Assessing Socioeconomic

Resiliency in 

Washington Counties

Jean M. Daniels

United States

Department of

Agriculture

Forest Service

Pacific Northwest

Research Station

General Technical

Report

PNW-GTR-607

April 2004
Author Jean M. Daniels is a research forester, Forestry Sciences Laboratory, 620 S. Main, 

Suite 400, Portland, OR 97205.
Daniels, Jean M. 2004. Assessing socioeconomic resiliency in Washington counties.

Gen. Tech. Rep. PNW-GTR-607. Portland, OR: U.S. Department of Agriculture,

Forest Service, Pacific Northwest Research Station. 35 p.

The link between forest management and the well-being of communities in forested

areas has traditionally been defined by forest sector employment opportunities.

Attempts to redefine this relationship have produced methods that use a more 

comprehensive approach by combining both economic and social indicators to 

evaluate community well-being. The goal of this study is to evaluate socioeconomic

resilience and forest dependence in Washington counties in order to identify counties

where changes in forest management could negatively affect the well-being of nearby

residents, allowing land managers and decisionmakers to anticipate the effects 

of land management policies. Results indicate that Ferry, Pend Oreille, Pacific,

Skamania, Stevens, and Wahkiakum Counties all have socioeconomic systems that

could be particularly vulnerable to forest management changes. The same analyses

were performed for the Washington Department of Natural Resources (DNR) by

using only counties on the west side of the Cascade Range. Results show that two

counties, Wahkiakum and Pacific, may experience disproportionate negative impacts

from changes in DNR state forest management. These findings are preliminary in

nature; findings should be reassessed using community-level data to determine the

optimum geographic scale necessary for detailed evaluation of policy effects.

Keywords: Sustainable forest management, socioeconomic resilience, forest dependency, criteria and indicators, Washington Department of Natural Resources.

Abstract
1 Introduction

2 Key Definitions and Concepts

2 Community Viability and Adaptability

4 Forest Dependency

5 Areas of Concern

6 The Washington Department of Natural Resources

9 Methods

10 Assessing Socioeconomic Resiliency

12 Forest Dependence

12 Identifying Counties of Concern

12 Identifying Washington Department of Natural Resources Counties of Concern

13 Results

13 Lifestyle Diversity

16 Economic Diversity

18 Population Density

20 Socioeconomic Resiliency

23 Forest Dependence

23 Counties of Concern

23 Washington Department of Natural Resources Reliance

29 Washington Department of Natural Resources Counties of Concern

31 Discussion

32 Acknowledgments

32 Metric Equivalents

32 References

34 Appendix 1

35 Appendix 2

Contents
1

Sustainability, the notion of sustaining economic prosperity that is socially just and

environmentally sound, has become a new paradigm in forest resource management.

Although sustainability has become popular as a concept, measuring, or even 

defining sustainability in a forest management context has been fraught with difficulty,

because sustainable forest management encompasses not only the production of 

timber and nontimber forest products, but also economic and social impacts on 

communities in forested areas. The need to establish a standardized international

framework for assessing sustainable forest management led to the drafting of the

Montreal Process of Criteria and Indicators for the Conservation and Sustainable

Management of Temperate and Boreal Forests in 1995, hereafter referred to as the

Montreal Process (Montreal Process Working Group 1998). The United States has

demonstrated commitment to sustainable forest management by participating in the

international working group that developed criteria and indicators and by subsequently becoming a signatory participant in the Montreal Process.

The Montreal Process establishes a list of 7 criteria and 67 indicators for the sustainable management of temperate and boreal forests. Criterion 6, titled “Maintenance

and Enhancement of Long-Term Multiple Socioeconomic Benefits to Meet the Needs

of Societies” contains 19 indicators to reflect the role that forest management plays 

in promoting and sustaining social, economic, and community well-being in forested

areas. This study focuses particularly on Indicator 46, titled “Viability and Adaptability

to Changing Economic Conditions of Forest Dependent Communities.”

Indicator 46 combines two concepts, first, community viability and adaptability and

second, forest dependency. These two concepts are combined to describe the link

between forest management and community well-being. In particular, it reflects 

concerns that implementing sustainable forest management policies may disrupt 

economic systems and therefore negatively affect the well-being of forested communities. Forest land management agencies, such as the Washington Department of

Natural Resources (DNR), logically have an interest in identifying areas that may

encounter obstacles on the path to sustainable forest management.

After a discussion of key definitions and concepts surrounding community viability

and adaptability and forest dependency, this study focuses on five objectives.

First, the study assesses viability and adaptability for each of the 39 counties in

Washington by using a socioeconomic resiliency index. Counties are used as a 

measure because of the ready availability of data at the county level. Figure 1 provides a base map displaying 39 counties in Washington. Second, forest dependency

is assessed for all Washington counties. Then, for each Washington county, socioeconomic resilience ratings are combined with forest dependency ratings to produce 

a list of “counties of concern” where socioeconomic resilience is low and forest 

dependency is high. Next, this method is repeated to determine reliance of western

Washington counties (hereafter referred to as west-side counties) on DNR forest

resources and identify “DNR counties of concern.” These are west-side counties that

have both a low socioeconomic resiliency rating and a relatively high percentage of

DNR forest land ownership. Findings are then compared against traditional approaches used by the state of Washington to identify areas experiencing economic distress

and areas considered dependent on public timber.

Introduction
There are several definitions that are key in understanding how to evaluate the

propensity of communities to adapt to changing economic conditions. These include

community viability and adaptability, forest dependency, and areas of concern.

Background information describing the operations of the DNR is also important to

gain perspective on how its forest management policies can affect forested areas.

Although community viability and adaptability are not defined in the Montreal

Process, their inclusion reflects an expansion of traditional thinking about the connection between communities and the forest resources that surround them. Early notions

of community stability refer to jobs and income generated through the harvest and

processing of timber from public or private forest lands by residents of communities

dependent on income from such activities (Society of American Foresters 1989).

However, such notions often treat forest industry employment (both direct and 

indirect) as the sole determinant of community well-being in forested areas. This

approach to community stability ignores the social component of a community

(McCool et al. 1997). Communities are made up of individual residents who base

their decision to relocate into, remain in, or leave the community on a variety of 

factors that may or may not include forest sector employment opportunities. Also,

community stability oversimplifies the relationship between residents and surrounding

forest resources because it does not account for nontimber goods and services 

that residents obtain from the forest. In other words, communities located in forested

areas represent complex dynamic economic and social systems that are ignored by

the simple “timber and jobs” perspective of community stability.

2

Figure 1—Counties in Washington.

Key Definitions

and Concepts

Community Viability

and Adaptability
3

The state of Washington continues to apply economic measures to assess community well-being. Washington’s Employment Security Department (ESD) determines 

“distressed areas” by using each county’s 3-year average unemployment rate. If the

average unemployment rate is 120 percent or more of the statewide unemployment

rate, the county is considered distressed and placed on the distressed area list

(Washington ESD 2003). The following 19 counties were designated as distressed

areas in 2003:

Adams Klickitat

Chelan Lewis

Clallam Mason

Columbia Okanogan

Cowlitz Pacific

Douglas Pend Oreille

Ferry Skamania

Franklin Stevens

Grant Yakima

Grays Harbor

Although the state of Washington has devised a simple, universally understandable

method to gauge economic well-being, it relies exclusively on one variable, unemployment. When community well-being is exclusively considered a function of the

wage income of residents, complex interactions between social and economic systems are not addressed. Combining measures of economic well-being with social

variables presents a more comprehensive representation of community life. In addition, unemployment itself is not always a consideration when residents decide to

leave or remain in a community. Lifestyle choices and traditions are other important

determinants of where individuals choose to live.

Expanding beyond traditional employment indicators means incorporating other

important aspects of community life, including social variables, into the analysis.

The notion of community viability and adaptability broadens employment-based

approaches to encompass all factors that contribute to community well-being.

One factor influencing community well-being is the ability of communities to adapt to

change. Shocks to social or economic systems occur regularly within communities;

indeed, change is an inevitable part of the human condition. So then, why are 

some communities better equipped to adapt to change than others? Why does an

economic downturn spell disaster in some communities whereas others are able to

shake off negative impacts with relative ease? 

The term “socioeconomic resiliency” is one of many that social scientists have used

to describe community health. It reflects the ability of people to manage individual

and community transitions with reasonable comfort and confidence in the face of the

forces of change. In other words, socioeconomic resilience is the ability of a community to adapt to change. Haynes et al. (1996) initiated discussions linking natural

resource policy and community resilience. Researchers have identified several characteristics that likely are all valid determinants of community resilience. These include

community character, cohesiveness, services and infrastructure, cultural diversity,

economic diversity, resource dependence, attractiveness to business, quality of life,

and civic leadership (McCool et al. 1997).
4

Because techniques to directly measure socioeconomic resiliency do not exist, 

measuring the socioeconomic resiliency of a community requires the use of social

and economic indices. These indices serve as proxies, which are frequently used in

economics to estimate the value of a good that has no market in which to establish 

a price. A model of socioeconomic resiliency requires a method to use social and

economic indices to discern the link between a community’s social and economic 

systems and its ability to adapt to change. Horne and Haynes (1999) formulated such

a model in an attempt to use indices to assess conditions contributing to socioeconomic resilience of counties in the interior Columbia River basin.

For the purposes of this study, it is assumed that socioeconomic resiliency is directly

related to diversity in social and economic systems. In other words, a community with

higher social and economic diversity is less affected by change than a community

with lower diversity, making the first community more resilient. A community having a

highly resilient socioeconomic system is able to adapt to and recover from negative

impacts quickly. Residents of areas demonstrating high resiliency have a wide variety

of skills and access to a variety of employment opportunities. Should specific firms or

industries experience downturns, unemployment rates may rise, but only until displaced workers find other employment. On the other hand, socioeconomic systems

having low resiliency may experience longer term negative impacts, such as persistent unemployment or even outmigration. Thus, resilience is directly related to diversity, a relationship that is widely documented in the ecological sciences (Moffat 1996).

Indicator 46 of the Montreal Process combines community viability and adaptability

with a second concept, forest dependency. Like community viability and adaptability,

forest dependency is not defined in the Montreal Process; it stems from an expansion

of the concept of timber dependency. Traditionally, timber dependence was a criterion

used to identify communities with significant economic dependence on timber harvesting and processing. In fact, provisions in the National Forest Management Act

(1976) directed the U.S. Forest Service to publish a list of communities deemed

dependent on national forest timber since 1977, although the Forest Service has 

not produced a list since 1987.1

Economic benefits from forest land are not limited to timber commodity production.

Forest dependence reflects the growing acknowledgment that there is more to the

relationship between forests and communities than commodity wood production and

forest sector employment. Measures of timber dependency fail to recognize that 

noncommodity economic opportunities arising from forests, such as tourism and

recreation, nontimber forest products, and other nontraditional forest uses also 

provide income and jobs to residents. In many areas, nontimber uses generate more

Forest Dependency

1 The Federal Register (42[106]); 28258. Thursday, June 2, 1977)

defines communities dependent on national forest timber as

“...areas with common social and economic interests bounded by

established daily marketing and work force commuting patterns,

and encompassing one or more primary wood products manufacturing facilities located within or adjacent to a specific area of

National Forest upon which it is dependent for its timber supply

and where 10 percent or more of the community work force is

employed in the primary manufacture of wood products, including

logging and log transportation, and National Forest timber

accounts for at least 30 percent of the timber used in the primary

wood products manufacturing facilities in the last 5 calendar

years.”
5

income for residents than does timber production. In addition, forests contribute to

higher quality of life, making communities more attractive to potential new residents

and businesses and for retirement settlement areas. Last, most communities possess

a mix of industries, of which wood products manufacturing is only one component.

To assert that the well-being of a community can be judged entirely by its production

of timber ignores the direct and indirect economic impacts made by other local industries. Thus, while jobs and income stemming from timber production do provide 

important economic opportunities to some forested communities, valuing the forest

exclusively in terms of timber production undervalues the forest to residents and oversimplifies its value to society.

For the purposes of this study, forest dependency is defined as a function of forest

land, rather than timberland area. Forested landscapes classified as forest land are 

at least 10 percent stocked with forest trees of any size; timberland is forest land

capable of producing industrial timber and not withdrawn from timber use by statute

or regulation. All timberland is forest land, but not all forest land is timberland.

Communities located in heavily forested areas receive a higher forest dependency

rating than communities located outside of forested areas or in less densely stocked

forests. Logically, more forest area implies more opportunity for residents to reap 

benefits from forest resources.

One way to single out areas with economic systems that are especially sensitive to

changes in forest management strategies is to combine socioeconomic resilience with

forest dependence. For example, an area designated as having “high” socioeconomic

resilience and “low” forest dependence would not be expected to experience difficulty

with changing forest land management policies. On the other hand, areas with “low”

socioeconomic resilience and “high” forest dependence could experience negative

economic impacts from changes in forest management, because of both inability to

adapt to change and dependence on forest resources.

By combining areas that may experience difficulty adapting to change with areas

identified as forest dependent, a list of “communities of concern” can be compiled

(Donoghue and Haynes 2002). However, for the purposes of this study, analyses

were performed at the county, not community, level, owing to limitations associated

with release of Census 2000 demographic data. Therefore, rather than “communities

of concern,” this study identifies “counties of concern” where low socioeconomic

resilience coincides with high forest dependence (Haynes 2003, Horne and Haynes

1999). The intent is to identify for stakeholders in land management agencies those

counties that might require mitigation, development assistance, or further understanding of the impacts of decisions before they are implemented as policies, in an attempt

to prevent unforeseen negative impacts on county residents. Aggregating the data to

the county level may sacrifice spatial detail evident at the community level. Readers

are cautioned not to extrapolate results to make inferences at the community level.

Areas of Concern
6

Federal, state, and local land management agencies own and control vast areas of

public forest land across Washington (table 1). Forest management policies favoring

reduced harvest from public forests have been a fact of life in the Pacific Northwest

since the early 1990s. As a result, revenues generated from timber harvests have

declined substantially. Traditionally, a portion of these revenues was distributed back

to counties and other public entities in some form, either in lieu of property and other

taxes or as the result of fiduciary responsibilities to various public trust beneficiaries.

Declining revenues from timber harvesting have raised concerns that evolving timber

management policies negatively affect counties containing a significant amount of

public forest land. Appendix 1 displays forest land acreage by ownership.

Presumably, managers of public agencies are interested in identifying counties that

may experience disproportionate negative impacts from changing public timber management policies. The DNR is one such agency. Specifically, the DNR has expressed

an interest in identifying counties that may experience difficulty adapting to changing

harvest levels on state forest lands.

The DNR was established in 1957 to serve as land manager for a variety of lands

owned by the state of Washington, including forest lands. The DNR manages approximately 2.1 million acres of state forest land for two main purposes. First, the DNR

has a fiduciary responsibility to create revenue as manager of public lands. Revenues

generated from DNR operations provide income to public schools and universities, 

a variety of state institutions, and many counties, in addition to contributions to the

state general fund. Second, with the signing of a Habitat Conservation Plan with the

U.S. Fish and Wildlife Service and the National Marine Fisheries Service in 1997, the

DNR is responsible for maintaining habitat to protect native fish and wildlife species

and other sensitive resources on lands under its jurisdiction. In addition, state law

(RCW 79.68.050) directs the DNR to provide recreation opportunities (including

camping facilities and hiking and biking trails), hunting and fishing, and other multipleuse benefits for all Washington residents.

Washington state forest land management is conducted within the framework of 

policy direction approved by the Board of Natural Resources, one of which is the

DNR’s 1992 Forest Resource Plan (FRP). The FRP was developed as a management plan for 2.1 million acres of state forest land over the 10-year period from 1992

to 2002 (Washington DNR 1992). This 10-year period was extended until June 2005

to allow for the completion of sustainable harvest calculations for western and 

eastern Washington. The DNR is directed by state law (RCW 79.68.040) to apply

sustained yield management strategies on state forest lands. As part of this directive,

the DNR is required to periodically adjust acreages designated for inclusion in the

sustained yield management program and recalculate a 10-year sustainable timber

harvest level. The sustainable harvest level is the volume of timber offered for sale

from state-owned forest land during a planning decade.

As directed by law, the DNR is recalculating a new sustainable harvest level for state

forest land in 2003. By establishing a new sustainable timber harvest level, the DNR

not only is fulfilling its legislative mandate, but also is provided the opportunity to

make changes to the 1992 Forest Resource Plan. By revising the FRP, the DNR 

can update its management strategies to better meet the goals of its Habitat

The Washington

Department of 

Natural Resources
7

Table 1—Forest land ownership in Washington counties

County and Total public

County Federal State municipal ownership Private

Percent

Adams 0 0 0 0 0

Asotin 61.1 .7 0 61.8 38.2

Benton 0 0 0 0 0

Chelan 83.7 3.8 .9 88.3 11.7

Clallam 48.5 15.0 0 63.4 36.6

Clark 3.4 26.9 .7 31.0 69.0

Columbia 73.2 4.4 0 77.6 22.4

Cowlitz 3.6 11.6 0 15.2 84.8

Douglas 50.0 50.0 0 100 0

Ferry 39.3 3.2 .5 42.9 57.1

Franklin 0 0 0 0 0

Garfield 90.8 0 0 90.8 9.2

Grant 0 0 0 00

Grays Harbor 12.1 8.0 4.5 24.6 75.4

Island 5.5 13.2 .2 18.9 81.1

Jefferson 61.9 17.2 0 79.1 20.9

King 32.5 7.2 11.7 51.4 48.6

Kitsap 1.8 13.8 9.9 25.5 74.5

Kittitas 52.1 11.1 0 63.2 36.8

Klickitat 5.3 14.4 1.4 21.1 78.9

Lewis 35.4 9.1 .6 45.1 54.9

Lincoln 3.5 3.3 0 6.8 93.2

Mason 27.5 14.3 0 41.8 58.2

Okanogan 66.0 10.4 0 76.4 23.6

Pacific .6 13.6 .6 14.8 85.2

Pend Oreille 58.2 5.1 .1 63.3 36.7

Pierce 42.1 4.2 1.2 47.4 52.6

San Juan .8 13.6 .2 14.6 85.4

Skagit 47.9 15.9 1.0 64.8 35.2

Skamania 80.3 7.8 .4 88.4 11.6

Snohomish 53.4 14.5 .9 68.8 31.2

Spokane 3.4 12.0 1.1 16.5 83.5

Stevens 24.2 14.5 0 38.8 61.2

Thurston 2.9 21.7 0 24.6 75.4

Wahkiakum 0 26.6 0 26.6 73.4

Walla Walla .3 0 0 0 99.7

Whatcom 66.7 10.5 .6 77.8 22.2

Whitman 0 0 1.4 1.4 98.6

Yakima 41.4 8.7 0 50.0 50.0

Total 43.2 10.5 1.1 54.9 45.1
8

Conservation Plan. In addition, the FRP can more fully address the impacts of DNR

timber harvesting operations on water quality, salmon habitat, and other aquatic and

riparian resources. Changing the FRP requires the preparation of an environmental

impact statement (EIS) presenting a range of alternatives that must all meet DNR

management objectives.

One question not addressed by the EIS is whether changing DNR harvesting 

operations will have a negative impact on communities located near state forest

lands. The DNR conducts financial analyses by using projections of timber revenues

to estimate cash flows to its beneficiaries. However, such analyses cannot fully 

capture the direct and indirect effects felt by social and economic systems in these

areas—the links are too complex. Examination of socioeconomic resiliency in these

forest-dependent areas provides the DNR with an idea of where disproportionate

effects may occur from evolving forest land management strategies. For the purposes

of this study, Washington counties that may be especially vulnerable to changes in

DNR land management policies are called “DNR counties of concern.”

One way the DNR currently acknowledges the relationship between communities 

and forest resources is through measures of regional mill dependency published in

Washington Mill Survey 2000 (Larsen et al., n.d.). Washington counties are combined

into five economic areas: Puget Sound, Olympic Peninsula, Lower Columbia, Central

Washington, and Inland Empire (fig. 2). Wood processing facilities located within 

each economic area report the percentage of total raw materials obtained from each 

Figure 2—Department of Natural Resources economic areas.
9

category of forest land owner. Forest land ownership is subdivided into national 

forest, state, Bureau of Land Management, other public, forest industry, Native

American, and farmer and miscellaneous private ownerships. For each mill, raw

material source dependency is measured by using four groupings: no dependency, 

1 to 33 percent dependency, 34 to 66 percent dependency, and 67 to 100 percent

dependency.

Regions containing a relatively large number of mills highly dependent upon any 

single forest land owner for timber supply are considered “dependent” on that source.

DNR timber dependence arises when several mills within a region are highly dependent on state forests for raw material supply. Table 2 contains mill dependency measures for state-owned timber only. Note that one mill in the Puget Sound Economic

Area and four mills in the Olympic Peninsula Economic Area are between 67 and 

100 percent dependent on state lands for raw materials. Ideally, counties identified 

as highly reliant on DNR forest resources by this study are located within the two

economic regions where mills display high dependency on state timber, making the

two measures compatible.

Assessing socioeconomic resiliency, forest and timber dependency, and reliance 

on DNR timber of Washington counties is a four-step process. First, each county is

assigned a socioeconomic resiliency rating by combining indices to represent lifestyle

diversity, economic resiliency, and population density. Second, forest dependence is

determined by rating all Washington counties based on proportion of forest land per

county. Next, statewide counties of concern are identified, as these are potentially

sensitive to changes in regional forest management policies. Last, focus shifts to

west-side counties to rate their reliance on DNR forest resources. The DNR reliance

ratings for each west-side county are calculated by combining ratings for the proportion of forest land in DNR ownership with socioeconomic resiliency ratings revised to

include only west-side counties. The result is a list of west-side “DNR counties of concern” that may experience difficulty adapting to changes in DNR forest management

policies.

Methods

Table 2—Number of mills identified by percentage of dependency on state

lands for raw material requirements

Dependency percentage

Economic area 0 1-33 34-66 67-100

Number of mills

Puget Sound 30 16 6 1

Olympic Peninsula 63 37 4 4

Lower Columbia 21 10 1 --

Central Washington 5 8 -- --

Inland Empire 10 11 -- --

Total 129 82 11 5
10

Socioeconomic resiliency in Washington counties was assessed by using methods

described in Horne and Haynes 1999. Horne and Haynes developed an operational

definition of socioeconomic resilience at the county level by using an index composed

of three factors: social and cultural diversity, economic diversity, and civic infrastructure. This study also uses these three factors, but social and cultural diversity and

economic diversity are measured differently. Social and cultural diversity are represented by the diversity of lifestyles in each county. Economic diversity is measured 

by using an index of regional specialization. Civic infrastructure is estimated by using

population density as a proxy measure.

Lifestyle diversity—The first component of socioeconomic resiliency is lifestyle

diversity. Counties exhibiting greater diversity of lifestyles are assumed to be more

resilient to change than those with less diversity of lifestyles. Because lifestyle 

diversity is not something that can be directly measured, proxy measures were developed by using demographic data from Census 2000 Gateway (USDC Bureau of the

Census 2002). Demographic factors for mobility, ethnicity, degree of urbanness, race,

income, and education in each Washington county were used as proxies in model

construction.

Mobility measures the proportion of people who changed their residence between

1995 and 2000 versus those who did not. Ethnicity measures were obtained from

census categories of native and born in state of residence, native and born in another

state in the United States, native and born outside the United States, and foreign

born. Degree of urban and rural was categorized as urban and inside urbanized

areas, urban and inside urban clusters, rural farm, and rural nonfarm. Race included

the proportion of census respondents identifying themselves as White, African

American, Asian, Hispanic, American Indian, Pacific Islanders, and other. Income

measured the proportion of people in each of 16 sequential income categories by

county. Lastly, education measured educational attainment for the proportion of 

people over age 25 who had not finished high school, obtained a high school diploma, had a diploma and some college, received an associate’s degree, received a

bachelor’s degree, and received a graduate degree.

Diversity measures for these six indicators were obtained by using the ShannonWeiner diversity index. This index is commonly used in the biological sciences to 

calculate, for example, habitat diversity.

D = -_______ Σpiln(pi) ,

ln(s) (1)

where

D = diversity measure, ranging from 0 to 1,

s = total number of subcategories for each of the six indicator variables, and

p = proportion of people in each subcategory for each variable.

Thus, D is calculated by considering the distribution of people across subcategories;

a relatively low value indicates uneven distribution of people across that indicator.

Note that any variable having high concentrations of people in a few subcategories

would obtain a low value for D. For example, because 95 percent of King County is

concentrated in urban centers, King County would receive a relatively low diversity

Assessing

Socioeconomic

Resiliency
11

rating for the indicator degree of urbanness. Summing the Shannon-Weiner index

value for each variable and dividing by six resulted in an overall lifestyle diversity 

rating for each county.

Economic diversity—Economic diversity is the second component of socioeconomic

resiliency and reflects diversity of employment opportunities. Employment by standard industrial classification (SIC) code for 2001 for each Washington county was

downloaded from the Labor Market and Economic Analysis Branch of Washington’s

Department of Employment Security Web site. Data missing owing to disclosure

issues were interpolated from the 1997 County Business Patterns CD-ROM (USDC

Bureau of the Census 1999). The year 1997 was selected to ensure data consistency, as subsequent releases use the newer North American industry classification

(NAIC) code system.

These county employment figures were then benchmarked against the state of

Washington as a whole by using the coefficient of regional specialization index:

Ri = Σ__ Eij Ej , - __

Ei E (2)

where

Eij = employment in county i in industry j,

Ei = total employment in county i,

Ej = total employment in industry j in all counties, and

E = total employment in all industries across all counties.

This index is commonly used in geography to determine if a region (county here) is

more or less specialized than a benchmark (Washington state here). Values close to

zero indicate the county has about the same proportion of people employed in each

industry as the state, whereas values around 1 indicate that employment is more 

specialized in the county than in the state. The assumption used for this analysis 

is that counties that are highly specialized have employment concentrated among 

relatively few industries, making those counties more vulnerable if negative impacts

occur. In other words, a relatively high value for the coefficient of regional specialization is interpreted to mean the county is less economically diverse and therefore less

able to adapt to change. Although using community, rather than county, data might

yield more detailed results, disclosure issues are formidable at the community level

as some individual businesses can be easily identified.

A coefficient of regional specialization was computed for each of the 39 Washington

counties. These values were then sorted from lowest to highest, divided into four

groups based on 25th, 50th, and 75th statistical quartiles, and rated from 1 to 4 with

low specialization corresponding with high economic diversity.

Population density—The third measure, population density, was used as a proxy for

civic infrastructure. Greater population density is assumed to lead to a more developed county infrastructure and so increases socioeconomic resiliency. The number of

people per square mile of county area determines population density for each county.

Population for 2000 by county was obtained from QuickFacts on Census 2000
12

Gateway (USDC Bureau of the Census 2002). The number of square miles in each

county was downloaded from the Washington state data page Web site (Washington

ESD 2001). By using a method similar to that of Horne and Haynes (1999), each of

the 39 counties was given a population density rating from 0 to 4.

Population density of county Rating

> 816 4

237 to 816 3

33 to 236 2

11 to 32 1

< 11 0

Socioeconomic resiliency—Each county received an overall socioeconomic

resiliency rating corresponding to an unweighted average of its ranks for lifestyle

diversity, economic resiliency, and population density. These values were then sorted

from highest to lowest value and divided into thirds. Counties in the top third had the

highest socioeconomic resilience and so were given a rating of “high.” Counties in the

middle third were given a “medium,” and counties in the last third were given a “low”

socioeconomic resiliency rating. Although there are alternative ways to rate the counties, of the methods considered, dividing them into three equal parts resulted in the

best agreement between the list of counties having a socioeconomic resilience rating

of “low” and those appearing on the 2003 distressed county list published by the state

of Washington.

Identifying the Washington counties with low socioeconomic resiliency is the first step

in determining counties whose economies might be strongly impacted by changing

forest management policies. The next part of the analysis identifies forest dependence in Washington counties.

The proportion of forest land in each county serves as a proxy for forest dependency;

it represents dependence of local residents on forest resources. Total acres and

acres of forest land for each Washington county were obtained from the USDA Forest

Service’s Forest Inventory and Analysis (FIA) database. These data were used to

compile a list of the proportion of forest land in each county. This list was then sorted

from highest to lowest value and divided into three equal parts. The top third were

assigned a forest dependency rating of “high,” the second third received a “medium”

rating, and the lowest third received a “low” rating.

Counties of concern are those where low socioeconomic resiliency and high forest

resource dependence increase the likelihood that changing forest management policies may disproportionately impact the well-being of residents. Counties having both

a “low” socioeconomic rating and a “high” forest dependency rating were classified as

counties of concern.

The DNR counties of concern were determined by combining socioeconomic 

resiliency ratings with measures of DNR reliance for only west-side counties. As 

the scale of analysis changed from the entire state to the west-side region, socioeconomic resiliency ratings were reassessed by using a regional instead of a statewide

benchmark. Thus, although relative socioeconomic resiliency values remain the

same, the dividing line for thirds, and thus some socioeconomic resiliency ratings 

(i.e., “high,” “medium,” or “low”) changed.

Forest Dependence

Identifying Counties

of Concern

Identifying

Washington

Department of 

Natural Resources

Counties of Concern
13

The relative proportion of total forest land in DNR ownership serves as a proxy for

reliance on DNR timber. Figures for DNR forest land ownership by county provided by

the DNR and total acres of forest land per county obtained from the FIA database

were used to calculate the proportion of DNR ownership of total forest land in each

west-side county. These proportions were then sorted in descending order. The third

of the counties with the highest proportions were rated with “high” DNR reliance. This

process was repeated for the second third (“medium” reliance) and the last third

(“low” reliance) of the counties.

The resulting list of DNR reliance is compared against the revised list of socioeconomic resiliency ratings. Counties identified as having “high” DNR reliance and “low”

socioeconomic resiliency are designated as “DNR counties of concern,” where 

disproportionate negative impacts may arise from evolving DNR forest management

strategies. We would expect to find these counties within the Puget Sound or Olympic

Peninsula Economic Areas, where, according to the Washington Mill Survey 2000

(Larsen et al., n.d.), five mills are between 67 and 100 percent dependent on state

timber.

Appendix 2 contains actual Shannon-Wiener lifestyle diversity index values for all 39

Washington counties for 6 demographic factors: mobility, ethnicity, urbanization, race,

income, and education. Lifestyle diversity ratings for each of these six factors and 

a composite lifestyle diversity rating for each county are presented in table 3.

Composite lifestyle diversity ratings are redisplayed in descending order for emphasis

in table 4 and arrayed graphically in figure 3. The mean rating was 2.47 with standard

deviation of 0.86. According to the Shannon-Wiener diversity index, Franklin County,

with an overall rating of 4.00, has the greatest diversity of lifestyles in Washington.

Closely following are Chelan and Grant Counties (3.83), Yakima County (3.67), and

Skagit and Whatcom Counties (3.50).

These results may seem counterintuitive, as King and Pierce Counties do not top the

list. Table 3 shows that both King and Pierce Counties receive a rating of 1 for urbanrural, because residents of both counties are heavily concentrated in urban areas.

Remember, the composite lifestyle diversity rating reflects the distribution of lifestyles

present in each county.

On the other hand, Pend Oreille and Wahkiakum Counties have overall lifestyle diversity ratings of 1.00, indicating that these counties have the least diversity of lifestyles

in the state. A tied rating of 1.33 for Asotin, Garfield, Lincoln, Skamania, and Stevens

Counties places these counties as second to lowest. Grays Harbor and Ferry

Counties, with ratings of 1.50, tied for third lowest diversity rating. Again, counties

having a low rating should be interpreted as having high concentrations of people in

fewer demographic groups, rather than a population that is evenly distributed across

demographic groups.

Results

Lifestyle Diversity
14

Table 3—Shannon-Wiener lifestyle diversity ratings, Washington counties

County Mobility Ethnicity Urban/rural Race Income Education Composite

Adams 4 4 4 4 1 3 3.33

Asotin 1 1 1 1 3 1 1.33

Benton 3 3 2 3 3 4 3.00

Chelan 4 4 4 3 4 4 3.83

Clallam 2 2 3 2 3 3 2.50

Clark 4 3 2 2 3 2 2.67

Columbia 1 1 4 1 1 3 1.83

Cowlitz 2 1 4 2 3 1 2.17

Douglas 3 3 3 3 2 4 3.00

Ferry 1 1 1 4 1 1 1.50

Franklin 4 4 4 4 4 4 4.00

Garfield 1 1 1 1 1 3 1.33

Grant 4 4 4 4 3 4 3.83

Grays Harbor 1 1 2 2 2 1 1.50

Island 3 3 3 3 3 2 2.83

Jefferson 2 2 3 1 4 2 2.33

King 4 4 1 4 4 3 3.33

Kitsap 3 3 2 4 4 2 3.00

Kittitas 2 2 3 2 1 3 2.17

Klickitat 2 2 4 3 1 2 2.33

Lewis 2 2 3 1 2 2 2.00

Lincoln 1 1 1 1 2 2 1.33

Mason 2 2 2 3 2 1 2.00

Okanogan 3 3 2 4 1 4 2.83

Pacific 2 2 3 2 1 2 2.00

Pend Oreille 1 1 1 1 1 1 1.00

Pierce 4 4 1 4 4 2 3.17

San Juan 2 2 1 1 4 1 1.83

Skagit 3 3 4 3 4 4 3.50

Skamania 1 2 1 1 2 1 1.33

Snohomish 3 3 2 3 2 2 2.50

Spokane 2 2 2 2 4 3 2.50

Stevens 1 1 2 2 1 1 1.33

Thurston 3 3 3 4 3 3 3.17

Wahkiakum 1 1 1 1 1 1 1.00

Walla Walla 4 4 2 3 2 4 3.17

Whatcom 4 4 4 2 4 3 3.50

Whitman 4 3 3 2 1 3 2.67

Yakima 3 4 4 4 3 4 3.67
15

Table 4—Composite lifestyle diversity rating in Washington counties

County Composite lifestyle diversity rating

Franklin 4.00

Chelan 3.83

Grant 3.83

Yakima 3.67

Skagit 3.50

Whatcom 3.50

Adams 3.33

King 3.33

Pierce 3.17

Thurston 3.17

Walla Walla 3.17

Benton 3.00

Douglas 3.00

Kitsap 3.00

Island 2.83

Okanogan 2.83

Clark 2.67

Whitman 2.67

Clallam 2.50

Snohomish 2.50

Spokane 2.50

Jefferson 2.33

Klickitat 2.33

Cowlitz 2.17

Kittitas 2.17

Lewis 2.00

Mason 2.00

Pacific 2.00

Columbia 1.83

San Juan 1.83

Ferry 1.50

Grays Harbor 1.50

Asotin 1.33

Garfield 1.33

Lincoln 1.33

Skamania 1.33

Stevens 1.33

Pend Oreille 1.00

Wahkiakum 1.00
16

Economic diversity values and ratings using the coefficient of regional specialization

index are presented for each Washington county in table 5. The average economic

diversity index value was 0.709 with standard deviation of 0.266. To reiterate, counties having low coefficients of regional specialization are interpreted as having high

economic resiliency because the distribution of employment across industries is 

similar to the state as a whole. In other words, jobs are widely distributed across a 

multitude of industries. A relatively high value for the coefficient of regional specialization means that jobs are highly concentrated into a few industries, implying a less

diverse economy.

According to this index, Spokane County, with a coefficient of 0.268, has the highest

economic resiliency of all Washington counties. Following closely is Whatcom County

on the Canadian border with a rating of 0.269. King and Pierce Counties have the

third and fourth highest economic resiliency ratings with 0.279 and 0.293, respectively. Not surprisingly, these counties tend to be highly urbanized and to possess large

transportation networks.

At the opposite end of the spectrum, Columbia has the greatest coefficient of 

regional specialization at 1.287, making it the Washington county with the least

diverse economy. Employment in Columbia County is heavily concentrated in highly

Economic Diversity

Figure 3—Composite lifestyle diversity ratings in Washington counties.
17

Table 5—Economic diversity in Washington counties

Coefficient of 

regional Economic diversity

County specialization rating

Spokane 0.268 4

Whatcom .269 4

King .279 4

Pierce .293 4

Clark .371 4

Snohomish .411 4

Skagit .443 4

Cowlitz .489 4

Kitsap .536 4

Thurston .548 4

Lewis .573 3

Grays Harbor .576 3

Clallam .583 3

Yakima .598 3

Island .613 3

Jefferson .625 3

Chelan .632 3

Asotin .649 3

Benton .652 3

Franklin .670 2

Kittitas .672 2

Walla Walla .698 2

Stevens .724 2

Mason .744 2

San Juan .805 2

Whitman .806 2

Grant .809 2

Douglas .816 2

Pacific .830 2

Okanogan .910 1

Klickitat .935 1

Lincoln .969 1

Adams 1.037 1

Pend Oreille 1.044 1

Skamania 1.050 1

Wahkiakum 1.075 1

Garfield 1.176 1

Ferry 1.181 1

Columbia 1.287 1
18

seasonal agriculture and food processing industries. Ferry, Garfield, and Wahkiakum

Counties have the second, third, and fourth lowest ratings of 1.181, 1.176, and 1.075,

respectively. Counties were assigned economic diversity ratings as shown in the 

following tabulation.

Coefficient of regional specialization Rating

0.268 to 0.548 4

0.547 to 0.652 3

0.651 to 0.830 2

> 0.830 1

The geographic distribution of these ratings is displayed in figure 4.

Population density values and population density ratings for each Washington county

are provided in table 6. The mean population density is 95 persons per square mile

with standard deviation of 152. As expected, King County is the most densely 

populated county with 817 persons per square mile. Because of the wide disparity

between King County and the next most densely populated county (Kitsap with 586

persons per square mile), King County is the only county selected for a 4 population

density rating. At the opposite end of the spectrum, counties containing 10 or fewer

persons per square mile were assigned a rating of 0. Recall that population density is

used here as a proxy measure for civic infrastructure. Intuitively, the relative isolation

of these counties results in a lower propensity to establish elements of civic infrastructure. A map of county population density ratings is provided in figure 5.

Figure 4—Economic diversity ratings in Washington counties.

Population Density
19

Table 6—Population density in Washington counties

Total People/square Population density

County population mile rating

King 1,737,034 817 4

Kitsap 231,969 586 3

Clark 345,238 550 3

Pierce 700,820 418 3

Island 71,558 343 3

Snohomish 606,024 290 3

Thurston 207,355 285 3

Spokane 417,939 237 3

Benton 142,475 84 2

Cowlitz 92,948 82 2

San Juan 14,077 80 2

Whatcom 166,814 79 2

Skagit 102,979 59 2

Yakima 222,581 52 2

Mason 49,405 51 2

Walla Walla 55,180 43 2

Franklin 49,347 40 2

Clallam 64,525 37 2

Grays Harbor 67,194 35 2

Asotin 20,551 32 2

Lewis 68,600 28 1

Grant 74,698 28 1

Chelan 66,616 23 1

Pacific 20,984 22 1

Whitman 40,740 19 1

Douglas 32,603 18 1

Stevens 40,066 16 1

Kittitas 33,362 15 1

Wahkiakum 3,824 14 1

Jefferson 25,953 14 1

Klickitat 19,161 10 0

Adams 16,428 9 0

Pend Oreille 11,732 8 0

Okanogan 39,564 8 0

Skamania 9,872 6 0

Columbia 4,064 5 0

Lincoln 10,184 4 0

Garfield 2,397 3 0

Ferry 7,260 3 0
20

For each Washington county, socioeconomic resilience values are calculated by

using an unweighted average of lifestyle diversity, economic diversity, and population

density ratings (table 7). The average socioeconomic resiliency value is 2.16 with

standard deviation of 0.90. King County is the most resilient county in the state 

with an overall socioeconomic resilience value of 3.78. Next, Pierce and Thurston

Counties tied for second highest with 3.39. Kitsap County, with a rank of 3.33, was

the third most resilient county in the state. All counties in the top third of overall

socioeconomic resilience ranks were assigned a rating of “high.” These counties 

are the most able to adapt to changes in their social and economic systems.

Counties containing the lowest third of socioeconomic resiliency ranks were assigned

a rating of “low.” These are counties that may experience difficulty adapting after

changes in social or economic systems. According to this analysis, Pend Oreille

County, with a rating of 0.67, is the least resilient county in the state. Garfield,

Lincoln, and Skamania Counties tie for next lowest with ratings of 0.78. Ferry

County’s resilience value of 0.83 is the third lowest in the state. The map in 

figure 6 displays socioeconomic resiliency ratings for all Washington counties.

Comparing socioeconomic resilience with areas designated by the state of

Washington as distressed yields interesting results (fig. 7). Of the 14 counties with a

“high” resilience rating, 3 appear on the 2003 distressed area list. Seven of thirteen

counties with “medium” resilience are designated as distressed counties. Nine of the

twelve counties receiving “low” socioeconomic resilience ratings were also identified

as distressed. Three counties assigned low socioeconomic resilience ratings do not

appear on the state’s list.

Figure 5—Population density ratings in Washington counties.

Socioeconomic

Resiliency
21

Table 7—Socioeconomic resiliency ratings for Washington counties

Socioeconomic resiliency

County Value Rating

King 3.78 High

Pierce 3.39 High

Thurston 3.39 High

Kitsap 3.33 High

Clark 3.22 High

Skagit 3.17 High

Snohomish 3.17 High

Spokane 3.17 High

Whatcom 3.17 High

Island 2.94 High

Yakima 2.89 High

Cowlitz 2.72 High

Benton 2.67 High

Franklin 2.67 High

Chelan 2.61 Medium

Clallam 2.50 Medium

Walla Walla 2.39 Medium

Grant 2.28 Medium

Grays Harbor 2.17 Medium

Asotin 2.11 Medium

Jefferson 2.11 Medium

Douglas 2.00 Medium

Lewis 2.00 Medium

Mason 2.00 Medium

San Juan 1.94 Medium

Whitman 1.89 Medium

Kittitas 1.72 Medium

Pacific 1.67 Low

Adams 1.44 Low

Stevens 1.44 Low

Okanogan 1.28 Low

Klickitat 1.11 Low

Wahkiakum 1.00 Low

Columbia .94 Low

Ferry .83 Low

Garfield .78 Low

Lincoln .78 Low

Skamania .78 Low

Pend Oreille .67 Low
22

Figure 6—Socioeconomic resilience ratings in Washington counties.

Figure 7—Socioeconomic resilience ratings and 2003 distressed area designations.
23

The proportion of forest land per county sorted from highest to lowest values is presented in table 8. Washington state contains an average of 51 percent forest land.

Counties with the highest proportion of forest land were Skamania, Grays Harbor,

and Pacific, with 89.9 percent, 88.9 percent, and 88.8 percent, respectively. These

counties are considered highly dependent on forest resources. At the other extreme,

Adams, Benton, Franklin, and Grant Counties have no forest land, implying no

dependence on forest resources.

Counties having 74 percent or more forest land were assigned a forest dependency

rating of “high.” Those containing between 39 and 73 percent forest land were classified with a “medium” forest dependency rating. All counties containing 38 percent or

less forest land were given a dependency rating of “low.” Observe that all counties in

the “low” class are located on the east side of the state (fig. 8).

Table 9 combines socioeconomic resiliency ratings with forest dependence ratings 

for each Washington county. Ferry, Pacific, Pend Oreille, Skamania, Stevens, and

Wahkiakum Counties all exhibit “low” socioeconomic resilience and “high” forest

dependence and therefore are designated counties of concern. Ferry, Stevens, and

Pend Oreille Counties are all located in the far northeast part of the state. Pacific and

Wahkiakum Counties are situated along the west coast; Skamania County lies on the

Oregon border (fig. 9).

A base map of Washington west-side counties is provided in figure 10. Table 10 

displays results after revision of the socioeconomic resilience ratings to include only

west-side counties. More than one-third of the counties have a “high” rating owing to

the three-way tie between Skagit, Snohomish, and Whatcom Counties. All counties

having a socioeconomic resiliency rating greater than 3.00 are considered highly

resilient. Counties rated as “medium” have a socioeconomic resilience value between

2.00 and 3.00. “Low” ratings are assigned to counties with a socioeconomic resilience

value less than 2.00 (fig. 11).

The DNR reliance measures in west-side counties are displayed in table 11.

Wahkiakum, Clark, and Thurston Counties contain the greatest proportions of DNR

forest land ownership, with 27.04, 24.59, and 20.08 percent DNR ownership, respectively. At the opposite end, Pierce, Island, and San Juan Counties have the lowest

proportion of DNR ownership of total county forest land, with 3.15, 2.01, and 1.39

percent, respectively.

Forest Dependence

Counties of Concern

Washington

Department of Natural

Resources Reliance
24

Table 8—Forest dependence in Washington counties

Forest land Forest land Dependency

County Total area area area rating

Acres Acres Percent

Skamania 1,060,160 953,994 89.99 High

Grays Harbor 1,227,072 1,090,765 88.89 High

Pacific 623,744 553,975 88.81 High

Jefferson 1,157,632 1,022,359 88.31 High

Ferry 1,410,560 1,224,035 86.78 High

Clallam 1,116,928 966,233 86.51 High

Mason 615,104 517,817 84.18 High

Cowlitz 728,768 612,863 84.10 High

Lewis 1,540,992 1,294,328 83.99 High

Wahkiakum 169,152 138,573 81.92 High

Pend Oreille 896,320 679,073 75.76 High

Stevens 1,586,112 1,200,530 75.69 High

Skagit 1,110,592 824,545 74.24 High

Pierce 1,072,320 761,776 71.04 Medium

Snohomish 1,337,728 939,869 70.26 Medium

Kitsap 253,440 165,472 65.29 Medium

King 1,360,704 877,928 64.52 Medium

Whatcom 1,356,864 871,009 64.19 Medium

Thurston 465,344 296,297 63.67 Medium

Okanogan 3,371,712 2,139,277 63.45 Medium

Chelan 1,869,824 1,172,636 62.71 Medium

San Juan 111,936 68,401 61.11 Medium

Island 133,504 80,206 60.08 Medium

Clark 401,856 218,280 54.32 Medium

Kittitas 1,470,272 746,863 50.80 Medium

Klickitat 1,198,400 507,325 42.33 Medium

Yakima 2,749,504 1,041,640 37.88 Low

Columbia 556,032 188,911 33.97 Low

Spokane 1,128,832 368,830 32.67 Low

Garfield 454,720 97,690 21.48 Low

Asotin 406,976 69,104 16.98 Low

Lincoln 1,479,168 66,777 4.51 Low

Walla Walla 813,120 17,934 2.21 Low

Whitman 1,382,016 14,862 1.08 Low

Douglas 1,165,184 4 0 Low

Adams 1,232,000 - 0 Low

Benton 1,089,984 - 0 Low

Franklin 795,008 - 0 Low

Grant 1,712,896 - 0 Low
25

Figure 8—Forest dependency in Washington counties.

Figure 9—Washington statewide counties of concern.
26

Table 9—Statewide counties of concern

Socioeconomic resilience Forest dependence

County rating rating

Adams Low Low

Asotin Medium Low

Benton High Low

Chelan Medium Medium

Clallam Medium High

Clark High Medium

Columbia Low Low

Cowlitz High High

Douglas Medium Low

Ferry Low High

Franklin High Low

Garfield Low Low

Grant Medium Low

Grays Harbor Medium High

Island High Medium

Jefferson Medium High

King High Medium

Kitsap High Medium

Kittitas Medium Medium

Klickitat Low Medium

Lewis Medium High

Lincoln Low Low

Mason Medium High

Okanogan Low Medium

Pacific Low High

Pend Oreille Low High

Pierce High Medium

San Juan Medium Medium

Skagit High High

Skamania Low High

Snohomish High Medium

Spokane High Low

Stevens Low High

Thurston High Medium

Wahkiakum Low High

Walla Walla Medium Low

Whatcom High Medium

Whitman Medium Low

Yakima High Low
27

Figure 10—Washington west side counties.

Table 10—Revised west-side socioeconomic resiliency ratings

Socioeconomic

County resilience value Revised rating

King 3.78 High

Thurston 3.39 High

Pierce 3.39 High

Kitsap 3.33 High

Clark 3.22 High

Skagit 3.17 High

Snohomish 3.17 High

Whatcom 3.17 High

Island 2.94 Medium

Cowlitz 2.72 Medium

Clallam 2.50 Medium

Grays Harbor 2.17 Medium

Jefferson 2.11 Medium

Mason 2.00 Medium

Lewis 2.00 Medium

San Juan 1.94 Low

Pacific 1.67 Low

Wahkiakum 1.00 Low

Skamania .78 Low
28

Figure 11—Revised west-side socioeconomic resiliency ratings.

Table 11—Department of Natural Resources (DNR) reliance in western

Washington counties

Area of Proportion of DNR

forest DNR DNR forest land reliance

County land ownership ownership rating

Acres Acres Percent

Wahkiakum 138,573 37,468 27.04 High

Clark 218,280 53,685 24.59 High

Thurston 296,297 59,492 20.08 High

Jefferson 1,022,359 188,602 18.45 High

Clallam 966,233 151,284 15.66 High

Skagit 824,545 127,950 15.52 High

Pacific 553,975 78,979 14.26 High

Snohomish 939,869 121,181 12.89 Medium

Cowlitz 612,863 75,735 12.36 Medium

Whatcom 871,009 91,468 10.50 Medium

Mason 517,817 52,174 10.08 Medium

King 877,928 72,844 8.30 Medium

Kitsap 165,472 12,868 7.78 Medium

Lewis 1,294,328 95,972 7.41 Low

Skamania 953,994 69,795 7.32 Low

Grays Harbor 1,090,765 74,625 6.84 Low

Pierce 761,776 24,019 3.15 Low

Island 80,206 1,614 2.01 Low

San Juan 68,401 950 1.39 Low
Analysis of regional DNR reliance is based on the same assumption as statewide 

forest dependence; counties with the greatest proportion of DNR forest land ownership are considered the most reliant on DNR forest resources. Therefore, counties

containing greater than 14 percent DNR ownership of forest land are assigned a

“high” DNR reliance rating. Counties where the DNR owns between 7.5 percent and

14 percent of total forest land are rated with “medium” DNR reliance. Last, counties

with less than 7.5 percent of forest land in DNR ownership have “low” DNR reliance.

These counties are unlikely to be affected by changes in DNR forest management

policies. A map of DNR reliance ratings in west-side counties appears in figure 12.

Of the seven counties identified as having “high” DNR reliance, one (Skagit County) is

located in the Puget Sound Economic Area and four (Clallam, Jefferson, Pacific, and

Thurston Counties) are in the Olympic Peninsula Economic Area. These findings are

consistent with measures of mill timber supply dependency in the DNR mill survey.

On the other hand, the two remaining counties (Wahkiakum and Clark) are positioned

within the Lower Columbia Economic Area, which does not contain mills identified as

highly dependent on state timber.

Table 12 and figure 13 delineate DNR counties of concern by combining DNR

reliance ratings with revised west-side socioeconomic resilience ratings. The DNR

counties of concern are those western Washington counties with both a low socioeconomic resiliency rating and a high DNR reliance rating. Only two counties meet

these criteria, Wahkiakum County and Pacific County. These are the two western

Washington counties that may experience difficulty adapting to changes in DNR forest

management strategies.

29

Washington

Department of Natural

Resources Counties

of Concern

Figure 12—Department of Natural Resources reliance ratings, west-side counties.
30

Table 12— Department of Natural Resources (DNR) counties of concern

Revised 

socioeconomic 

County resilience rating DNR reliance rating

Wahkiakum Low High

Clark High High

Thurston High High

Jefferson Medium High

Clallam Medium High

Skagit High High

Pacific Low High

Snohomish High Medium

Cowlitz Medium Medium

Whatcom High Medium

Mason Medium Medium

King High Medium

Kitsap High Medium

Lewis Medium Low

Skamania Low Low

Grays Harbor Medium Low

Pierce High Low

Island Medium Low

San Juan Low Low

Figure 13—Department of Natural Resources west-side counties of concern.
Washington counties receiving high socioeconomic resiliency ratings generally were

close to urban areas. Seattle, Tacoma, Olympia, Spokane, and the Tri-Cities are all

located in counties displaying a high degree of resiliency. In addition, highly developed transportation networks, including proximity to Interstate 5 and Interstate 90 

and coastal ports, characterize highly resilient counties. Last, highly resilient counties

contain a diversified industry mix with employment opportunities available in both

services and manufacturing sectors.

On the other hand, the Olympic Peninsula and the far southeast and far northeast

parts of the state have particularly low resilience. Remoteness, isolation, and poorly

developed transportation networks are all factors contributing to low ratings, a result

consistent with findings in Donoghue and Haynes (2002). In addition, the economies

of these counties primarily center on manufacturing, timber and agricultural production, and natural resource extraction with relatively fewer jobs in the service sector

than observed in highly resilient areas.

Forest-dependent counties may be vulnerable to changes in overall forest management. Since forest dependence incorporates nontimber values of forests, including

tourism and recreation, these counties will be affected by a greater variety of land

management decisions. As expected, forest dependence is greatest in areas containing the most forest cover. Consequently, west-side counties and the Tri-Counties

region composed of Ferry, Stevens, and Pend Oreille Counties in the northeast corner of the state have the greatest forest dependency. On the west side, counties with

greater proportions of residents living in urban areas are less forest dependent than

those with significant rural populations. This observation can be interpreted in two

ways; either it illustrates the industrial shift from manufacturing to services in urban

economies or it reflects land use changes following the expansion of urban and suburban boundaries into areas formerly devoted to timber production. All counties with

low forest dependence are located in eastern Washington, where no significant forest

resource exists.

Of the 39 counties in Washington, only 6 were identified as “counties of concern,”

which, for this study, is defined as low socioeconomic resilience combined with high

forest dependence. Ferry, Pacific, Pend Oreille, Skamania, Stevens, and Wahkiakum

Counties have several traits in common. The primary employer of residents in all of

these counties has traditionally been the forest products industry. As a result of

reduced volumes in timber harvesting, each is transitioning from an economy based

on manufacturing of lumber and wood products to one dominated by trade and services. Each is relatively isolated and rural. All but Wahkiakum County appear on the

2003 state of Washington distressed county list. However, all six counties were listed

as distressed in 2002.

Because of the relatively large proportion of DNR ownership of forest land, DNR

reliance is high in Wahkiakum, Clark, Thurston, Jefferson, Clallam, Skagit, and Pacific

Counties. Combining DNR reliance with low socioeconomic resiliency determines

“DNR counties of concern.” Only Wahkiakum County and Pacific County fit this category. These two counties should be examined carefully when evaluating changes in

DNR land management policies.

31

Discussion
This assessment is an attempt to identify forest-dependent Washington counties 

that, for a variety of reasons, may experience difficulty adapting to changing forest

management policies. Results demonstrate that combining social and economic indicators is a promising method for identifying such areas. Economic and social systems

present in each county respond differently when faced with significant challenges.

Taking a more comprehensive approach to evaluating well-being recognizes the

inherent differences and individual challenges faced by county residents. Although

change is a fact of life, land managers and the public should consider the differential

effects among Washington counties to fully anticipate impacts during the transition to

sustainable forest resource management on public lands.

Measures of socioeconomic systems are influenced by the size of the area measured

and by time. Identifying the appropriate spatial and temporal scales for analysis

requires further investigation. Readers are cautioned that potential limitations exist

when analyses are performed at the county, rather than community, scale. This

includes an “averaging over” effect that can mask severe distress in one community

when it is offset by prosperity in another. In addition, a duplication of this study using

1990 data would provide insight into how county social and economic conditions

have changed over time, a valuable aspect of monitoring consequences of public

policies.

Many thanks to the Washington Department of Natural Resources for the opportunity

to work on this project and for providing data, comments, and advice. Also, thanks to

Richard Haynes and John Perez-Garcia for assistance and guidance and Phil Hurvitz

for preparing the maps.

When you know: Multiply by: To find:

Acres 0.405 Hectares

Square miles 2.59 Square kilometers

People per square mile .386 People per square kilometer

Donoghue, E.M.; Haynes, R.W. 2002. Assessing the viability and adaptability 

of Oregon communities. Gen. Tech. Rep. PNW-GTR-549. Portland, OR: U.S.

Department of Agriculture, Forest Service, Pacific Northwest Research Station.

13 p.

Haynes, R.W. 2003. Assessing the viability and adaptability of forest-dependent 

communities in the United States. Gen. Tech. Rep. PNW-GTR-567. Portland, OR:

U.S. Department of Agriculture, Forest Service, Pacific Northwest Research

Station. 33 p.

Haynes, R.W.; Graham, R.T.; Quigley, T.M. 1996. A framework for ecosystem 

management in the interior Columbia basin and portions of the Klamath and 

Great Basins. Gen. Tech. Rep. PNW-GTR-374. Portland, OR: U.S. Department 

of Agriculture, Forest Service, Pacific Northwest Research Station. 46 p. [plus

appendices].

32

Acknowledgments

Metric

Equivalents

References
Horne, A.L.; Haynes, R.W. 1999. Developing measures of socioeconomic resiliency

in the interior Columbia basin. Gen. Tech. Rep. PNW-GTR-453. Portland, OR: U.S.

Department of Agriculture, Forest Service, Pacific Northwest Research Station.

41 p.

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Budget and Economics. On file with: David Larsen, Economist, Financial

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McCool, S.F.; Burchfield, J.A.; Allen, S.D. 1997. Social assessment. In: Quigley,

T.M.; Arbelbide, S.J., tech. eds. An assessment of ecosystem components in the

interior Columbia basin and portions of the Klamath and Great Basins. Gen. Tech.

Rep. PNW-GTR-405. Portland, OR: U.S. Department of Agriculture, Forest Service,

Pacific Northwest Research Station: 1871-2009. Chapter 6. Vol. 4.

Moffat, A.S. 1996. Biodiversity is a boon to ecosystems, not species. Science.

271(5255): 1497.

Montreal Process Working Group. 1998. The Montreal Process.

http://www.mpci.org. (July 6, 2003).

National Forest Management Act of 1976 [NFMA]; Act of October 22, 1976; 16

U.S.C. 1600.

Society of American Foresters. 1989. Community stability. SAF Resource Policy

Series SAF 89-06. Bethesda, MD. 42 p.

U.S. Department of Commerce, Bureau of the Census. 1999. County business 

patterns—Washington 1997 [CD-ROM]. Washington, DC: U.S. Government Printing

Office.

U.S. Department of Commerce, Bureau of the Census. 2002. Census 2000 gateway. http://www.census.gov/main/www/cen2000.html. (January 2003).

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policy plan, final. Olympia, WA: Forest Land Management Division. 53 p.

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labor market information. Olympia, WA: Labor Market and Economic Analysis

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areas. Olympia, WA: Labor Market and Economic Analysis Branch.

http://www.wa.gov/esd/lmea/pubs/affirm/distco03.htm. (August 12).

33
34

Table 13—Forest land acreage by ownership in Washington counties

County

and 

County Federal State municipal Private Total

Adams 0 0 0 0 0

Asotin 42,256 450 0 26,398 69,104

Benton 0 0 0 0 0

Chelan 980,965 44,171 10,282 137,218 1,172,636

Clallam 468,447 144,482 88 353,216 966,233

Clark 7,387 58,813 1,490 150,590 218,280

Columbia 138,330 8,250 0 42,331 188,911

Cowlitz 22,051 71,016 220 519,576 612,863

Douglas 2 2 0 0 4

Ferry 480,690 38,665 5,959 698,721 1,224,035

Franklin 0 0 0 0 0

Garfield 88,748 0 0 8,942 97,690

Grant 0 0 0 0 0

Grays Harbor 132,043 87,260 49,037 822,425 1,090,765

Island 4,411 10,576 170 65,049 80,206

Jefferson 632,428 176,111 50 213,770 1,022,359

King 285,272 63,611 102,344 426,701 877,928

Kitsap 3,058 22,811 16,314 123,289 165,472

Kittitas 388,796 83,104 0 274,963 746,863

Klickitat 26,996 73,235 6,948 400,146 507,325

Lewis 457,658 118,174 7,809 710,687 1,294,328

Lincoln 2,320 2,209 0 62,248 66,777

Mason 142,535 74,041 18 301,223 517,817

Okanogan 1,410,985 222,884 0 505,408 2,139,277

Pacific 3,177 75,422 3,257 472,119 553,975

Pend Oreille 395,209 34,340 410 249,114 679,073

Pierce 320,496 31,683 9,250 400,347 761,776

San Juan 516 9,304 154 58,427 68,401

Skagit 395,053 131,464 7,927 290,101 824,545

Skamania 765,850 74,380 3,511 110,253 953,994

Snohomish 502,302 136,012 8,784 292,771 939,869

Spokane 12,426 44,368 3,940 308,096 368,830

Stevens 291,113 174,259 0 735,158 1,200,530

Thurston 8,553 64,397 67 223,280 296,297

Wahkiakum 0 36,907 0 101,666 138,573

Walla Walla 50 0 0 17,884 17,934

Whatcom 580,750 91,292 5,255 193,712 871,009

Whitman 0 0 210 14,652 14,862

Yakima 430,762 90,530 0 520,348 1,041,640

Total 9,421,635 2,294,223 243,494 9,830,829 21,790,180

Appendix 1
35

Table 14—Shannon-Weiner diversity index values for Washington counties

County Mobility Ethnicity Urban/rural Race Income Education

Adams 0.773 0.770 0.623 0.390 0.931 0.906

Asotin .642 .485 .184 .137 .947 .866

Benton .712 .694 .467 .322 .951 .936

Chelan .732 .718 .784 .323 .963 .939

Clallam .679 .638 .524 .274 .947 .894

Clark .734 .674 .357 .294 .953 .886

Columbia .643 .594 .580 .201 .936 .889

Cowlitz .673 .613 .588 .229 .949 .864

Douglas .713 .710 .526 .318 .940 .909

Ferry .643 .587 .115 .371 .917 .858

Franklin .807 .794 .582 .457 .956 .933

Garfield .630 .553 .236 .076 .925 .901

Grant .773 .754 .594 .401 .947 .919

Grays Harbor .672 .594 .515 .278 .937 .869

Island .727 .694 .522 .319 .952 .870

Jefferson .692 .637 .530 .208 .957 .870

King .773 .778 .150 .472 .967 .905

Kitsap .714 .692 .457 .375 .958 .877

Kittitas .707 .629 .571 .236 .936 .891

Klickitat .690 .650 .603 .299 .933 .882

Lewis .682 .618 .535 .207 .942 .875

Lincoln .659 .540 .246 .143 .944 .870

Mason .676 .645 .444 .299 .945 .864

Okanogan .711 .683 .506 .434 .926 .909

Pacific .674 .664 .546 .254 .931 .888

Pend Oreille .647 .587 .077 .197 .935 .865

Pierce .737 .730 .206 .454 .955 .885

San Juan .684 .642 .100 .151 .977 .853

Skagit .725 .696 .772 .312 .960 .913

Skamania .661 .615 .064 .196 .944 .857

Snohomish .728 .710 .351 .340 .943 .883

Spokane .707 .646 .462 .242 .955 .900

Stevens .657 .587 .488 .226 .936 .861

Thurston .712 .705 .576 .352 .951 .901

Wahkiakum .606 .560 .083 .157 .925 .851

Walla Walla .743 .716 .474 .345 .943 .950

Whatcom .729 .723 .772 .291 .958 .903

Whitman .747 .695 .547 .281 .924 .894

Yakima .722 .733 .801 .473 .950 .926

Appendix 2
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