat least some of the local lay experts weren’t con-sulted. “People will not support what they don’tunderstand,” Clark and Stankey (1994:35) ob-serve, “and they cannot understand that in whichthey are not involved.” Who could blame a com-munity for being suspicious about a study con-ducted from afar that treats them as little morethan demographic data, or where outsiders dis-cuss with outsiders what must be occurring inthat community?

The Community Self-AssessmentProcess: How?In sum, the community self-assessment study wasthe major primary-data collection effort for theassessment research. For each of the 198 smalltowns sampled, we organized a focus group tar-geted for composition of eight different kinds ofresidents. The residents asked to participate were

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knowledgeable, active opinion leaders identifiedby fellow residents in each community as bestrepresenting eight specified categories of inter-ests, specialties, and perspectives, including localgovernment, education, health and human serv-ices, and business.

A modified snowball sampling design was de-veloped and applied, whereby five people in eachcommunity (including the city or town clerk, anelected official, the Chamber of Commerce ex-ecutive or administrative secretary, an officer ina major civic group, and the superintendent ofschools or a principal of a school in town) wereasked to provide a list of people to fit the spec-ified categories (some provided more than onename for each role, and others provided namesfor certain roles only). The people whose nameswere provided also were contacted and asked toprovide a list of eight, until five names for eachcategory were identified. The person mentionedmost often for each role was asked to participatein the assessment.

Although it was not always possible to find some-one for each category in some of the smaller com-munities, we included as many of them as pos-sible. This factor of finding willing participantswas an important one. It was not always easyto find people identified as being active and in-volved who were willing to donate the effort andtime needed to participate in the workshop proc-ess, much less uninvolved, or even apathetic orotherwise occupied, people. Occasionally, evensome of these involved, committed people wouldagree to attend a workshop but then would not doso. It thus is likely that residents already less in-volved than others in their community would beeven less likely to agree to participate in a time-consuming process or even a community work-shop. Indeed, people less knowledgeable abouttheir town would likely be much less motivatedto participate in a workshop.

Each workshop was facilitated to gather and syn-thesize information about a sample community onthe basis of responses to the community self-assessment workbooks completed before theworkshop. The workbook responses provided

information on the perceptions and insights of1,350 active and involved community membersacross the 198 communities.

Each participant in the assessment was asked,first, to fill out the community self-assessmentworkbook (which took about an hour to com-plete). The purpose of the workbook was to helpcommunity members describe the characteristicsof their communities and their aspirations fortheir towns, providing indepth information on 13key constructs depicting their town’s situation interms of various dimensions of characteristicsand conditions. The key constructs were:

• Attractiveness of the community

• Attractiveness and amenities of the regionsurrounding the community

• Community attachment (personal attachmentto the community)

• Community cohesiveness (sense ofcommunity)

• 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 theircommunity to change or remain the same)

The workbook was an instrument to obtain rat-ings for the key constructs. Its format was a seriesof questions for each construct. Each section wasorganized in the same general way: Most sectionsbegan by asking an open-ended question relatedto the central dimension of a particular constructto help the respondent start thinking in broadterms about that dimension of their community.Then, a series of more specific questions wereasked by using seven-point, bipolar scales to

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elicit quantitative ratings of the community onspecific aspects of that dimension. A more gen-eral, multiple choice question with descriptionsof alternative options was then asked whose pur-pose was to help respondents think about howthey would describe their community on that con-struct in general terms. Finally, a standard seven-point scale to measure the overall key constructwas presented to obtain an overall rating for it. Inmost cases, the question set alluded to the ques-tions just answered: “Keeping in mind the an-swers you have given above, how would yourate the ___ in your community?”

After completing the workbook, the communityparticipants attended a 2-hour community work-shop to discuss the answers given individually intheir workbooks. The purpose of the workshopswas to bring together a focus group represent-ing the diversity of knowledge and perspectiveswithin each community and explore the depth andcomplexity of conditions within the community.In the workshop, community residents met withone another to share and discuss the answers.Comparisons of their comments and the resultswere used to aid the group to better describe theircommunity. After discussing their ideas and in-formation, they were asked to rate the 13 keyconstructs a second time.

Thus, rather than simply aggregating the individ-ual ratings of community members on the keyconstructs for each town, the workshop was con-ducted so that the members themselves could pro-vide a group rating after sharing ideas and infor-mation. Some members of the workshop mighthave more information on a variable or knowmore about factors affecting it (for instance,an economic development official might havegreater knowledge about the community’s eco-nomic diversity). In other cases, a participantmight remind others of something they had notconsidered in rating a variable. The role of theworkshop facilitator was to clarify the questionsin the workbook, ask participants to discuss theirindividual rating for each construct variable, andconduct the group rating. The intention in thisprocess was not to compel the group to reachconsensus, although this sometimes happened.

The goal was to facilitate the sharing of informa-tion and ideas that might affect an individual’srating of a construct variable and ensure a grouprating that was as reliable and valid as possible.

Assessment of CommunityEconomiesThe economics group for the ICBEMP socialassessment team decided early in the assessmentprocess that regional information on the area’seconomy would be sufficient for its analysis. Al-though the value of data on the economies ofeach of the communities was recognized, the col-lection of these data was incorporated into thestudy only later in the research process. Conse-quently, the assessment of community economiesthat eventually was conducted was somewhatconstrained by time and resources available.

Profiles of the Economic Structureof Rural Basin CommunitiesThe economic assessment of the region’s com-munities (cities and towns) provided profiles ofthe economic structure of each of the 476 com-munities and CDPs in the region. These profilesconsisted of estimates of the proportion of totalemployment in a community attributable to eachindustrial sector contributing to that community’seconomy. The profiles were based on an inven-tory of all firms, businesses, and agencies in orotherwise affiliated with each community. For thepurposes of the profiles, all employment in thesejob-producing organizations was attributed to acommunity if the firm or agency had its addressin the community.

Trade, service and professional businesses, andgovernment offices typically are located physi-cally in a given community, and their employeesare likely to reside in that community. Primaryproducers, secondary processors, and other man-ufacturers, however, may have their address inone town but have a plant located between it andone or more other towns and employ residentsfrom all of them. Likewise, farmers and ranchersmay have farms and ranches located some dis-tance from the town where they get their mail and

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socialize, and most of their economic activity(i.e., their purchasing of goods and services forboth business and household and their sellingof their produce) takes place in trade centers or“central places” further up the trade hierarchyfrom these “home towns.”

The data in these profiles, therefore, do not repre-sent the results of economic base or economic im-pact models. They represent the economic base ofa community only in a very rough way, in that atown’s economic base depends to varying degreeson primary producers and secondary processorslocated beyond city limits (one could theorizethat the closer a mill or plant is to a town, thegreater its likely contribution to that community’seconomic base, although this was not investigatedhere). Given the interconnectedness of industriallinks across communities, and the important roleof central places in trade hierarchies that areespecially relevant in rural regions such as thestudy area, the economic importance of primaryproducers and secondary processors for a giventown cannot be surmised from our data. The dataalso do not indicate what the impact on the townwould be if a plant or mill closed. Different smalltowns located in farming country, for example,might be impacted to various degrees and in var-ious ways if, say, the multitude of small familyfarms and ranches in the area were consolidatedinto one or two large ones, as has been the trendin recent decades. Nonetheless, the data compiledand reported here provide a rough indicator ofimportance of various industries for specifictowns and thus provide a starting point for fur-ther economic analysis.

The profile of employment for each of the 476communities (cities and towns) and CDPs in theregion provided a representation of the economicstructure of these communities. These data, (esti-mates of the proportion of a town’s total employ-ment attributable to each industrial sector contri-buting to that town’s economy) were developedin collaboration with University of Idaho econo-mists (see, for example, Robison 1998, Robisonand Peterson 1995). These data provide a profileof each community’s economy in terms of 22categories of industrial sectors: agriculture,agricultural services, wood and paper products

manufacturing, food processing, miscellaneousmanufacturing, sand and gravel mining, othermining, construction, public utilities, communica-tion, business and personal services, transporta-tion, wholesale trade, retail trade, food and bever-age, lodging, amusement and recreation, medicaland social services, Federal Government, stateand local government, and finance, insurance, andreal estate.

These major categories represent an aggregationof all industrial activities included under the sub-categories for each standard industrial category(SIC); e.g, the major category of wood and paperproducts manufacturing includes lumber milling,paper milling, and logging activities among thevarious subcategories of industrial activity thatthe main category represents.

This data set represents an updating and dis-aggregation of 1992 employment and earningsdata from the U.S. Bureau of Economic AnalysisREIS (Regional Economic Information System;1994) and the Forest Service’s IMPLAN data(REIS data updated and estimated at the countylevel for all counties in the study area; seeRobison 1998). These data were resolved andallocated to all communities (towns, cities, andCDPs) in the region. This disaggregation wascompleted by using local sources such as phonelistings for businesses (InfoUSA, Inc. 1995) andrecent directories of businesses for the relevantstates. (For a discussion of the methods used andtheir theoretical basis, see Robison and Peterson1995.)

The only addition in the current research to themethodology described by Robison and Peterson(1995) was the ground-truthing of the employ-ment estimates through interviews conducted bytelephone with city clerks, U.S. Postal Serviceemployees, county extension agents, and repre-sentatives of major businesses for each town.This ground-truthing updated the employmentdata to the extent possible to the first quarter of1995, so that it would be temporally consistentwith the period when the community assessmentswere conducted. This consistency ensured thatvalid comparisons between the results of the twodatabases could be made.

25

Travel and Tourism EmploymentThe sectors above did not include estimates ofemployment for the travel and tourism industry.Consequently, although estimates of employmentattributable to other resource-related industrieswere available for comparison, this was not thecase for employment related to outdoor recrea-tion, travel, and tourism. Yet much of the employ-ment in travel and tourism is directly resourcelinked, as in the case of economic activity result-ing from recreation trips to the natural resourcesin the region. Part of this travel and tourism em-ployment also is indirectly resource linked, as inthe case of business travel by firms in variousresource-linked industries.

In the present analysis, a rough estimate of em-ployment attributable to travel and tourism wasobtained from an indirect measure based on aneconomic base technique applied at the level ofthe small community: the minimum requirementsapproach (Tiebout 1962; Ullman and Dacey1960, as cited in Tiebout 1962). This approachto estimating the economic base of a communityfocused on redistributing the proportion of em-ployment in the communities initially attributedto other sectors that could be attributed to thetravel and tourism sector.

As Tiebout (1962) explains, this approach as-sumes that a minimum amount of employment ina given sector of a community’s economy canbe attributed to local requirements (nonbasic em-ployment), with the remaining employment attri-butable to the production of exported goods orservices (i.e., basic employment). In the case oftravel, tourism, and recreation, for which keysectors include lodging, food and beverage, retailtrade, and amusements, an estimate of the con-tribution of this industry to a community’s econ-omy can be derived from estimates of the basicportion of employment in these travel and tour-ism subsectors. All lodging employment could beassumed to be basic (scarcely ever do residentsof a town stay in the motels, hotels, etc., in theirown town), while the contributions of residents

to the other sectors needed to be estimated. Oncethe local requirements for consumption of foodand beverages, retail trade, and amusements weredetermined in proportion to each town’s total em-ployment, these proportions for each sector weresubtracted from the proportion of employment forthat sector in each town. The remainder couldthen be attributed to either basic employment orthe travel and tourism sector. Thus, the propor-tion of employment in the travel and tourism sec-tor was the sum of the proportions attributable tobasic employment for all the travel and tourismsubsectors.

For our research it was assumed, followingTiebout (1962), that the smallest communitiesof the basin might not be typical of either thelocation or the economies of the target popula-tion of rural communities. Accordingly, thesmallest 5 percent of the basin’s communitieswere excluded from the analysis of minimum re-quirements for employment in the sectors underconsideration. Next, to calculate a town’s mini-mum requirements, the mean proportion of em-ployment in each of the travel and tourism sub-sectors for the remaining towns was calculated,along with a display of these proportions byquartiles. After the lowest quartile proportionswere compared with the mean proportions for thesubsectors, it was decided that the former propor-tion (i.e., the cutoff proportion for the 25 percentof the towns with the smallest proportions of em-ployment in a given subsector) would provide themore sound and conservative estimate of non-basic employment needed to meet local serviceneeds.

A constant proportion of employment represent-ing the local requirement for consumption in eachof the food and beverage, retail trade, and amuse-ment subsectors was determined, and each ofthese constants was subtracted from employmentfor each sector in each town. As table 1 shows,these constants included 2 percent for the foodand beverage subsector, 6 percent for the retailtrade subsector, and 1 percent for the amusementssubsector.

26

The remainder then could be attributed to basicemployment, or the travel and tourism sector.The average “excess” proportion of total employ-ment, or basic employment, across the small ruraltowns in the basin included 2 percent for lodging,4 percent for food and beverage, 5 percent for re-tail trade, and 1 percent for amusement and rec-reation (table 1). When these percentages forbasic employment in these subsectors weretotaled, they provided an estimate of an averageof 12.2 percent of total employment attributableto travel and tourism across all rural communitiesin the region.

Because the estimation of travel and tourism em-ployment lumps both recreational travel (outdoorrecreation, tourism, etc.) and business travel to-gether, the comparability of the travel and tour-ism sector with, say, the outdoor recreation andtourism industry assessed in the community self-assessment workbooks was somewhat limited.The travel and tourism sector actually was mostclosely related to the workbook question askingthe extent to which an economy centered pri-marily around retail stores or tourism services.

Surveys of Residents ofChelan County and SignificantChange CommunitiesA representative survey of residents of ChelanCounty, located in central Washington, was con-ducted to assess the opinions and attitudes of onecounty’s residents about growth and resource

management issues in a rapidly growing county.Following Dillman’s (1978) survey procedures, amail questionnaire was used to collect data froma population defined as adult representatives ofall households in the county. A total of 700 ques-tionnaires was sent to a sample of residents ran-domly selected from local phone books. Eightwere undeliverable. After two questionnaire mail-ings and a postcard-reminder mailing, 222 com-pleted questionnaires were returned, for a re-sponse rate of 32 percent. These data provideduseful insights into the preferences and concernsof residents in a county typical of those in theinterior West experiencing significant populationgrowth and community development.

A fourth component of the assessment researchwas to examine and analyze the characteristicsand experiences of 145 communities in the re-gions identified as “significant change communi-ties.” These communities were indicated asundergoing major change by (1) state economicdevelopment officials, agricultural extensionexperts, and Forest Service forest planners oreconomic development coordinators; or (2) U.S.census population estimates of changes of + 20percent since 1980. Data collection on thesecommunities focused on identifying the kinds ofchanges occurring, the kinds of communityresponses, and the effects or characteristics of allthese factors in terms of community conditions,activities, and lifestyles. A random sample of 80of the 145 communities indicated them to be

Table 1—Estimated mean proportion of total employment in rural Columbia basin communitiesthat is attributable to the travel and tourism sector, with mean proportions of total employmentattributable to basic and nonbasic employment for subsectors

Sectors of traveland tourism Total employment Nonbasic employment Basic employment

Percent of jobs

Lodging 2 0 2Food beverage 6 2 4Retail trade 11 6 5Amusement recreation 2 1 1

Total 12

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significant change communities, which weresurveyed about the major changes affecting themand the impacts of these changes and their re-sponse to them. Initial contacts were made withcity clerks, who were asked to suggest the nameof the person who would have the greatest knowl-edge of the changes the town had experiencedand its response to them. The survey was con-ducted with a structured telephone interview ofthis representative of the town.

The primary purpose of collecting these initialdata was to better identify communities to studyas part of the indepth case studies of 10 townsalso conducted as part of the assessment, as wellas to better understand factors or variables to con-sider in those case studies. The findings for the10 communities identified as having undergonemajor changes in recent decades of the kindsmost prevalent in the study area are reported else-where (Harris 1996). Harris describes the indepthcase studies of these communities, which focusedon gaining greater understanding of the majorchanges influencing a diversity of communities,the impacts of those changes, and responses bythe community.

Analysis and PresentationQuantitative data were gathered for the assess-ment and were analyzed statistically with the“Statistical Package for the Social Sciences”

software (SPSS, Inc. 1989). Univariate analyseswere performed on the census data, economicprofiles, and data obtained at the communityworkshops. Mean values for relevant variablesare presented in the following sections of thispaper.

Where the data analyses presented here werefrom the community self-assessment, they repre-sent a community’s overall response; i.e., thecommunity is the unit of analysis. In the case ofcontinuous data collected with numerical scales,the data reported in the following sections are themean values of the workshop responses. Fre-quencies for the nominal-level data were obtainedfrom the workbook results for all 198 sampledcommunities. Where only one value is reportedfor these kinds of data, it represents the mode forresponses from the workshop participants. Theinitial results of the survey of significant changecommunities also are presented in tabular form.

Multivariate analyses were performed on contin-uous data with one-way analysis of variance(with appropriate posthoc tests of difference),stepwise regression, and cross-tabulations (withappropriate tests of strength of relationship). Thelevel of statistical significance used was p<0.05.

28

IntroductionThis chapter describes some of the initial majorfindings of the rural community assessment.Given the extensiveness of the data collected, thispaper focuses on providing an overview of thestatus of small rural communities in the basin andthe influences of public land management on theregion’s rural communities. Further analysis andreporting will explore the full breadth and depthof these data and their implications for under-standing the region’s rural communities.

Gathering and AnalyzingCommunity Scale Social DataVarious possible levels of scale for studying hu-man beings and their sociocultural and economicorganizations and processes were considered inthe assessment research. Units for such an analy-sis can be based on levels of social organizationand everyday human activities—individuals aswell as collectivities, geography, and politicalboundaries. Levels of social collectivities includegroups of individuals in service clubs, civicgroups, and special interest groups, and the locusfor each of these groups and their activities canrange from the local level to state, regional, andnational levels. Commonly recognized levels ofsocial organization based on geography and hu-man activities range from households to neigh-

borhoods, communities (towns and cities), coun-ties, multicounty regions, and states.

Communities were selected as the most appro-priate unit of analysis primarily because townsand cities typically are the center of daily life formost people living in rural America. Rural com-munities are the places where individuals andgroups of individuals carry on much of theirwork, play, and civic activities as well as theplaces where they go for services important intheir lives (school, church, shopping, health,sports, and recreation). Because of these impor-tant factors, social scientists studying socialgroups (for instance, sociologists and anthropo-logists) most often focus on the community as theprimary unit of analysis.

Results of the Chelan County survey are instruc-tive here. The analysis of that survey confirmsthat, although 38 percent of county residentslived outside a recognized town or city, most(79 percent) felt that the town or city where theycollect their mail was a somewhat to very im-portant aspect of their lives. Only 18 percent ofthose surveyed rated the community where theycollect their mail as being only slightly importantin their lives. These results affirm that, althoughmany residents of a county live outside the citylimits of any town or cities, nearby communitiesare important elements of rural living for all but asmall segment.

An Overview of the Statusof the Rural Communitiesin the Basin

29

A secondary reason for choosing the communityas a scale of analysis for an assessment is thathigher levels of scale can always be examined byaggregation of community data, which them-selves represent the aggregation of individual andhousehold data. The primary locus for the rela-tionship between residents of rural areas andplace is the community. Rural towns are suf-ficiently small that neighborhoods are not themeaningful unit of analysis that they are in largercities. County-level activities and responses canbe examined by aggregating community-leveldata, but county-level aggregation cannot depictthe differences in characteristics of different com-munities within a given county or the impacts ofFederal, state, and county policies on them. Fur-ther, communities are composed of both the indi-vidual residents and the social groups they join orbecome a part of, and an accurate understandingand description of communities requires data onthese elements.

The results of our research confirm that, in manyplaces, social conditions and key changes in thoseconditions, when depicted at the broader levelof counties, mask important differences in thoseconditions and changes across communities. Forinstance, the population of a county and itsgrowth may not represent the situation for townswithin that county, as in the example of threerural communities in Wallowa County, Oregon(table 2). As table 2 shows, changes in past andfuture trends in, say, population clearly differ bycommunity and are not reflected in county-leveldata.

The economic links among communities indifferent counties and even different states maybe equally significant. An initial analysis of thesocial networks linking communities confirmedthat these networks are as important as politicaland economic ties. Thus, the issue of scale under-scores that the county as a sociocultural realitymay not be meaningful for many residents andthus not as relevant a unit of analysis.

A final reason for the focus on small towns re-lates to policy development and its real-worldconsequences. Many people are concerned aboutthe impacts of resource planning on their com-munities, as well as on individuals, families,and a region’s customs and cultures. During the1980s, when significant impacts of changes inFederal resource management began to be felt incommunities, the focus of resource managementreflected the concerns of communities in transi-tion and the concept of community stability. Al-though many people may not want to return to thekinds of conditions that resulted in the boom-and-bust cycles once characterizing many communi-ties in the American West, the reality is that ruralcommunities will continue to evolve and change.Accordingly, it is important to remember that anydescription of a community’s characteristics andconditions is a snapshot of its situation at onepoint in time and the context in which that partic-ular situation unfolded. Looking at the recent pastand current conditions surrounding a particularsituation can provide a better understanding ofwhere a community has been and where it seemsto be heading.

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

County and 1980 1990 Population change 1994 Population changecommunities population population (1980-1990)a population (1990-2000) a

Percent change Percent changeWallowa County: 7,273 6,911 -5 7,200 10

Enterprise 2,003 1,905 -5 1,935 5Joseph 999 1,073 7 1,165 21Wallowa 847 762 -10 755 -2

a Straight-line projections based on 1990 and 1994 population estimates obtained from the Oregon Center for PopulationResearch and Census (1995).

30

Most Rural Communities AreSmallAt the time the research was being conducted inthe region, the 387 small rural communitiesranged from 26 to 9,760 in population (1992-94population estimates). Differences in the commu-nities based on their population size were ana-lyzed for a subsample of 198 towns by categoriz-ing communities into one of four population sizeclasses: 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 townshad fewer than 1,500 residents (68 percent) andwere classified as rural villages by Johansen andFuguitt (1984). Within this class, communitiesranged from 22 to 1,500 people with an averageof 520. The second class of towns ranged from1,501 to 3,000 residents. These communitiescomprised 19 percent of all communities in thebasin, with an average size of 2,162. The thirdlargest class, which ranged from 3,001 to 5,000people, accounted for 7 percent of the communi-ties and were on average 3,974 in population. Theremaining 6 percent of the communities were inthe largest class, ranging from 5,001 to 10,000people, with an average of 7,087.

The Geography of theCommunitiesThe role of geographic location in characterizingcommunities and assessing community resiliencewas considered in the analysis of the communitydata. The selection and study of a large randomsample of towns across the region ensured an as-sessment representative of the entire basin. Whenanalyzed by state boundaries, the survey of 198communities indicated that the largest proportionof small rural towns were in Idaho (41 percent,or 81 towns), with major proportions in easternWashington (28 percent, or 55 towns) and Oregonas well (23 percent, or 46 towns). A much smallerproportion of small towns were in westernMontana (7 percent, or 14 towns) and Wyoming(1 percent, or 2 towns).

The Forest Service denotes major geographicalregions based on the ecology of the landscape byclassifying regions into ecological reporting units(ERUs). These ERUs are aggregations of individ-ual watersheds within major ecosystem types.Thirteen ERUs, some spanning parts of two ormore states, were identified for the study area(fig. 2).

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

31

In terms of the largest number of rural communi-ties within different ERUs, the most significantunit was the Columbia Plateau ERU, where morethan 32 percent of all the communities were lo-cated. Another 15 percent of the region’s ruralcommunities were in the Northern GlaciatedMountains ERU, 9 percent were in the OwyheeUplands ERU, and another 9 percent were in theBlue Mountains ERU. The Central Idaho Mount-ains ERU followed closely with 8 percent of ruralcommunities, and the Upper Snake, Snake Head-waters, and Lower Clark Fork ERUs had the nexthighest numbers of rural communities, withbetween 5.1 and 6.1 percent. The Northern andSouthern Cascades, the Upper Clark Fork, andthe Upper Klamath ERUs accounted for the re-maining communities in the region with between2.0 and 3.5 percent. Only a few communities(0.5 percent) were located in the Northern GreatBasin ERU.

Characteristics ofParticipants in CommunitySelf-AssessmentsThe characteristics of residents who participatedin the community assessment workshops wereanalyzed and the results compared with thosefrom the survey of all Chelan County residents.This comparison was based on the assumptionthat similarities between the characteristics of thegeneral populace of a randomly selected countyand those of the workshop participants wouldminimize concerns about how representative“opinion leaders” were of other citizens in theircommunities. As discussed previously, a concernof people who reviewed and commented on theassessment methodology was that the participantsselected may not have adequately represented theresidents 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 liveoutside an incorporated town, and they can com-prise the majority of people living in a county). Amajor objective of the survey of Chelan Countyresidents was to address these concerns and, tothe extent possible, assess their validity (seeKrull 1995).

Data collected on characteristics of the workshopparticipants showed that 43 percent of partici-pants were female and 57 percent were male. Theaverage age was 51, with ages ranging from 23 to94 years and a median age of 49 years. Individu-als aged 40 to 60 years old constituted the largestage class, with nearly 60 percent of all partici-pants. In addition, a greater percentage of olderindividuals (over 60 years of age) participated inthe study than younger ones (less than 30 years ofage). Similarly, the mean age of Chelan Countyrespondents was 53 years old, with the proportionof males and females found to be 53 and 47 per-cent, respectively.

Consistent with the age of the workshop partici-pants, about 37 percent of the participants hadlived in their community for 25 years or more.We targeted relative newcomers to elicit theirperspectives as well. About 21 percent of theworkshop participants had lived in their commu-nities 5 years or less. In addition, workshop par-ticipants also represented a number of practicaland philosophical perspectives that differed withoccupation and civic activity within their com-munity. To identify their ideological perspectives,workshop participants were asked to rate them-selves on a scale from 1 (liberal) to 7 (conserva-tive) that allowed them to define these conceptsthemselves. The resulting distribution wasskewed toward the conservative end of the scalewith a median rating of 5 and a mode of 6 (seefig. 3). Similarly, the same mean and medianwere obtained for the Chelan County residents,affirming the ideological representation by theworkshop participants of other residents.

Workshop participants also were asked to selectthe one category that best reflected the role orposition 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 busi-ness leaders (271, or 20 percent). Other rolesrepresented by the participants included educa-tional leaders (171, or 13 percent), civic groupleaders (117, or 9 percent), retired individuals(44, or 3 percent), self-identified environmenta-lists (40, or 3 percent), and individuals involvedin the community health services (38, or 3 per-cent). The remaining 29 percent of workshopparticipants were “other leaders” and people ofmiscellaneous 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 partici-pants ranged from less than $5,000 (0.4 percent)to more than $100,000 (6.7 percent). Most par-ticipants’ household incomes fell within the$25,000 to $34,999 range (21.5 percent), the$35,000 to $49,999 range (22.0 percent), andthe $50,000 to $74,999 range (22.8 percent).

Perceived Characteristics andCurrent ConditionsThe geography and ecology of the landscape areimportant for describing communities in the basinand understanding differences and similarities intheir characteristics and experiences. The geogra-phy of these communities in large part predeter-mines their economic base and thus their econo-mic structure. This condition along with locationand interrelations with other communities under-lies a community’s way of life and subsequentlyits social condition. In many cases, this geograph-ic basis for community characteristics and con-ditions transcends political boundaries (e.g.,counties and states) and, in some cases, severalERUs. Based on geography alone and the attend-ant uniqueness of each community, the communi-ty becomes the scale for understanding the variedcharacteristics and conditions in the region.

33

Current Characteristics andConditions of Rural CommunitiesPrimary data were provided on the community’scurrent characteristics and conditions by the par-ticipants in the community assessment work-shops. These data included responses on the keycommunity constructs described in “Methodologyfor Assessing Communities,” above. The resultsdiscussed below reflect the end points, or anchor-ing descriptors, used in the seven-point scales forthe overall construct ratings, which helped work-

shop participants rate their community on eachconstruct. These responses represent the result ofa cumulative assessment for each dimension ofcommunity.

Community attractiveness—A community’scharacter was defined as a combination of at-tributes ranging from a town’s visual appearanceto special places in the region where the town islocated. One key dimension of community char-acter is a town’s physical attractiveness, as per-ceived 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 attractive-ness tended to be on the high end (extremely at-tractive) 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, ex-tremely attractive). The distribution of attractive-ness scores ranged from values of 2.0 to 7.0 witha bimodal distribution of towns concentrated just

below and above the mean value. These resultsconfirm that many communities in the region per-ceived themselves to be as attractive as othercommunities, and in some cases, more so.

Regional attractiveness—Another characteristiccontributing to a community’s character was itsregional attractiveness. This characteristic refers

Figure 5—Distribution of meanratings for communityattractiveness across ruralColumbia basin communities.

Figure 6—Distribution of meanratings for regional attractivenessacross rural Columbia basincommunities.

Regional attractiveness

7.00 6.50 6.00 5.50 5.00 4.50

Fre

quen

cy

60

50

40

30

20

10

0

Std. Dev = 0.50 Mean = 6.11 N = 198

Community attractiveness

7.00 6.50 6.00 5.50 5.00 4.50 4.00 3.50 3.00 2.50 2.00

Fre

quen

cy

30

20

10

0

Std. Dev. = .093 Mean = 4.75

N = 198

35

to the attractiveness of the area beyond the com-munity’s city limits for a distance of 100 miles.Attractiveness in this case referred to a variety ofattributes, including the importance of the sce-nery and outdoor recreational opportunities in theregion where a community is located.

As figure 6 shows, the distribution of ratings forcommunity attractiveness tended be very high(close to the extremely attractive end of thescale), with a mean rating of 6.1 on a seven-point scale (ranging from 1, extremely unattrac-tive, to 7, extremely attractive). This mean wasthe highest of any key construct, indicating therelative abundance of scenery and amenities thatwas perceived across the region, regardless ofspecific location. Likewise, the distribution of theregional attractiveness scores ranged from valuesof 4.4 to 7.0, with a normal distribution of towns,except for a concentration of communities aroundthe mean value, and a standard deviation of 0.76.These results confirmed that most communities inthe study area perceived the region in which theywere located to be as attractive as that for othercommunities, if not more so.

Community cohesion—A community’s socialcohesiveness was defined as “the degree to whichthe residents of a community work together toget things done” and their “sense of community.”

The distribution of mean values for the region’ssocial cohesion scores was relatively small(standard deviation of 0.78), with ratings rangingfrom 2.3 to 6.6 and a mean of 4.9 on a seven-point scale (ranging from 1, an extremely weaksense of community, to 7, an extremely strongsense of community) (fig. 7).

Table 3 shows that, in response to a categoricalquestion on the extent of a strong sense of com-munity, a very small segment of communities wasso diverse with respect to the values of the com-munities’ residents that there was no agreementamong those values. Alternatively, in about halfof the remaining communities, residents were notonly in agreement but also held similar values.

Community services—Community services in-cluded those provided by either the governmentor the private sector. The mean rating of the ad-equacy of services in the sampled communitieswas 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). Thedistribution of responses for satisfaction withcommunity services was skewed with a dispro-portionate share of towns between 4.7 and 5.8.

Community autonomy—The autonomy of acommunity was defined as “the degree to which a

Figure 7—Distribution of mean ratings forcommunity cohesiveness across 198rural Columbia basin communities.

Community cohesiveness

6.25 5.75 5.25 4.75 4.25 3.75 3.25 2.75 2.25

Fre

quen

cy

30

20

10

0

Std. Dev = 0.80 Mean = 4.88 N = 198

36

community is linked economically, socially, andphysically to neighboring communities and to theregion as a whole.” For instance, a communitythat perceived itself as lacking autonomy con-sidered itself highly linked and dependent onsurrounding towns for its economic and socialwell-being. On the other hand, a community per-ceiving itself as being highly autonomous wasone that considered itself to be very independent,economically, socially, and physically, of othercommunities.

On a seven-point scale (ranging from 1, not atall autonomous, to 7, extremely autonomous),the community autonomy construct was compara-tively low with a mean rating of 3.4. This finding

underscores the relative dependence of small ruralcommunities on other towns. As figure 9 shows,however, a comparatively large standard deviation(1.14) and the rectangular distribution of theratings, with a wide range of values from 1.1 to6.3, indicate a wide spread of means across thescale for the community autonomy construct. Thisresult suggests that autonomy may not have beenconceived of strictly in terms of economics or thesupply of goods and services, but in the broadersocial context the concept was meant to represent.

In comparison with constructs for other keydimensions of community characteristics andconditions, the mean rating for communityautonomy was found to be the lowest: 3.4 on a

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

CumulativeSense of community Frequency Percentage percentage

Most residents hold similar values and are inagreement 90 45.5 45.5

The community has diverse values, butresidents have learned to work together 96 48.5 94.0

The community is very diverse and there isno real agreement in the community 12 6.0 100.0

Total 198 100.0 100.0

Figure 8—Distribution of mean ratings forcommunity services across 198 ruralColumbia basin communities.

Community services

6.25 5.75 5.25 4.75 4.25 3.75 3.25 2.75 2.25 1.75

Fre

qu

en

cy

30

20

10

0

Std. Dev = 0.96 Mean = 4.72 N = 198

37

seven-point scale, in comparison with meansranging from the next lowest mean of 3.9 forattractiveness for business to a high of 6.1 for re-gional attractiveness. This low mean ratingindicates that the towns in the region have arelatively low level of autonomy.

Table 4 confirms that the region’s communitiesare split between those very dependent on othertowns and those dependent on other towns forsome things but not for others. When workshopparticipants were asked to categorize their towns,those sampled were split between those sayingtheir community was very dependent on other

towns (43.9 percent), and those saying their com-munity was dependent on other towns for somethings 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 functionspretty independently of other communities).These three towns—St. John, Washington;Stanley, Idaho; and Sandpoint, Idaho—range inpopulation from 70 to 5,725 (1992-94 estimates).

Table 5 indicates that a community’s autonomywas significantly related to a variety of othercharacteristics and conditions. It confirms thatvarious aspects of community life, such as social

Figure 9—Distribution of mean ratings forcommunity autonomy across rural ICRBcommunities.

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

CumulativeLevel of community autonomy Frequency Percentage percentage

The community is very dependent on othercommunities 87 43.9 43.9

The community depends on other towns for somethings but is independent on other things 108 54.5 98.5

The community stands alone and functionsrelatively independently of other communities 3 1.5 100.0

Total 198 100.0 100.0

Community autonomy

6.25 5.75 5.25 4.75 4.25 3.75 3.25 2.75 2.25 1.75 1.25

Fre

quen

cy

30

20

10

0

Std. Dev. = 1.14 Mean = 3.42

N = 198

38

cohesion, community attractiveness, and popula-tion size were as important as economic con-structs (e.g., economic diversity and attractive-ness for business) for understanding a commu-nity’s sense of self-reliance and independence,and consequently that autonomy is moderatelyrelated to a community’s quality of life.

Quality of life—The quality of life of a com-munity refers to a range of physical and socialaspects reflecting how good the good life is with-in a community and includes air and water qual-ity, traffic congestion, perceived safety, socialproblems, overall friendliness, and the abundanceof stimulating social activities. Most communitiesrated 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, to7, extremely high quality of life). A small stand-ard deviation of 0.56 for the mean (half of thatfor the community autonomy construct) and theconcentration of mean ratings between 4.0 and6.5 confirmed this construct’s narrow distributionand the high quality of life perceived by residentsof most towns in the region (see fig. 10). Addi-tionally, when asked if their communities weresafe, friendly, and good places to live, more than80 percent of the respondents felt that few otherrural communities could match their quality of life

(table 6). Together, these results indicate that thevast majority of towns surveyed perceived theirquality of life to be quite high.

Community leadership—Community leadershipreferred to leadership from a variety of sources,including the business community, governmentagencies, other organizations, active individuals,and elected officials. A relatively normal distribu-tion was obtained from the mean ratings of thestudy communities on the construct effectivenessof community leaders. On a seven-point scale(ranging from 1, extremely ineffective leadership,to 7, extremely effective leadership), the meanresponse was 4.8 with a range of 2.4 to 6.4 and astandard deviation of 0.76 (fig. 11).

Community government—Workshop partici-pants also rated the effectiveness of their com-munity government, which referred to their per-ceptions of their local government’s ability tomake and carry out plans and projects, as well asits performance in acting in accordance with thewill of the citizens and in ways that earned trustin the government. The resulting distribution wascharacterized by a mean rating of 4.8 on a seven-point scale (ranging from 1, extremely ineffec-tive, to 7, extremely effective), with a range of1.8 to 6.4 and a standard deviation of 0.85(fig. 12). These results are consistent with thefindings shown in table 7, which indicate that lessthan 5 percent of the communities felt that theirgovernment did not know what to do, or that itdid only what influential people wanted it to do.

Not surprisingly, ratings of the effectiveness ofthe community’s government were highly cor-related with ratings of the effectiveness of thecommunity’s leadership (Pearson correlationcoefficient, r = 0.72). It also was significant thatthe perceptions of elected officials about theirperformance differed statistically from other par-ticipants’ perception of the effectiveness of thecommunity’s government. Analyses comparingmean values for the two groups revealed a statis-tically significant difference (p<0.05) of about0.5 in the scale means for effectiveness of com-munity government and the community’s leader-ship. This difference may be explained by the

Table 5—Pearson correlation coefficients ofcharacteristics of 198 Columbia basin studycommunities significantly correlated withcommunity autonomy

CommunityVariable 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 basincommunities.

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

CumulativeLevel of quality of life Frequency Percentage percentage

The community is safe, friendly, and a goodplace to live; few rural communities can matchits quality of life 159 80.3 80.3

The community is not the best place to live forhealth, safety, or social reasons, but it offers areasonable quality of life 38 19.2 99.5

The community has serious social problems; mostother 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 leadersacross rural Columbia basin communities.

Figure 12—Distribution ofmean ratings of governmenteffectiveness across ruralColumbia basin communities.

Effectiveness of community leadership

6.50 6.00 5.50 5.00 4.50 4.00 3.50 3.00 2.50

Fre

quen

cy

30

20

10

0

Std. Dev. = 0.76

Mean = 4.81

N = 198

Government effectiveness

6.25 5.75 5.25 4.75 4.25 3.75 3.25 2.75 2.25 1.75

Fre

qu

en

cy

30

20

10

0

Std. Dev. = 0.85

Mean = 4.80

N = 198

41

fact that the elected officials have a different, butvalid, perspective on the effectiveness of theirleadership than do other workshop participants.An alternative explanation is that the systematicdifferences in ratings on leadership effectivenessrepresent a self-interested bias in the perceptionsof elected officials in evaluating their perfor-mance and the extent to which they represent theviews and desires of their constituents.

Preparedness for the future—Community pre-paredness for the future is defined in the self-assessment workbook as the “degree to which acommunity is looking towards the future and pre-paring for its future.” The section devoted to thiskey construct focused on questions about theways community members perceived their com-munities were already changing, the extent ofthose changes, and how much residents werediscussing whether or how they wanted theircommunity to change.

The mean rating of the extent to which communi-ties perceived being prepared for the future wasrelatively low (4.1) on a seven-point scale (rang-ing from 1, or totally unprepared, to 7, totallyprepared). In fact, only the autonomy constructhad a lower mean rating. The distribution of thepreparedness for the future construct across ruralcommunities was relatively normal (fig. 13).More communities fell at the upper end of thescale, however, and perceived themselves asbeing more prepared for the future than others.Table 8, which looks at the results for a fixed-response question about a community’s prepared-

ness for the future, indicates that about a third ofthe communities (3.5 + 30.8 = 34.3 percent) wereones where citizens had plans and projects forrealizing some desired future. An additional39.4 percent of the towns were ones where citi-zens had begun identifying future directions fortheir community, but they were yet to identify anyactions, much less take any. In contrast, morethan one-fourth of the towns (18.2 + 8.1 = 26.3percent) had little or no discussion about theirfuture, or about whether or how they wanted tochange. These data reveal that, while nearly 22percent of the communities in the region haddecided they wanted to stay the same (3.5 + 18.2= 21.7 percent), more than 38 percent wanted tochange (30.8 + 8.1 = 38.9 percent). Of thosecommunities already actively making plans andtaking action (34.3 percent), 90 percent had doneso to allow them to change to achieve a desiredfuture. Conversely, of the little more than a quar-ter (26.3 percent) of the towns whose citizens hadnot as yet made any plans or taken any action,only 31 percent were open to changing to achievea desired future, and 69 percent wanted to staythe same.

A conclusion from these results is that theproactive communities were the ones that hadrealized change was coming and were readilymoving forward in dealing with that change andtrying to manage it. Communities desiring not tochange tended to be ones that were ignoring theirsituation, or at least were not discussing orconsidering change.

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

How the community’s Cumulativegovernment operates Frequency Percentage percentage

Does pretty much what the citizens want 63 31.8 31.8Does what some influential people want 15 7.6 39.4Does what it thinks is best for the citizens 117 59.1 98.5Does 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 futureacross the Columbia basin.

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

How prepared for the future Cumulativeis your community? Frequency Percentage percentage

Citizens have plans and projects identified thatwill allow them to stay the same 7 3.5 3.5

Citizens have plans and projects identified that willallow them to change to achieve a desired future 61 30.8 34.3

Citizens have discussed and identified futuredirections for the community, but no actionsidentified 78 39.4 73.7

Citizens have not had much discussion about thetown’s future, but they want to stay the same 36 18.2 91.9

Citizens have not had much discussion, about thetown’s future, but they are willing to change 16 8.1 100.0

Total 198 100.0 100.0

43

Economics: Perceptions andRealityThe perceptions of workshop participants aboutthe economy of their community were assessedwith a variety of questions. These perceptionscould then be compared to the actual economicstructure of the region’s communities.

Perceptions of WorkshopParticipantsPerceptions of community’s dependence onresource-based industries—In the section of theworkbook on the economy of a community, work-shop participants first were asked to name themajor businesses and industries in their commu-nities. The residents then were asked to rate theextent to which their towns were dependent onvarious industries for their economic stability, aseven-point scale ranging from 1 (extremely in-dependent) to 7 (extremely dependent). The re-sults for the major resource-based industries,which included farming and agriculture, woodproducts manufacturing, grazing and ranching,outdoor recreation and tourism, and minerals andmining, are reported in table 9 across all 198communities for each of these industries.

Overall, residents of the rural communities of theregion perceived farming and agriculture as themost important natural resource industry in termsof their economic dependence, followed by graz-

ing and ranching and outdoor recreation and tour-ism. Also significant is the finding that, on averageacross the total sample of workshop participants,the outdoor recreation and tourism sector wasperceived as being more important than woodproducts as a contributor to small rural economies.The validity of these perceptions was assessedby comparing the industries in terms of the actualcontribution of the different industrial sectors torural economies, which was based onprossportions of total employment, as discussed ina later section of this paper.

Figure 14 shows that perceptions of overall de-pendence of the region’s communities on naturalresource industries was rated very highly by theworkshop participants, with a comparatively highmean rating of 5.8 on a seven-point scale (from1, extremely independent, to 7, extremely de-pendent). The distribution of mean ratings wasskewed, with an overall mean of 6.0; only 25 per-cent of the informants indicated a rating of 5.4or less.

Classification of communities by dominantindustry—Many people have promoted the ideaof classifying communities on the basis of theireconomic structure (Branch et al. 1982, Gale andCordray 1991). Here, just one application of thecommunity typology idea is presented, with com-munities identified as resource dependent andclassified by the industrial sector that residentsperceived their community was most dependenton. This sector was termed the “dominantindustry.”

To operationalize this classification, communitiesthat were highly resource dependent were firstidentified. These were communities with a meanrating of 5.0 or higher on the seven-point scalerating their perceived resource dependence. Com-munities meeting this criterion then were clas-sified in terms of the dominant industry as per-ceived by residents; this classification was basedon that industry receiving the highest rating onthe economic dependence scale. Table 10 showsthe number and proportion of all communitiesthat citizens indicated as having economies dom-inated by particular natural resource industries.

Table 9—Mean ratings of perceiveddependence of rural Columbia basincommunities 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 1.7aMean ratings based on results from community self-assessment workshops in 198 communities in the basin.

44

Only 11 towns, or 5.5 percent of all towns in theregion, were perceived as not being significantlydependent on natural resources. Of the othercommunities, residents perceived them to bemost dependent on one of four types of naturalresource-based industries: farming, ranching, tim-ber, and recreation and tourism. Only four com-munities were found to be mining dominant;given this small sample size, any results ofstatistical analysis must be viewed as tentative.

Based on residents’ perceptions of their commu-nities, about 46 percent of all communities inthe region could be labeled as primarily farm-ing dominated, and another 8 percent perceivedthemselves to be primarily ranching communi-ties. In addition to these farming- and ranching-dominated communities, another 10 percent ofthe region’s communities reported that they weremoderately highly dependent to very highly de-pendent on agriculture. Many of these communi-ties also were dependent on wood products, tour-ism and recreation, and mining.

Figure 14—Distribution of meanratings of dependence on naturalresources across rural Columbiabasin communities.

Dependence on natural resources

7.00 6.00 5.00 4.00 3.00 2.00 1.00

Fre

quen

cy

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 industrydominance

CumulativePerceived 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 198 100.0 100.0a Because of the small number of communities rated highly dependent on mining and minerals (1 community), it wasnot broken out for this analysis.

45

Nearly 24 percent of the region’s communitieswere perceived by participants as being timber-dominant communities. Many of these, however,also perceived themselves to be dependent onmining and outdoor recreation and tourism. Fullytwo-thirds of all communities in the region per-ceived themselves as being somewhat dependentto highly dependent on wood products.

Communities perceiving themselves as havingeconomies dominated primarily by tourism andrecreational activities totaled 17 percent of alltowns in the region. Another 11 percent perceivedthemselves as being moderately highly dependentto very highly dependent on tourism andrecreation.

Yet another 11 percent of the communities de-scribed their economy as primarily based ongovernment jobs.

Although the region’s towns were classified inthis way, most had mixed economies that wereperceived as being at least somewhat dependenton a number of resource-based industries. Forexample, only 9 percent of the communities ex-amined were reported to be highly independentof farming and ranching, only 13 percent werereported to be highly independent of outdoor rec-reation and tourism, and 37 percent of the com-munities were not dependent on wood products toa significant extent. Almost one-quarter of allcommunities in the region (22 percent) were per-ceived by workshop participants as having pri-marily a mixed economy with no particulardominant industry.

The relation of the ratings of perceived depen-dence on resource industries to community pop-ulation size also was examined. As describedabove, the towns were classified by four sizeclasses (i.e., less than 1,500 people, 1,501 to3,000 people, 3,001 to 5,000 people, and 5,001to 10,000 people). Statistical differences (p<0.05)in communities’ ratings of perceived dependenceon timber, mining, or farming in relation to popu-lation size classes were not found. An analysis oftowns based on their dominant industry classi-fication, however, indicated that different kindsof industry-dominated communities did indeeddiffer in their population size.

The majority (58 percent) of communities in thesmallest size category (communities under 1,500in population) were ones in which agriculture(farming, ranching, and food processing) wasperceived to be the dominant industry. Moreover,the perceived dominant industry in the largestsegment of towns in every size category also wasfarming.

Towns perceived as timber dominant were wellrepresented across each size category, with pro-portions ranging from 20 to 38 percent of com-munities in each class. In contrast, most of theranching-dominant communities (87 percent)were among the smallest towns (under 1,500 inpopulation); the other ranching-dominant com-munities fell under the next smallest size class(1,500 to 3,000 in population).

Interestingly, outdoor recreation and tourism wasparticularly dominant in the smallest (under 1,500in population) and the midsized (3,000 to 5,000in population) communities with 19 and 29 per-cent, respectively. Not surprisingly, the largestcommunities in the region, which were among themost economically diverse, were the ones mostlikely to be perceived as not being highly naturalresource dependent.

In a related question, communities were askedwhich best characterized their economic base:(1) one centered mainly around the growing,gathering, or harvesting of raw materials; (2) onecentered around adding value to or processingraw materials; (3) one centered primarily aroundretail stores or tourism services; (4) one centeredprimarily around government jobs; or (5) one toodiverse to be described by the preceding cate-gories of economic activity. As table 11 shows,nearly 16 percent of the communities identifiedthemselves as too diverse to classify, and the ma-jority (58 percent) perceived themselves to bedominated by traditionally extractive or agri-culturally based sectors. Only 8 percent reportedthemselves to be dominated by manufacturingindustries or ones adding value to or processingraw or harvested materials. Twelve percent re-ported themselves to be dominated by retail andtourism services, much the same proportion asthe average employment in that sector, which isreported in the next section.

46

Perceptions of the economic diversity ofcommunities—A related focus of the communityself-assessment was on perceptions by the work-shop participants of their community’s economicdiversity. These perceptions were rated on aseven-point scale (ranging from 1, extremely non-diverse, to 7, extremely diverse). As shown infigure 15, responses were broadly distributedwith a comparatively low mean of 3.91 and arange of 1.83 to 6.67.

Community’s attractiveness for business—Perceptions by residents of the attractiveness oftheir communities for business were rated on aseven-point scale (ranging from 1, extremely un-

attractive, to 7, extremely attractive). Responseswere skewed toward the low end of the scale;with a comparatively low mean of 3.85 and arange of 1.57 to 6.0 (fig. 16) indicating that amajority of the communities assessed themselvesas being more unattractive than attractive forbusiness.

Profiles of the Economies by ActualEmploymentA profile of each community’s actual economiccomposition was developed for the assessmentresearch, based on the estimated proportion of atown’s total employment in each industrial sector.

Table 11—Types of industries perceived to dominate the economies of 198 Columbia basin studycommunities

CumulativeIndustry Frequency Percentage percentage

Growing, gathering, or harvesting of rawmaterials 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 thediversity of the economies of rural Columbiabasin communities.

Economic diversity

6.75 6.25 5.75 5.25 4.75 3.25 2.75 2.25 1.75

Fre

quen

cy

30

20

10

0

Std. Dev.= 1.11

Mean = 3.89

N = 198

4.25 3.75

47

Figure 16—Distribution of meanratings of communityattractiveness for businessacross rural Columbia basincommunities.

Table 12—Percentage of total employment across rural Columbia basin communities byindustrial sectora b

Industrial sector Type of sector Total employment

Percent

Agriculture Basic industry and processing 23.1State and local government Service 15.2Travel and tourism Service 12.4Retail trade (nontourism related) Service 11.1Food and beverage (nontourismrelated) Service 5.1

Federal Government Service 4.8Medical and social services Service 4.7Mining and minerals Basic industry and processing 3.3Harvesting and manufacturingof wood products Basic industry and processing 5.6

Other 14.4

Total 100.0

a Rural 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

6.00 5.50 5.00 4.50 4.00 3.50 3.00 2.50 2.00 1.50

Fre

quen

cy

30

20

10

0

Std. Dev. = 0.95 Mean = 3.84

N = 198

48

These profile data were then used to assess dif-ferences among communities based on their sizeand actual economic diversity. The actual econo-mic structure of communities also could be com-pared with perceptions by the residents of theircommunity’s economy.

Employment profiles of the region’scommunities—Table 12 shows the extent towhich different industrial sectors directly con-tributed to rural economies in the first quarterof 1995, as indicated by the average proportionof total employment in each sector across the re-gion’s rural communities. These results providesupport for the proposition that both resource-based industries (i.e., farming, ranching, timber,and travel and tourism) and various service sec-tors are important components of rural econo-mies. Of the industries displayed, agriculture,manufacturing of wood products, and mining andminerals are the more traditional, extractive (orbasic) resource-related industries, and the otherslisted are service industries.

Agriculture is the region’s top employer (at anaverage of 23.1 percent of total employment inrural communities), and state and local govern-ment (15.2 percent) is the second largest employ-er. An average of 12.4 percent for the travel andtourism industry places it third: This includesemployment attributable to such sectors as lodg-ing, nonlocal retail trade, and nonlocal food andbeverage. Other significant employers in the re-gion’s rural communities are a number of servicesectors, including locally based (nontourism re-lated) retail trade and food and beverage. Thewood manufacturing and timber harvesting sec-tor, on average, accounts for 5.6 percent of ruralcommunity employment, and the mining and min-erals sector accounts for 3.3 percent.

In terms of jobs in the average rural community,the majority (62.1 percent) are ones in the servicesector. Excluding government jobs, which pro-vide 21.4 percent of all jobs, other service jobsaccount for 40.7 percent of all employment inrural communities. A difference is found, how-ever, between large and small communities in theproportion of total employment in traditional eco-nomic base industries. Those towns having morethan 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 accountedfor 34 percent of all jobs.

Economic profiles of communities based onsize class—Table 13 presents the results of theeconomic profiles of the region’s towns, based onan analysis of employment in different industrialsectors in towns grouped by population size.Mean proportions of employment in each indus-trial sector are reported for each of the four sizeclasses of towns. For this analysis, which wouldbe indicative of the extent to which towns ofdifferent sizes (based on population) were eco-nomically diverse, the 198 communities were clas-sified according to the population size classesdescribed earlier (i.e., less than 1,500 people;1,501 to 3,000 people; 3,001 to 5,000 people; and5,001 to 10,000 people). (The results presentedin table 13 are for only the 198 communities forwhich complete community assessment dataexist, for the sake of comparability, and not forall 387 small rural communities; nonetheless, thelarge sample size ensures the representationalvalue of these results for all towns in the region.)

Table 13 suggests that larger communities are,indeed, somewhat less dependent on one or a fewindustrial sectors and thus are more economicallydiverse. Smaller communities are more dependenton a number of natural resource industries foremployment, including agriculture and woodproducts, than are larger communities; a majorexception here is the mining and minerals sector.Just as telling was the finding that the proportionof employment in the other industries categorywas much greater for the two larger size classesof communities (20.0 and 21.2 percent) than thesmaller town classes (9.2 and 15.0 percent).

In the smallest size class of communities (lessthan 1,500), employment in agriculture accountedfor the greatest percentage of jobs (26.0 percent),followed by state and local government (16.2percent) and the travel and tourism sector (13.7percent). Agriculture accounted for only 9.1 per-cent of all jobs in communities having between3,001 and 5,000 people and 6.2 percent in com-munities with 5,001 to 10,000 people. The har-vesting of timber and manufacturing of woodproducts accounted for similar proportions of

49

Tab

le 1

3—P

erce

ntag

e of

199

5 to

tal e

mpl

oym

ent b

y in

dust

rial s

ecto

r an

d po

pula

tion

size

in 1

93 C

olum

bia

basi

n co

mm

uniti

es

Sta

te a

nd lo

cal

Pop

ulat

ion

size

(N

)a

Agr

icul

ture

Woo

d pr

oduc

tsT

rave

l and

tou

rism

gove

rnm

ent

Fed

eral

Gov

ernm

ent

Pe

rce

nt

Less

tha

n 1,

500

(128

)26

.06.

313

.716

.25.

61,

501

to 3

,000

(36

)14

.46.

811

.717

.74.

43,

001

to 5

,000

(16

) 9

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812

.515

.94.

85,

001

to 1

0,00

0 (1

3) 6

.25.

017

.411

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0

Me

an

21.3

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13.5

16.2

5.3

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ing

and

Foo

d an

dM

edic

al a

ndm

iner

als

Ret

ail t

radeb

beve

rageb

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al s

ervi

ces

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93.

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51,

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115

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512

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911

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513

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221

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a N

umbe

r of

stu

dy c

omm

uniti

es w

ith c

ompl

ete

data

on

rele

vant

var

iabl

es (

N=

193)

.

b N

onto

uris

m r

elat

ed.

50

employment: regardless of community size, thissector accounted for 6.3 and 6.8 percent of alljobs in communities with less than 1,500 peopleand 1,501 to 3,000 people, respectively, and foronly 5.0 percent in large communities (5,001 to10,000 people). Sectors providing the greatestpercentage of jobs in larger communities (3,001to 5,000 and 5,001 to 10,000 people) were pri-marily service oriented. In the largest class ofcommunities (5,001 to 10,000 people), the traveland tourism sector provided the largest propor-tion of jobs (17.4 percent), followed by the retailsector (13.2 percent), state and local government(11.9 percent), medical and social services (8.2percent), and food and beverage establishments(7.4 percent).

Development of an economic diversity index—A rough-and-ready indicator of actual economicdiversity was developed from the above employ-ment data to measure the degree to which the 387small rural communities in the region actuallywere economically diverse. This index is a sum-mative one of relative economic diversity. It wascalculated with two measures of the extent towhich a community actually was dependent ona wide variety of industries as opposed to onlya few.

One component of the index was a measure of theextent to which a given community’s economywas comprised of only a few or, alternatively,many sectors. This measure was an average num-ber of industrial sectors having some proportionof employment in that community and rangedfrom an average of 0.04 for communities withone sector (one-twenty-third of all 23 sectors) to1.0 for all 23 sectors. This average was recordedas a standardized score.

The second component of the index was a meas-ure of the preponderance of total employment inany one sector: this measure was first set at zeroand then increased by one for each sector forwhich the proportion of a community’s total em-ployment exceeded one-third (33 percent). Themost sectors for which the proportion of employ-ment for any community exceeded this amountwas 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 eco-nomy, so its sign was changed to provide an in-dicator consistent with the first measure. Again,the average was standardized. Then, both meas-urements were summed for a cumulative indexof economic diversity.

The towns were classified by their level of econo-mic diversity, based on the index score calculatedfor each. Levels of economic diversity based onthe index ranged from low (-1.00 to 0.01) andmedium-low (0.01 to 0.35) to medium-high (0.35to 0.90) and high (0.90 to 1.00). These levelswere relative ones with ranges based on quartilesof the towns’ economic diversity index scores,and each class thus representing an equal propor-tion of the communities under study (25 percenteach). The one-quarter of the towns receiving thelowest economic diversity index scores (less than0.01) were labeled as “low,” and so forth.

For example, Conconully, Washington, was atown in which employment was found for only7 of the 23 industrial sectors estimated, and 2 ofthose sectors accounted for over 33 percent of thetown’s employment. Accordingly, it was very lowin economic diversity, receiving a standardizedscore of -0.65 that ranked it 15 of all the region’stowns and cities in being economically diverse(in the lowest quartile), so it was classified in the“low” economic diversity class. At the other endof the diversity scale, a highly diverse communitylike Bend, Oregon, was found to have employ-ment in 20 sectors, with no sectors having amajor predominance of employment (over 33 per-cent). It received a diversity index score of 1.00that ranked it 442—in the highest quartile as oneof the region’s most diversified towns—and itwas included in the high economic diversityclass. A more moderate level of diversity wasfound for Troy, Idaho, which had employmentin 15 sectors; but because one of those sectorshad a major predominance of employment, itwas ranked as 192 in economic diversity with anindex score of 0.25, placing it in the medium-lowcategory of being economically diverse.

Text continues on page 68.

51

Tab

le 1

4—E

cono

mic

div

ersi

ty in

dex

scor

es a

nd le

vels

of d

irect

em

ploy

men

t by

indu

stria

l sec

tor

(bas

ed o

n pr

opor

tions

of

empl

oym

ent)

by

rura

l Col

umbi

a ba

sin

com

mun

ity, w

ith 1

992-

94 p

opul

atio

n es

timat

es (

N=

387)

Leve

ls o

f dire

ct e

mpl

oym

ent b

y in

dust

rial s

ecto

ra

1992

-94

Eco

nom

icT

rave

l and

Sta

te a

nd lo

cal

Fed

eral

Min

ing

and

Sta

te a

nd to

wn

popu

latio

ndi

vers

ity in

dex

Agr

icul

ture

Tim

ber

tour

ism

gove

rnm

ent

Gov

ernm

ent

min

eral

s

Idah

o:A

berd

een

1,54

8M

ed. h

igh

Med

. hig

hLo

wLo

wM

ed. l

owM

ed. l

owLo

wA

cequ

ia1

03

Low

Hig

hLo

wLo

wH

igh

Low

Low

Alb

ion

29

3Lo

wH

igh

Low

Low

Hig

hLo

wLo

wA

mer

ican

Fal

ls4,

008

Med

. low

Med

. hig

hLo

wLo

wM

ed. h

igh

Low

Hig

hA

mm

on5

,46

9Lo

wM

ed

. hig

hLo

wH

igh

Low

Low

Low

Arb

on V

alle

y6

28

Low

Hig

hLo

wLo

wH

igh

Low

Low

Arc

o 1

,029

Med

. hig

hLo

wLo

wLo

wLo

wM

ed. h

igh

Hig

hA

rimo

31

4M

ed. l

owM

ed. h

igh

Low

Low

Hig

hLo

wLo

wA

shto

n 1

,104

Med

. hig

hM

ed. h

igh

Hig

hM

ed. l

owLo

wLo

wLo

wA

thol

40

9H

igh

Low

Med

. hig

hH

igh

Med

. hig

hLo

wLo

wA

tom

ic C

ity2

6Lo

wH

igh

Low

Low

Low

Low

Low

Ban

crof

t4

17

Med

. low

Hig

hLo

wM

ed. h

igh

Med

. hig

hLo

wLo

wB

anks

57

0Lo

wLo

wLo

wH

igh

Low

Low

Low

Bas

alt

45

0Lo

wLo

wLo

wLo

wLo

wM

ed. h

igh

Low

Bel

levu

e1,

433

Med

. hig

hM

ed. h

igh

Low

Hig

hM

ed. l

owLo

wLo

wB

lack

foot

10,6

28H

igh

Low

Low

Med

. hig

hM

ed. h

igh

Low

Low

Blis

s1

96

Med

. hig

hH

igh

Low

Hig

hM

ed. h

igh

Low

Low

Blo

omin

gton

18

4Lo

wH

igh

Low

Low

Low

Low

Low

Bo

ise

13

5,5

06

Hig

hLo

wLo

wM

ed. h

igh

Med

. hig

hLo

wM

ed. l

owB

onne

rs F

erry

2,24

4H

igh

Med

. low

Med

. low

Med

. hig

hM

ed. h

igh

Low

Low

Buh

l3,

743

Med

. hig

hM

ed. h

igh

Low

Med

. low

Low

Low

Low

Bur

ley

8,91

8H

igh

Low

Low

Med

. hig

hM

ed. h

igh

Low

Low

But

te C

ity6

5Lo

wH

igh

Low

Low

Low

Low

Low

Cal

dwel

l20

,800

Hig

hLo

wLo

wM

ed. l

owM

ed. h

igh

Low

Med

. hig

hC

ambr

idge

36

7M

ed. h

igh

Hig

hM

ed. h

igh

Med

. low

Med

. hig

hLo

wLo

wC

arm

en0

Low

Hig

hLo

wM

ed. h

igh

Hig

hLo

wLo

wC

asc

ad

e1

,00

1H

igh

Low

Med

. low

Med

. hig

hH

igh

Med

. hig

hLo

wC

astle

ford

17

6M

ed. h

igh

Hig

hLo

wLo

wH

igh

Low

Low

52

Tab

le 1

4—E

cono

mic

div

ersi

ty in

dex

scor

es a

nd le

vels

of d

irect

em

ploy

men

t by

indu

stria

l sec

tor

(bas

ed o

n pr

opor

tions

of

empl

oym

ent)

by

rura

l Col

umbi

a ba

sin

com

mun

ity, w

ith 1

992-

94 p

opul

atio

n es

timat

es (

N=

387)

(co

ntin

ued)

Leve

ls o

f dire

ct e

mpl

oym

ent b

y in

dust

rial s

ecto

ra

1992

-94

Eco

nom

icT

rave

l and

Sta

te a

nd lo

cal

Fed

eral

Min

ing

and

Sta

te a

nd to

wn

popu

latio

ndi

vers

ity in

dex

Agr

icul

ture

Tim

ber

tour

ism

gove

rnm

ent

Gov

ernm

ent

min

eral

s

Cha

llis

99

5H

igh

Med

. hig

hLo

wM

ed. l

owM

ed. h

igh

Med

. low

Hig

hC

hatc

olet

73

Low

Hig

hLo

wLo

wLo

wLo

wM

ed. l

owC

hubb

uck

8,35

4M

ed. h

igh

Med

. hig

hLo

wH

igh

Med

. hig

hLo

wLo

wC

lark

For

k4

71

Med

. low

Low

Med

. low

Med

. hig

hH

igh

Low

Low

Cla

yton

20

Low

Med

. low

Low

Hig

hLo

wH

igh

Low

Coe

ur d

’Ale

ne26

,611

Hig

hLo

wLo

wM

ed. h

igh

Med

. hig

hLo

wM

ed. l

owC

otto

nwoo

d8

52

Med

. hig

hM

ed. h

igh

Low

Med

. hig

hH

igh

Low

Low

Cou

ncil

95

1M

ed. h

igh

Med

. hig

hLo

wM

ed. h

igh

Med

. low

Med

. hig

hLo

wC

raig

mon

t5

71

Med

. low

Hig

hLo

wM

ed. l

owM

ed. l

owLo

wLo

wC

ulde

sac

28

9M

ed. l

owH

igh

Low

Med

. hig

hH

igh

Low

Low

Da

lton

Ga

rde

ns

2,1

70

NA

NA

NA

NA

NA

NA

NA

Day

ton

38

2Lo

wH

igh

Low

Low

Hig

hLo

wLo

wD

eary

54

8M

ed. h

igh

Low

Hig

hM

ed. h

igh

Hig

hLo

wM

ed. l

owD

eclo

28

9Lo

wH

igh

Low

Hig

hLo

wLo

wLo

wD

ietr

ich

12

9Lo

wH

igh

Low

Low

Hig

hLo

wLo

wD

ingl

eLo

wLo

wLo

wLo

wLo

wLo

wLo

wD

onne

lly1

55

Med

. low

Low

Low

Hig

hM

ed. l

owLo

wLo

wD

over

33

5Lo

wLo

wLo

wH

igh

Low

Low

Hig

hD

owne

y6

72

Med

. hig

hM

ed. h

igh

Low

Hig

hH

igh

Low

Low

Drig

gs9

80

Hig

hM

ed. h

igh

Low

Med

. hig

hM

ed. h

igh

Low

Low

Dru

mm

ond

33

Low

Hig

hLo

wLo

wLo

wLo

wLo

wD

uboi

s4

80

Med

. low

Hig

hLo

wLo

wM

ed. h

igh

Med

. low

Low

Ea

gle

3,6

94

Hig

hM

ed. l

owLo

wM

ed. h

igh

Med

. low

Low

Low

Eas

t Hop

e2

31

Low

Low

Low

Low

Low

Low

Hig

hE

den

32

9M

ed. l

owH

igh

Low

Low

Med

. low

Low

Low

Elk

City

Med

. hig

hH

igh

Hig

hM

ed. h

igh

Low

Hig

hLo

wE

lk R

iver

15

3Lo

wH

igh

Low

Med

. low

Low

Med

. low

Low

Elli

sLo

wLo

wLo

wM

ed. h

igh

Low

Hig

hLo

wE

mm

ett

4,8

88

Hig

hM

ed. h

igh

Med

. hig

hM

ed. l

owM

ed. h

igh

Low

Low

53

Tab

le 1

4—E

cono

mic

div

ersi

ty in

dex

scor

es a

nd le

vels

of d

irect

em

ploy

men

t by

indu

stria

l sec

tor

(bas

ed o

n pr

opor

tions

of

empl

oym

ent)

by

rura

l Col

umbi

a ba

sin

com

mun

ity, w

ith 1

992-

94 p

opul

atio

n es

timat

es (

N=

387)

(co

ntin

ued)

Leve

ls o

f dire

ct e

mpl

oym

ent b

y in

dust

rial s

ecto

ra

1992

-94

Eco

nom

icT

rave

l and

Sta

te a

nd lo

cal

Fed

eral

Min

ing

and

Sta

te a

nd to

wn

popu

latio

ndi

vers

ity in

dex

Agr

icul

ture

Tim

ber

tour

ism

gove

rnm

ent

Gov

ernm

ent

min

eral

s

Fai

rfie

d3

76

Hig

hH

igh

Low

Low

Med

. hig

hLo

wLo

wF

erdi

nand

14

1N

AN

AN

AN

AN

AN

AN

AF

erna

n La

ke1

86

Low

Low

Hig

hLo

wLo

wLo

wLo

wF

iler

1,7

16

Hig

hM

ed. h

igh

Low

Med

. low

Med

. hig

hLo

wLo

wF

irth

45

6M

ed. l

owH

igh

Low

Low

Low

Low

Low

Fis

hhav

enLo

wLo

wLo

wH

igh

Low

Low

Low

For

t Hal

l1,

453

Med

. low

Med

. low

Low

Med

. low

Med

. low

Hig

hLo

wF

rank

linM

ed. l

owLo

wLo

wH

igh

Low

Low

Low

Fru

itlan

d2,

668

Hig

hM

ed. h

igh

Med

. hig

hM

ed. l

owM

ed. l

owLo

wLo

wG

ard

en

City

7,0

34

Low

Hig

hLo

wLo

wLo

wLo

wLo

wG

arde

n V

alle

yM

ed. l

owLo

wLo

wH

igh

Low

Low

Low

Gen

esee

78

3M

ed. l

owH

igh

Low

Low

Hig

hLo

wLo

wG

enev

aLo

wH

igh

Low

Low

Med

. hig

hLo

wLo

wG

eorg

etow

n6

59

Low

Hig

hLo

wM

ed. l

owM

ed. h

igh

Low

Low

Gib

bonv

ille

Low

Low

Low

Hig

hLo

wLo

wLo

wG

lenn

s F

erry

1,35

9M

ed. l

owM

ed. h

igh

Low

Low

Med

. hig

hH

igh

Low

Glif

ton

Low

Low

Low

Low

Low

Low

Low

Go

od

ing

3,0

66

Hig

hM

ed. l

owLo

wLo

wM

ed. h

igh

Med

. low

Low

Gra

nd V

iew

35

5M

ed. l

owH

igh

Low

Med

. low

Med

. hig

hM

ed. l

owLo

wG

rang

evill

e3,

208

Hig

hM

ed. l

owM

ed. l

owM

ed. h

igh

Med

. hig

hM

ed. h

igh

Low

Gre

enle

af6

81

Low

Low

Low

Hig

hH

igh

Low

Low

Hag

erm

an6

69

Hig

hM

ed. h

igh

Low

Low

Med

. low

Hig

hLo

wH

aile

y4,

252

Hig

hLo

wLo

wM

ed. l

owM

ed. h

igh

Low

Low

Ham

er8

6M

ed. l

owH

igh

Low

Low

Med

. low

Low

Low

Han

sen

94

6M

ed. l

owM

ed. h

igh

Low

Med

. hig

hH

igh

Low

Low

Har

rison

23

2M

ed. h

igh

Hig

hLo

wH

igh

Med

. low

Low

Low

Hau

ser

42

7Lo

wLo

wLo

wH

igh

Low

Low

Low

Hay

den

4,69

3M

ed. l

owLo

wH

igh

Low

Low

Low

Low

Hay

den

Lake

37

4M

ed. h

igh

Low

Low

Hig

hLo

wLo

wLo

w

54

Tab

le 1

4—E

cono

mic

div

ersi

ty in

dex

scor

es a

nd le

vels

of d

irect

em

ploy

men

t by

indu

stria

l sec

tor

(bas

ed o

n pr

opor

tions

of

empl

oym

ent)

by

rura

l Col

umbi

a ba

sin

com

mun

ity, w

ith 1

992-

94 p

opul

atio

n es

timat

es (

N=

387)

(co

ntin

ued)

Leve

ls o

f dire

ct e

mpl

oym

ent b

y in

dust

rial s

ecto

ra

1992

-94

Eco

nom

icT

rave

l and

Sta

te a

nd lo

cal

Fed

eral

Min

ing

and

Sta

te a

nd to

wn

popu

latio

ndi

vers

ity in

dex

Agr

icul

ture

Tim

ber

tour

ism

gove

rnm

ent

Gov

ernm

ent

min

eral

s

Haz

elto

n4

26

Med

. low

Hig

hLo

wLo

wM

ed. h

igh

Low

Low

Hey

burn

2,83

6M

ed. l

owH

igh

Low

Low

Med

. low

Low

Low

Hol

broo

kLo

wH

igh

Low

Low

Med

. low

Low

Low

Hol

liste

r1

51

Low

Hig

hLo

wLo

wLo

wLo

wLo

wH

omed

ale

2,09

7H

igh

Med

. hig

hLo

wLo

wM

ed. h

igh

Low

Med

. hig

hH

ope

11

6M

ed. l

owLo

wH

igh

Hig

hM

ed. l

owLo

wLo

wH

orse

shoe

Ben

d7

26

Med

. hig

hM

ed. l

owH

igh

Low

Med

. low

Hig

hLo

wH

uette

r8

5Lo

wLo

wH

igh

Low

Low

Low

Low

Idah

o C

ity3

73

Med

. low

Low

Low

Hig

hH

igh

Low

Low

Ida

ho

Fa

lls4

8,2

26

Hig

hLo

wLo

wM

ed. h

igh

Med

. hig

hLo

wLo

wIn

dian

Val

ley

Low

Hig

hLo

wLo

wLo

wLo

wLo

wIn

kom

75

3M

ed. h

igh

Low

Low

Med

. low

Hig

hLo

wH

igh

Iona

1,10

7M

ed. l

owLo

wLo

whi

ghLo

wLo

wLo

wIr

win

11

6M

ed. l

owLo

wLo

wH

igh

Low

Low

Low

Isla

nd P

ark

16

3M

ed. l

owLo

wLo

wM

ed. l

owH

igh

Hig

hLo

wJe

rom

e7,

077

Hig

hM

ed. h

igh

Low

Med

. low

Med

. low

Low

Med

. low

Julia

etta

51

4M

ed. l

owM

ed. h

igh

Hig

hLo

wH

igh

Low

Low

Kam

iah

1,19

0M

ed. h

igh

Med

. low

Hig

hM

ed. h

igh

Med

. hig

hLo

wLo

wK

ello

gg2,

495

Hig

hLo

wLo

wM

ed. h

igh

Med

. low

Low

Med

. hig

hK

etch

um2

,68

5M

ed

. hig

hLo

wLo

wH

igh

Low

Low

Low

Kim

berly

2,6

56

Me

d. h

igh

Hig

hLo

wM

ed. h

igh

Low

Low

Low

Koo

skia

70

8M

ed. h

igh

Hig

hH

igh

Med

. low

Med

. hig

hLo

wLo

wK

oote

nai

31

7M

ed. l

owH

igh

Low

Med

. hig

hLo

wLo

wM

ed. l

owK

una

2,2

38

Hig

hM

ed. h

igh

Low

Med

. low

Hig

hLo

wLo

wLa

kefo

rkLo

wM

ed. h

igh

Low

Low

Hig

hLo

wLo

wLa

pwai

1,00

6M

ed. l

owM

ed. h

igh

Low

Low

Med

. hig

hH

igh

Low

Lava

Hot

Spr

ings

46

4M

ed. l

owM

ed. l

owLo

wH

igh

Low

Low

Low

Lead

ore

85

Med

. low

Hig

hLo

wM

ed. l

owH

igh

Low

Low

Lem

hiLo

wH

igh

Low

Low

Med

. hig

hLo

wLo

w

55

Tab

le 1

4—E

cono

mic

div

ersi

ty in

dex

scor

es a

nd le

vels

of d

irect

em

ploy

men

t by

indu

stria

l sec

tor

(bas

ed o

n pr

opor

tions

of

empl

oym

ent)

by

rura

l Col

umbi

a ba

sin

com

mun

ity, w

ith 1

992-

94 p

opul

atio

n es

timat

es (

N=

387)

(co

ntin

ued)

Leve

ls o

f dire

ct e

mpl

oym

ent b

y in

dust

rial s

ecto

ra

1992

-94

Eco

nom

icT

rave

l and

Sta

te a

nd lo

cal

Fed

eral

Min

ing

and

Sta

te a

nd to

wn

popu

latio

ndi

vers

ity in

dex

Agr

icul

ture

Tim

ber

tour

ism

gove

rnm

ent

Gov

ernm

ent

min

eral

s

Leno

reLo

wH

igh

Low

Low

Low

Med

. hig

hLo

wLe

tha

Low

Hig

hLo

wLo

wLo

wLo

wLo

wLe

wis

ton

29

,11

9H

igh

Low

Med

. hig

hM

ed. h

igh

Med

. hig

hLo

wM

ed. l

owLe

wis

ville

54

9Lo

wH

igh

Low

Low

Low

Low

Low

Mac

kay

59

2M

ed. h

igh

Hig

hLo

wH

igh

Low

Med

. hig

hLo

wM

alad

City

Hig

hH

igh

Low

Med

. low

Hig

hLo

wM

ed. l

owM

alta

18

0M

ed. l

owM

ed. h

igh

Low

Hig

hLo

wLo

wLo

wM

ansf

ield

36

5M

ed. h

igh

Hig

hLo

wM

ed. h

igh

Hig

hLo

wLo

wM

arsi

ng8

09

Med

. hig

hH

igh

Low

Low

Med

. hig

hM

ed. l

owLo

wM

ayLo

wH

igh

Low

Low

Hig

hLo

wLo

wM

cCal

l2,

329

Hig

hLo

wLo

wH

igh

Med

. low

Low

Low

McC

amm

on7

63

Med

. low

Hig

hLo

wM

ed. h

igh

Low

Low

Low

Mel

ba2

72

Med

. low

Med

. hig

hLo

wM

ed. l

owH

igh

Low

Low

Men

an7

68

Med

. low

Hig

hLo

wLo

wLo

wLo

wLo

wM

erid

ian

11,1

81H

igh

Low

Low

Med

. low

Med

. low

Low

Med

. low

Mid

dlet

on2,

081

Med

. hig

hH

igh

Low

Med

. low

Med

. low

Low

Low

Mid

vale

11

6Lo

wH

igh

Low

Low

Med

. low

Low

Hig

hM

inid

oka

64

Low

Hig

hLo

wLo

wH

igh

Low

Med

. Low

Mon

tour

Low

Hig

hH

igh

Low

Low

Low

Low

Mon

tpel

ier

Hig

hM

ed. l

owLo

wH

igh

Med

. hig

hM

ed. l

owM

ed. l

owM

oo

re1

96

Low

Low

Low

Low

Low

Low

Low

Mos

cow

19,1

22M

ed. h

igh

Low

Low

Hig

hH

igh

Low

Low

Mou

ntai

n H

ome

8,10

7M

ed. h

igh

Med

. low

Low

Med

. low

Med

. low

Hig

hLo

wM

oyie

Spr

ings

43

5M

ed. l

owM

ed. l

owH

igh

Med

. low

Med

. low

Low

Low

Mud

Lak

e1

82

Med

. hig

hM

ed. h

igh

Low

Low

Hig

hM

ed. l

owLo

wM

ulla

n8

15

Low

Low

Low

Low

Low

Hig

hH

igh

Mur

taug

h1

41

Med

. low

Hig

hLo

wLo

wM

ed. h

igh

Low

Low

Nam

pa31

,416

Hig

hLo

wLo

wM

ed. h

igh

Low

Low

Med

. low

New

Mea

dow

s6

20

Med

. low

Low

Hig

hLo

wH

igh

Med

. low

Low

56

Tab

le 1

4—E

cono

mic

div

ersi

ty in

dex

scor

es a

nd le

vels

of d

irect

em

ploy

men

t by

indu

stria

l sec

tor

(bas

ed o

n pr

opor

tions

of

empl

oym

ent)

by

rura

l Col

umbi

a ba

sin

com

mun

ity, w

ith 1

992-

94 p

opul

atio

n es

timat

es (

N=

387)

(co

ntin

ued)

Leve

ls o

f dire

ct e

mpl

oym

ent b

y in

dust

rial s

ecto

ra

1992

-94

Eco

nom

icT

rave

l and

Sta

te a

nd lo

cal

Fed

eral

Min

ing

and

Sta

te a

nd to

wn

popu

latio

ndi

vers

ity in

dex

Agr

icul

ture

Tim

ber

tour

ism

gove

rnm

ent

Gov

ernm

ent

min

eral

s

New

Ply

mou

th1,

465

Med

. low

Hig

hLo

wLo

wLo

wLo

wM

ed. l

owN

ewda

le3

61

Low

Hig

hLo

wLo

wH

igh

Low

Low

Nez

Per

ce4

71

Med

. low

Hig

hLo

wM

ed. l

owM

ed. l

owLo

wLo

wN

orth

Pow

der

51

5Lo

wH

igh

Hig

hLo

wM

ed. h

igh

Med

. low

Low

Not

us4

11

Med

. hig

hH

igh

Low

Med

. hig

hH

igh

Low

Low

Oak

ley

60

7M

ed. h

igh

Hig

hLo

wH

igh

Low

Low

Med

. low

Ola

0Lo

wH

igh

Low

Med

. hig

hH

igh

Low

Low

Old

tow

n1

66

Hig

hM

ed. h

igh

Med

. hig

hM

ed. h

igh

Low

Med

. hig

hLo

wO

naw

ay2

08

NA

NA

NA

NA

NA

NA

NA

Oro

fino

3,01

0H

igh

Med

. low

Med

. hig

hM

ed. l

owH

igh

Med

. low

Low

Osb

urn

1,50

7M

ed. h

igh

Low

Med

. low

Med

. hig

hH

igh

Low

Med

. low

Ovi

dLo

wLo

wH

igh

Low

Low

Low

Low

Par

ker

31

4Lo

wH

igh

Low

Low

Hig

hLo

wLo

wP

arm

a1,

702

Hig

hH

igh

Low

Low

Med

. low

Low

Low

Pau

l1,

000

Med

. hig

hH

igh

Low

Low

Med

. hig

hLo

wLo

wP

ayet

te6,

170

Hig

hLo

wM

ed. h

igh

Low

Med

. hig

hLo

wLo

wP

eck

16

6M

ed. h

igh

Med

. hig

hLo

wM

ed. h

igh

Med

. hig

hLo

wM

ed. h

igh

Pie

rce

75

5M

ed. l

owLo

wH

igh

Low

Med

. low

Low

Low

Pilo

t Roc

k1,

540

Med

. hig

hM

ed. h

igh

Hig

hLo

wH

igh

Low

Low

Pin

eh

urs

t1

,78

5H

igh

Low

Med

. hig

hH

igh

Med

. hig

hLo

wLo

wP

lum

mer

76

3M

ed. h

igh

Med

. hig

hH

igh

Med

. hig

hM

ed. l

owH

igh

Low

Po

cate

llo4

7,9

14

Hig

hLo

wLo

wM

ed. h

igh

Hig

hLo

wM

ed. l

owP

onde

ray

49

1M

ed. l

owH

igh

Med

. low

Low

Med

. low

Low

Hig

hP

ost

Fa

lls8

,49

4H

igh

Low

Low

Hig

hLo

wLo

wM

ed. l

owP

otla

tch

74

3M

ed. h

igh

Med

. low

Hig

hM

ed. l

owM

ed. h

igh

Low

Med

. hig

hP

rest

onH

igh

Med

. hig

hLo

wM

ed. h

igh

Med

. hig

hLo

wLo

wP

rie

st R

ive

r1

,67

9H

igh

Low

Hig

hH

igh

Med

. low

Low

Low

Rat

hdru

m2

,38

2H

igh

Low

Med

. low

Hig

hM

ed. h

igh

Low

Med

. low

Rex

burg

14,4

97H

igh

Low

Low

Med

. hig

hM

ed. h

igh

Low

Med

. low

57

Tab

le 1

4—E

cono

mic

div

ersi

ty in

dex

scor

es a

nd le

vels

of d

irect

em

ploy

men

t by

indu

stria

l sec

tor

(bas

ed o

n pr

opor

tions

of e

mpl

oym

ent

)by

rur

al C

olum

bia

basi

n co

mm

unity

, with

199

2-94

pop

ulat

ion

estim

ates

(N

=38

7) (

cont

inue

d)

Leve

ls o

f dire

ct e

mpl

oym

ent b

y in

dust

rial s

ecto

ra

19

92

-94

Eco

no

mic

Tra

vel a

ndS

tate

and

loca

lF

ed

era

lM

inin

g a

nd

Sta

te a

nd

tow

np

op

ula

tion

div

ers

ity in

de

xA

gri

cultu

reT

imb

er

tou

rism

go

vern

me

nt

Go

vern

me

nt

min

era

ls

Ric

hfie

ld3

80

Med

. low

Hig

hLo

wLo

wM

ed. l

owLo

wLo

wR

igby

2,9

50

Hig

hM

ed. l

owLo

wM

ed. h

igh

Med

. hig

hLo

wLo

wR

iggi

ns4

60

Med

. hig

hM

ed. h

igh

Low

Hig

hM

ed. h

igh

Med

. hig

hLo

wR

irie

66

5M

ed. l

owM

ed. h

igh

Low

Low

Med

. hig

hLo

wLo

wR

ober

ts6

47

Med

. hig

hH

igh

Low

Med

. low

Hig

hLo

wLo

wR

ockl

and

30

5Lo

wH

igh

Low

Low

Hig

hLo

wLo

wR

uper

t5,

636

Hig

hM

ed. l

owLo

wM

ed. l

owM

ed. h

igh

Low

Low

Sal

mon

3,0

93

Hig

hM

ed. l

owM

ed. l

owM

ed. h

igh

Med

. hig

hM

ed. l

owLo

wS

andp

oint

5,72

5H

igh

Low

Med

. low

Hig

hM

ed. h

igh

Low

Low

She

lley

3,7

44

Hig

hM

ed. h

igh

Low

Med

. low

Med

. hig

hLo

wLo

wS

hosh

one

1,27

3H

igh

Hig

hLo

wLo

wH

igh

Med

. low

Low

Sm

elte

rvill

e4

53

Med

. low

Low

Low

Hig

hH

igh

Low

Med

. hig

hS

mith

s F

erry

Low

Hig

hLo

wH

igh

Low

Low

Low

Sod

a S

prin

gs3,

182

Hig

hM

ed. h

igh

Low

Low

Med

. hig

hLo

wH

igh

Spa

ldin

gLo

wLo

wLo

wLo

wLo

wH

igh

Low

Spi

rit L

ake

88

3M

ed. h

igh

Low

Low

Hig

hH

igh

Med

. low

Low

St.

Ant

hony

3,39

3M

ed. h

igh

Hig

hM

ed. l

owM

ed. h

igh

Med

. hig

hLo

wLo

wS

t. C

harle

s2

05

Low

Hig

hLo

wLo

wLo

wLo

wLo

wS

t. M

ari

e2

,66

9H

igh

Low

Hig

hM

ed. L

owM

ed. h

igh

Low

Low

Sta

nley

70

Med

. low

Low

Low

Hig

hM

ed. h

igh

Low

Low

Sug

ar C

ity1,

410

Med

. low

Hig

hLo

wM

ed. l

owM

ed. l

owLo

wLo

wS

un V

alle

y9

97

Med

. hig

hLo

wLo

wH

igh

Med

. low

Med

. hig

hLo

wS

wan

Val

ley

13

9Lo

wH

igh

Low

Hig

hLo

wLo

wLo

wS

wee

tLo

wH

igh

Med

. low

Low

Low

Low

Low

Ten

doy

Low

Low

Low

Hig

hLo

wLo

wLo

wT

ense

d9

1Lo

wH

igh

Low

Med

. low

Med

. low

Med

. low

Low

Tet

on5

63

Low

Hig

hLo

wLo

wM

ed. l

owLo

wLo

wT

eton

ia1

53

Med

. low

Hig

hM

ed. l

owM

ed. l

owM

ed. l

owLo

wLo

wT

roy

78

2M

ed. l

owH

igh

Med

. low

Low

Hig

hLo

wLo

w

58

Tab

le 1

4—E

cono

mic

div

ersi

ty in

dex

scor

es a

nd le

vels

of d

irect

em

ploy

men

t by

indu

stria

l sec

tor

(bas

ed o

n pr

opor

tions

of e

mpl

oym

ent

)by

rur

al C

olum

bia

basi

n co

mm

unity

, with

199

2-94

pop

ulat

ion

estim

ates

(N

=38

7) (

cont

inue

d)

Leve

ls o

f dire

ct e

mpl

oym

ent b

y in

dust

rial s

ecto

ra

1992

-94

Eco

nom

icT

rave

l and

Sta

te a

nd lo

cal

Fed

eral

Min

ing

and

Sta

te a

nd t

own

popu

latio

ndi

vers

ity in

dex

Agr

icul

ture

Tim

ber

tour

ism

gove

rnm

ent

Gov

ernm

ent

min

eral

s

Tw

in F

alls

29

,68

4H

igh

Low

Low

Med

. hig

hM

ed. h

igh

Low

Low

Uco

n9

32

Low

Hig

hLo

wLo

wLo

wLo

wLo

wV

icto

r3

41

Med

. low

Hig

hLo

wM

ed. h

igh

Med

. low

Low

Low

Wal

lace

99

4M

ed. l

owLo

wLo

wM

ed. h

igh

Hig

hM

ed. l

owM

ed. h

igh

War

dner

24

7Lo

wH

igh

Low

Low

Low

Low

Low

Wei

ppe

52

3M

ed. l

owM

ed. l

owH

igh

Low

Hig

hLo

wLo

wW

eis

er

4,8

91

Hig

hM

ed. h

igh

Low

Med

. low

Med

. hig

hLo

wM

ed. l

owW

ende

ll2,

179

Med

. hig

hH

igh

Low

Low

Med

. low

Low

Low

Wes

ton

42

6Lo

wH

igh

Low

Low

Low

Low

Low

Whi

te B

ird1

09

Med

. low

Hig

hM

ed. l

owM

ed. h

igh

Low

Hig

hLo

wW

idle

r1

,42

6H

igh

Hig

hLo

wLo

wM

ed. h

igh

Med

. low

Low

Win

ches

ter

27

2N

AN

AN

AN

AN

AN

AN

AW

orle

y1

94

Med

. low

Hig

hLo

wM

ed. h

igh

Med

. hig

hM

ed. h

igh

Low

Mon

tana

:A

lber

ton

35

8M

ed. l

owLo

wM

ed. l

owH

igh

Hig

hLo

wLo

wA

naco

nda

10,0

37H

igh

Low

Low

Hig

hH

igh

Low

Low

Arle

e4

86

Med

. hig

hLo

wLo

wM

ed. h

igh

Med

. hig

hM

ed. l

owLo

wB

onne

r-W

. Riv

ersi

de1,

654

Med

. low

Low

Hig

hM

ed. l

owM

ed. h

igh

Low

Low

But

te33

,555

Hig

hLo

wLo

wH

igh

Med

. hig

hLo

wM

ed. l

owC

harlo

40

6M

ed. l

owH

igh

Low

Low

Hig

hLo

wLo

wC

olum

bia

Fal

ls3,

044

Hig

hLo

wM

ed. h

igh

Med

. hig

hM

ed. l

owLo

wM

ed. h

igh

Dar

by6

79

Hig

hM

ed. l

owH

igh

Med

. low

Med

. low

Med

. hig

hLo

wD

ee

r L

od

ge

3,4

94

Hig

hLo

wM

ed. l

owM

ed. l

owH

igh

Low

Med

. hig

hD

rum

mon

d2

70

Med

. hig

hM

ed. h

igh

Hig

hM

ed. l

owH

igh

Low

Low

Eur

eka

1,03

9H

igh

Low

Hig

hM

ed. l

owM

ed. h

igh

Med

. hig

hM

ed. h

igh

Fin

ley

Poi

nt3

76

Med

. low

Hig

hLo

wLo

wLo

wLo

wH

igh

Ham

ilton

3,02

3H

igh

Med

. low

Low

Med

. hig

hM

ed. h

igh

Low

Low

Hot

Spr

ings

41

3M

ed. l

owM

ed. l

owLo

wH

igh

Med

. hig

hLo

wLo

wK

alis

pell

12,4

56H

igh

Low

Med

. low

Med

. low

Med

. low

Low

Low

59

Tab

le 1

4—E

cono

mic

div

ersi

ty in

dex

scor

es a

nd le

vels

of d

irect

em

ploy

men

t by

indu

stria

l sec

tor

(bas

ed o

n pr

opor

tions

of e

mpl

oym

ent

)by

rur

al C

olum

bia

basi

n co

mm

unity

, with

199

2-94

pop

ulat

ion

estim

ates

(N

=38

7) (

cont

inue

d)

Leve

ls o

f dire

ct e

mpl

oym

ent b

y in

dust

rial s

ecto

ra

19

92

-94

Eco

no

mic

Tra

vel a

ndS

tate

and

loca

lF

ed

era

lM

inin

g a

nd

Sta

te a

nd

tow

np

op

ula

tion

div

ers

ity in

de

xA

gri

cultu

reT

imb

er

tou

rism

go

vern

me

nt

Go

vern

me

nt

min

era

ls

Kic

king

Hor

se2

88

Low

Low

Low

Low

Low

Low

Low

Libb

y2

,54

1H

igh

Low

Med

. low

Med

. hig

hM

ed. h

igh

Med

. low

Low

Lo

lo2

,74

6H

igh

Low

Low

Hig

hM

ed. h

igh

Low

Low

Mis

soul

a44

,522

Hig

hLo

wLo

wM

ed. h

igh

Med

. hig

hLo

wLo

wO

rcha

rd H

omes

10,3

17Lo

wH

igh

Low

Low

Low

Low

Med

. low

Pa

blo

1,2

64

Me

d. h

igh

Low

Hig

hLo

wH

igh

Low

Med

. low

Phi

lipsb

urg

90

2H

igh

Med

. hig

hM

ed. h

igh

Med

. hig

hM

ed. l

owM

ed. l

owLo

wP

ines

dale

66

5Lo

wLo

wLo

wH

igh

Hig

hLo

wM

ed. h

igh

Pla

ins

1,01

4H

igh

Hig

hLo

wM

ed. h

igh

Med

. low

Med

. hig

hLo

wP

ols

on

3,6

21

Hig

hM

ed. l

owLo

wH

igh

Med

. low

Low

Low

Rex

ford

13

4M

ed. l

owM

ed. l

owH

igh

Low

Low

Low

Low

Ron

an1,

630

Hig

hM

ed. h

igh

Low

Med

. hig

hM

ed. h

igh

Low

Low

St.

Igna

tius

84

9H

igh

Hig

hLo

wLo

wLo

wM

ed. l

owLo

wS

teve

nsvi

lle1,

340

Hig

hM

ed. h

igh

Low

Med

. hig

hLo

wM

ed. l

owLo

wS

uper

ior

87

9H

igh

Low

Hig

hH

igh

Med

. hig

hM

ed. l

owLo

wT

hom

pson

Fal

ls1,

313

Hig

hLo

wH

igh

Med

. hig

hM

ed. h

igh

Low

Low

Tro

y1,

054

Med

. hig

hLo

wM

ed. l

owH

igh

Med

. hig

hM

ed. h

igh

Low

Wal

kerv

ille

57

3Lo

wLo

wLo

wH

igh

Low

Low

Low

Whi

tefis

h4,

551

Hig

hLo

wLo

wH

igh

Med

. low

Low

Low

Ore

gon:

Ada

ms

24

5Lo

wH

igh

Low

Low

Low

Med

. low

Low

Adr

ian

13

5Lo

wH

igh

Low

Low

Med

. hig

hLo

wLo

wA

ltam

ont

18

,59

1Lo

wH

igh

Low

Low

Low

Low

Low

Ant

elop

e3

5Lo

wH

igh

Low

Low

Low

Low

Low

Arli

ngto

n4

60

Med

. hig

hH

igh

Med

. low

Med

. hig

hH

igh

Low

Low

Ath

ena

1,0

50

Me

d. h

igh

Hig

hLo

wM

ed. l

owM

ed. h

igh

Low

Low

Ba

ker

City

9,5

85

Hig

hM

ed. l

owM

ed. l

owM

ed. h

igh

Med

. low

Med

. low

Low

Be

nd

29

,42

5H

igh

Low

Med

. low

Hig

hM

ed. l

owLo

wLo

wB

oard

man

2,14

5M

ed. h

igh

Med

. hig

hLo

wM

ed. l

owM

ed. h

igh

Med

. low

Low

60

Tab

le 1

4—E

cono

mic

div

ersi

ty in

dex

scor

es a

nd le

vels

of d

irect

em

ploy

men

t by

indu

stria

l sec

tor

(bas

ed o

n pr

opor

tions

of e

mpl

oym

ent

)by

rur

al C

olum

bia

basi

n co

mm

unity

, with

199

2-94

pop

ulat

ion

estim

ates

(N

=38

7) (

cont

inue

d)

Leve

ls o

f dire

ct e

mpl

oym

ent b

y in

dust

rial s

ecto

ra

1992

-94

Eco

nom

icT

rave

l and

Sta

te a

nd lo

cal

Fed

eral

Min

ing

and

Sta

te a

nd to

wn

popu

latio

ndi

vers

ity in

dex

Agr

icul

ture

Tim

ber

tour

ism

gove

rnm

ent

Gov

ernm

ent

min

eral

s

Bon

anza

35

5M

ed. l

owH

igh

Low

Med

. low

Med

. low

Med

. hig

hLo

wB

urns

2,8

70

Hig

hM

ed. h

igh

Med

. low

Med

. hig

hM

ed. h

igh

Low

Low

Can

yon

City

66

0M

ed. l

owH

igh

Low

Low

Hig

hLo

wLo

wC

hilo

quin

70

0M

ed. h

igh

Med

. low

Low

Hig

hM

ed. l

owM

ed. l

owLo

wC

ondo

n7

25

Hig

hH

igh

Low

Low

Med

. hig

hLo

wLo

wC

ove

57

0M

ed. l

owH

igh

Low

Low

Low

Med

. low

Low

Cul

ver

66

0M

ed. h

igh

Med

. hig

hLo

wH

igh

Med

. low

Low

Med

. hig

hD

ayvi

lle1

45

Med

. low

Med

. hig

hM

ed. l

owM

ed. l

owH

igh

Hig

hLo

wD

ufur

58

0M

ed. l

owH

igh

Low

Low

Med

. hig

hLo

wLo

wE

cho

51

5M

ed. h

igh

Hig

hLo

wM

ed. h

igh

Hig

hM

ed. l

owLo

wE

lgin

1,65

5M

ed. h

igh

Med

. hig

hH

igh

Med

. hig

hM

ed. h

igh

Low

Low

Ent

erpr

ise

1,93

5H

igh

Med

. hig

hLo

wM

ed. h

igh

Hig

hM

ed. h

igh

Med

. low

Fos

sil

47

0M

ed. l

owH

igh

Low

Low

Hig

hLo

wLo

wG

rani

te1

0Lo

wLo

wLo

wLo

wLo

wLo

wLo

wG

rass

Val

ley

16

0M

ed. l

owH

igh

Low

Med

. hig

hM

ed. l

owLo

wLo

wG

reen

horn

3Lo

wH

igh

Low

Low

Low

Low

Low

Hai

nes

41

0M

ed. l

owH

igh

Low

Low

Med

. hig

hLo

wLo

wH

alfw

ay3

40

Hig

hM

ed. h

igh

Low

Hig

hM

ed. h

igh

Low

Low

Hel

ix1

55

Low

Low

Low

Med

. low

Hig

hLo

wLo

wH

eppn

er1,

465

Med

. hig

hH

igh

Med

. low

Low

Hig

hM

ed. l

owLo

wH

erm

isto

n10

,330

Hig

hM

ed. l

owLo

wM

ed. h

igh

Med

. low

Low

Low

Hin

es1,

445

Med

. hig

hH

igh

Hig

hLo

wM

ed. l

owH

igh

Low

Ho

od

Riv

er

4,8

75

NA

NA

NA

NA

NA

NA

NA

Hun

tingt

on5

60

Low

Med

. hig

hLo

wM

ed. h

igh

Hig

hLo

wH

igh

Imbl

er3

10

Med

. hig

hM

ed. l

owM

ed. h

igh

Med

. hig

hM

ed. h

igh

Med

. hig

hLo

wIo

ne2

50

Low

Med

. hig

hLo

wH

igh

Hig

hLo

wLo

wIr

rigon

89

0M

ed. l

owM

ed. l

owLo

wM

ed. l

owH

igh

Low

Low

Isla

nd

City

82

5Lo

wLo

wLo

wLo

wLo

wLo

wLo

wJo

hn

Da

y1

,90

0H

igh

Med

. low

Hig

hM

ed. h

igh

Med

. low

Med

. hig

hLo

w

61

Tab

le 1

4—E

cono

mic

div

ersi

ty in

dex

scor

es a

nd le

vels

of d

irect

em

ploy

men

t by

indu

stria

l sec

tor

(bas

ed o

n pr

opor

tions

of e

mpl

oym

ent

)by

rur

al C

olum

bia

basi

n co

mm

unity

, with

199

2-94

pop

ulat

ion

estim

ates

(N

=38

7) (

cont

inue

d)

Leve

ls o

f dire

ct e

mpl

oym

ent b

y in

dust

rial s

ecto

ra

19

92

-94

Eco

no

mic

Tra

vel a

ndS

tate

and

loca

lF

ed

era

lM

inin

g a

nd

Sta

te a

nd

tow

np

op

ula

tion

div

ers

ity in

de

xA

gri

cultu

reT

imb

er

tou

rism

go

vern

me

nt

Go

vern

me

nt

min

era

ls

Jord

an V

alle

y4

00

Med

. hig

hM

ed. h

igh

Low

Med

. hig

hH

igh

Med

. hig

hM

ed. l

owJo

seph

1,16

5M

ed. l

owH

igh

Hig

hM

ed. h

igh

Med

. hig

hLo

wLo

wK

lam

ath

Fal

ls18

,405

Hig

hLo

wLo

wH

igh

Med

. hig

hLo

wLo

wL

a G

ran

de

12

,19

5H

igh

Low

Med

. low

Med

. hig

hM

ed. h

igh

Low

Low

Lake

view

2,5

75

Hig

hM

ed. l

owM

ed. h

igh

Med

. hig

hM

ed. h

igh

Med

. hig

hLo

wLe

xing

ton

28

5M

ed. l

owH

igh

Low

Low

Low

Low

Low

Lone

rock

20

Low

Hig

hLo

wLo

wLo

wLo

wLo

wLo

ng C

reek

24

0M

ed. h

igh

Hig

hM

ed. h

igh

Hig

hH

igh

Low

Low

Lost

ine

23

0M

ed. h

igh

Low

Hig

hM

ed. h

igh

Low

Hig

hM

ed. l

owM

ad

ras

4,2

90

Hig

hM

ed. l

owM

ed. h

igh

Med

. hig

hM

ed. h

igh

Low

Low

Mal

in7

40

Med

. low

Med

. hig

hH

igh

Low

Low

Low

Low

Mau

pin

48

5M

ed. h

igh

Hig

hLo

wH

igh

Med

. low

Med

. hig

hLo

wM

erril

l8

35

Hig

hM

ed. h

igh

Med

. hig

hM

ed. l

owM

ed. h

igh

Low

Low

Met

oliu

s5

45

Low

Low

Low

Low

Low

Low

Low

Milt

on-F

reew

ater

5,86

5H

igh

Med

. low

Low

Hig

hM

ed. h

igh

Low

Low

Mitc

hell

16

5M

ed. l

owH

igh

Med

. low

Med

. hig

hH

igh

Low

Low

Mon

umen

t1

70

Low

Hig

hLo

wM

ed. l

owM

ed. h

igh

Low

Low

Mor

o2

95

Med

. hig

hH

igh

Low

Med

. hig

hH

igh

Med

. hig

hLo

wM

osie

r2

75

Med

. hig

hLo

wLo

wH

igh

Hig

hLo

wLo

wM

ount

Ver

non

62

5M

ed. l

owLo

wH

igh

Med

. low

Hig

hLo

wLo

wN

orth

fork

Med

. low

Low

Low

Hig

hLo

wLo

wLo

wN

yssa

2,6

75

Hig

hM

ed. h

igh

Low

Med

. hig

hM

ed. h

igh

Low

Low

On

tari

o9

,76

0H

igh

Med

. hig

hLo

wM

ed. h

igh

Med

. low

Hig

hLo

wP

aisl

ey3

45

Med

. low

Hig

hM

ed. l

owLo

wLo

wLo

wLo

wP

aris

Med

. low

Hig

hLo

wLo

wH

igh

Low

Low

Pe

nd

leto

n1

5,7

15

Hig

hLo

wLo

wM

ed. h

igh

Med

. hig

hM

ed. l

owM

ed. l

owP

rairi

e C

ity1,

160

Med

. hig

hM

ed. h

igh

Med

. hig

hM

ed. l

owM

ed. l

owH

igh

Low

Prin

evill

e5,

945

Hig

hM

ed. l

owH

igh

Med

. low

Med

. low

Med

. low

Low

Red

mon

d9,

650

Hig

hM

ed. l

owM

ed. l

owH

igh

Med

. low

Low

Low

62

Tab

le 1

4—E

cono

mic

div

ersi

ty in

dex

scor

es a

nd le

vels

of d

irect

em

ploy

men

t by

indu

stria

l sec

tor

(bas

ed o

n pr

opor

tions

of e

mpl

oym

ent

)by

rur

al C

olum

bia

basi

n co

mm

unity

, with

199

2-94

pop

ulat

ion

estim

ates

(N

=38

7) (

cont

inue

d)

Leve

ls o

f dire

ct e

mpl

oym

ent b

y in

dust

rial s

ecto

ra

19

92

-94

Eco

no

mic

Tra

vel a

ndS

tate

and

loca

lF

ed

era

lM

inin

g a

nd

Sta

te a

nd

tow

np

op

ula

tion

div

ers

ity in

de

xA

gri

cultu

reT

imb

er

tou

rism

go

vern

me

nt

Go

vern

me

nt

min

era

ls

Ric

hlan

d1

80

Med

. hig

hH

igh

Low

Hig

hM

ed. h

igh

Low

Low

Ruf

us2

90

Med

. low

Hig

hLo

wH

igh

Med

. low

Hig

hLo

wS

enec

a1

90

Med

. low

Low

Low

Low

Med

. low

Low

Low

Sha

niko

30

Low

Hig

hM

ed. l

owLo

wLo

wLo

wH

igh

Sis

ters

76

5M

ed. h

igh

Med

. low

Low

Hig

hM

ed. l

owLo

wLo

wS

pray

15

5M

ed. l

owH

igh

Med

. low

Med

. low

Med

. hig

hLo

wLo

wS

tanf

ield

1,62

0M

ed. l

owH

igh

Low

Med

. low

Hig

hLo

wLo

wS

umm

ervi

lle1

50

Med

. low

Low

Hig

hLo

wLo

wLo

wM

ed. h

igh

Sum

pter

16

5M

ed. h

igh

Med

. hig

hM

ed. l

owH

igh

Med

. hig

hLo

wM

ed. h

igh

Te

rre

bo

nn

e1

,08

3M

ed

. lo

wLo

wLo

wH

igh

Low

Low

Low

Th

e D

alle

s1

1,3

25

Hig

hLo

wLo

wH

igh

Med

. hig

hLo

wM

ed. l

owT

hre

e R

ive

rs1

,23

0Lo

wH

igh

Low

Low

Low

Low

Low

Uki

ah2

60

Low

Hig

hLo

wH

igh

Med

. low

Low

Low

Um

atill

a3,

155

Med

. hig

hH

igh

Low

Med

. hig

hH

igh

Med

. low

Low

Uni

on1,

915

Hig

hH

igh

Low

Med

. hig

hH

igh

Low

Low

Uni

ty1

10

Med

. hig

hM

ed. h

igh

Med

. low

Low

Med

. low

Hig

hLo

wV

ale

1,49

5M

ed. h

igh

Hig

hLo

wLo

wH

igh

Low

Low

Wal

low

a7

55

Med

. low

Hig

hM

ed. h

igh

Med

. hig

hM

ed. h

igh

Med

. low

Med

. low

War

m S

prin

gs2,

287

Med

. low

Med

. hig

hH

igh

Low

Low

Med

. low

Med

. low

Wes

ton

64

0M

ed. l

owLo

wLo

wLo

wM

ed. h

igh

Low

Low

Was

hing

ton:

Afto

n1

,53

4H

igh

Med

. hig

hLo

wM

ed. h

igh

Hig

hLo

wLo

wA

irway

Hei

ghts

2,52

0M

ed. h

igh

Low

Low

Hig

hH

igh

Low

Low

Alb

ion

65

5Lo

wH

igh

Low

Low

Low

Low

Low

Alm

ira3

15

Low

Hig

hLo

wLo

wH

igh

Low

Low

Alp

ine

22

2M

ed. l

owLo

wLo

wH

igh

Med

. low

Low

Low

Aso

tin1,

108

Low

Hig

hLo

wH

igh

Hig

hLo

wLo

wB

ento

n C

ity2,

090

Med

. hig

hH

igh

Low

Med

. hig

hM

ed. h

igh

Med

. low

Low

Bin

gen

66

0H

igh

Med

. hig

hM

ed. h

igh

Med

. low

Med

. hig

hM

ed. l

owLo

w

63

Tab

le 1

4—E

cono

mic

div

ersi

ty in

dex

scor

es a

nd le

vels

of d

irect

em

ploy

men

t by

indu

stria

l sec

tor

(bas

ed o

n pr

opor

tions

of e

mpl

oym

ent

)by

rur

al C

olum

bia

basi

n co

mm

unity

, with

199

2-94

pop

ulat

ion

estim

ates

(N

=38

7) (

cont

inue

d)

Leve

ls o

f dire

ct e

mpl

oym

ent b

y in

dust

rial s

ecto

ra

19

92

-94

Eco

no

mic

Tra

vel a

ndS

tate

and

loca

lF

ed

era

lM

inin

g a

nd

Sta

te a

nd

tow

np

op

ula

tion

div

ers

ity in

de

xA

gri

cultu

reT

imb

er

tou

rism

go

vern

me

nt

Go

vern

me

nt

min

era

ls

Bre

wst

er1,

645

Med

. hig

hM

ed. l

owLo

wM

ed. h

igh

Low

Low

Low

Brid

gepo

rt1,

705

Med

. hig

hH

igh

Low

Low

Med

. hig

hLo

wLo

wB

urba

nk1,

695

Med

. low

Med

. hig

hLo

wLo

wH

igh

Hig

hLo

wC

ashm

ere

2,6

60

Hig

hH

igh

Low

Med

. low

Med

. low

Low

Med

. hig

hC

hela

n3,

200

Hig

hM

ed. h

igh

Low

Med

. hig

hH

igh

Low

Low

Che

ney

8,2

20

Hig

hLo

wLo

wM

ed. h

igh

Hig

hLo

wLo

wC

hew

elah

2,2

43

Hig

hM

ed. h

igh

Med

. low

Med

. hig

hM

ed. l

owLo

wM

ed. l

owC

lark

ston

6,75

0H

igh

Low

Low

Hig

hM

ed. h

igh

Low

Low

Cle

Elu

m1,

785

Hig

hM

ed. l

owLo

wH

igh

Med

. hig

hLo

wLo

wC

olfa

x2,

810

Med

. hig

hLo

wLo

wM

ed. l

owH

igh

Low

Low

Col

lege

Pla

ce6,

710

Med

. low

Med

. hig

hLo

wLo

wM

ed. h

igh

Low

Med

. low

Col

ton

35

0Lo

wH

igh

Low

Low

Hig

hLo

wM

ed. l

owC

olvi

lle4,

440

Hig

hLo

wM

ed. l

owM

ed. h

igh

Hig

hLo

wLo

wC

onco

nully

18

0Lo

wH

igh

Low

Hig

hM

ed. h

igh

Low

Low

Con

nell

2,64

0H

igh

Hig

hLo

wM

ed. l

owM

ed. l

owLo

wLo

wC

oule

e C

ity6

12

Med

. hig

hH

igh

Low

Med

. low

Hig

hLo

wLo

wC

oule

e D

am2

06

Med

. low

Low

Low

Med

. hig

hH

igh

Med

. hig

hLo

wC

ount

ry H

omes

5,12

6Lo

wLo

wLo

wH

igh

Low

Hig

hLo

wC

rest

on2

39

Low

Hig

hLo

wLo

wH

igh

Low

Low

Cus

ick

25

6M

ed. h

igh

Low

Med

. low

Med

. hig

hH

igh

Med

. hig

hLo

wD

aven

port

1,55

0H

igh

Med

. hig

hLo

wLo

wH

igh

Low

Low

Day

ton

2,5

05

Hig

hM

ed. h

igh

Low

Med

. low

Hig

hLo

wLo

wD

ee

rpa

rk2

,57

0H

igh

Med

. low

Low

Hig

hM

ed. l

owLo

wLo

wE

ast W

enat

chee

4,01

0H

igh

Med

. hig

hLo

wM

ed. h

igh

Med

. low

Low

Low

Ele

ctric

City

94

5M

ed. l

owLo

wLo

wM

ed. h

igh

Hig

hLo

wLo

wE

llens

burg

12,8

60H

igh

Low

Low

Hig

hH

igh

Low

Low

Elm

er C

ity3

10

Low

Low

Low

Hig

hM

ed. h

igh

Low

Low

End

icot

t3

60

Med

. low

Med

. hig

hLo

wM

ed. l

owH

igh

Low

Low

Ent

iat

54

5M

ed. l

owH

igh

Low

Low

Hig

hM

ed. h

igh

Low

Ep

hra

ta5

,58

5H

igh

Med

. hig

hLo

wM

ed. l

owH

igh

Low

Low

64

Tab

le 1

4—E

cono

mic

div

ersi

ty in

dex

scor

es a

nd le

vels

of d

irect

em

ploy

men

t by

indu

stria

l sec

tor

(bas

ed o

n pr

opor

tions

of

empl

oym

ent)

by

rura

l Col

umbi

a ba

sin

com

mun

ity, w

ith 1

992-

94 p

opul

atio

n es

timat

es (

N=

387)

(co

ntin

ued)

Leve

ls o

f di

rect

em

ploy

men

t by

ind

ustr

ial

sect

ora

19

92

-94

Eco

no

mic

Tra

vel a

ndS

tate

and

loca

lF

ed

era

lM

inin

g a

nd

Sta

te a

nd

tow

np

op

ula

tion

div

ers

ity in

de

xA

gri

cultu

reT

imb

er

tou

rism

go

vern

me

nt

Go

vern

me

nt

min

era

ls

Fa

irch

ild4

,85

4M

ed.

low

Low

Low

Low

Low

Hig

hLo

wF

air

field

59

9M

ed. h

igh

Hig

hLo

wLo

wH

igh

Low

Low

Far

min

gton

13

0M

ed. l

owH

igh

Low

Low

Hig

hLo

wLo

wG

arfie

ld5

94

Med

. low

Med

. hig

hLo

wLo

wH

igh

Low

Med

. low

Geo

rge

36

5M

ed. l

owH

igh

Low

Low

Low

Low

Low

Go

lde

nd

ale

3,4

25

Hig

hM

ed. h

igh

Med

. low

Low

Med

. hig

hLo

wM

ed. h

igh

Gra

nd C

oule

e1,

045

Med

. hig

hM

ed. l

owLo

wH

igh

Hig

hLo

wLo

wG

rand

view

7,69

0M

ed. h

igh

Hig

hLo

wM

ed. l

owM

ed. h

igh

Low

Low

Gra

nger

2,08

5M

ed. h

igh

Med

. hig

hLo

wLo

wM

ed. h

igh

Low

Med

. hig

hG

reen

acre

s4,

626

Hig

hLo

wLo

wLo

wM

ed. l

owLo

wLo

wH

arra

h4

53

Med

. low

Hig

hLo

wM

ed. l

owH

igh

Low

Low

Har

ringt

on4

92

Med

. low

Hig

hLo

wLo

wM

ed. h

igh

Low

Low

Har

tline

18

5Lo

wH

igh

Low

Low

Low

Low

Low

Inch

eliu

m3

92

Med

. low

Low

Med

. hig

hM

ed. l

owM

ed. l

owH

igh

Low

Ione

50

1M

ed. h

igh

Low

Hig

hH

igh

Med

. hig

hLo

wLo

wK

ahlo

tus

20

0M

ed. h

igh

Hig

hLo

wLo

wH

igh

Med

. low

Low

Ken

new

ick

46

,96

0H

igh

Low

Low

Med

. hig

hM

ed. l

owLo

wLo

wK

ettle

Fal

ls1,

435

Med

. hig

hM

ed. l

owH

igh

Med

. hig

hM

ed. l

owM

ed. l

owLo

wK

ittita

s1,

060

Med

. low

Hig

hLo

wM

ed. h

igh

Hig

hLo

wLo

wK

rupp

65

Low

Hig

hLo

wLo

wLo

wLo

wLo

wLa

Cro

sse

39

0M

ed. l

owH

igh

Low

Hig

hLo

wLo

wLo

wLa

mon

t9

3Lo

wH

igh

Low

Low

Hig

hLo

wLo

wLa

tah

21

1M

ed. l

owH

igh

Low

Low

Low

Low

Low

Leav

enw

orth

2,0

20

Hig

hM

ed. h

igh

Low

Hig

hM

ed. l

owLo

wLo

wLi

bert

y La

ke2,

036

Low

Low

Low

Med

. low

Low

Low

Hig

hLi

nd4

70

Med

. hig

hH

igh

Low

Low

Med

. hig

hLo

wLo

wM

abto

n1,

615

Med

. low

Hig

hLo

wLo

wH

igh

Low

Low

Mal

den

21

5Lo

wH

igh

Low

Low

Low

Low

Low

Mar

cus

15

4M

ed. l

owLo

wLo

wH

igh

Med

. low

Low

Low

Mat

taw

a1,

535

Med

. low

Hig

hLo

wLo

wM

ed. h

igh

Low

Low

Med

ical

Lak

e3,

660

Med

. hig

hLo

wLo

wM

ed. h

igh

Hig

hM

ed. l

owM

ed. h

igh

65

Tab

le 1

4—E

cono

mic

div

ersi

ty in

dex

scor

es a

nd le

vels

of d

irect

em

ploy

men

t by

indu

stria

l sec

tor

(bas

ed o

n pr

opor

tions

of

empl

oym

ent)

by

rura

l Col

umbi

a ba

sin

com

mun

ity, w

ith 1

992-

94 p

opul

atio

n es

timat

es (

N=

387)

(co

ntin

ued)

Leve

ls o

f dire

ct e

mpl

oym

ent b

y in

dust

rial s

ecto

ra

1992

-94

Eco

nom

icT

rave

l and

Sta

te a

nd lo

cal

Fed

eral

Min

ing

and

Sta

te a

nd to

wn

popu

latio

ndi

vers

ity in

dex

Agr

icul

ture

Tim

ber

tour

ism

gove

rnm

ent

Gov

ernm

ent

min

eral

s

Mes

a3

15

Med

. low

Hig

hLo

wM

ed. h

igh

Med

. hig

hLo

wLo

wM

etal

ine

19

3Lo

wLo

wLo

wH

igh

Hig

hLo

wLo

wM

etal

ine

Fal

ls2

27

Med

. low

Low

Low

Low

Hig

hH

igh

Low

Mo

ses

La

ke1

2,1

90

Hig

hLo

wLo

wLo

wM

ed. l

owLo

wLo

wM

oxee

92

5M

ed. h

igh

Hig

hM

ed. l

owLo

wLo

wLo

wM

ed. h

igh

Nac

hes

68

9H

igh

Med

. low

Med

. hig

hM

ed. h

igh

Hig

hLo

wLo

wN

espe

lem

22

5M

ed. l

owLo

wLo

wLo

wLo

wH

igh

Low

New

port

1,78

0H

igh

Med

. hig

hLo

wM

ed. l

owH

igh

Low

Med

. hig

hN

orth

port

34

2M

ed. h

igh

Med

. hig

hH

igh

Low

Med

. hig

hH

igh

Med

. low

Oak

esda

le4

33

Med

. hig

hH

igh

Low

Low

Hig

hLo

wLo

wO

dess

a9

57

Hig

hH

igh

Low

Low

Med

. hig

hLo

wLo

wO

kano

gan

2,40

0M

ed. l

owM

ed. h

igh

Low

Med

. low

Hig

hM

ed. l

owLo

wO

mak

4,2

20

Hig

hH

igh

Med

. low

Low

Med

. low

Med

. low

Low

Opp

ortu

nity

22,3

26M

ed. l

owH

igh

Low

Low

Med

. hig

hH

igh

Med

. hig

hO

rovi

lle1,

520

Hig

hH

igh

Med

. low

Med

. low

Med

. hig

hLo

wLo

wO

thel

lo4,

780

Hig

hM

ed. h

igh

Low

Low

Med

. hig

hLo

wLo

wO

tis O

rch

ard

5,7

90

Hig

hLo

wLo

wH

igh

Low

Low

Med

. low

Pal

ouse

96

0M

ed. l

owH

igh

Low

Low

Hig

hLo

wLo

wP

asc

o2

2,1

70

Hig

hM

ed. l

owLo

wM

ed. h

igh

Med

. hig

hLo

wLo

wP

ater

os5

85

Hig

hM

ed. l

owH

igh

Med

. hig

hM

ed. l

owLo

wLo

wP

omer

oy1,

460

Hig

hH

igh

Low

Low

Hig

hM

ed. h

igh

Low

Pre

scot

t3

05

Med

. hig

hH

igh

Low

Med

. low

Hig

hLo

wLo

wP

ross

er

4,6

30

Hig

hH

igh

Low

Low

Hig

hLo

wLo

wP

ullm

an23

,770

Med

. hig

hLo

wLo

wM

ed. h

igh

Hig

hLo

wLo

wQ

uinc

y3

,86

0H

igh

Hig

hLo

wM

ed. l

owM

ed. l

owLo

wLo

wR

eard

an4

97

Med

. hig

hM

ed. h

igh

Low

Med

. hig

hH

igh

Med

. low

Low

Rep

ublic

1,08

0H

igh

Med

. hig

hM

ed. h

igh

Med

. low

Hig

hLo

wM

ed. h

igh

Ric

hlan

d35

,430

Med

. hig

hLo

wLo

wM

ed. l

owM

ed. h

igh

Low

Low

Ritz

ville

1,75

0M

ed. l

owH

igh

Low

Med

. hig

hM

ed. h

igh

Low

Low

Riv

ersi

de2

50

Med

. hig

hH

igh

Low

Hig

hM

ed. l

owLo

wLo

wR

ock

Isla

nd5

55

Med

. hig

hH

igh

Low

Med

. low

Hig

hM

ed. l

owLo

w

66

Tab

le 1

4—E

cono

mic

div

ersi

ty in

dex

scor

es a

nd le

vels

of d

irect

em

ploy

men

t by

indu

stria

l sec

tor

(bas

ed o

n pr

opor

tions

of e

mpl

oym

ent

)by

rur

al C

olum

bia

basi

n co

mm

unity

, with

199

2-94

pop

ulat

ion

estim

ates

(N

=38

7) (

cont

inue

d)

Leve

ls o

f di

rect

em

ploy

men

t by

ind

ustr

ial

sect

ora

19

92

-94

Eco

no

mic

Tra

vel a

ndS

tate

and

loca

lF

ed

era

lM

inin

g a

nd

Sta

te a

nd

tow

np

op

ula

tion

div

ers

ity in

de

xA

gri

cultu

reT

imb

er

tou

rism

go

vern

me

nt

Go

vern

me

nt

min

era

ls

Roc

kfor

d5

05

Med

. hig

hH

igh

Low

Med

. low

Hig

hLo

wLo

wR

osal

ia6

20

Hig

hM

ed. l

owLo

wH

igh

Med

. hig

hLo

wLo

wR

osly

n8

85

Med

. hig

hM

ed. h

igh

Low

Hig

hH

igh

Low

Low

Roy

al C

ity1,

200

Med

. hig

hH

igh

Low

Med

. low

Hig

hLo

wLo

wS

elah

5,17

0M

ed. h

igh

Med

. low

Low

Med

. low

Hig

hLo

wM

ed. l

owS

oap

Lake

1,30

0M

ed. h

igh

Low

Low

Hig

hH

igh

Low

Low

Sou

th B

road

way

2,84

3Lo

wLo

wLo

wLo

wLo

wLo

wLo

wS

pang

le2

45

Med

. hig

hH

igh

Low

Low

Hig

hLo

wM

ed. l

owS

poka

ne18

5,60

0H

igh

Low

Low

Low

Low

Low

Low

Spr

ague

46

5M

ed. l

owH

igh

Low

Low

Med

. hig

hLo

wLo

wS

prin

gdal

e3

55

Med

. low

Hig

hM

ed. l

owLo

wLo

wLo

wH

igh

St.

John

50

8M

ed. h

igh

Med

. low

Low

Med

. low

Hig

hLo

wLo

wS

tarb

uck

16

5Lo

wH

igh

Low

Low

Med

. hig

hH

igh

Low

Sun

nysi

de1

1,6

60

Hig

hM

ed. h

igh

Low

Med

. low

Med

. hig

hLo

wLo

wT

ekoa

87

0M

ed. l

owM

ed. h

igh

Low

Med

. low

Hig

hLo

wLo

wT

err

ace

He

igh

ts4

,22

3Lo

wLo

wLo

wLo

wLo

wLo

wLo

wT

ieto

n8

91

Med

. hig

hM

ed. h

igh

Low

Low

Med

. hig

hLo

wLo

wT

onas

ket

1,02

0M

ed. l

owH

igh

Low

Med

. low

Med

. hig

hLo

wLo

wT

op

pe

nis

h7

,73

4H

igh

Low

Low

Med

. low

Hig

hM

ed. h

igh

Low

Tw

isp

91

0H

igh

Med

. hig

hLo

wH

igh

Med

. low

Low

Low

Un

ion

Ga

p3

,22

0H

igh

Med

. low

Med

. low

Hig

hM

ed. h

igh

Low

Low

Uni

onto

wn

30

5M

ed. l

owH

igh

Low

Low

Hig

hLo

wLo

wV

era

da

le7

,83

6H

igh

Low

Low

Hig

hM

ed. l

owLo

wM

ed. l

owW

aits

burg

1,13

0M

ed. h

igh

Hig

hF

PH

igh

Med

. low

Low

Low

Wal

la W

alla

28,7

30H

igh

Low

Low

Med

. hig

hM

ed. h

igh

Low

Med

. low

Wa

pa

to3

,79

0H

igh

Hig

hLo

wM

ed. l

owM

ed. h

igh

Low

Low

Wa

rde

n1

,76

5M

ed

. lo

wH

igh

Low

Med

. low

Med

. hig

hLo

wLo

wW

asco

38

5M

ed. h

igh

Hig

hLo

wH

igh

Med

. low

Low

Low

Was

htuc

na2

70

Med

. low

Hig

hLo

wLo

wH

igh

Low

Low

67

Tab

le 1

4—E

cono

mic

div

ersi

ty in

dex

scor

es a

nd le

vels

of d

irect

em

ploy

men

t by

indu

stria

l sec

tor

(bas

ed o

n pr

opor

tions

of e

mpl

oym

ent

)by

rur

al C

olum

bia

basi

n co

mm

unity

, with

199

2-94

pop

ulat

ion

estim

ates

(N

=38

7) (

cont

inue

d)

Leve

ls o

f di

rect

em

ploy

men

t by

ind

ustr

ial

sect

ora

19

92

-94

Eco

no

mic

Tra

vel a

ndS

tate

and

loca

lF

ed

era

lM

inin

g a

nd

Sta

te a

nd

tow

np

op

ula

tion

div

ers

ity in

de

xA

gri

cultu

reT

imb

er

tou

rism

go

vern

me

nt

Go

vern

me

nt

min

era

ls

Wat

ervi

lle1,

065

Med

. low

Hig

hLo

wLo

wLo

wLo

wLo

wW

aver

ly1

11

Low

Hig

hLo

wLo

wLo

wLo

wH

igh

Wen

atch

ee23

,460

Hig

hM

ed. l

owLo

wM

ed. h

igh

Med

. low

Low

Med

. low

Whi

te S

alm

on1,

915

Hig

hLo

wM

ed. l

owM

ed. l

owH

igh

Med

. low

Low

Whi

te S

wan

2,75

5M

ed. l

owH

igh

Med

. low

Med

. low

Med

. hig

hM

ed. h

igh

Low

Wilb

ur8

75

Med

. hig

hM

ed. h

igh

Low

Med

. hig

hM

ed. h

igh

Low

Low

Wils

on C

reek

22

4M

ed. l

owH

igh

Low

Med

. low

Med

. low

Low

Low

Win

thro

p3

45

Hig

hM

ed. h

igh

Med

. low

Hig

hLo

wM

ed. l

owLo

wY

akim

a5

9,7

40

Hig

hM

ed. l

owLo

wM

ed. h

igh

Low

Low

Low

Zill

ah2,

190

Med

. hig

hH

igh

Low

Low

Med

. low

Low

Med

. Low

Wyo

min

g:Ja

ckso

n5,

605

Med

. hig

hLo

wLo

wH

igh

Med

. low

Low

Low

Tha

yne

28

8M

ed. h

igh

Low

Low

Med

. low

Low

Low

Hig

h

NA

= n

ot a

vaila

ble.

a A “

low

” le

vel o

f dire

ct e

mpl

oym

ent r

epre

sent

s 5

perc

ent o

f tot

al e

mpl

oym

ent i

n a

give

n in

dust

ry, o

r le

ss; “

med

ium

-low

,” 6

to 1

0 pe

rcen

t; “m

ediu

m h

igh,

” 11

to 1

9 pe

rcen

t;an

d “h

igh”

20

perc

ent o

r m

ore

of to

tal e

mpl

oym

ent i

n a

give

n in

dust

ry.

68

Table 14 displays all the region’s cities and townsand gives their 1992-94 population estimates,scores on the economic diversity index, andlevels of employment per industrial sector. Forthis analysis, the levels of actual employmentdeveloped were low (5 percent of total employ-ment 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 employ-ment in a given industry). These levels, whichwere based on a comparison of quartiles for allindustries, provided absolute—as opposed torelative—indicators of the level of communitydependence on the various industries for employ-ment. That is, few communities would be ratedhighly in construction employment, which rep-resented a low proportion of employment acrossmost communities, but the levels would be muchmore revealing for sectors representing higherproportions and greater variation, such as agricul-ture or wood products.

Overall, as table 14 shows, although larger townsand cities tended to have a comparatively higherdegree of economic diversity, many of the re-gion’s smaller towns also were found to be com-paratively diverse economically. For instance,Bonners Ferry, Idaho, was found to have a highlydiverse economy, yet had only 2,244 residents in1992-94.

Comparing Perceived and ActualIndustry DominanceAn analysis of industry employment bydominant industry classifications—Economic-profile data for the 198 communities were ana-lyzed to identify differences in actual employ-ment in various sectors in those towns classifiedas dominated by the various natural resourceindustries (i.e., farming, ranching, timber, andoutdoor recreation and tourism; see the earlierdiscussion on classification of communities bydominant industry). As table 15 shows, the per-centage of actual employment within each of themajor industrial sectors is reported for each typeof town classified as dominated by a particularindustry.

As one would expect, the 93 communities thatperceived themselves to be farming and ranch-

ing dependent had higher percentages of employ-ment in the agricultural sector (28.5 percent).Similarly, the 45 communities perceiving them-selves to be timber dependent were primarilyrepresented by the higher levels of employment(16.7 percent) in the harvesting of timber andmanufacturing of wood products. Additionally,within the 33 communities that perceived them-selves to be primarily dependent on travel andtourism, over one-quarter of all jobs, on average,were directly related to that industry. Higher pro-portions of employment in travel-related service-oriented sectors, such as food and beverageand retail trade, that were attributed to local(nontourism related) trade, also were found fortravel and tourism communities as well. Thisfinding suggests that the attribution of nonlocal(or tourism) jobs in these travel-related servicesectors based on the minimum requirements ap-proach may be conservative; that is, the traveland tourism sector may account for higher levelsof employment than indicated by the minimumrequirements approach applied here. In addition,the travel and tourism sector was an importantprovider of jobs in both timber- and ranching-dependent communities (13.0 and 14.3 percent,respectively, of total employment), reflecting theimportant role that resource amenities are playingin many rural areas in the basin. The FederalGovernment was also a comparatively importantsector in these towns, with nearly double themean estimate of employment as other kinds ofindustrial-dominant towns.

Alternatively, industries such as wood productsand Federal Government are relatively unimport-ant to travel and tourism communities. Likewise,mining and minerals was a relatively unimportanteconomic sector, in terms of direct jobs provided,in farming-, ranching-, and timber- dependentcommunities. However, wood products manufac-turing had the second-highest proportion of totalemployment, next to timber-dependent towns, inranching communities. Aside from these timber-dependent communities having the largest pe-rcentage of manufacturing of wood and paperproducts, they also had the most diverse econo-mies based on relatively high proportions of jobsin each of the major industries.

69

Tab

le 1

5—P

erce

ntag

e of

tota

l em

ploy

men

t in

sele

cted

eco

nom

ic s

ecto

rs, b

y in

dust

ry d

omin

ance

cla

ssifi

catio

n, C

olum

bia

basi

n

Per

ceiv

ed d

omin

ant

Sta

te a

nd lo

cal

Fed

eral

indu

stry

(N

)aA

gric

ultu

reW

ood

prod

ucts

Tra

vel a

nd t

ouris

mgo

vern

men

tG

over

nmen

t

Pe

rce

nt

Far

min

g (9

3)28

.52.

09.

117

.94.

0R

anch

ing

(18)

26.4

7.7

14.3

15.6

8.4

Tim

ber

(45)

12.6

16.7

13.0

14.9

8.5

Tra

vel a

nd t

ouris

m (

33)

11.6

2.6

26.5

13.3

3.6

Min

ing

(4)

7.0

5.8

10.0

17.7

2.1

Me

an

21.3

6.2

13.5

16.2

5.3

Min

ing

and

Foo

d an

dM

edic

al a

nd s

ocia

lm

iner

als

Ret

ail t

radeb

beve

rage

b s

ervi

ces

Oth

er in

dust

ries

Pe

rce

nt

Far

min

g (9

3)1.

59.

84.

74.

418

.1R

anch

ing

(18)

1.2

13.5

6.1

5.3

1.5

Tim

ber

(45)

3.2

12.5

4.8

7.1

6.7

Tra

vel a

nd t

ouris

m (

33)

5.1

15.0

9.5

5.2

7.6

Min

ing

(4)

13.5

12.4

3.6

10.0

17.9

Me

an

2.7

11.7

5.7

5.4

12.0

a To

tal n

umbe

r of

rur

al c

omm

uniti

es in

eac

h cl

ass

base

d on

a s

ampl

e of

193

com

mun

ities

that

par

ticip

ated

in a

sel

f-as

sess

men

t wor

ksho

p.

b N

onto

uris

m r

elat

ed.

70

How accurately do perceptions of residentsmatch the reality of a community’s employmentprofile?—The preceding discussion of residents’perceptions of their community’s economy and itsactual composition in terms of employment pro-vides insight on the question, How well and howaccurately do these perceptions reflect the realityof any given community’s actual employmentbase? One indication can be found in comparingperceived levels of dependence on natural re-source industries in the 198 sample communitieswith levels based on proportions of actual em-ployment for those communities (see table 16).For this comparison, the levels of actual employ-ment applied again ranged were low (5 percent oftotal employment in a given industry or less),medium low (6 to 10 percent), medium high (11to 19 percent), and high (20 percent or more, oftotal employment).

Table 16 indicates that the accuracy of perceiveddependence with actual dependence in terms ofjobs differed greatly, depending on community.The perceptions by some communities (e.g.,Genessee or Bellevue, Idaho) were consistentwith levels of employment, and those for othercommunities (e.g., Clayton or Lava Hot Springs,Idaho) were not.

An analysis of the match between perceptionand reality on two traditionally impor tantresource-related industries—One difficultywith assessing the accuracy of resident percep-tions of their community’s economy and its diver-sity is that of determining an acceptable standardfor declaring a community “resource dependent.”As a Forest Service (1977) policy statementnotes, “The definition of dependency has longbeen debated...[with] no clear-cut definition ofdependency.” The criterion in the 1977 memo-randum on dependent communities establishesthat, “if mills and/or communities utilize at least50 percent of the annual capacity from NationalForest timber sales and have at least 10 percent oftheir total employment in this industry, then themills and/or communities are dependent uponNational Forest timber sales.” Another approachis presented by Bender et al. (1985), whose studyof mining-dependent counties classified all coun-ties with 20 percent or more of total county in-

come attributable to the mining industry as min-ing dependent. Our analysis used the broader,more inclusive criterion used by the ForestService of 10 percent.

Our analysis focused in particular on data on twokey resource-based industrial sectors, wood prod-ucts and agriculture, as well as on the relation ofperceptions to reality for all sectors of a commu-nity’s economy. The economic profile data wereanalyzed by using this benchmark of 10 percentor more of employment in an industry as an in-dicator that the industry was a major one in atown’s economy. The analysis indicated that amuch higher percentage (about 70 percent) of thetowns were ones in which farming and ranchingwere major industries than were perceived bycommunity residents (58 percent) to be agricul-ture dominant. (In the economic profile data,ranching is combined with farming as part of theagricultural sector for comparison with the em-ployment data for that sector.) When the averageproportion of employment in agriculture (20 per-cent) was applied as the benchmark across allcommunities, the percentage of towns in whichagriculture was the major industry (58 percent)was much closer to the proportion based on per-ceptions. In contrast, a lower percentage of thetowns (17 percent) were found to be ones inwhich timber was a major industry than were per-ceived by workshop participants (23 percent) tobe timber dominant. Communities in which tim-ber played a significant role, as indicated byhaving more than 10 percent in manufacturingof wood products, included 71 communities rep-resenting 15 percent of all towns and cities in theregion (see table 17).

Of the 198 sample communities for which data onresident perceptions of resource dependence werecollected, 37 (18.7 percent) had high employment(10 percent or more of all jobs) in manufacturingof wood products. As table 18 shows, workshopparticipants in 3 (8 percent) of these 37 commu-nities that actually had high dependence on woodproducts manufacturing for employment (greaterthan 10 percent of all jobs) perceived them tohave fairly low dependence on this sector.


71

Tab

le 1

6—C

ompa

rison

s of

per

ceiv

ed a

nd a

ctua

l lev

els

of d

epen

denc

e on

nat

ural

res

ourc

e in

dust

ries

(bas

ed o

n pr

opor

tions

of a

ctua

lem

ploy

men

t) in

the

198

stud

y co

mm

uniti

es, C

olum

bia

basi

n

Tim

ber

and

Tra

vel a

ndM

inin

g an

dA

gric

ultu

re w

ood

prod

ucts

tour

ism

min

eral

s

Pe

rce

ive

dT

ota

lP

erc

eiv

ed

To

tal

Pe

rce

ive

dT

ota

lP

erc

eiv

ed

To

tal

Sta

te a

nd to

wn

depe

nden

cea

empl

oym

entb

depe

nden

ceem

ploy

men

tde

pend

ence

empl

oym

ent

depe

nden

ceem

ploy

men

t

Idah

o:A

mm

onLo

wM

ed. h

igh

Low

Low

Low

Hig

hLo

wLo

wA

shto

nH

igh

Med

. hig

hH

igh

Hig

hH

igh

Med

. low

Low

Low

Ath

olLo

wLo

wH

igh

Med

. hig

hH

igh

Hig

hLo

wLo

wB

ancr

oft

Hig

hH

igh

Low

Low

Hig

hM

ed. h

igh

Hig

hLo

wB

elle

vue

Hig

hM

ed. h

igh

Low

Low

Hig

hH

igh

Low

Low

Bla

ckfo

otH

igh

Low

Low

Low

Hig

hM

ed. h

igh

Low

Low

Blis

sH

igh

Hig

hLo

wLo

wH

igh

Hig

hLo

wLo

wB

onne

rs F

erry

Hig

hM

ed. l

owH

igh

Med

. low

Low

Med

. hig

hLo

wLo

wC

asca

deLo

wLo

wH

igh

Med

. low

Hig

hM

ed. h

igh

Low

Low

Cha

llis

Hig

hM

ed. h

igh

Low

Low

Hig

hM

ed. l

owH

igh

Hig

hC

hubb

uck

Hig

hM

ed. h

igh

Low

Low

Low

Hig

hH

igh

Low

Cla

rk F

ork

Low

Low

Hig

hM

ed. l

owH

igh

Med

. hig

hLo

wLo

wC

layt

onH

igh

Med

. low

Hig

hLo

wLo

wH

igh

Hig

hLo

wC

raig

mon

tH

igh

Hig

hH

igh

Low

Low

Med

. low

Low

Low

Cul

desa

cH

igh

Hig

hH

igh

Low

Low

Med

. hig

hLo

wLo

wD

alto

n G

arde

nsLo

wN

ALo

wN

AH

igh

NA

Low

NA

Dec

loH

igh

Hig

hLo

wLo

wLo

wH

igh

Low

Low

Don

nelly

Hig

hLo

wH

igh

Low

Hig

hH

igh

Low

Low

Drig

gsH

igh

Med

. hig

hH

igh

Low

Hig

hM

ed. h

igh

Low

Low

Dub

ois

Hig

hH

igh

Low

Low

Low

Low

Low

Low

Elk

Riv

erLo

wH

igh

Hig

hLo

wH

igh

Med

. low

Low

Low

Em

met

tH

igh

Med

. hig

hH

igh

Med

. hig

hH

igh

Med

. low

Hig

hLo

wF

erdi

nand

Hig

hN

ALo

wN

ALo

wN

ALo

wN

AF

iler

Hig

hM

ed. h

igh

Low

Low

Low

Med

. low

Low

Low

Firt

hH

igh

Hig

hLo

wLo

wLo

wLo

wLo

wLo

wF

ort H

all

Hig

hM

ed. l

owLo

wLo

wH

igh

Med

. low

Low

Low

Fru

itlan

dH

igh

Med

. hig

hH

igh

Med

. hig

hLo

wM

ed. l

owLo

wLo

wG

enes

eeH

igh

Hig

hLo

wLo

wLo

wLo

wLo

wLo

wG

rang

evill

eH

igh

Med

. low

Hig

hM

ed. l

owH

igh

Med

. hig

hLo

wLo

w

72

Tab

le 1

6—C

ompa

rison

s of

per

ceiv

ed a

nd a

ctua

l lev

els

of d

epen

denc

e on

nat

ural

res

ourc

e in

dust

ries

(bas

ed o

n pr

opor

tions

of a

ctua

lem

ploy

men

t) in

the

198

stud

y co

mm

uniti

es, C

olum

bia

basi

n (c

ontin

ued)

Tim

ber

and

Tra

vel a

ndM

inin

g an

dA

gric

ultu

re w

ood

prod

ucts

tour

ism

min

eral

s

Pe

rce

ive

dT

ota

lP

erc

eiv

ed

To

tal

Pe

rce

ive

dT

ota

lP

erc

eiv

ed

To

tal

Sta

te a

nd to

wn

depe

nden

cea

empl

oym

entb

depe

nden

ceem

ploy

men

tde

pend

ence

empl

oym

ent

depe

nden

ceem

ploy

men

t

Hag

erm

anH

igh

Med

. hig

hLo

wLo

wH

igh

Low

Low

Low

Hai

ley

Low

Low

Low

Low

Hig

hM

ed. l

owLo

wLo

wH

arris

onH

igh

Hig

hH

igh

Low

Hig

hH

igh

Low

Low

Haz

elto

nH

igh

Hig

hLo

wLo

wLo

wLo

wLo

wLo

wH

omed

ale

Hig

hM

ed. h

igh

Low

Low

Low

Low

Low

Med

. hig

hId

aho

City

Low

Low

Hig

hLo

wH

igh

Hig

hLo

wLo

wIr

win

Hig

hLo

wLo

wLo

wH

igh

Hig

hLo

wLo

wIs

land

Par

kLo

wLo

wH

igh

Low

Hig

hM

ed. l

owLo

wLo

wK

amia

hH

igh

Med

. low

Hig

hH

igh

Hig

hM

ed. h

igh

Low

Low

Kel

logg

Low

Low

Hig

hLo

wH

igh

Med

. hig

hH

igh

Med

. hig

hK

etch

umLo

wLo

wLo

wLo

wH

igh

Hig

hLo

wLo

wK

oosk

iaH

igh

Hig

hH

igh

Hig

hH

igh

Med

. low

Low

Low

Koo

tena

iLo

wH

igh

Hig

hLo

wLo

wM

ed. h

igh

Low

Med

. low

Lapw

aiH

igh

Med

. hig

hH

igh

Low

Low

Low

Low

Low

Lava

Hot

Spr

ings

Hig

hM

ed. l

owLo

wLo

wH

igh

Hig

hH

igh

Low

Lead

ore

Hig

hN

AH

igh

NA

Hig

hN

ALo

wN

AM

cCam

mon

Low

Hig

hLo

wLo

wLo

wM

ed. h

igh

Low

Low

Mel

baH

igh

Med

. hig

hLo

wLo

wLo

wM

ed. l

owLo

wLo

wM

erid

ian

Low

Low

Low

Low

Low

Med

. low

Low

Med

. low

Mou

ntai

n H

ome

Hig

hM

ed. l

owLo

wLo

wH

igh

Med

. low

Low

Low

Moy

ie S

prin

gsH

igh

Med

. low

Hig

hH

igh

Low

Med

. low

Low

Low

Mur

taug

hH

igh

Hig

hLo

wLo

wLo

wLo

wLo

wLo

wN

ew M

eado

ws

Hig

hLo

wH

igh

Hig

hH

igh

Low

Low

Low

Old

tow

nLo

wM

ed. h

igh

Hig

hM

ed. h

igh

Hig

hM

ed. h

igh

Low

Low

Ona

way

Low

NA

Hig

hN

ALo

wN

ALo

wN

AO

rofin

oLo

wM

ed. l

owH

igh

Med

. hig

hH

igh

Med

. low

Low

Low

Osb

urn

Low

Low

Hig

hM

ed. l

owH

igh

Med

. hig

hH

igh

Med

. low

Par

ker

Hig

hH

igh

Low

Low

Hig

hLo

wLo

wLo

wP

arm

aH

igh

Hig

hLo

wLo

wH

igh

Low

Low

Low

73

Tab

le 1

6—C

ompa

rison

s of

per

ceiv

ed a

nd a

ctua

l lev

els

of d

epen

denc

e on

nat

ural

res

ourc

e in

dust

ries

(bas

ed o

n pr

opor

tions

of a

ctua

lem

ploy

men

t) in

the

198

stud

y co

mm

uniti

es, C

olum

bia

basi

n (c

ontin

ued)

Tim

ber

and

Tra

vel a

ndM

inin

g an

dA

gric

ultu

re w

ood

prod

ucts

tour

ism

min

eral

s

Pe

rce

ive

dT

ota

lP

erc

eiv

ed

To

tal

Pe

rce

ive

dT

ota

lP

erc

eiv

ed

To

tal

Sta

te a

nd to

wn

depe

nden

cea

empl

oym

entb

depe

nden

ceem

ploy

men

tde

pend

ence

empl

oym

ent

depe

nden

ceem

ploy

men

t

Pay

ette

Low

Low

Low

Med

. hig

hLo

wLo

wLo

wLo

wP

ierc

eLo

wLo

wH

igh

Hig

hH

igh

Low

Low

Low

Pilo

t Roc

kH

igh

Med

. hig

hH

igh

Hig

hLo

wLo

wLo

wLo

wP

riest

Riv

erLo

wLo

wH

igh

Hig

hH

igh

Hig

hLo

wLo

wR

athd

rum

Low

Low

Hig

hM

ed. l

owLo

wH

igh

Low

Med

. low

Ric

hfie

ldH

igh

Hig

hLo

wLo

wLo

wLo

wLo

wLo

wR

iggi

nsH

igh

Med

. hig

hH

igh

Low

Hig

hH

igh

Low

Low

Riri

eH

igh

Med

. hig

hLo

wLo

wLo

wLo

wLo

wLo

wR

ober

tsH

igh

Hig

hLo

wLo

wLo

wM

ed. l

owLo

wLo

wS

alm

onH

igh

Med

. low

Hig

hM

ed. l

owH

igh

Med

. hig

hH

igh

Low

San

dpoi

ntLo

wLo

wH

igh

Med

. low

Hig

hH

igh

Low

Low

Sho

shon

eH

igh

Hig

hLo

wLo

wLo

wLo

wLo

wLo

wS

mel

terv

ille

Low

Low

Hig

hLo

wH

igh

Hig

hH

igh

Med

. hig

hS

tanl

eyLo

wLo

wH

igh

Low

Hig

hH

igh

Hig

hLo

wS

ugar

City

Hig

hH

igh

Low

Low

Low

Med

. low

Low

Low

Sw

an V

alle

yLo

wH

igh

Low

Low

Hig

hH

igh

Low

Low

Ten

sed

Hig

hH

igh

Low

Low

Low

Med

. low

Low

Low

Tet

onH

igh

Hig

hLo

wLo

wH

igh

Low

Low

Low

Tet

onia

Hig

hH

igh

Low

Med

. low

Low

Med

. low

Low

Low

Wal

lace

Low

Low

Hig

hLo

wH

igh

Med

. hig

hH

igh

Med

. hig

hW

eipp

eH

igh

Med

. low

Hig

hH

igh

Hig

hLo

wLo

wLo

wW

eise

rH

igh

Med

. hig

hH

igh

Low

Hig

hM

ed. l

owLo

wM

ed. l

owW

inch

este

rH

igh

NA

Low

NA

Hig

hN

ALo

wN

AW

orle

yH

igh

Hig

hH

igh

Low

Hig

hM

ed. h

igh

Low

Low

Mon

tana

:A

lber

ton

Low

Low

Hig

hM

ed. l

owH

igh

Hig

hLo

wLo

wC

olum

bia

Fal

lsLo

wLo

wH

igh

Med

. hig

hH

igh

Med

. hig

hH

igh

Med

. hig

hD

arby

Hig

hM

ed. l

owH

igh

Hig

hH

igh

Med

. low

Low

Low

Dee

r Lo

dge

Hig

hLo

wH

igh

Med

. low

Hig

hM

ed. l

owLo

wM

ed. h

igh

74

Tab

le 1

6—C

ompa

rison

s of

per

ceiv

ed a

nd a

ctua

l lev

els

of d

epen

denc

e on

nat

ural

res

ourc

e in

dust

ries

(bas

ed o

n pr

opor

tions

of a

ctua

lem

ploy

men

t) in

the

198

stud

y co

mm

uniti

es, C

olum

bia

basi

n (c

ontin

ued)

Tim

ber

and

Tra

vel a

ndM

inin

g an

dA

gric

ultu

re w

ood

prod

ucts

tour

ism

min

eral

s

Pe

rce

ive

dT

ota

lP

erc

eiv

ed

To

tal

Pe

rce

ive

dT

ota

lP

erc

eiv

ed

To

tal

Sta

te a

nd to

wn

depe

nden

cea

empl

oym

entb

depe

nden

ceem

ploy

men

tde

pend

ence

empl

oym

ent

depe

nden

ceem

ploy

men

t

Dru

mm

ond

Hig

hM

ed. h

igh

Hig

hH

igh

Hig

hM

ed. l

owLo

wLo

wE

urek

aH

igh

Low

Hig

hH

igh

Hig

hM

ed. l

owLo

wM

ed. h

igh

Libb

yLo

wLo

wH

igh

Med

. low

Hig

hM

ed. h

igh

Hig

hLo

wP

lain

sH

igh

Hig

hH

igh

Low

Low

Med

. hig

hLo

wLo

wP

olso

nLo

wM

ed. l

owH

igh

Low

Hig

hH

igh

Low

Low

Ron

anH

igh

Med

. hig

hH

igh

Low

Hig

hM

ed. h

igh

Low

Low

St.

Igna

tius

Hig

hH

igh

Hig

hLo

wH

igh

Low

Low

Low

Ste

vens

ville

Hig

hM

ed. h

igh

Hig

hLo

wH

igh

Med

. hig

hLo

wLo

wS

uper

ior

Low

Low

Hig

hH

igh

Hig

hH

igh

Low

Low

Whi

tefis

hLo

wLo

wH

igh

Low

Hig

hH

igh

Low

Low

Ore

gon:

Ada

ms

Hig

hH

igh

Low

Low

Low

Low

Low

Low

Adr

ian

Hig

hH

igh

Low

Low

Low

Low

Low

Low

Ant

elop

eH

igh

Hig

hLo

wLo

wLo

wLo

wLo

wLo

wA

rling

ton

Hig

hH

igh

Low

Med

. low

Low

Med

. hig

hLo

wLo

wB

aker

City

Hig

hM

ed. l

owH

igh

Med

. low

Hig

hM

ed. h

igh

Hig

hLo

wB

urns

Hig

hM

ed. h

igh

Hig

hM

ed. l

owH

igh

Med

. hig

hLo

wLo

wC

hilo

quin

Hig

hM

ed. l

owH

igh

Low

Hig

hH

igh

Low

Low

Day

ville

Hig

hM

ed. h

igh

Hig

hM

ed. l

owH

igh

Med

. low

Low

Low

Duf

urH

igh

Hig

hLo

wLo

wH

igh

Low

Low

Low

Ech

oH

igh

Hig

hLo

wLo

wLo

wM

ed. h

igh

Low

Low

Ent

erpr

ise

Hig

hM

ed. h

igh

Hig

hLo

wH

igh

Med

. hig

hLo

wM

ed. l

owG

rass

Val

ley

Hig

hH

igh

Low

Low

Hig

hM

ed. h

igh

Low

Low

Hal

fway

Hig

hM

ed. h

igh

Low

Low

Hig

hH

igh

Low

Low

Hel

ixH

igh

Low

Low

Low

Low

Med

. low

Low

Low

Hep

pner

Hig

hH

igh

Hig

hM

ed. l

owH

igh

Low

Low

Low

Hoo

d R

iver

Hig

hN

AH

igh

NA

Hig

hN

ALo

wN

AIm

bler

Hig

hM

ed. l

owH

igh

Med

. hig

hLo

wM

ed. h

igh

Low

Low

Irrig

onH

igh

Med

. low

Low

Low

Hig

hM

ed. l

owLo

wLo

w

75

Tab

le 1

6—C

ompa

rison

s of

per

ceiv

ed a

nd a

ctua

l lev

els

of d

epen

denc

e on

nat

ural

res

ourc

e in

dust

ries

(bas

ed o

n pr

opor

tions

of a

ctua

lem

ploy

men

t) in

the

198

stud

y co

mm

uniti

es, C

olum

bia

basi

n (c

ontin

ued)

Tim

ber

and

Tra

vel a

ndM

inin

g an

dA

gric

ultu

re w

ood

prod

ucts

tour

ism

min

eral

s

Pe

rce

ive

dT

ota

lP

erc

eiv

ed

To

tal

Pe

rce

ive

dT

ota

lP

erc

eiv

ed

To

tal

Sta

te a

nd to

wn

depe

nden

cea

empl

oym

entb

depe

nden

ceem

ploy

men

tde

pend

ence

empl

oym

ent

depe

nden

ceem

ploy

men

t

John

Day

Hig

hM

ed. l

owH

igh

Hig

hLo

wM

ed. h

igh

Low

Low

Jord

an V

alle

yH

igh

Med

. hig

hLo

wLo

wH

igh

Med

. hig

hH

igh

Med

. low

Jose

phH

igh

Hig

hH

igh

Hig

hH

igh

Med

. hig

hLo

wLo

wLa

kevi

ewH

igh

Med

. low

Hig

hM

ed. h

igh

Low

Med

. hig

hLo

wLo

wLo

ng C

reek

Hig

hH

igh

Hig

hM

ed. h

igh

Low

Hig

hLo

wLo

wM

alin

Hig

hM

ed. h

igh

Low

Hig

hLo

wLo

wLo

wLo

wM

aupi

nH

igh

Hig

hLo

wLo

wH

igh

Hig

hLo

wLo

wM

erril

lH

igh

Med

. hig

hLo

wM

ed. h

igh

Low

Med

. low

Low

Low

Mos

ier

Low

Low

Low

Low

Hig

hH

igh

Low

Low

Nys

saH

igh

Med

. hig

hLo

wLo

wH

igh

Med

. hig

hLo

wLo

wP

aisl

eyH

igh

Hig

hH

igh

Med

. low

Hig

hLo

wLo

wLo

wP

rairi

e C

ityH

igh

Med

. hig

hH

igh

Med

. hig

hLo

wM

ed. l

owLo

wLo

wP

rinev

ille

Hig

hM

ed. l

owH

igh

Hig

hH

igh

Med

. low

Low

Low

Red

mon

dH

igh

Med

. low

Hig

hM

ed. l

owH

igh

Hig

hLo

wLo

wR

ichl

and

Hig

hH

igh

Hig

hLo

wH

igh

Hig

hLo

wLo

wS

hani

koH

igh

Hig

hLo

wM

ed. l

owH

igh

Low

Low

Hig

hS

iste

rsLo

wM

ed. l

owLo

wLo

wH

igh

Hig

hLo

wLo

wS

pray

Hig

hH

igh

Hig

hM

ed. l

owLo

wM

ed. l

owLo

wLo

wS

tanf

ield

Hig

hH

igh

Low

Low

Low

Med

. low

Low

Low

Sum

pter

Low

Med

. hig

hH

igh

Med

. Low

Hig

hH

igh

Hig

hM

ed. h

igh

Um

atill

aH

igh

Hig

hLo

wLo

wH

igh

Med

. hig

hLo

wLo

wU

nion

Hig

hH

igh

Hig

hLo

wH

igh

Med

. hig

hLo

wLo

wU

nity

Hig

hM

ed. h

igh

Hig

hM

ed. l

owH

igh

Low

Hig

hLo

wW

allo

wa

Hig

hH

igh

Hig

hM

ed. h

igh

Hig

hM

ed. h

igh

Low

Med

. low

War

m S

prin

gsLo

wM

ed. h

igh

Hig

hH

igh

Hig

hLo

wLo

wM

ed. l

owW

esto

nH

igh

Low

Low

Low

Hig

hLo

wLo

wLo

wW

ashi

ngto

n:A

irway

Hei

ghts

Low

Low

Low

Low

Low

Hig

hLo

wLo

wA

lmira

Hig

hH

igh

Low

Low

Low

Low

Low

Low

Ben

ton

City

Hig

hH

igh

Low

Low

Low

Med

. hig

hLo

wLo

w

76

Tab

le 1

6—C

ompa

rison

s of

per

ceiv

ed a

nd a

ctua

l lev

els

of d

epen

denc

e on

nat

ural

res

ourc

e in

dust

ries

(bas

ed o

n pr

opor

tions

of a

ctua

lem

ploy

men

t) in

the

198

stud

y co

mm

uniti

es, C

olum

bia

basi

n (c

ontin

ued)

Tim

ber

and

Tra

vel a

ndM

inin

g an

dA

gric

ultu

re w

ood

prod

ucts

tour

ism

min

eral

s

Pe

rce

ive

dT

ota

lP

erc

eiv

ed

To

tal

Pe

rce

ive

dT

ota

lP

erc

eiv

ed

To

tal

Sta

te a

nd to

wn

depe

nden

cea

empl

oym

entb

depe

nden

ceem

ploy

men

tde

pend

ence

empl

oym

ent

depe

nden

ceem

ploy

men

t

Bur

bank

Hig

hM

ed. h

igh

Hig

hLo

wH

igh

Low

Low

Low

Cas

hmer

eH

igh

Hig

hLo

wLo

wH

igh

Med

. low

Low

Med

. hig

hC

hela

nH

igh

Med

. hig

hLo

wLo

wH

igh

Med

. hig

hLo

wLo

wC

hew

elah

Hig

hM

ed. h

igh

Hig

hM

ed. l

owH

igh

Med

. hig

hH

igh

Med

. low

Cle

Elu

mH

igh

Med

. low

Hig

hLo

wH

igh

Hig

hLo

wLo

wC

olfa

xH

igh

Low

Low

Low

Low

Med

. low

Low

Low

Col

ville

Hig

hLo

wH

igh

Med

. low

Hig

hM

ed. h

igh

Low

Low

Con

conu

llyH

igh

Hig

hLo

wLo

wH

igh

Hig

hLo

wLo

wC

rest

onH

igh

Hig

hLo

wLo

wH

igh

Low

Low

Low

Day

ton

Hig

hM

ed. h

igh

Hig

hLo

wH

igh

Med

. low

Low

Low

Elm

er C

ityLo

wLo

wLo

wLo

wLo

wH

igh

Low

Low

End

icot

tH

igh

Med

. hig

hLo

wLo

wLo

wM

ed. l

owLo

wLo

wE

ntia

tH

igh

Hig

hH

igh

Low

Hig

hLo

wLo

wLo

wG

eorg

eH

igh

Hig

hLo

wLo

wH

igh

Low

Low

Low

Gra

nd C

oule

eLo

wM

ed. l

owLo

wLo

wH

igh

Hig

hLo

wLo

wG

rang

erH

igh

NA

Low

NA

Low

NA

Low

NA

Har

rah

Hig

hN

ALo

wN

ALo

wN

ALo

wN

AH

arrin

gton

Hig

hH

igh

Low

Low

Hig

hLo

wLo

wLo

wH

artli

neH

igh

Hig

hLo

wLo

wLo

wLo

wLo

wLo

wIn

chel

ium

Hig

hLo

wH

igh

Med

. hig

hH

igh

Med

. low

Low

Low

Ione

Low

Low

Hig

hH

igh

Hig

hH

igh

Low

Low

Ket

tle F

alls

Hig

hM

ed. l

owH

igh

Hig

hH

igh

Med

. hig

hLo

wLo

wK

ittita

sH

igh

Hig

hLo

wLo

wLo

wM

ed. h

igh

Low

Low

Kru

ppH

igh

Hig

hLo

wLo

wLo

wLo

wLo

wLo

wM

abto

nH

igh

NA

Low

NA

Low

NA

Low

NA

Mat

taw

aH

igh

Hig

hLo

wLo

wLo

wLo

wLo

wLo

wM

edic

al L

ake

Low

Low

Low

Low

Hig

hM

ed. h

igh

Low

Med

. hig

hM

esa

Hig

hH

igh

Low

Low

Low

Med

. hig

hLo

wLo

wN

ewpo

rtLo

wM

ed. h

igh

Hig

hLo

wH

igh

Med

. low

Low

Med

. hig

hO

dess

aH

igh

Hig

hLo

wLo

wLo

wLo

wLo

wLo

w

77

Tab

le 1

6—C

ompa

rison

s of

per

ceiv

ed a

nd a

ctua

l lev

els

of d

epen

denc

e on

nat

ural

res

ourc

e in

dust

ries

(bas

ed o

n pr

opor

tions

of a

ctua

lem

ploy

men

t) in

the

198

stud

y co

mm

uniti

es, C

olum

bia

basi

n (c

ontin

ued)

Tim

ber

and

Tra

vel a

ndM

inin

g an

dA

gric

ultu

re w

ood

prod

ucts

tour

ism

min

eral

s

Pe

rce

ive

dT

ota

lP

erc

eiv

ed

To

tal

Pe

rce

ive

dT

ota

lP

erc

eiv

ed

To

tal

Sta

te a

nd to

wn

depe

nden

cea

empl

oym

entb

depe

nden

ceem

ploy

men

tde

pend

ence

empl

oym

ent

depe

nden

ceem

ploy

men

t

Oka

noga

nH

igh

Med

. hig

hH

igh

Low

Hig

hM

ed. l

owLo

wLo

wO

thel

loH

igh

Med

. hig

hLo

wLo

wH

igh

Low

Low

Low

Pal

ouse

Hig

hH

igh

Low

Low

Low

Low

Low

Low

Pom

eroy

Hig

hH

igh

Hig

hLo

wLo

wLo

wLo

wLo

wP

resc

ott

Hig

hH

igh

Low

Low

Hig

hM

ed. l

owLo

wLo

wQ

uinc

yH

igh

Hig

hLo

wLo

wH

igh

Med

. low

Low

Low

Rep

ublic

Low

Med

. hig

hH

igh

Med

. hig

hLo

wM

ed. l

owH

igh

Med

. hig

hR

itzvi

lleH

igh

Hig

hLo

wLo

wLo

wM

ed. h

igh

Low

Low

Roc

k Is

land

Hig

hH

igh

Low

Low

Low

Med

. low

Hig

hLo

wR

osal

iaH

igh

Med

. low

Low

Low

Low

Hig

hLo

wLo

wS

elah

Hig

hM

ed. l

owH

igh

Low

Low

Med

. low

Low

Med

. low

Spr

ague

Hig

hH

igh

Low

Low

Low

Low

Low

Low

St.

John

Hig

hM

ed. l

owLo

wLo

wLo

wM

ed. l

owLo

wLo

wT

ekoa

Hig

hM

ed. h

igh

Low

Low

Low

Med

. low

Low

Low

Tie

ton

Hig

hM

ed. h

igh

Low

Low

Low

Low

Low

Low

Ton

aske

tH

igh

Hig

hH

igh

Low

Hig

hM

ed. l

owLo

wLo

wT

oppe

nish

Hig

hLo

wLo

wLo

wH

igh

Med

. low

Low

Low

Tw

isp

Hig

hM

ed. h

igh

Hig

hLo

wH

igh

Hig

hLo

wLo

wW

asco

Hig

hH

igh

Low

Low

Low

Hig

hLo

wLo

wW

asht

ucna

Hig

hH

igh

Low

Low

Low

Low

Low

Low

Whi

te S

alm

onH

igh

Low

Hig

hM

ed. l

owH

igh

Med

. low

Low

Low

Wils

on C

reek

Hig

hH

igh

Low

Low

Hig

hM

ed. l

owLo

wLo

wW

inth

rop

Low

Med

. hig

hH

igh

Med

. low

Hig

hH

igh

Low

Low

Wyo

min

g:Ja

ckso

nLo

wLo

wLo

wLo

wH

igh

Hig

hLo

wLo

wT

hayn

eH

igh

Low

Low

Low

Hig

hM

ed. l

owLo

wH

igh

NA

= n

o es

timat

es o

f em

ploy

men

t ava

ilabl

e.

a A

rat

ing

of “

low

” re

pres

ente

d a

num

eric

al r

atin

g of

4 o

r le

ss o

n a

7-po

int s

cale

(fr

om 1

, ext

rem

ely

inde

pend

ent,

to 7

, ext

rem

ely

dep

ende

nt),

and

a r

atin

g of

“hi

gh”

repr

esen

ted

a nu

mer

ical

rat

ing

of m

ore

than

4 o

n th

at 7

-poi

nt s

cale

.

b A

“lo

w”

leve

l of d

irect

em

ploy

men

t rep

rese

nts

5 pe

rcen

t of t

otal

em

ploy

men

t in

a gi

ven

indu

stry

, or

less

; “m

ediu

m lo

w,”

6 to

10

perc

ent;

“med

ium

hig

h,”

11 to

19

perc

ent;

and

“hig

h,”

20 p

erce

nt o

r m

ore

of to

tal e

mpl

oym

ent i

n a

give

n in

dust

ry.

78

Table 17—Rural Columbia basin communities with high percentages of total employment inwood 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. highAthol 12 HighCambridge 17 Med. highDeary 30 Med. highElk City 27 Med. highEmmett 14 HighFernan Lake 89 LowFruitland 18 HighHayden 21 Med. lowHope 21 Med. lowHorseshoe Bend 32 Med. highHuetter 100 LowJuliaetta 33 Med. lowKamiah 22 Med. highKooskia 30 Med. highLewiston 11 HighMontour 63 LowMoyie Springs 64 Med. lowNew Meadows 37 Med. lowNorth Powder 44 LowOldtown 16 HighOrofino 12 HighOvid 86 LowPayette 11 HighPierce 64 Med. lowPilot Rock 33 Med. highPinehurst 12 HighPlummer 20 Med. highPotlatch 25 Med. highPriest River 29 HighSt. Marie 30 HighWeippe 42 Med. low

Montana:Bonner-W. Riverside 47 Med. lowColumbia Falls 11 HighDarby 30 HighDrummond 26 LowEureka 22 HighPablo 22 Med. highPhilipsburg 11 HighRexford 55 Med. lowSuperior 21 HighThompson Falls 21 High

79

Table 17—Rural Columbia basin communities with high percentages of total employment inwood 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. highHines 20 Med. highImbler 12 Med. highJohn Day 20 HighJoseph 34 Med. lowLakeview 11 HighLong Creek 12 Med. highLostine 31 Med. highMadras 11 HighMalin 66 Med. lowMerrill 16 HighMount Vernon 38 Med. lowPrairie City 16 Med. highPrineville 27 HighSummerville 33 Med. lowWallowa 19 Med. lowWarm Springs 51 Med. low

Washington:Bingen 17 HighInchelium 12 Med. lowIone 27 LowKettle Falls 22 Med. highNaches 11 HighNorthport 31 Med. highPateros 21 HighRepublic 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) tomedium high (0.35 to 0.90) and high (0.90 to 1.00).

Table 18—Number and percentage of rural Columbia basin communitieswith a high actual degree of dependence on wood products (based on10 percent or more employment in wood products), by perception ofdependence (N=37)

High employment dependencePerceived dependence Number of cases Percentage

High 34 92Low 3 8

Total 37 100

80

In contrast, as table 19 shows, 162 communitieshad less than 10 percent of their total employ-ment in the wood products manufacturing sectorand could not be deemed timber dependent bythis measure. Of these, 58 (36.6 percent) wereperceived by key informants to have fairly highdependence on wood products manufacturing.

Table 20 shows the names and statistics of townsperceived to be independent of timber harvest andwood products manufacturing but that actuallydid have a significant proportion of employmentin wood products manufacturing. Table 21 showsthe names and statistics for towns perceived to bedependent on timber, but that were found to haveno significant proportion of employment in woodproducts manufacturing.

The correlation between perception of commu-nity dependence on timber harvesting and pro-cessing and empirical data on actual amount ofemployment in manufacturing of wood productsas opposed to other industrial sectors (i.e., rela-tive proportion) was measured with a Pearsoncorrelation coefficient, which produced a mod-erately strong correlation of 0.50. Although thisresult suggests some degree of consistency be-tween resident perceptions of the wood productsindustry’s importance and its actual significance,over a third of the region’s communities per-ceived that they were dependent on the timberindustry to an extent that they really were not.

In the case of agriculture, the 321 communities inthe region with high employment in agriculture(10 percent or more of all jobs) represented 68.0percent of the total of 476 communities. Of the198 communities sampled and for which datawere gathered on resident perceptions of resourcedependence, 60 communities (25 percent) werecharacterized by a moderately small proportion(less than 10 percent) of employment in agricul-ture. Of the 60, the citizens of 35 of them (58.3

percent) indicated that they perceived that theirtowns had a fairly high dependence on agriculture(table 22).

Table 23 shows that, of the 138 sample communi-ties characterized by a high proportion (more than10 percent) of employment in agriculture, only 12(8.6 percent) had residents who perceived them tohave a low rating of dependence on agriculture.

Pearson correlation coefficients were calculatedto indicate the strength of the relation of citizenperceptions of community dependence on farm-ing and ranching to actual results of empiricaldata on agricultural employment. These coef-ficients were calculated to be 0.36 for farmingand 0.24 for ranching, which were statisticallysignificant (p<0.05) but indicated only a mod-erately weak relation between perceived andactual economic dependence.

These results suggest that residents in some of theregion’s communities misperceived the extent towhich they were dependent on farming andranching. An alternative explanation is that thisquestion focused workshop participants’ attentionon dependence on particular industries, and thatthis represented a different, more comparativemeasure that focused on resource-related indus-tries, which could be expected to provide differ-ent results from the employment profiles.

In summary, the empirical data suggest that mostworkshop participants perceive their communitiesto be dependent on traditional resource industries.However, 37 percent of all communities in thecase of timber and 58 percent for agriculture per-ceived themselves to be moderately to highly de-pendent on timber and agriculture, yet these in-dustries employed less than 10 percent of totalemployees in those towns. The discrepanciesbetween perceptions and realities in these com-munities suggest that their economies mayactually be more diverse than first perceived.


81

Table 19—Number and percentage of rural ICRB communities with a low actualdegree of dependence on wood products (based on 10 percent or more employmentin wood products), by perception of dependence (N=162)

Low employment dependencePerceived dependence Number of cases Percentage

High 59 37Low 103 63

Total 162 100

Table 20—Rural Columbia basin communities perceived to be independent oftimber but with a significant proportion of employment in wood products (N=3)

Perceived timber Wood productsTown dependencea employment

Percent

Malin, Oregon 3.29 0.66Merrill, Oregon 3.38 .16Payette, Idaho .83 .11

a Perceived 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 bedependent on timber but with no significant proportion ofemployment in wood products (N=64)

Perceived timber Employment inState and town dependencea wood products

Percent

Idaho:Bonners Ferry 6.50 10Cascade 6.43 9Clark Fork 5.57 10Clayton 5.80 0Craigmont 5.13 3Culdesac 4.86 0Donnelly 5.25 0Driggs 4.20 3Elk River 5.00 4Grangeville 5.14 8Harrison 5.43 2Idaho City 5.33 0Island Park 4.43 0Kellogg 5.67 2Kootenai 5.00 0Lapwai 5.00 0Leadore 6.13 0Osburn 5.67 8

82

Table 21—Rural Columbia basin communities perceived to bedependent on timber but with no significant proportion ofemployment in wood products (N=64) (continued)


Percent

Rathdrum 5.00 7Riggins 5.63 3Salmon 6.00 7Sandpoint 6.00 8Smelterville 5.17 0Stanley 5.29 0Wallace 5.63 0Weiser 4.17 0Worley 4.33 0

Montana:Alberton 4.75 9Deer Lodge 5.63 7Libby 6.50 9Plains 5.40 0Polson 5.33 4Ronan 5.14 2St. Ignatius 4.38 0Stevensville 4.57 4Whitefish 4.86 5

OregonBaker City 6.20 7Burns 5.75 10Chiloquin 6.33 2Dayville 4.29 6Enterprise 6.38 2Heppner 6.63 7Paisley 7.00 6Redmond 5.00 8Richland 4.50 2Spray 5.86 6Sumpter 4.67 8Union 5.71 2Unity 6.57 6

Washington:Burbank 6.17 0Chewelah 6.00 8Cle Elum 4.57 3Colville 6.00 8Dayton 4.71 1Entiat 4.17 0

83

Table 21—Rural Columbia basin communities perceived to bedependent on timber but with no significant proportion ofemployment in wood products (N=64) (continued)


Percent

Harrah 4.43 0Newport 5.17 2Okanogan 6.40 0Pomeroy 4.63 0Selah 4.29 0Tonasket 5.00 0Twisp 4.00 3White Salmon 6.29 7Winthrop 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 communitieswith a low actual degree of dependence on agriculture (based on 10 percentor more employment in agriculture), by perception of dependence (N=60)

Low employment dependencePerceived dependence Number of cases Percentage

High 35 58Low 25 42

Total 60 100

Table 23—Number and percentage of rural Columbia basin communitieswith a high actual degree of dependence on agriculture (based on 10 percentor more employment in agriculture), by perception of dependence (N=138)

High employment dependencePerceived dependence Number of cases Percentage

High 126 91Low 12 9

Total 138 100

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A Community Resilience IndexThe concept of community resilience refers to atown’s ability to manage change and adapt to it inpositive, constructive ways relative to other com-munities. A measure of this construct, termed the“community resilience index” (CRI), was devel-oped to indicate a town’s likely response tochange. The higher the index, the greater thecommunity’s resilience in comparison to that ofother communities, and the more vital, attractive,and healthy the community was compared toother communities in the region. The index wasbased on community characteristics critical to atown’s capacity to adapt to future changes, in-cluding strong civic leadership, a highly cohesivesocial organization, local amenities and attrac-tiveness, and a diversified or stable economy, allreflecting or contributing to civic pride, excite-ment, and typically proactive responses tochanges facing a community.

Development and ValidationThe CRI was developed in the course of the re-search as a relative indicator of the degree of acommunity’s resilience, based on patterns in theperceptions of residents of their community’scharacteristics and current conditions. Commu-nity resilience emerged as a function of a numberof major dimensions of the attributes and charac-teristics of communities. Specifically, a high de-gree of resilience reflects:

• Strong civic leadership. A high commitmentof individual leaders and groups to commu-nity and active involvement in creating or re-sponding to change; a strong sense of localcontrol regardless of external events orinfluences.

• Positive, proactive attitude toward change.Either residents promote change and thusvitality in community development, or if

change is occurring on its own, residentsrespond positively and create a desirablealternative future.

• Strong social cohesion. A high degree of con-sensus in values and goals for desired future;working together to achieve goals.

• Strong economic structure. A high continuityor endurance in a few major industries, or ahigh degree of diversity in economic base, orsome combination that provides a stable eco-nomy in the community.

• High degree of physical amenities. The his-toric character of a community’s downtown;the attractiveness of its downtown, surround-ing scenery, and region.

• Larger population. The larger the populationin rural towns (all other things being equal),the more developed the infrastructure is andthe greater the resilience.

The CRI was an additive function of scales basedon the dimensions above. The relative importanceplaced on the various scales, applied in the indexthrough the weighting of these scales, was basedon the results of empirical analysis—specifically,factor analysis—as detailed in the next section.The most important construct was civic leader-ship, which was weighted by a factor of 4, rela-tive to the least important factor, physical ameni-ties and attractiveness; also important were socialorganization (weighted by a factor of 3.3 overphysical amenities) and economic structure(weighted by a factor of 2.7).

Significantly, the weightings applied to thesescales were mirrored by their overall importancefor a community’s response to change, as ratedby workshop participants in the 10 significantchange communities examined in depth with casestudies. As part of the case studies, retrospectiveworkshops were held at which residents involvedin their community when it underwent recent

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major changes were asked to assess the impor-tance of various community characteristics formanaging those changes. During the workshops,participants were asked to list responses of theircommunity to the changes they had experienced.These responses were then broadly categorized,and participants were asked to indicate the threemost important for responding to change. Acrossall 10 communities, the results of this processwere highly consistent with results of the factoranalysis: the economic diversity construct wasindicated to be more important by a magnitude of2+ than the attractiveness construct, the cohesive-ness construct was more important by a magni-tude of 3+, and the leadership construct wasmore important by a magnitude of about 4. Thiscross-validation of the weightings used for theCRI lent important additional support for thevalidity of this index.

Developing an index for measuring resiliency—The specifics in developing an index for measur-ing community resilience were as follows: Aninitial analysis was conducted to assess thevalidity of the particular dimensions theorized tocontribute to the community resilience concept.All the workbook items for the key constructswere analyzed through factor analysis (principalcomponents, varimax rotation). The first fourfactors reflected the findings of earlier research:

Construct Variance

Percent

Civic leadership 32.7Economic structure 12.3Social organization 7.4Physical amenities 5.5

These factors, whose component items are de-tailed in table 24, became the basis for construct-ing the four scales comprising the CRI and thatroughly corresponded to several key constructs(amenities, economic structure, social organiza-tion, and civic leadership) measured in the com-munity workshops:

Starting constructScale from workshop

Civic leadership Community leadershipSocial organization Community cohesivenessEconomic structure Economic diversityRegional amenities Regional attractiveness

The four scales developed are displayed intable 25.

Each scale was developed with a scale reliabilityanalysis, which ensured that its component itemsachieved the maximum Cronbach’s alpha possi-ble. (Items that did not contribute to the greatestalpha value were dropped from the scales.) Asa final check, factor analysis (principal compo-nents, varimax rotation) was run against the com-plete set of workbook and workshop variables.Again, the four scales emerged as the most im-portant factors, although the percentage of var-iance explained by each scale was slightlyreduced:

Construct Variance

Percent

Civic leadership 26.9Social organization 7.1Economic structure 11.4Physical amenities 5.2

Finally, from results of the full factor analysis,loadings were examined to see if any variablesshould be included that did not appear in the pre-vious steps. An adjustment was made to theeconomic structure scale by adding two items:business attractiveness and economic diversity.When the scale reliability analysis was performeda second time, the two additional items (businessattractiveness and economic diversity) adjustedCronbach’s alpha upward slightly for the econo-mic structure scale. Table 25 shows the finalscales and the items comprising them.

Mean values for the items in each scale wereaveraged for each community, to produce a scalescore for each community. Then the scores wereweighted and totaled to provide a resilience scorefor each community.

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Table 24—Results of the factor analysis of workshop ratings in 198 Columbia basin studycommunities

Factor Factor items Factor loadings

Government effectiveness

q10 2 Extent of competence of community government 0.86q10 3 Level of trust in community government .82q10 4 Extent to which government’s positions reflect those of community .85q9 2a Contribution of elected officials to leadership .82q9 3 How visionary community leaders are .76q9 4 How flexible and creative community leaders are .78q9 5 Consistency of opinions and values of community leaders with

your own .81Eigenvalue = 13.75 Percent of variance = 26.4

Economic structure

q4 3 Extent that people shop inside the community .79q4 4 Extent that people work inside the community .72q5 2 Extent that the community’s economy is comprised of different

types of businesses .78q8 8 Abundance of social activities in community .63q1A 2 Attractiveness of community’s downtown area .53q9 2b Contribution of business community to leadership in the community .70q9 2c Contribution of government agency to community leadership .57q9 2d Contribution of nongovernment organizations to community leadership .66q9 2e Contribution of other active individuals to community leadership .52

Economic diversity index .66Eigenvalue = 6.44 Percent of variance = 12.4

Social organization

q2 2 Extent to which people work together to get things done .70q2 3 Extent to which people are supportive of one another .74q2 4 Extent to which people are committed to the community .74q2 5 Extent that people’s beliefs and values are similar .69q2 6 Extent to which people identify with community .64q8 10 Social problems .51q1A 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 .43q1B 4 Importance of nearby recreation areas to community’s character .46q1B 5 Importance of wilderness, parks, etc., to community’s character .69q1B 6 Importance of history, customs, and cultures to community’s character .58q1B 7 Uniqueness of region in special qualities and travel attractions .80q6 1e Community’s dependence on recreation and tourism .49

Eigenvalue = 2.81 Percent of variance = 5.4

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Tables 25—Scales comprising the community resilience index, with component scale items,alphas (index of scale reliability), and item-total scale correlations

Item-scaleScale item correlation

Civic leadership scale (alpha = 0.95)

q9 4 How flexible and creative community leaders are 0.84q9 5 Consistency of opinions and values of community leaders with

your own .83q10 2 Extent of competence of community government .80q10 3 Level of trust in community government .79q10 4 Extent to which government’s positions reflect those of community .79q9 3 How visionary community leaders are .79Construct Government effectiveness .73Construct Community leadership .68q9 2a Contribution of elected officials to leadership .62

Social organization scale (alpha = 0.92)

Construct Community cohesion .80q2 3 Extent to which people are supportive of one another .74q2 4 Extent to which people are committed to the community .74q2 2 Extent to which people work together to get things done .70q2 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 ofdifferent types of businesses .82

q4 3 Extent that people shop inside the community .77Construct Community autonomy .76q9 2b Contribution of business community to leadership in the community .68q4 4 Extent that people work inside the community .66q9 2d Contribution of nongovernment organizations to community

leadership .63q9 2c Contribution of government agency to community leadership .57Construct Attractiveness for business .57Construct Economic diversity .57

Regional amenities scale (alpha = 0.82)

q1B 7 Uniqueness of region in special qualities and travel attractions .80q1B 4 Importance of nearby recreation areas .73q1B 2 Importance of scenery outside the community .67Construct Attractiveness of region .59q1B 3 Abundance of special places .51q1B 5 Importance of wilderness and parks to community’s character .50

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Resilience ClassesFor ease in interpreting and displaying the resultsof the community resilience index, the 198 studycommunities were classified with a continuumof levels of different community resiliences: low,medium low, medium high, and high. The townswere classified by their level of resilience, basedon the CRI score calculated for each; these resil-ience classes helped to clarify a community’scomparative resilience and its implications. Theselevels were relative ones with ranges based onquartiles of the towns’ index scores, and eachclass thus representing an equal proportion ofthe communities under study (25 percent each).The one-quarter of the towns receiving the lowestCRI scores were labeled low, and so forth. Therange 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, 402.8to 466.98.

Statistical analysis of the CRI revealed that,although population size was significantly cor-related to the resilience index (Pearson correla-tion coefficient of 0.325; p<0.05), the relation is amoderate one. There is evidence, then, that thesmaller a community, the less resilient it tends tobe, as might be theorized, although this evidenceis not strong. The proposition that there may besome critical mass in terms of a populationthreshold that is related to community growthand development needs to be further examined.

Unquestionably, several small communities wererated as highly resilient, again affirming thequalification underlying the assessment that allcommunities are unique and all generalizationsabout them have their exceptions. Further, theresults suggest that several large towns were lesshealthy and resilient than some of the smallertowns characterized by a greater degree of socialorganization and civic leadership. For example,several small “timber communities” in the BlueMountains region of northeast Oregon wererated as highly resilient (e.g., Joseph, pop. 1165;Wallowa, pop. 755; and Weston, pop. 640), whileother larger towns in the “scablands” of south-eastern Washington and north-central Oregonwere rated as being less so (Umatilla, Oregon,

pop. 3155; Benton City, Washington, pop. 2090;and Othello, Washington, pop. 4730). The CRIscores for all 198 communities, along with thecomponent scale ratings and their resilience class,are displayed in table 26; figure 17 shows geo-graphic locations and the levels of resilience clas-sifying the 198 communities in the basin.

The spatial mapping of resilience ratings in-dicates that communities in particular types ofgeographic areas tended to be higher or lower inresilience, depending on physical characteristicsof the surrounding area (fig. 17). Analysis ofcommunity resilience by ecoregion suggests thatdifferent communities in the same basic type ofecosystem may differ in their resilience. In theecosystems of the Blue Mountains of northeastOregon, for example, several “timber communi-ties” are rated as highly resilient (John Day,Joseph, Enterprise), while others are judged to beless so (Long Creek, Prairie City, Unity). Patternsof a greater prevalence of lower resiliency areapparent, however, for communities in the agri-cultural and ranching regions of the Snake RiverPlain in southern Idaho and the Columbia Plateauin north-central Idaho and eastern Washingtonand Oregon. Results of the spatial mapping ofresilience scores are discussed further later in thissection.

Of the 198 surveyed communities, 10 communi-ties were examined in depth (table 27). Of the 10communities, 6 were among those rated as beinghighly resilient, 2 were classified as moderatelyhigh in resilience, and the remaining 2 communi-ties were rated as moderately low. Interestingly,one inference from the case studies is that com-munities experiencing major changes in the past(for instance population growth or decline, timbermill closures, or major business closures) aremore prepared for the future and better able toadopt to change. Results of an analysis of changein these communities since 1990 affirms thisconclusion, as do the results of the indepth casestudies (see Harris 1996 for additional detailsconcerning the results of the community casestudies). Table 27 summarizes the resiliencyindex results for the case-study communities aswell as results from the retrospective workshops.


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Table 26—Level of community resilience for 198 Columbia basin study communities, with levelsof scores on scales comprising the community resilience index

Levels of scores on scales comprisingcommunity resilience indexa

Levels of scoreson community Social Economic Physical

State and town resilience indexa Civic leadership cohesion structure amenitiesIdaho:

Ammon Med. high High High Low Med. lowAshton High High Med. high Med. high HighAthol Low Low Low Low HighBancroft Med. high Med. high Med. high Med. low Med. lowBellevue Med. low Med. high Low Med. low Med. highBlackfoot Med. high Med. low Low High Med. lowBliss Med. low High Low Low LowBonners Ferry High High High High LowCascade High High Med. high High HighChallis Med. low Low Med. high High Med. highChubbuck Med. high High Med. high Med. high Med. lowClark Fork Low Med. low Low Med. low Med. lowClayton High Med. high High High Med. lowCraigmont Med. high Med. high Med. low Med. high LowCuldesac Med. low High Med. high Low LowDalton Gardens Low Med. low High Low Med. lowDeclo High High High Low HighDonnelly Med. low Med. high Med. high Med. low Med. lowDriggs Med. low Low Med. low Med. high Med. highDubois Med. high Med. low Med. low High LowElk River Low Low Low Low Med. lowEmmett High High Med. high High Med. highFerdinand Med. low Med. high Low Med. low Med. highFiler Low Low Low Low Med. highFirth Med. high High Med. low Med. low Med. lowFort Hall Low Low Low Med. low Med. highFruitland Med. high High Med. high Med. high LowGenesee Med. low Med. low Med. high Low LowGrangeville Med. high Med. low Med. high High HighHagerman Low Low Low Med. high HighHailey High High High High HighHarrison Med. low Low Med. high Med. low Med. highHazelton Low Med. low Med. high Low LowHomedale Med. low Med. low Med. low Med. low LowIdaho City Low Low Low Med. low HighIrwin Med. low Med. high Med. low Low HighIsland Park Med. low Low Med. low Med. low HighKamiah High Med. high High High HighKellogg High High High High HighKetchum High Med. high High High High

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Table 26—Level of community resilience for 198 Columbia basin study communities, with levelsof scores on scales comprising the community resilience index (continued)



State and town resilience indexa Civic leadership cohesion structure amenities

Kooskia High High Med. high Med. high Med. highKootenai Med. high High Med. low Med. Low HighLapwai Med. low Low Med. low Med. Low Med. lowLava Hot Springs Low Low Low Low LowLeadore Med. low Med. low High Med. low LowMcCammon Med. low High Med. low Low Med. lowMelba Med. low Med. low Med. high Med. low Med. lowMeridian High High High High HighMountain Home High High Med. low High Med. lowMoyie Springs Med. low Med. high Low Low Med. highMurtaugh Med. low Med. low High Low Med. highNew Meadows Med. low Low Med. low Med. high Med. highOldtown Med. high High Low Med. low Med. highOnaway Low Med. low Med. low Low Med. lowOrofino Med. high Med. low Med. low High Med. highOsburn High High High Med. high HighParker Low Med. high Med. low Low Med. highParma Med. high Med. low Med. high Med. high Med. lowPayette Med. low Low Low Med. high Med. highPierce Med. low Low Low Med. high Med. highPilot Rock Med. low Med. high Med. low Med. low Med. lowPriest River High High Med. high High HighRathdrum Med. low Med. high Low Med. low HighRichfield Med. high High High Med. low LowRiggins High High High Med. high HighRirie Med. low Med. low Med. high Med. low Med. highRoberts Low Low Low Low Med. lowSalmon High Med. high High High HighSandpoint Med. low Low Med. low High Med. highShoshone Low Low Low Med. low LowSmelterville Med. high Med. high Med. low Med. high Med. lowStanley High Med. high High High HighSugar City Med. high High High Low Med. highSwan Valley Low Low Low Med. low HighTensed Low Low Low Low LowTeton Med. high High High Low Med. highTetonia Low Low Low Low Med. lowWallace High High High High HighWeippe High High Med. low Med. low HighWeiser Med. high Med. low Med. high Med. high Med. high

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Winchester High High Med. high Med. low HighWorley Low Med. low Low Low Med. low

Montana:Alberton Med. low Med. low Med. low Low Med. highColumbia Falls Med. high Med. low Med. high High HighDarby Med. high Med. low High Med. high HighDeer Lodge High High Med. high High Med. highDrummond Low Low Med. low Med. high Med. highEureka Med. high Med. low High Med. high HighLibby High Med. high High Med. high HighPlains Med. high Med. low Med. high High Med. highPolson Med. high Med. low Low High HighRonan High Med. low Med. high High HighSt. Ignatius Med. low Med. low Med. low Med. low HighStevensville Med. high Med. low High Med. high HighSuperior Med. high Med. low Med. high Med. high Med. highWhitefish Med. low Low Med. low Med. high High

Oregon:Adams Low Med. low Low Low LowAdrian Low Low Med. high Med. low LowAntelope Low Low Low Low LowArlington Med. low Med. low Low Med. low LowBaker City High High High High HighBurns Med. high Med. high Med. low Med. high Med. lowChiloquin Low Low Low Med. low HighDayville Low Low Med. high Low LowDufur High Med. high Med. high High Med. lowEcho Med. low Med. high Low Low LowEnterprise Med. high Med. low Med. low High Med. highGrass Valley Med. high Med. high High Med. low Med. lowHalfway High High High Med. high Med. highHelix Med. low High High Low LowHeppner High High High High LowHood River Med. high Med. low Med. low High HighImbler Med. low Med. low High Med. low Med. lowIrrigon Low Low Low Low LowJohn Day High Med. high Med. high Med. high Med. highJordan Valley Med. low Med. low High Low LowJoseph High Med. high Med. high High HighLakeview High High High High LowLong Creek Low Med. high Low Low Low

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Malin Med. high Med. high High Med. high LowMaupin Med. high Med. high Med. low Med. high Med. highMerrill Med. low Low Med. high Med. high Med. lowMosier Low Low Med. low Low Med. lowNyssa Med. low Med. high Med. low Med. low Med. lowPaisley Med. high Med. low High Med. high Med. lowPrairie City Med. low Low Med. high Med. high Med. highPrineville Med. high Low Med. high High HighRedmond High High High High HighRichland Med. high Med. high High Med. high Med. highShaniko High High High Med. high HighSisters Med. high Med. low Med. high Med. high HighSpray Low Low Med. low Low LowStanfield Low Low Low Low Med. lowSumpter Med. low Med. high Med. high Low HighUmatilla Low Med. low Low Med. low LowUnion Med. low Med. high Low Med. low LowUnity Med. low Med. low Med. high Med. low Med. lowWallowa High High High Med. high HighWarm Springs Low Low Low Med. low Med. lowWeston High High High Med. high Med. low

Washington:Airway Heights Low Low Low Med. low HighAlmira Med. low Med. high Med. low Med. low LowBenton City Low Med. high Med. low Low LowBurbank Low Low Low Low LowCashmere High Med. high High High HighChelan Med. high Med. low Med. low High Med. highChewelah High Med. high High High Med. lowCle Elum Low Low Low Med. high Med. highColfax High High Med. high High Med. lowColville Med. high Med. low Med. low High Med. lowConconully Low Low Low Low Med. lowCreston Low Med. low Med. high Low Med. lowDayton High Med. high High High HighElmer City Med. low Med. high Med. low Low Med. lowEndicott Med. high High Med. low Med. low LowEntiat Med. low Med. low Med. low Med. low LowGeorge Med. low Med. high High Low LowGrand Coulee High Med. high Med. high Med. high HighGranger Low Med. low Low Med. low Med. low

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Harrah Low Med. high Low Low LowHarrington Low Med. low Med. low Med. low LowHartline High High High Med. low Med. highInchelium Low Med. low Med. low Med. low Med. lowIone Low Low Med. low Med. low Med. highKettle Falls Low Low Low Med. high Med. lowKittitas Low Low Low Low Med. highKrupp Low High Med. high Low LowMabton Low Med. high Low Low LowMattawa High Med. high Med. high High LowMedical Lake Med. high High Med. low Med. low Med. lowMesa Med. low Med. high Low Med. low LowNewport Med. high Med. low Med. low High Med. highOdessa High Med. high High Med. high Med. lowOkanogan Low Low Med. high Med. high LowOthello Med. low Med. low Low Med. high Med. lowPalouse Med. low Med. high Med. low Med. low Med. highPomeroy Med. high Med. low High Med. high LowPrescott Low Med. low Low Low LowQuincy High Med. high Med. low High Med. highRepublic Med. low Low Med. high High Med. lowRitzville Med. high Med. low Med. high High Med. lowRock Island Low Med. high Low Low Med. lowRosalia Med. high High High Med. low Med. highSelah High High Med. high Med. high LowSprague Low Med. low Low Low LowSt. John High High High High LowTekoa Low Low Med. low Med. high Med. highTieton Med. low High Low Low Med. lowTonasket Med. high Med. high Med. high Med. high Med. lowToppenish High High Med. low High Med. lowTwisp High Med. high High High Med. highWasco Low Low Low Low Med. lowWashtucna Med. high Med. high Med. high Med. high LowWhite Salmon Low Low Low Med. low HighWilson Creek Med. high Med. high High Med. low LowWinthrop Med. high Med. low High Med. high Med. high

Wyoming:Jackson Med. high Med. low Med. high High HighThayne 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.

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Table 27—Community resilience scores, ranks, and resilience classes for 10 case studycommunities, Columbia basin

1992-94 ResilienceTowna population Scoreb Rankc Classd

Baker City, Oregon 9,585 457 4 HighSalmon, Idaho 3,093 438 9 HighJoseph, Oregon 1,165 433 14 HighRiggins, Idaho 460 429 17 HighKellogg, Idaho 2,495 425 20 HighMattawa, Washington 1,535 404 46 HighPomeroy, Washington 1,460 399 57 Med. highBurns, Oregon 2,870 396 64 Med. highWhitefish, Montana 4,551 354 132 Med. lowDriggs, Idaho 980 351 139 Med. low

a Towns are listed in order of magnitude of resilience, from highest to lowest.

b Resilience scores based on summation of social cohesion, civic leadership, economic structure, and physical amenitiesscales of rural communities.

c Resilience 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 managechange and mitigate its impacts) was used to as-sess rural communities and their likely responsesto change, as well as the nature and possible ex-tent of impacts. Tables 28 and 29 provideexamples of communities that differ in popula-tion size, the dominant industry characterizingthem, and resilience ratings. Examples include acomparative listing of the sampled communitiesand their dominant industries from highest tolowest in terms of resilience scores. They affirmthat different kinds of communities, regardless ofdifferent economic bases and sizes, can have re-silience levels from low to high, depending on theunique situation within each town.

Other FindingsAn analysis of the resilience of communities withdifferent industry dominance classificationsfound that a community’s economy was relatedto its resilience (table 30). Larger proportions ofcommunities in which timber and outdoor recrea-tion and tourism were perceived dominant wererated as moderately high and high in resilience,and ranching communities were rated as lower inresilience.

Communities also were classified by actual in-dustry dependence, where employment propor-tion greater than 10 percent for a given sectormeant that it was important to the community’seconomy. Table 31 shows that economically di-verse communities were perceived to havechanged the most since 1990 and to have had thehighest resilience scores; farming and ranchingchanged the least and had the lowest resiliencescores. Interestingly, timber towns also were per-ceived to be changing while resilient; the rapidpopulation growth of tourism and recreationtowns had also caused them to undergo signifi-cant change but resulted in lower resilience. Animportant complementary finding was that com-munities that had changed the most since 1990tended to be more resilient, which likely was dueto their greater experience in coping with change.Analysis of the variance in communities’ ratingsof the amount they had changed since 1990 indi-cated that the most resilient towns were ratedwith a mean of 4.7, and the least resilient townswere rated with a mean of 3.5 (statistically sig-nificant, p<0.05).

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Table 28—A sample of communities having different dominant industries, by extent of resilienceand population size, Columbia basin

Community Community1992–94 Perceived resilience resilience

Town population dominant industry score class

Stanfield, Oregon 1,620 Farming 284 LowChiloquin, Oregon 700 Timber 300 LowSpray, Oregon 155 Ranching 310 LowLava Hot Springs, Idaho 464 Travel and tourism 320 LowWhitefish, Montana 4,551 Travel and tourism 354 Med. lowRepublic, Washington 1,080 Timber 365 Med. lowChallis, Idaho 995 Ranching 373 Med. lowAlmira, Washington 315 Farming 366 Med. lowSisters, Oregon 765 Travel and tourism 385 Med. highPaisley, Oregon 345 Timber 390 Med. highBurns, Oregon 2,870 Ranching 396 Med. highPomeroy, Washington 1,460 Farming 399 Med. highHalfway, Oregon 340 Ranching 415 HighBaker City, Oregon 9,585 Timber 457 HighSt. John, Washington 508 Farming 459 HighWallace, 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

CommunityTowns by type of resilience Economic Social Civic Preparednessdominant industry class diversity cohesion leadership for future

Towns where ranchingis 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 farmingis 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-

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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

CommunityTowns by type of resilience Economic Social Civic Preparednessdominant industry class diversity cohesion leadership for future

Towns where traveland tourism isperceived 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 timberis 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 studycommunities by perceived industry dominanceclassification and level of community resilience

Level of communityresilience

Towns by industry dominanceclassification High Low

PercentTimber (N=44) 62 38Travel and tourism (N=34) 53 47Farming (N=90) 48 52Ranching (N=16) 37 63Not resource dependent (N=11) 27 73

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Several relations between the community resil-ience scores and community characteristics werestatistically significant (p<0.05). The higher acommunity’s resilience rating, the more autono-mous the town was, the larger its population, thehigher the towns’ perceived quality of life, andthe more likely its economy was perceived to bediverse. Other factors related to resilience werethat, in the more resilient communities, govern-ments were more likely to be rated as doing whatthe public wants and that the town had developedplans involving future change.

Of the factors related to community resiliency, re-spondents’ perceived community autonomy wasfound to be the strongest predictor (Pearson cor-relation coefficient of 0.63; p<0.05). Autonomywas defined in the self-assessment workbooksand community workshops as the degree to whicha community is linked, economically, socially,and physically, to neighboring communities andto the region as a whole. Not surprisingly, giventhat correlational analyses suggest that largertowns are somewhat more autonomous than smallones, the larger communities in the region weregenerally more resilient. Analysis of varianceconducted on the 1992-94 population estimatesbased on CRI class indicated a statistically signi-ficant difference (p<0.05) between the averagesize of communities in the medium-low and lowresilience classes (764 and 1,131 people, respec-tively) and the medium-high and high resilienceclasses (2,028 and 2,420 people, respectively).

The largest towns in the region also tended to havemore diversified economies, as did the moreresilient communities, which had a mean econo-mic diversity rating of 1.4 in comparison with amean of -0.30 for the least resilient towns (statis-tically significant difference, p<0.05). In FEMAT(1993), the community assessment suggested thatcommunities with high capacity to adapt tendedto be larger communities; as indicated above,those with larger populations tended to have amore developed, extensive infrastructure andworkforce to build on. It is noted in FEMAT(1993) that communities less able to adapt tend tohave less developed infrastructure, less economicdiversity and active leadership, and fewer links tocenters of political and economic influence, withgreater dependence on nearby communities. Incontrast to these findings, our research docu-mented that autonomous communities were moreresilient, with spatial factors (e.g., transportationcorridors, isolation) being relatively insignificantin their adaptive capacity; in fact, a statisticallysignificant, positive, though weak, relation (0.19,p<0.05) between distance from an interstate high-way and community resilience was found. Thesefindings were consistent with the relation be-tween resilience and industry dominance indi-cated by table 31. Interestingly, towns perceivedas timber dominant tended to be farther from aninterstate highway and relatively isolated, andthey also tended to be relatively resilient com-pared to towns in which other industries wereperceived to be dominant.

Table 31—Average community resilience scores of Columbia basin study communities andratings of perceived change since 1990, by perceived industry dominance classification (N=198)

Average communityDominant industrial sector a resilience score Perceived change rating

Farming and ranching (N=81) 361 3.8Timber (N=20) 380 4.3Travel and tourism (N=49) 386 4.6Economically 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) wasfound between the CRI score and communitygrowth in the 1980s (0.09); the strength of therelation between the CRI score and perceiveddegree of change in community in the 1990s wasa moderate 0.37 (as indicated by a Pearson cor-relation coefficient). The former result clarifiesthat resilience is not simply a matter of a com-munity’s growth in population, and the latter sug-gests it may be coupled with change, but of amore complex kind than simply population in-creases. Supporting these results were the find-ings on population changes in towns smaller than10,000 where mills manufacturing wood or paperproducts have closed since 1980: 52 percent ofthese towns have suffered population declines,although the populations of 48 percent have in-creased. In total, the change in population ofsmall towns in which mills have closed has beena net increase of 8 percent since 1980.

Rural Economies and CommunityResilienceThe match between the perceived and actualimportance of particular industries in ruralcommunities—The results described earlier formatching workshop participants’ perceptionswith the actual economic profiles for those com-munities suggested some inconsistency betweenthe two. In spite of this, however, a strong posi-tive relation was found between the scores cal-culated to measure perceptions of the economicstructure of communities and the economic diver-sity index based on actual employment figures: aPearson correlation coefficient of 0.62 (p<0.05)was found across the 198 study communities.This finding suggests that most participants in thecommunity workshops were reasonably accuratein their assessment of the relative extent to whichtheir towns’ economies were diverse. This find-ing lends support to the suggestion made earlierthat the measure of perceived dependence onresource-related industries focused the attentionof workshop participants on their perceptions ofthe absolute (noncomparative) importance ofthese industries. In contrast, percentages of totalemployment and the economic diversity data re-

flected the relative importance of these industriesvis a vis all the other manufacturing, service, andindustrial sectors. Nonetheless, the lack of aneven stronger relation between the mean scoreson the economic structure scale and the scores onthe economic diversity index could be due to in-accurate perceptions by workshop participants inthose towns perceived to be dependent on indus-tries such as farming and timber harveting andmilling.

The past prominence of these industries or theirhigh visibility in a community may be the basisfor residents’ assumptions that these industriesare more important than they actually are. To testthis hypothesis, Pearson correlation coefficientsbetween perceived diversity and actual diversitywere calculated for those communities foundto have high agriculture and timber employmenttotals. These coefficients were only 0.32 and0.44; and although statistically significant(p<0.05), they indicated only a weak to moder-ate strength of relation. Further, when the 48 agri-cultural and 46 timber communities were omittedfrom the analyses owing to overestimates orunderestimates of workshop participants’ per-ceived dependence on the respective industries,the resulting correlation coefficients increased to0.65 (p>0.05) for agriculture communities and0.70 (p>0.05) for timber communities. Thus, thisanalysis indicated possible merit in the abovehypothesis.

Actual employment and communityresilience—Proportions of total employment innatural resource industries such as agriculture,manufacturing of wood and paper products, traveland tourism, mining, Federal and state govern-ment, and others did not differ significantly bydegree of community resilience, with a few ex-ceptions (table 32). In general, towns rated higherin resilience tended to be more dependent, interms of total employment, on a mix of servicesectors (e.g., medical and social services, retailtrade, and travel and tourism) and less dependenton some basic industries, such as agriculture andmining. This relation did not exist, however, forthe wood products manufacturing sector: con-sistent with previous findings, communities with

100

Tab

le 3

2—P

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ntag

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f tot

al e

mpl

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bia

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n co

mm

uniti

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, by

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mun

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ss(N

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Leve

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101

higher resilience ratings also had a higher per-centage of wood products manufacturing employ-ment. Conversely, towns with lower degrees ofresilience 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 employ-ment in the travel and tourism industry, as wellas in the wood products manufacturing industry,compared to communities that were moreresilient.

The picture provided by these findings is not asclear and definitive as that provided by people’sperceptions, which suggest that the more resilienttowns are the ones perceived as timber dominantand less resilient ones are those in which farming,especially ranching, is dominant. Perhaps it is thecase that, regardless of the actual employmentstructure of the communities, those towns per-ceiving themselves as timber towns have beenundergoing change and increasing in resilience,and the towns perceived as agriculture dominanthave not. The situation for timber-dependentcommunities is examined in greater depth in thenext section.

Other Findings on RuralCommunitiesTimber-Dependent CommunitiesOf the 198 rural communities sampled for thecommunity assessment in the basin, 34 wereidentified by Forest Service policy analysts astimber dependent (USDA Forest Service 1996).Workshop participants from 20 of these perceivedtheir communities as timber dominant, 3 as di-verse and extractive, 7 as nonresource dependent,2 as travel and tourism, and 1 each as agricultureand government dominant. Analysis of direct em-ployment in the wood products manufacturingindustry indicated that only 40 percent of the For-est Service-designated towns were actually de-pendent on wood products manufacturing for asignificant amount of their employment (employ-ment in the wood products manufacturing indus-

try greater than 10 percent of total communityemployment). Equally significant, 40 percent ofthe 198 communities in the study region that hadmore than 10 percent of their employment in thewood products industry were not designated bythe Forest Service as timber dependent. There-fore, an important use of these data is to clarifythe situation for communities designated by theForest Service as not timber dependent buthaving a large percentage of their total employ-ment in the wood products industry.

In general, communities designated as timberdependent by the Forest Service were ratedhigher in resilience than other, nondesignatedcommunities, although the difference from othercommunities was not statistically significant onthe economic structure scale discussed as part ofthe CRI. Higher proportions of timber-dependentcommunities were moderately small (1,500 to3,000 people) to moderately large (3,000 to 5,000people) in size. But these communities generallydid not differ significantly from other communi-ties in their economic structure, or in averagepopulation size.

Communities in the study region designated bythe Forest Service as timber dependent differedfrom other communities in the proportions ofemployment across several key industries, suchas agriculture, wood products and Federal Gov-ernment. They did not differ from towns per-ceived by their residents to be timber dependent(table 33). Interestingly, these Forest Service-designated communities were rated as actuallybeing more economically diverse (in their aver-age score on the economic diversity index) thantowns perceived to be timber dependent. In addi-tion, the average index scores for both kinds oftowns were much greater than the mean score forother kinds of communities. Given these compar-able diversity ratings, mean levels of employmentin the travel and tourism sector were about ashigh in both the Forest Service-designated timbercommunities and towns perceived to be timberdependent as they were in other kinds ofcommunities.

102

Tab

le 3

3—P

erce

ntag

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tota

l em

ploy

men

t in

Col

umbi

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sin

com

mun

ities

, by

indu

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type

of c

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iver

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ityT

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ser

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– –

– –

– –

– –

– –

– –

– –

– –

– –

– P

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t – –

– –

– –

– –

– –

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Des

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ted

by U

SD

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ores

t S

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4

Not surprisingly, communities across the basinwere found to have significant differences in theircharacteristics associated with their biophysicalgeography and their location relative to specificERUs (table 34). In examining communities ineach of the 13 ERUs by level of resilience, it wasfound that a high proportion of communities inthe Southern Cascades, the Upper Clark Fork,and the Central Idaho Mountains ERUs had ahigh level of resilience (fig. 17). Many of thecommunities in these ERUs are in mountainousregions possessing a high degree of physicalamenities, and these communities are respondingconstructively and proactively to a changing eco-nomic structure and growing population.

Other ERUs having communities with high levelsof resilience include the Blue Mountains ofnortheastern Oregon, the Lower Clark Fork, andthe Upper Snake River ERUs. These ERUs alsoare well endowed with amenity resources and in-creasingly diversified economies. In contrast, theColumbia Plateau, Snake River Headwaters, andthe Owyhee Uplands ERUs generally had lowerresilience ratings and were dominated by farmingand ranching industries. These ERUs are charac-terized by high plains deserts and “scablands”that are perceived as comparatively lacking inphysical amenities.

Communities in these 13 ERUs show consistentpatterns in characteristics and conditions for per-ceived and empirical data that are most easily dis-played by combining the units into four majorregions: a Coastal Mountains ERU (comprised ofthe Northern and Southern Cascades ERUs), aHigh Plains Desert and Prairie ERU (ColumbiaPlateau and Owyhee Uplands ERUs), a NorthernRocky Mountains ERU (the Northern GlaciatedMountains, the Blue Mountains, the CentralIdaho Mountains, the Lower Clark Fork, and theSnake Headwaters ERUs), and the Upper SnakeERU. The Upper Klamath and Northern GreatBasin ERUs, which accounted for only 2.5 per-cent of all communities, were not included in theanalysis.

103

Table 34—Percentage of Columbia basin study communities in ecological reporting units, withmajority proportions in high or low community resilience classes

Percentage of all Majority proportions of communitiesEcological reporting unit communities (N=387)a by resilience class

PercentColumbia Plateau 32 60 – lowNorthern Glaciated Mountains 15 NAOwyhee Uplands 9 56 – lowBlue Mountains 9 60 – highCentral Idaho Mountains 8 60 – highUpper Snake 6 58 – highSnake Headwaters 6 63 – lowLower Clark Fork 5 60 – highNorthern Cascades 4 NASouthern Cascades 3 67 – highUpper Clark Fork 2 75 – highUpper Klamath 2 NANorthern Great Basin 1 NA

NA = not applicable: a large majority of communities in the ecological reporting unit was classified as neither low nor high inresilience.

a Number of rural communities with less than 10,000 population, based on 1992-94 estimates (Oregon Center for PopulationResearch and Census 1995 ).

An analysis of the trends in responses on per-ceived community characteristics revealed thefollowing differences in absolute scale ratingsor in the number of communities across differentERUs:

• Statistically significant increases in commu-nity attractiveness from towns of the HighPlains Desert and Prairie and the Upper SnakeERUs to the Northern Rocky Mountain andCoastal Mountain ERUs.

• Statistically significant increases in commu-nity autonomy from the Upper Snake andHigh Plains Desert and Prairie ERUs to theCoastal Mountain and Northern RockyMountain ERUs.

• Increases in regional attractiveness, unique-ness of community, and community resiliencefrom the High Plains Desert and Prairie andthe Upper Snake ERUs to the Coastal Moun-tain and Northern Rocky Mountain ERUs.

• Increases from the Upper Snake and the HighPlains Desert and Prairie ERUs to the North-ern Rocky Mountain and Coastal MountainERUs in perceived levels of economic diver-sity, dependence on travel and tourism andtimber, degree of perceived change in com-munities between 1990 and 1995, and popu-lation migration patterns as indicated by thepercentage of households living in a differenthouse but in the same state (perhaps indicat-ing a migration within the state to more resi-dentially attractive areas).

• Increases from the Upper Snake and HighPlains Desert and Prairie ERUs to theCoastal Mountain and Northern RockyMountain ERUs in distance in miles to aninterstate highway.

• Increases from the Coastal Mountain andNorthern Rocky Mountain ERUs to the HighPlains Desert and Prairie and the UpperSnake ERUs in percentage of householdswith farm income and dependence onranching.

104

• Increases from the Coastal Mountain and theNorthern Rocky Mountain ERUs to the UpperSnake and High Plains Desert and PrairieERUs in traffic congestion.

• Increases from the Northern Rocky Mountainand Coastal Mountain ERUs to the UpperSnake and High Plains Desert and PrairieERUs in percentage of people employed inagriculture, forestry, fisheries.

• No statistical differences were found in per-ceived characteristics such as social cohesive-ness, services, business attractiveness, de-pendence on natural resources, governmentand civic leadership, preparedness for thefuture, or quality of life.

Is Bigger Better—Or Is Being MoreAutonomous?Analyses of both the documented data obtainedfrom town officials and perceptions gatheredfrom the community self-assessment workshopsindicated that population size is among the vari-ables that are related to a community’s currentcondition and likely responses to change. As pre-viously indicated, however, even though popula-tion size is statistically related to a community’slevel of resilience, this relation is not a strongone. Therefore, any strategic decision by a com-munity to grow in size should not necessarily bemade to increase its resilience, which depends ona variety of interrelated factors.

Nonetheless, as the variety of statistically signifi-cant findings on a community’s population sizesuggests, the size of a community is statisticallyrelated to a number of different community char-acteristics 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 eco-nomy, as indicated by the relation between acommunity’s population size and its score onthe economic diversity index (r = 0.45).

• Statistically significant but weak correlationswere found for several characteristics: Thelarger the community, the more successful it

was in obtaining grant funding for communityimprovement (r = 0.28), the more it grew inthe 1980s (r = 0.24), the lower the proportionof households receiving social security in-come (r = -0.22), and the higher the cost ofhousing (r = 0.15).

• The economies of rural communities, in termsof proportions of employment in various sec-tors, were not significantly different based ontheir population size: the only statisticallysignificant correlations were those betweenpopulation size and proportions of employ-ment in agriculture and medical services. Thestrongest of these correlations were foundfor communities with high proportions of em-ployment in farm and ranching, with thosetowns showing some tendency to be smallerin population, but even that correlation wasrelatively weak (r = -0.18).

• Characteristics for which population sizemade no statistically significant differenceincluded geographic isolation and the pro-portion of households receiving public as-sistance income or retirement income.

Community perception data:

• The larger the community, the lower itsrating of traffic congestion (r = -0.53), andthe greater its attractiveness for business(r = 0.49), social and economic changes since1990 (r = 0.42), and autonomy (r = 0.35).

• Statistically significant but weak correlationswere found for several perceived characteris-tics of rural communities: the larger the com-munity, the higher the rating of its overallresilience (r = 0.29), its preparedness forthe future (r = 0.26), the adequacy of its ser-vices (r = 0. 25), its attractiveness (r = 0.22),and how interesting it is as a community(r = 0.22).

• Characteristics for which population sizemade no statistically significant differenceincluded the extent of a community’s socialcohesion, friendliness, leadership, quality oflife, regional attractiveness, and how safe thecommunity is perceived to be.

105

In general, then, rural communities perceivedto have greater suitability and capability for eco-nomic development tended to be larger ones; thatis, larger towns were somewhat more likely tohave a more developed infrastructure, increasedavailability of services, especially education, andgreater preparedness for the future. A ruraltown’s population size is related to its currentconditions and likely response to change: statisti-cal analyses indicated that larger towns tend to bemore economically diverse, autonomous, and at-tractive for business. The conclusion here is con-sistent with the basic premise of the plethora ofcommunity development handbooks and work-shops provided in the 1970s and 1980s: if mem-bers of a small rural community want to “de-velop” their town, they should work to attractnew industries and expand the economic base,which would result in population increase.

Significantly, the findings of both the self-assessment study and the community economicprofiles suggest that the impacts of this improve-ment extend beyond the economic aspects ofcommunity development, whose significance haslong been recognized and is reaffirmed here, toits social elements as well. Larger rural communi-ties tend to represent a more advanced stage ofsocial and civic development than small ones.The importance for community vitality of activesocial groups and civic organizations, increasededucational infrastructure, availability of ser-vices, success in obtaining development grants,and greater preparedness for the future—all ofwhich increase with a town’s size—reflects thebenefits that towns with a critical mass of socialcapital and infrastructure are more likely to real-ize. An interesting question for future research,however, is at what size and level of communitydevelopment the net benefits of growth are maxi-mized, beyond which the social costs of furthergrowth begin to exceed the benefits.

A factor as important as a community’s size forits future development is its autonomy, which hasbeen defined in terms of the extent to which com-munities are linked—economically, socially, andpolitically—to neighboring communities and theregion as a whole. (The following discussion is

excerpted from the work of Harris and Russell,4

which focused on “timber communities,” thosehaving some employment in the wood productssector.)

The more autonomous a community is, the less itis linked to the “outside.” Implicitly, then, use ofthe term “autonomy” underscores a community’sindependence, self-reliance and ability to func-tion as a cohesive and functional unit. Commu-nity autonomy is a complex, multidimensionalconstruct, as reflected in the duality of its defini-tion. The autonomy of a town can reflect a stateof isolation and remoteness, which can be viewednegatively, as well as a condition of independ-ence and self-reliance that are positiveattributes—and this duality can suggest ambigu-ous implications for change in rural communities.

Many researchers (e.g., Lackey et al. 1987,Warren 1971), nonetheless, emphasize the posi-tive connotation of autonomy as a communityattribute characteristic of strong, healthy commu-nities, and our research results support this as-sessment. The autonomy construct was moststrongly correlated with residents’ perceptions ofthe availability of services in their communities(r = 0.52), their communities’ overall attractive-ness (r = 0.52), their attractiveness for business(r = 0.40), and the social cohesion and qualityof life in their communities (r = 0.40, 0.32). Theautonomy construct also was strongly correlatedwith documented data, such as economic diver-sity (r = 0.45) and, as noted above, populationsize (r = 0.35). It is not significantly related toemployment in most key industries, such as tour-ism or wood products, and it is negatively related,although fairly weakly, to agriculture (r = -0.28).There is some tendency, then, for more agri-culturally dominated towns to be less autono-mous, but even this is not the case for all suchtowns; autonomy is not as strongly tied to anyparticular kind of economy as it is to economiesthat have a more diverse mix of sectors on whichthey are dependent.

4 Russell, K.; Harris, C.C. [In press]. Economic, social andpolitical dimensions of community autonomy in timber towns.Society and Natural Resources.

106

These results suggest that autonomous commu-nities, whether isolated or not, are those that havedeveloped the service and economic infrastruc-ture to provide residents with goods, services, andjobs. Our results also indicate that community re-sidents perceive that the most autonomous townsare those that not only have a diversity of servicesand economy but also have a social resiliencythat enables them to manage the changes they ex-perience. This result challenges the notion thatautonomous communities are either higher ordercommunities with an ability to provide for citi-zens internally or isolated towns; rather, townsthat are autonomous are likely to be isolated,have a strong sense of community, and havehealthy, diversified economies.

Quality of Life in SmallCommunitiesMost communities in the region, whether large orsmall, rated themselves as having a high level of

quality of life. As previously shown, fully 80 per-cent of the communities rated their quality of lifeas very high, and another 19 percent indicated itwas moderately high; only one of the 198 com-munities indicated that their quality of life waslow (see table 6). Part of a community’s qualityof life is due to the presence of scenic and rec-reational amenities in the surrounding area thatare related to the natural resources. The resultsof regression analysis, as shown below, confirmthat a town’s quality of life is partially dependenton the attractiveness of the region where it is lo-cated. Even more important, however, are socialfactors, such as how interesting a community is,the extent a community is plagued with socialproblems, how safe its residents feel, and thesocial cohesiveness. Significantly, the extent ofa town’s quality of life is strongly related to itsresilience (r = 0.50):

Multiple R 0.7727 Analysis of varianceR-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

StandardVariable B error Beta t Significant t

(Constant) 0.7679 0.4057 1.893 0.0599

How interesting thecommunity 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 thecommunities in the study area indicate that, onaverage, the populations of these towns increasedby 7 percent between 1980 and the early 1990s.(The most recent population estimates availablefrom the states at the time of this analysis werefrom 1992 or 1994, depending on the state; seecitations in next paragraph). Population-changeproportions range from a minimum of a 60 per-cent decline to a maximum increase of 413 per-cent, but the distribution of these proportions isskewed toward population growth: 60 percent ofall towns in the region increased in populationbetween 1980 and 1992-94, with the bottom 20percent of all towns in the region decreasing inpopulation by -9.6 percent and the top 20 percentincreasing by over two times as much, or 19.9percent.

In the 1990s, this trend accelerated. The averagepopulations of rural communities in all five statesin the Columbia River basin are estimated tohave increased since 1990, although in differentamounts: these increases ranged from an averageof about 3 percent in communities in Montana(Montana Department of Commerce 1995) and4 percent in Idaho communities (Idaho Divisionof Financial Management 1995), to a high of anaverage 12 percent in communities in Wyoming(Wyoming Department of Administration andInformation 1995). Likewise, as table 35 shows,the vast majority (86 percent) of all towns in theregion have been growing since 1990—a signifi-cant change from the ingrowth in the 1980s.

Recent estimates also indicate that, statewide, thepopulation growth between 1988 and 1994 hasbeen 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, OregonCenter for Population Research and Census1995a, Washington Office of Financial Manage-ment 1994, Wyoming Department of Administra-tion and Information 1995). In contrast, the U.S.population grew only 4 percent during this sameperiod. Significantly, even for as short a period as

Also significant is the finding that a town’s sizeis unrelated to its quality of life, which begs thequestion of the goal or desired future for townsseeking to become more viable, healthy, vital, andthus resilient in the face of change. But it alsosuggests that, just because a town grows, thischange does not mean that a community’s qualityof life is necessarily compromised.

Change in Small RuralCommunitiesWorkshop participants in a large majority of com-munities (70 percent) in the basin reported thatthey had experienced a moderate to high degreeof change since 1990. Similarly, the indepth casestudy of Chelan County, Washington, residentsfound that a majority (68 percent) perceived thattheir communities had experienced a moderateto high degree of change in the 1980s. Whenasked about the kinds of change that had oc-curred, 68 percent of Chelan County residentsreported growth and population increases as oneof the major changes. Other important changesincluded the conversion of agricultural lands toresidential and commercial development (32 per-cent), increase in retail stores (26 percent), in-creased traffic (23 percent), and increased crime(22 percent). More than half of the residents(55 percent) were somewhat to extremely con-cerned about the overall changes taking place intheir community (for instance, population growthand economic structure).

Table 35—Percentage of Columbia basincommunities with increasing populations

Communities withState increasing populations

Percent

Idaho 85Montana 73Washington 86Wyoming 100

Average, all communities 86

Sources: State departments of administration, finance, andinformation.

108

1990 to 1994, the present study indicates thatresidents of larger towns are more likely to reportthat their town has changed (Pearson correlationcoefficient of 0.44; p<0.05).

In addition to population growth, a multiplicity ofchanges and influences are affecting the characterof rural communities in the study area. They in-clude not only changing natural resource suppliesand resource-management policies, but also so-cial changes due to aging populations and the in-migration of commuters, welfare recipients, re-tirees, and new ethnic groups. In many instances,these new types of residents are changing the so-cial makeup and character of communities, alongwith their traditions, customs, and cultures.

Economic changes—The assessment of the 145communities identified as having changed signi-ficantly in recent years affirmed that the eco-nomies of the region’s small communities havechanged throughout their history and continue tochange (see previous section, “Surveys of Resi-dents of Chelan County and Significant ChangeCommunities”). Significantly, our assessmentsof community resilience and significant changecommunities made clear that change and resil-ience to it are found across the various economictypes of communities. Government policies onpublic lands clearly have affected the economiesof some rural communities in significant ways,thereby influencing their resilience. Other in-fluences, including the decisions and actions ofsmall business owners and large corporations,and the methods with which the public sector hassubsidized these industries (e.g., crop paymentprograms, logging road construction, bidding-preference systems for small sawmills), also havelong affected the development of small rural com-munities in the region.

For towns with wood products mills, mining andminerals processing, and the like, concerns of re-sidents and agency resource managers have tra-ditionally focused on “community stability” interms of the economic stability assured by asteady, dependable flow of resources from public

lands and the resulting stable employment base.Some congressional acts (e.g., Organic Admin-istration Act of 1897, Multiple Use-SustainedYield Act of 1960, Forest and Rangeland Renew-able Resources Planning Act of 1974, and Na-tional Forest Management Act of 1976) reflectthis concern and, as a Forest Service policy mem-orandum notes, they mandate that the Forest Ser-vice provide a continuous supply of outputs forthe nation (USDA Forest Service 1977). Thisdocument, while noting that “none of the langu-age [in these acts] specifically addresses ‘com-munity stability,’” also recognizes that “the basiccharge [of the agency] to provide the goods andservices is well ingrained” (USDA Forest Service1977:1).

Recent changes in communities have resultedfrom a variety of broader economic influences,such as global economic forces, economic div-ersification, plant modernization, and industrialdownsizing (e.g., laying off company loggers andhiring independents to reduce the costs of bene-fit payments). Significantly, growth in employ-ment in the Pacific Northwest has far exceededthe national rate: employment increased nation-wide 7.7 percent between 1988 and 1994, but itincreased 27.7 percent in Idaho in that same per-iod, and around 17 percent in the other states inthe region (Idaho Division of Financial Manage-ment 1995, Montana Department of Commerce1995, Oregon Center for Population Research andCensus 1995a, Washington Office of FinancialManagement 1994, Wyoming Department ofAdministration and Information 1995).

As discussed in “Context for the Assessment,”key characteristics of communities include eco-nomic ones, such as the levels of economicdevelopment 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). Althoughthe literature has often asserted that resource-extraction industries are essential industries for

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rural economic survival, some researchers (e.g.,Power 1994; Rasker 1993, 1995) note that tradi-tional extractive industries are decreasing andservice industries increasing in importance acrossthe Pacific Northwest; for example, The Wilder-ness Society (Rasker 1995), examines U.S. Bu-reau of the Census statistics on income and em-ployment in the Columbia River basin since thelate 1960s. These statistics clearly document that,across the region as a whole, traditional, extrac-tive economic base industries (e.g., agriculture,forestry, and mining) have remained at fixedlevels over the last two decades, while the majorincreases in the region’s economy have occurredin service sectors.

The analysis conducted by The Wilderness Soci-ety reflects only part of the current situation inthe region, however. By focusing on the region asa whole, their analysis overlooks the significantdifferences between the economic base of smallrural communities vs. that of large cities. Whenour research assessed the importance of industrialsectors in rural communities in 1995 as propor-tions of the total employment, a different pictureof the region’s economy emerges: harvesting andprocessing (e.g., agriculture, timber) were amongthe most important employers in small ruraltowns across the region. So too are newer indus-tries, such as travel and tourism, with retail tradeand food and beverage (mainstays of tourism aswell as important for meeting local needs) andthe Federal and local governments becoming in-creasingly important. Yet analyses like Power’s(1994), which ostensibly focus on the region’srural communities, present few data representingthat scale.

In some cases, the total dependence of a town ona particular industry may be less important thanthe proportion of that industry controlled by oneentity, such as a government agency’s control oftimber supply or a company’s control of process-ing plants. Finally, economic sectors are oftencomplementary rather than substitutable or com-petitors for one another; consequently, economicdiversification began occurring long before pub-lic policy started restricting commodity supplies

on public lands and companies in extractive in-dustries began plant improvements and employeelayoffs to increase company competitiveness. Akey point here is that the economies of thesecommunities are more complex and unique thansimplistic, ideologically driven analyses maysuggest.

Significant change communities—Anothercomponent of the research focused on assessingand analyzing the characteristics and experiencesof 145 towns in the region identified as signifi-cant change communities. These communitieswere identified as undergoing major economic orsocial change, or both, by state economic devel-opment officials, agricultural extension experts,Forest Service forest planners or economic devel-opment coordinators, and population estimatesindicating changes of greater than 20 percentsince 1980, from the U.S. Bureau of the Census(1995a, 1995b). Data collection focused on iden-tifying the types of changes occurring in ruralcommunities, community responses to thesechanges, and the effects or characteristics of allthese factors in terms of community conditions,activities, and lifestyles. Of the 145 identifiedcommunities having experienced significantchange, 80 communities (55 percent) were sur-veyed. The following is a brief summary of thosefindings. (For more detailed results of significantchange communities, see Harris et al. 1996.)

• Of the 80 significant change communitiessurveyed, 3 percent were perceived as non-resource dependent, 13 percent as havingpredominately ranching economies, 20 per-cent as farming based, 29 percent as primarilytravel and tourism based, and 35 percent aspredominately timber based.

• Of the surveyed communities, 35 percent hadpopulations that were growing, and 39 percenthad populations that had decreased.

• In addition, 36 percent of the surveyed com-munities were not responding to change, andthe other 64 percent were much more pro-active in responding to change.

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Results from the surveyed communities suggestthat the impacts of population growth, and thesocial and land-use changes that have come aboutas a result of that growth, were as critical or moreso than any recent changes in resource manage-ment. Additionally, a community’s degree ofresilience was more related to how it respondedto changes than to its economic structure. Moreresilient communities were more likely to takeproactive actions to respond to changes affectingtheir quality of life. Of the sample of 80 commu-nities, 34 percent were rated as being highly re-silient, and 26 percent and 21 percent were ratedas being moderately high to moderately low inresilience, respectively. Of those having a highresilience rating, 44 percent were perceived aspredominantly timber dependent, 30 percent as

travel and tourism, 22 percent as agricultural, and4 percent as not resource dependent. Conversely,of the communities having a low level of resil-ience, 52 percent were perceived as predomi-nantly agricultural based and only 20 percent astimber dependent.

Among communities having undergone signifi-cant changes, higher proportions of communitiesin the higher resilience classes were perceived astimber dependent, and they reported activitiessuggesting that they were proactive in respondingto change. The number of respondents perceivingtheir communities as travel and tourism depend-ent were represented equally across the four re-silience classes, as were trends in populationchanges, thereby reaffirming that these charac-teristics do not fully predict resiliency.

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The structures and functions of small rural com-munities in the region are more complex thansome analyses would suggest, especially thoseconducted at a regional level or that used countydata to assess local conditions. The extent towhich communities depend on different indus-tries differs, both in the perceptions of active andinvolved residents and in actual employmentnumbers. Generalizations about individual com-munities or industries must be made with care.

To develop constructive strategies for managingchanges, it is important to assess the currentcharacteristics and conditions of communities inthe region, changes affecting them, and the majorfactors influencing responses to change. If com-munities are to develop in a coherent, managed,and well-planned way, residents must deal withthe realities and potentialities of their towns,including their social, economic and politicaladvantages and disadvantages, attractions anddrawbacks. Importantly, although a community’sresources, including its amenities and attractive-ness, can be a factor influencing development, adecisive, major determinant of a community’sresilience clearly is its residents, in particular, thewillingness of residents to take leadership roles,organize, and realize their community’s potential.Community residents are a central defining ele-ment in creating the future of rural communities.

Some major conclusions of the research are:

• Small, rural communities in the interior andupper Columbia River basin have always beenchanging and will continue to change. Theidea of community stability is a myth belyingvarious influences: the volatility of marketsfor timber, mining, and other traditional ex-

tractive industries; the actions of privatecompanies in modernizing and closing plantsand periodically laying off or terminatingworkers; the decreasing supply of timber fromNational Forests; and the rapidly increasingin-migration of new workers and residents(e.g., retirees and new ethnic groups).

• Although closures of mills, mines, and otherresource-processing plants have had a signifi-cant impact on many communities, pastclosures have had little social and economiceffects on other communities. Many mills, forexample, have been closed, sold, reopened,and closed again as a result of a series ofchanges over past decades not necessarilyrelated to public land management decisions.Community growth, as indicated by popula-tion increase, has occurred in many communi-ties that have lost mills.

• Rural communities tend to be more resilient(able to adapt to change in positive, construc-tive ways) than is commonly assumed. Smallcommunities in the basin are unique and com-plex; generalizing from the types of townsthat are resilient thus should be done withgreat care.

• Many of the region’s timber towns had arelatively high level of resilience and wereperceived to be healthy when compared tosmall ranching and farming towns. With theiramenities, diversified economies, and degreeof population growth, the face of many ofthese communities is changing. New policyinitiatives are needed to help smallcommunities cope with the external forcesresulting in change.

Conclusions

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• Public policy analysts could view the role ofresilience in one of two ways. One is that, ifgovernment resources (i.e., funding, work-force, and other kinds of subsidies) are to beexpended on rural communities, those lowestin resilience (ranching and farming communi-ties, in particular) are the ones most needingsupport.

• The second alternative view is that, in thename of economic efficiency and equity,America should “cut its losses” in terms ofcommunities that are “on the skids” andlosing their human capital. Expending anymore societal resources on these communitieswould not be worth the benefits derived;rather, government resources would be mosteffectively used on communities that are atrisk but have the potential to benefit mostfrom those resources.

• The history of Forest Service commitmentsand impacts on rural communities has con-tinually evolved. The nature of this evolu-tion, along with changing societal values, achanging Agency workforce reflecting thosevalues, and the learning that occurs within theAgency, underscores the importance of soundforest planning (for example, see Blattner etal., in press; Brown 1994; Clark and Stankey1994; FEMAT 1993; Gale and Cordray 1991;Grumbine 1994; Krannich et al. 1994; Lee etal. 1990; Machlis and Force 1988; Rasker1995; Waggener 1977). Information such asthat provided in this research may be an im-portant tool for revising forest plans and plan-ning individual projects. Additionally, it alsomay be useful for planning and managementefforts of the communities themselves andtheir counties and states.

Various approaches may help rural communitiesadapt to their changing environments and condi-tions. The CRI suggests that different communi-ties require different mixes of solutions or re-

sponses, depending on the nature of the changesaffecting them and their strengths and weaknessesas indicated by the resilience index. From theindex, solutions and responses could be tailoredto the situations of individual communities. Theymight include programs for rebuilding social net-works and increasing a community’s socialcohesion; leadership training programs; growthmanagement strategies; investments in improvingphysical infrastructure; and financial and infra-structure support for traditional industries tomaintain their role in local economies. Mitigationprograms could include a process for indepthcommunity self-assessment that further clarifiesand details community needs. This process couldhelp communities and their leaders assess theircurrent conditions, evaluate the challenges andopportunities facing their community, and de-velop short- and long-range strategies to respondto changes that make the most effective, efficientuse of outside funding.

As detailed in the research done on significantchange communities (see Harris 1996a, 1996b),distrust of government, issues of self-relianceversus dependence on public resources, concernswith private property rights, and conflicts overresource uses of Federal lands are commonplacein the interior and upper Columbia basins. Ac-cordingly, resource management agencies need totake actions that advance a positive, proactive ap-proach promoting consensus building and col-laborative problem solving across the region,rather than one that fans the flames of conflict,confrontation, and divisiveness among the var-ious publics in the inland Northwest. Recentsocial changes are altering small rural commu-nities across the basin as much as the changingsupplies of natural resources. Residents of theserural communities need to focus their attentionand efforts on dealing with the many comingchanges constructively and resolving resultingproblems as expediently as possible.

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Funding for the original assessment research wasprovided by the Interior Columbia Basin Ecosys-tem Management Project, USDA Forest Service,and U.S. Department of the Interior Bureau ofLand Management, Walla Walla, Washington.Additional support was provided with funds fromthe Pacific Northwest Research Station, USDAForest Service, Portland, Oregon, and the USDAMcIntire-Stennis research program at the IdahoForestry, Wildlife and Range Experiment Station,

Acknowledgments

College of Forestry, Wildlife and Range Sciences,University of Idaho, Moscow, Idaho. The authorsexpress their appreciation for the assistance ofresearch assistants Chris Wall and Jean Haley inplanning, conducting, and reporting the assess-ment. The economic data reported in this paperwere developed in collaboration with regionaleconomists Hank Robison and Steve Peterson ofthe University of Idaho.

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Wilkinson, Kenneth P. 1986. In search of the community in the changing countryside. RuralSociology. 51(1): 1-17.

Williams, Anne S. 1976. Planning service delivery systems for rural, sparsely populated areas. In:Rogers, David L.; Whiting, Larry R., eds. Aspects of planning for public services in rural areas.Ames, IA: North Central Regional Center for Rural Development.

Wyoming Department of Administration and Information. 1995. Population for Wyomingcounties and municipalities in 1990 and 1994. Cheyenne, WY: Division of Economic Analysis.

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The Forest Service of the U.S. Department of Agriculture isdedicated to the principle of multiple use management of theNation’s forest resources for sustained yields of wood, water,forage, wildlife, and recreation. Through forestry research, co-operation with the States and private forest owners, and man-agement of the National Forests and National Grasslands, itstrives—as directed by Congress—to provide increasinglygreater service to a growing Nation.

The U.S. Department of Agriculture (USDA) prohibits discrim-ination in all its programs and activities on the basis of race,color, national origin, gender, religion, age, disability, politicalbeliefs, sexual orientation, or marital or family status. (Not allprohibited bases apply to all programs.) Persons with disabilitieswho require alternative means for communication of programinformation (Braille, large print, audiotape, etc.) should contactUSDA’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, 14thand Independence Avenue, SW, Washington, DC 20250-9410or call (202) 720-5964 (voice and TDD). USDA is an equalopportunity provider and employer.

Pacific Northwest Research Station333 S.W. First AvenueP.O. Box 3890Portland, OR 97208-3890

rural communities in the inland northwest: an assessment ofquality of life, business attractiveness,...

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