Socioeconomic Monitoring Results. Volume III: Rural Communities and Economies
Seattle
Portland
Percent change in population,
1990–2000
Decreased population (-74–0)
Population increased, below
regional average (1–20)
High increase in population
(21–75)
Very high increase in population
(75–200+)
Metropolitan areas
Northwest Forest Plan region
States
Major lakes and rivers
Major roads
o
0
25
50
100 Miles
San Francisco
Figure 2-4—Change in community population, 1999–2000.
13
GENERAL TECHNICAL REPORT PNW-GTR-649, VOL. III
Seattle
Portland
Number of people per square
mile on nonpublic lands, 2000
1–100
101–500
501–1,500
1,501+
Bureau of Land Management
Forest Service
Tribal lands
National Park Service
State lands
U.S. Fish and Wildlife Service
Metropolitan areas
Northwest Forest Plan region
States
Major lakes and rivers
Major roads
o
0
25
50
100 Miles
San Francisco
Figure 2-5—Community population density on nonpublic lands, 2000.
14
Socioeconomic Monitoring Results. Volume III: Rural Communities and Economies
Age distribution—
As is true throughout the United States, the aging of the
population in the 1,314 communities in the region reflects
the aging of the baby-boomer generation. The average
median age for all communities in the Plan region in 1990
was 36.4 years, but rose in 2000 to 40.0 years,5 putting it
higher than the median age for the United States, which rose
from 32.9 years in 1990 to 35.3 years in 2000. Trends in age
distribution are also similar for the Plan region and for the
entire United States. For instance, in the Plan region, the
45-to-64-year-old cohort increased by 53 percent, on average, for all communities between 1990 and 2000, which
was by far the largest percentage increase (figure 2-6).
An aging population has implications on the demand for
the health care and other social services, social security
benefits, and employment opportunities for older workers.
1,000
900
920
856
825
766
800
702
700
600
600
500
400
300
500
447
413
432
242
228
200
100
up
an
d
64
4
9
Age range, years
65
45
–
–4
30
–2
20
5–
0–
1990
2000
19
0
4
Average size of age group per community
The majority of communities in the region (58.9
percent) had between 1 and 100 people per square mile on
nonpublic lands. About one-third of the communities (30.9
percent) had between 101 and 500 people per square mile.
And 8.9 percent had between 501 and 1,500 people, and 1.3
percent of the communities had between 1,501 and 5,381
people per square mile. The smaller communities tended
to have lower population densities, although there were
some communities in the 505–2,000 people per square mile
category that had relatively high population densities. Most
of the larger communities (>5,000 people) also had higher
population densities, though exceptions were found. However, of the communities with a population increase greater
than 20 percent between 1990 and 2000 (20 percent to more
than 200 percent), half (49.5 percent) had the lowest population density in 2000 (1 to 100 people per square mile).
More than one-third (36.3 percent) had densities in 2000
between 101 and 500 people per square mile. Although the
fast-growing communities tended to have higher population
densities than the slower-growing communities, the density
data suggest that some of the fast-growing communities
had relatively lower densities (<500 people per square mile)
and were relatively small (501–2,000 people). Although no
notable statistical correlation was found between the percentage change in population and population density (year
2000), a positive correlation was found between population
density (year 2000) and community population in 2000
(Pearson r = 0.51, p < 0.0001). This correlation suggests
that larger communities tended to have higher densities.
Comparison of the three population maps shows relations
between location of public lands and changes in population.
For instance, some communities adjacent to large areas of
public lands had high percentage increases in population
and had relatively high density.
Figure 2-6—Community age distribution, 1990 and 2000.
Race—
Change in ethnicity cannot be reported from census data
because information on race was collected differently in the
1990 and 2000 censuses. Race in the Plan region for 2000
was based on averages of all communities in the Plan region
(figure 2-7). Compared to the Nation, communities in the
Plan region have higher percentages of White (86.04 percent) and American Indian people (2.08 percent), and lower
5 Data
throughout this chapter are reported as an average of all
communities in a data category, unless otherwise noted.
15
GENERAL TECHNICAL REPORT PNW-GTR-649, VOL. III
Table 2-1—Community averages for educational
achievement and school enrollment
Native American, 2.07
Asian and Pacific
Islands, 2.08
Black, 0.88
Two or more races, 3.30
Other, 5.63
White, 86.04
Figure 2-7—Percentage of the population by race in communities
in the Plan region, 2000.
percentages of Black (0.88 percent) and Asian and Pacific
Islands (2.08 percent) people. The percentages among races
for the United States in 2000 were White 75.10 percent,
Black 12.21 percent, Native American 0.87 percent, Asian
and Pacific Islands 3.75 percent, other 5.49 percent, and
two or more races 2.58 percent.
The census asked similar questions pertaining to
Spanish, Hispanic, and Latino origin in 1990 and 2000,
thus comparisons can be made. On average, for all communities in the Plan region, the population of Hispanic or
Latino origin was 5.8 percent in 1990 and 9.0 percent in
2000, an increase of 46 percent. For the United States,
the percentage of the population of Hispanic or Latino
origin was 9.0 percent in 1990 and 12.5 percent in 2000,
an increase of 38 percent.
Educational attainment and school enrollment—
Data on three education indicators are shown in table 2-1.
Although school districts and counties may have more
16
Educational indicator
1990
Percentage
2000
change
Completed high school
(percent)
77.6
82.8
6.7
Bachelor’s degree or higher
(percent)
15.4
19.3
25.3
School enrollment (persons)
621
811
30.6
accurate and periodic data on indicators of education, the
census asked about school enrollment and educational
attainment in comparable ways from one decade to the
next. On average for communities in the region, there was
a moderate increase in the percentage of the population 25
years and older who had completed high school and a more
sizable increase in the percentage of the population that
had bachelor’s degrees or higher. These data also reflect
that school enrollment in the region went up by 31 percent
between 1990 and 2000, which is higher than the national
increase in school enrollment of 26 percent. This increase
in enrollment is consistent with the higher than average
increase in population in the region.
Employment by industry—
Employment by industry is a measure that shows the kind
of business conducted by the organization where the person
taking the census is employed, but does not necessarily represent the kind of work a person performs. For example, a
person could be an accountant for a clothing manufacturer,
and this measure would denote clothing manufacturing not
accounting. Also, the actual place of employment may be
outside the community. The measure provides a sense of the
types and diversity of knowledge, skills, and abilities of the
members of a community, based on the type of businesses
where they work, as well as the types of opportunities that
may be available for individuals to use their skills and make
a living. The average percentage for communities in the
Plan region of employment, by industry, for 11 industry
sectors between 1990 and 2000 is shown in figure 2-8. The
entire working population is represented in the 11 sectors
provided by the census.
Socioeconomic Monitoring Results. Volume III: Rural Communities and Economies
Percentage of labor force
25
20
21
1990
2000
15
15 15
12 12
12
10
5
18
16
6 5
7
8 8
8
5 5
4 4
5 5
5 6
f is
hi
ng Ag
, h r ic
un u l
t i n t ur
g, e,
an f o r
d es
m tr y
in ,
in
C
g
on
st
ru
ct
io
n
M
an
uf
ac
tu
r in
W
g
ho
le
sa
le
tra
Tr
de
an
sp
R
or
et
ta
ai
lt
t
Fi
io
ra
n,
na
de
w
nc
a
e
r
e
,i
Ed
n
an h o
uc
a n su
d us
at
r
d an
u t in
io
re c
ilit g,
n,
nt e,
he
al re ie s
,a a
al
th
nd l e
Ar
,
an
ts
l e st a
,r
as te
d
ec
so
in
g
re
ci
at
al
io
se
n,
rv
ac
ic
an c o
es
d m
fo m
od od
Pu
se ati
Pr
bl
r v on
ic
of
ic ,
es
ad
es
si
m
on
in
is
al
tra
an
tio
d
n
ot
he
rs
er
vi
ce
s
0
Employment by industry
Figure 2-8—Employment by industry, 1990 and 2000.
The census modified the sector categories in 2000 to
be consistent with economic classifications used by the
North American Industry Classification System, which
replaced the U.S. Standard Industrial Classification
system to provide comparability in statistics about business activity across North America. For instance, in 1990
the subcategory of logging was under the manufacturing
sector, but it was under the agriculture, forestry, fisheries,
and mining sector in 2000. Although category names are
similar, actual comparison of categories between 1990
and 2000 is only possible if a proportions crosswalk
program provided by the census is applied to the data.
The result of applying the proportions crosswalk to the
1990 data and producing employment by industry data
that are comparable from 1990 to 2000 is shown in figure
2-8. Thus, logging appears under the agriculture, forestry,
fishing, hunting, and mining sector for both years. Wood
product manufacturing, including sawmills and other
millwork, falls under the manufacturing sector.
The four industry sectors with the highest percentage of
people employed on average across the Plan region communities for 1990 and 2000 were education, health, and social
services; professional and other services; manufacturing;
and retail trade. The manufacturing sector, however, which
includes mills and millwork, had the highest percentage decrease of any sector: a 25-percent decrease from 16 percent
to 12 percent. And education, health, and social services
had the greatest increase of any sector: an increase of 17
percent from 18 percent to 21 percent of the employed labor
force in a sector. All other employment by industry sectors
remained largely the same between 1990 and 2000. Agriculture, forestry, fishing, hunting, and mining—the sector that
includes logging—decreased from 6 to 5 percent.
Income, poverty, and unemployment—
Data on income, poverty, and unemployment are shown
in table 2-2. Income data provided by the census are often
criticized because of suspected underreporting of income
17
GENERAL TECHNICAL REPORT PNW-GTR-649, VOL. III
Community Socioeconomic Well-Being
Table 2-2—Community economic indicators
Economic indicator
Median household income
Unemployment
Poverty
1990
2000
Change
2000 dollars
35,214 42,351
Percent
20.3
7.3
12.9
7.3
11.8
Percent
0.0
-8.5
by census takers. Nonetheless, the census asks several
questions that encourage people to account for their many
forms of income when they report their total household
income. The average median household income (adjusted
for inflation to 2000 dollars) for communities in the region
went up 20.3 percent, from $35,214 to $42,351. This change
is higher than the change in national median household
income that was $37,300 in 1990 and $41,994 in 2000, an
increase of 12.6 percent. Average unemployment for communities was about the same in 1990 as in 2000, although
this lack of change does not reflect the likely yearly fluctuations. The percentage of the population in a community
living in poverty decreased from 12.9 percent in 1990 to
11.8 percent in 2000, a decrease of 8.5 percent. The United
States had slightly higher poverty rates (13.1 percent in 1990
and 12.4 percent in 2000) and a slightly lower percentage
decrease in poverty (5.3 percent).
Changes in income distribution between 1990 and
2000 are difficult to report because, after the changes are
adjusted for inflation, the income categories cannot be
compared from one decade to the next. Although lowerincome brackets changed slightly (±2 percent) between
1990 and 2000 for the Plan region, the most notable changes
are in the higher income brackets. In 1990, 13 percent of
the population in communities reported incomes between
$62,051 and $93,077 (adjusted to 2000 dollars), but, in 2000,
20.5 percent of the population reported incomes between
$60,000 and $99,000. Similarly, in 1990, 6.3 percent of
the population reported adjusted incomes of greater than
$93,077, but, in 2000, 9.8 percent reported incomes greater
than $100,000.
18
One of the overarching goals of the Plan was to balance the
need for forest protection with the need to provide a steady
and sustainable supply of timber and nontimber resources to
benefit rural communities and economies. This broad-scale,
multifaceted goal does not lend itself to convenient methods
for measuring progress toward achieving it. One way to
address the goal is to assess how social and economic
conditions have been changing in communities under the
Plan. Are communities better or worse off? This section
offers a regional perspective on how socioeconomic conditions for Plan-region communities have been changing.
We developed a composite measure to serve as a proxy for
community socioeconomic well-being. We then examined
this composite measure at the regional level and also based
on the proximity of communities to FS and BLM lands.
The notion of “well-being” has been widely discussed
by social scientists, but it has not been rigorously defined
at either conceptual or operational levels. Well-being is a
normative concept based on how “the good life” is defined.
It often reflects the general conditions of people’s lives, or
the state of a social system that may include many dimensions of community life. Well-being has been defined on
the basis of capabilities and achievements of individuals
(Sen 1985) and on the social, cultural, and psychological
needs of people and communities (Wilkinson 1991). Wellbeing is often used to represent general community welfare
(Richardson and Christensen 1997) and has been assessed
through measures of socioeconomic status and community
capacity (Doak and Kusel 1996). Studies of community
well-being have focused on understanding the contribution
of the economic, social, cultural, and political components
of a community in maintaining itself and fulfilling the
various needs of local residents (Christakopoulou et al.
2001, Kusel and Fortmann 1991).
How to measure complex sociological constructs, such
as socioeconomic well-being, is often debated. Although no
definitive conceptual or operational definition of community socioeconomic well-being exists, it is an accepted
notion that measures of socioeconomic well-being should
represent multiple dimensions of the human community,
Socioeconomic Monitoring Results. Volume III: Rural Communities and Economies
such as social, economic, and human concerns (Force and
Machlis 1997). Also, social scientists increasingly emphasize the need to combine secondary data with primary data
from fieldwork in communities to fully understand the
relations between socioeconomic indicators and community
well-being (Beckley 1995, Kusel 1996, Parkins 1999). We
agree with the importance of a multimethod approach for
understanding complex processes at the community scale.
We suggest that this regional perspective on community
socioeconomic well-being complement data and findings
provided in the community case studies and other parts of
this report in assessing the progress toward achieving the
Plan’s socioeconomic goals.
Measuring Socioeconomic Well-Being for
Communities in the Plan Region
Because we wanted to examine change in community
socioeconomic well-being for hundreds of communities
in a large region where collecting primary data was not
feasible, we relied on census data to develop a composite
measure (index) that served as a proxy for community
socioeconomic well-being, and was comparable between
the 1990 and 2000 censuses. Developing an index enabled
us to reduce a large data set of socioeconomic indicators to
a convenient single numeric score, while still retaining the
meaning of underlying variables.
We conducted principal component analysis on about
50 socioeconomic variables to reduce the data set to factors
and variables that contributed to high variation in the data
set. We then examined a list of about a dozen variables
and looked for those that not only reflected the economic
health of community members, such as unemployment,
poverty, and income, but also indicators that reflected other
dimensions of community life. In particular, we wanted to
include variables that might provide some insight into how
equipped the communities were to deal with social and
economic change. The intent was to identify measures that
reflect dimensions of a social construct commonly referred
to as community capacity.
Social, human, and physical capital are dimensions of
community capacity that are difficult to approximate by
using secondary data, such as from the census. Census data
do not provide useful approximations for the amount of
physical capital in a community, for example. But some
indicators may approximate some dimensions of human
capital, such as the skills and abilities of residents of a
community. For instance, employment diversity may reflect the diversity of workforce skills in a community. The
assumption is that a more diverse workforce will be better
able to deal with changes in the economy. Other indicators,
such as poverty and education, may also reflect amounts
of human capital in a community. Diverse skills in a
community may also contribute to social capital, which
includes the ability of a community to come together, solve
problems, and make decisions. In contrast, residents who
spend a lot of time commuting may have less time to commit to civic activities, thus reducing the social capital of a
community. An income inequality ratio provides insight
into community well-being that a single measure, such
as median household income, does not. The assumption
of the income inequality measure is that social equality
contributes to community well-being. When income is
concentrated among a small proportion of residents, issues
of equality and the distribution of benefits detract from
general well-being (Beckley and Burkosky 1999, Parkins
and Beckley 2001).
Our basic assumption of the concept and measure
of community socioeconomic well-being is that it can
be enhanced or reduced. Thus, indicators must clearly
contribute in a positive or negative way to community
socioeconomic well-being. Although secondary data are
sometimes perceived as useful in social science research
because they are generally easy to collect, not based on
perception, and generally understandable, secondary data
also have many limitations (Diener and Suh 1997). For
census data, many indicators are not measured in the same
way from one census to the next, and complex procedures
to make data comparable are only available for some indicators. Also, some census data may reflect characteristics
of community life, such as age or ethnicity, that would help
us differentiate among communities, but changes in such
indicators may not clearly indicate enhanced or reduced
community socioeconomic well-being.
19
GENERAL TECHNICAL REPORT PNW-GTR-649, VOL. III
The index of community socioeconomic well-being
was calculated for each of the 1,314 communities in the Plan
region. The index consists of six indicators derived from
U.S. Census data: diversity of employment by industry,
percentage of population 25 years and older with bachelor’s
degree or higher, percentage unemployed, percentage of
persons living below the poverty level, household income
inequality, and the average travel time to work.
The community socioeconomic well-being (SEWB)
index is the summation of standardized and normalized
equally weighted socioeconomic indicators and was
calculated as SEWB = EmD + Ed - PUn - PP - InIn - ATT
(see table 2-3 for definitions). Two indicators, diversity of
employment by industry and percentage of the population
with bachelor’s degree or higher, positively contribute to the
socioeconomic well-being index. The other four indicators,
percentage unemployed, percentage in poverty, household
income inequality, and average travel time to work, are
thought of as negatively contributing to the socioeconomic
well-being index. The assumption is that higher amounts
Table 2-3—Indicators included in socioeconomic well-being index
Indicator
Indicator name
Description
EmD
Diversity of employment
Employment by industry relates to the kind of business conducted by the
by industry organization where the person is employed. Diversity of employment by
industry is a single measure of diversity, or variety, of industries that employ
people from the community (the actual place of employment may be outside
the community). This measure was generated for each community by using
a Shannon-Weaver index. The diversity index varies from a value of 0 (least
diverse) for communities with only a single employment industry to 1 (most
diverse) for communities having equal employment among all of the reported
employment industries.
Ed
Percentage of population Persons with a bachelor’s degree or higher are those who have received a
25 years and older having
bachelor’s degree from a college or university, or a master’s, professional,
bachelor’s degree or higher or doctorate degree. These data include only persons 25 years old and over.
PUn
Percentage of the All civilians 16 years old and over are classified as unemployed if they (1) were
population unemployed neither “at work” nor “with a job but not at work” during the reference week,
and (2) were looking for work during the last 4 weeks, and (3) were available
to start a job. Also included as unemployed are civilians who did not work
at all during the reference week but were waiting to be called back to a job
from which they had been laid off and were available for work except for
temporary illness. (For more information on census unemployment data,
see http://www.census.gov.)
PP
Percentage of persons living Number of persons below poverty threshold divided by total population for
below the poverty level whom poverty status is determined. Total population for whom poverty
status is determined does not include people in institutions, military group
quarters, or college dormitories, and unrelated individuals under 15 years
old. (For more information on census poverty data, see http://www.census.gov.)
InIn
Household income inequality Ratio of total household income of the 50 percent of households earning the
highest income to total household income of the 50 percent of households
earning the lowest income. Higher ratios indicates greater income inequality.
Calculations used group data.
ATT
Average travel time to work Average travel time to work (in minutes) for workers ages 16 years and older.
Calculations used group data.
20
Socioeconomic Monitoring Results. Volume III: Rural Communities and Economies
Table 2-4—Community socioeconomic well-being
of education and employment diversity in a commucategories, 1990 and 2000
nity indicate higher socioeconomic well-being, but
Community Socioeconomic
higher unemployment, poverty, income inequality,
socioeconomic
Standard deviations
well-being
and commute time indicate lower socioeconomic
well-being categories
from the mean (67.2)
score range
well-being.
Very low
<-1.5
0 to 48.72
To assess change in community socioeconomic
Low
-1.5 to -0.51
48.73 to 61.07
Medium
-0.5 to 0.49
61.08 to 73.36
well-being, we categorized the 1990 well-being data
High
0.5
to
1.49
73.37
to 85.58
and treated them as a baseline. We transformed the
Very high
≥1.5
85.59 to 100.00
raw data for both years to a range of 0 to 100. Using
the boundaries for the 1990 categories, we fitted the
2000 data into them, allowing us to see, on a scale of Table 2-5—Change in community socioeconomic well-being
score between 1990 and 2000
0 to 100, how communities increased or decreased
Community
relative to each other in socioeconomic well-being
socioeconomic
Change in score
between the two decades.
well-being change (scale of 0 to 100)
Communities
To create the baseline categories, the socioeco
Number
Percent
nomic well-being scores for all communities in 1990 Decrease
-51 to <-3
484
37
Little change
-3 to 3
353
27
were standardized and graphed as a histogram.
Increase >3 to 44
477
36
Based on the distribution of the data, the scores were
divided into five categories that reflect levels of
(again, scores are on a 0 to 100 scale) (table 2-5). The
community socioeconomic well-being. The categories are
locations of communities and their respective socioecobased on standard deviations from the 1990 baseline mean
nomic well-being scores for 1990 are shown in figure 2-9.
(table 2-4). Because the 1990 socioeconomic scores used in
The very low and low categories were combined, as well as
creating the categories are standardized by using z-scores
the high and very high categories. The 2000 socioeconomic
and normally distributed, roughly the same number of
well-being scores and whether the scores increased,
communities were in the very low and very high categories,
decreased, or stayed roughly the same are mapped for
and the low and high categories for both years. The medium
western Washington (fig. 2-10), western Oregon (fig. 2-11),
category contains the largest number of communities,
and northern California (fig. 2-12).7 The number of comreflecting that most of the community scores fall somemunities in each of the socioeconomic well-being categories
where near the mean.6 For additional information on
for 1990 and 2000 are shown in table 2-6. A Stewart
methods, see appendix A.
Maxwell statistical test for overall marginal homogeneity
Community Socioeconomic
was not significant (p = 0.0520), suggesting that the proporWell-Being at the Regional Scale
tion of communities in each category did not change from
Socioeconomic well-being has changed for many communione year to the next, which likely reflects the use of standties in the Plan region between 1990 and 2000, with a few
ardized z-scores to define the categories.
communities changing scores by more than 40 points
7 An
6 See
also Donoghue, E.M.; Sutton, N.L. [In prep.]. Strategies
and methods for measuring socioeconomic well-being at multiple
spatial and temporal scales as part of socioeconomic monitoring
of the Northwest Forest Plan. Gen. Tech. Rep. Portland, OR: U.S.
Department of Agriculture, Forest Service, Pacific Northwest
Research Station.
analysis of a histogram of change in well-being scores shows
that several communities (27 percent) had only slight changes in
scores between 1990 and 2000 (±3 percent). We added this characterization—communities with little change—to our analysis and
spatial displays. Other communities were classified as decreasing
by more than 3 percent or increasing by more than 3 percent.
21
GENERAL TECHNICAL REPORT PNW-GTR-649, VOL. III
Seattle
Portland
Socioeconomic well-being
categories, 1990
Very low, low
Medium
Very high, high
Bureau of Land Management
Forest Service
Metropolitan areas
Northwest Forest Plan region
States
Major lakes and rivers
Major roads
o
0
25
50
100 Miles
San Francisco
Figure 2-9—Community socioeconomic well-being, 1990.
22