Recent figures from the U.S. Census Bureau’s Small Area Health Insurance Estimates (SAHIE) show the number of Americans without health insurance increased from 42.9 million in 2008 to 45 million in 2009.  Similarly, in Oregon (see Table 1), the number of uninsured residents also increased.  In 2008, 579,893 (+/- 14,020) of the state’s residents, or 18 percent (+/- 0.4 percent), were without health insurance.  In 2009, the ranks of the uninsured grew to 628,573 residents (+/- 14,264), or 19.4 percent (+/- 0.4 percent)*.

Males in Oregon were more likely to be uninsured compared with females.  The number of uninsured males increased from 19.3 percent (+/-0.6 percent) in 2008 to 20.9 percent (+/-0.6 percent) in 2009.  The number of uninsured females also increased during the two-year period (16.6 percent [+/-0.6 percent] to 17.8 percent [+/-0.6 percent]).

Table 1: Percentage of Uninsured Oregon Residents by Sex, Race/Ethnicity, and Income

*These categories indicate ratio between family income level to the federal poverty threshold. Lower ratios define lower income.

SAHIE estimates also demonstrate inequitable rates of health insurance coverage according to race/ethnicity.  In 2009, Hispanics in Oregon had the highest overall rate of uninsured population, at 34 percent (+/- 1.4 percent).  The Black alone, non-Hispanic population had an uninsured rate of 20.4 percent (+/- 1.9 percent); the White alone, non-Hispanic population had an uninsured rate of 17 percent (+/- 0.5 percent).

Every income bracket — which is defined by the ratio of family income to the federal poverty threshold — showed a decrease in Oregonians’ health insurance coverage, the data show. Poorer people, however, were most likely to be uninsured.

Oregon Counties

Across Oregon, the highest proportion of uninsured residents was in Wheeler County (see Table 2), where approximately 31.9 percent (+/- 2.7 percent) of people were uninsured in 2009.

For counties with more than 40,000 residents, Marion County had the highest levels of uninsured people with rates of 22.1 percent (+/- 1.3 percent) and 23.9 percent (+/- 1.3 percent) during 2008 and 2009, respectively.

In the Portland metropolitan area, residents of Multnomah County were more likely to be uninsured compared with their metro neighbors.  What’s more, Multnomah County also experienced the largest gain in the percentage of the uninsured (16.8 percent [+/- 0.8 percent] to 18.9 percent [+/- 0.9 percent])*.

Table 2: 2008 and 2009 Percentage of Uninsured Oregon Residents by County

SAHIE Estimates

Unlike the U.S. Census Bureau’s American Community Survey (ACS), which provides single-year health insurance estimates for populations of at least 65,000, SAHIE produces model-based[i] estimates of health insurance coverage for all counties and states, regardless of population size. What’s more, SAHIE data[ii] provide information about health insurance coverage by sex, age, race/ethnicity, and income at the state level. Health insurance coverage by sex, age and income are available at the county level.

SAHIE 2008 and 2009 estimates include ACS data for the first time. Health insurance coverage and income group distributions (from the ACS) provide significant improvements in the precision of SAHIE estimates. An important caveat, however, is that because SAHIE data from 2008 and 2009 are estimated with a different data source than earlier estimates, SAHIE data from previous years should not be compared with 2008 and 2009 estimates.

To download and view SAHIE estimate data, click here.

*Statistically significant difference at the 90 percent confidence level.

Note: SAHIE data are based on survey estimates.  As such, all data should be interpreted with the corresponding Margin of Error (MOE) estimate.  MOE is the difference between an estimate and its upper or lower confidence bounds.  Confidence bounds can be created by adding the MOE to the estimate (for an upper bound) and subtracting the MOE from the estimate (for a lower bound).  All published MOE values are based on a 90 percent confidence level.

The SAHIE program uses the following data sources to model health insurance coverage: The American Community Survey; demographic population estimates; aggregated federal tax returns; participation records for the Supplemental Nutrition Assistance Program (SNAP), formerly known as the Food Stamp program; county business patterns; Medicaid and Children’s Health Insurance Program (CHIP) participation records; and Census 2000.

All data pertain to those younger than 65 years of age.

Charles Rynerson, Risa Proehl and Michael Burnham contributed to this report.

Where we live and who we live with have a huge influence on our everyday lives. Do we live alone or share a home with family or friends? Do we own or rent? How affordable is our housing when compared to our income and how is the value of our house changing? All of these issues shape how we live our lives.

Similarly, housing and households influence the character of neighborhoods and communities. The share of households with children and seniors, the share of households that rent or own, the size of households, and the cost and supply of housing can affect community resources, demographics and livability.

Every ten years, we get a snapshot of the nation’s households from the decennial census. In between census years, we get an update of that picture plus additional information from the American Community Survey. This article explores changes in the size, structure and financial condition of Oregon’s households, from 2000 to 2010, using data from the U.S. Census Bureau’s 2000 and 2010 decennial censuses and the 2005-2009 American Community Survey.

Households are occupied housing units. The type of people who occupy them is changing slowly over time. The composition of households is descriptive of the population in general and is indicative of the dynamics of an area’s demographic and socioeconomic characteristics.

Household Type and Average Size

In Oregon and in the United States, the share of households with children has been declining. Conversely, the share of households that are non-family households, including those where the householder lives alone, has been increasing. Contributing to the rising share of one-person households is the increasing share of senior citizens, especially women, who outlive their spouses.

According to the U.S. 2010 Census, roughly two-thirds of all households in Oregon were family households (households with spouses, children, or other relatives). More than a quarter of all households were composed of persons living alone, and the rest were composed of either group-living situations or non-family households (e.g., roommates, boarders). From 2000 to 2010, the percentage of households with people living alone increased by about a percentage point, while there was a decrease among family households and households with children. Counties with student populations or migrant workers generally have the highest percentages of non-family households. As college enrollments have been rising, so has the student resident population living in roommate situations; this is also true of agricultural workers.

Statewide, about a third of households have children, and almost a third of family households with children were headed by single parents. More than two-thirds of these single-parent households were headed by single-mothers; however, nationally and in Oregon, the share that was headed by single-fathers increased, although slightly, from 2000 to 2010.

Oregon counties with the smallest shares of households with children are counties that are popular destinations for retirees and are not necessarily attracting many young families (Figure 1). Thus, the population is aging in place.

The share of households with residents age 65 or older increased slightly faster in Oregon compared with the United States as a whole. From 2000 to 2010, Multnomah and Hood River counties were the only counties in Oregon without an increase in the share of households with persons age 65 or older. This is likely due to a higher rate of young adults moving to Portland for creative and employment opportunities and to Hood River County for recreational and outdoor amenities.

Alternatively, the counties with the largest share of households with one or more persons age 65 or older in 2010 were: Wheeler, Curry, Josephine, Grant, Baker, Coos, Douglas, Lincoln, Sherman, Tillamook, Wallowa, Gilliam, Lake, Crook, Harney, Malheur, Jackson, Wasco, Jefferson and Klamath; all had 30 percent or more households with seniors. Some of these counties are popular destinations for retirees (Curry, Josephine, Coos, Lincoln, Tillamook, Jackson), and others have aging populations.

Figure 1

Source: US Census Bureau

The average number of persons per household (PPH) changes along with shifts in household type. A smaller share of family households and a larger share of one-person households lead to smaller average household sizes.

Generally in Oregon, as in the United States, the PPH has been declining over the past few decades. Typically, the PPH changes slowly over time; it also depends on characteristics of the population and how those characteristics change over time. The PPH is largely influenced by the age structure of the population (e.g., a large number of children implies larger household sizes), and to cultural practices (e.g. fertility levels and the propensity for extended families to live together). The economy affects the PPH to a lesser extent and on more of a temporary basis (e.g., promotes the opportunity to live alone or the need to double-up).

Overall, in the United States, declining fertility rates of the white non-Hispanic majority population contributed to the decline in PPH in recent decades. However, during the 2000s, declining fertility and PPH started to become offset by the changing ethnic make-up of the population. Consequently, the pace at which PPH had been shrinking has decelerated.

Nationally, the average household size barely decreased from 2000 to 2010 (from 2.59 to 2.58). Because of a larger white non-Hispanic population, Oregon’s PPH dropped from 2.51 in 2000 to 2.47 in 2010. Still, the drop was not as great during the previous decade. Additionally, the PPH didn’t change much in the Portland three-county metropolitan area during the decade. The decline that had been occurring previously was likely offset by the increasing share of Hispanic population, particularly in Washington and Multnomah counties. PPH for the three-county Portland area was about the same as for Oregon in 2010.

All counties in Oregon, except Marion and Umatilla, experienced a decline in PPH, however slight (Figure 2). The counties that experienced the greatest decline were those with aging populations where the share of seniors is increasing, or those where fertility rates were lower, or were declining at a faster pace, than other Oregon counties.

The counties with the smallest PPH in 2010 were: Curry, Gilliam, Wheeler, Grant, Lake, Lincoln and Wallowa. These counties have relatively large retirement communities or aging populations.

Figure 2

Source: US Census Bureau

Most families reside in single-family housing units, which means single-family units generally have a higher household size than multi-family units. And since most multi-family units are occupied by persons who rent rather than own their home, renter-occupied households typically have a lower household size than owner-occupied households.

Housing Tenure

In general, most households are occupied by homeowners rather than renters. In 2010, 62 percent of Oregon households were owner-occupied, which is slightly lower than the national percentage (Figure 3). In all but one of Oregon’s counties, the percentage of owner-occupied households declined from 2000 to 2010; Washington County’s share of owner-occupied households was about the same in 2010 as it was in 2000. Counties with the largest share of owner-occupied households were: Columbia, Grant, Wallowa and Crook. Ownership in these counties ranged from 70-75 percent, which means their share of renter-occupied households was lower than the state average of 38 percent.

Figure 3

Source: US Census Bureau

Usually, the highest share of renter households is in more urbanized counties and especially where there is a large college student population. Rental housing must be extensive enough to accommodate the demand from persons who require temporary living situations. Still, in these areas, renter-occupied households consistently represent less than the majority of households.

The disparity between the share of owners and the share of renters was the least in Multnomah County, followed by Benton and Lane counties. According the Census 2010, the share of owner-occupied households was 55 percent while the share of renter-occupied households was 45 percent in Multnomah County.

Housing Affordability

Another difference between renters and owners is housing affordability. Many renters are renting because they cannot afford to purchase their own home. However, this does not necessarily mean that renters can afford their housing expenses.

Housing affordability is measured by the level of cost-burden. A household is considered cost-burdened if 30 percent or more of the household income is spent on housing costs.

According to the 2005-09 ACS, more than a third of households in Oregon were cost-burdened, which is slightly higher than the national figure. Additionally, the share of households, both nationally and in Oregon, that was cost-burdened increased from 2000 to the 2005-09 period by several percentage points (Figure 4). The counties in Oregon that tend to bear the highest share of cost-burdened households were in the most urbanized areas. Oregon counties with the highest shares of cost-burdened households reported in the 2005-09 ACS are: Multnomah, Jackson, Josephine, Lane, Benton, Curry, Lincoln, Clatsop, Marion and Deschutes. Here, approximately 40 percent of the households were reported to be paying 30 percent or more on housing costs. Multnomah County was highest in percent and number: More than 122,000 households were estimated to be paying 30 percent or more of their income on housing costs.

The counties consistently reported in 2000 and in the 2005-09 ACS to have the smallest percentage of cost-burdened households were: Wheeler, Grant, Morrow, Lake, Harney, Gilliam and Sherman.

Figure 4

Source: US Census Bureau

Typically, a larger share of renters than owners is cost-burdened. This is likely due a larger share of lower-income households being renters than owners. Both nationally and in Oregon, about half of renter-occupied households were cost-burdened. Slightly less than one-third of owner-occupied households were cost-burdened, according to the 2005-09 ACS data.

In all Oregon counties, a greater percentage of renters than owners was paying 30 percent or more of their income on housing. Four of the five Oregon counties with the highest percentage of renter cost-burden were counties with public universities: Multnomah, Lane, Benton and Jackson. The most disparity between renters and owners paying 30 percent or more on housing was in Baker, Gilliam, Wheeler and Benton counties. Home to Oregon State University, Benton County has a large percentage of student population, and the fact that most students don’t work full-time could explain this difference.

Housing Value and Vacancy

Housing costs, coupled with income, contribute to cost-burden and housing affordability. Thus, a high share of cost-burdened households does not necessarily indicate expensive housing. However, in Oregon, the median housing value was approximately 32 percent higher than the national average of $185,400.

According to the 2005-09 ACS, the median housing value for owner-occupied housing units in Oregon was about $244,200. Median housing values in about half of Oregon counties were between $200,000 and $300,000. Counties in Oregon with the highest median housing value were: Clackamas, Deschutes and Washington. The median housing value in these counties was about $300,000 or more.

Counties with the lowest housing values were east of the Cascades; here, the median housing value was $200,000 or lower. Wheeler, Gilliam, Morrow, Lake, Sherman, and Harney counties were most affordable, with values closer to $100,000.

Housing value is affected by the economy, but more so by the demand for housing. The counties with the lowest values are also the counties with the highest vacancy rates (Figure 5). Again, the ACS data show that most of these counties are east of the Cascades.

Figure 5

Source: US Census Bureau

A housing unit is considered to be vacant if it is not anyone’s usual place of residence. Statewide, about 9 percent of housing units were vacant in 2010 (the national vacancy rate was 11 percent). This percentage, however, includes seasonal housing (e.g., vacation homes); when these units are excluded, the Oregon rate is less than 8 percent. This shows that not all vacancy rates reflect a surplus of housing. The share of housing units that is seasonal in Oregon and the United States was reported to be around 3.5 percent. Nationally, the share of seasonal units declined by 3 percentage points from 2000 to 2010. However, the share increased in Oregon by 4 percentage points. The counties in Oregon with the largest share of housing stock in 2010 that is seasonal were: Tillamook, Lincoln and Clatsop on the coast; Wallowa to the east, in the Wallowa Mountains; and centrally located Wheeler, surrounded by the John Day Fossil Beds.

Nationwide, an increase in housing vacancy from 2000 to 2010 was commonplace. Vacancy rates increased in all Oregon counties but one during the same time period. Benton County was the only Oregon county where the vacancy rate actually decreased during the decade, but by a just a fraction of a percent.

Oregon counties where vacancy rates increased the most in the 2000s were Tillamook, Crook, Wheeler, Lincoln and Hood River. Each county’s vacancy rate increased by at least 5 percentage points during the decade. Although the highest vacancy rates were in Tillamook, Lincoln, Wheeler and Clatsop counties, when seasonal housing is factored out, Wheeler is the only county that remains amongst the counties with the rates above 10 percent. The other counties with the highest vacancy rates were Gilliam, Harney, Lake and Grant counties.

Generally in Oregon, regardless of seasonal housing, vacancy rates were lowest in counties along the Interstate 5 and the Interstate 84 corridors. The lowest vacancy rates in Oregon reported by Census 2010 were in the Willamette Valley counties (Benton Washington, Multnomah, Clackamas, Yamhill, Marion, Polk and Lane), Columbia County, Union County, and in the Columbia River area counties of Wasco, Morrow, Umatilla and Hood River.

In conclusion, some of Oregon’s more rural counties, east of the Cascades, tend to have a higher percentage of single-family residences and a greater supply of affordable housing. Conversely, and perhaps not surprising, the most expensive housing in the state is generally in the state’s most-populated counties, and those along the Interstate 5 corridor.

Additionally, over the past decade homeownership in Oregon generally has decreased. This condition is affected by the economy and could turn around as the economy improves over the next several years. Household composition, however, which is not so affected by the economy, is prone to certain trends that are expected to continue over the near future: The share of households with seniors is increasing; the share of children’s households is declining; and, the average household size is stabilizing.

FOOTNOTES:

1 Housing units were living quarters for 97.7 percent of Oregon’s population according to Census 2010; the remaining population, or 2.3 percent (86,600 persons), resided in group quarters facilities (group living situations such as prisons, nursing homes, and student dormitories). Depending on the living situation, the homeless population are included as living in special housing units (classified by the Census Bureau as ‘Other’ housing units, such as tents or vans) or in group quarters facilities such as emergency shelters. The share of population residing in group quarters generally hasn’t changed much throughout recent decades, but it could increase as the share of elderly is on the rise and the demand for nursing homes may become more prominent. Nationally, about 2.6 percent of the population resides in group quarters.

2 Single-family residences can be separate, stand-alone structures or can be attached as long as the separating wall reaches from the ground to the roof. Multi-family residences exist in structures that hold multiple dwelling units such as duplexes, apartments, townhouses, and some condos.

Editing assistance from IMS/PRC Director Sheila Martin, PRC Assistant Director Jason Jurjevich, and graduate research assistants Michael Burnham and Ryan Dann.

The combination of three components: fertility, mortality, and migration, collectively lead to population change. Fertility and mortality events in recent history, including escalated fertility levels associated with the ‘baby boom cohort’ and spikes in mortality linked to the Spanish Flu of 1918 for example, have produced demonstrative demographic effects.

However, both the unpredictability and ability of migration to produce virtually immediate impacts, often enduring for decades across social, cultural, demographic, and economic landscapes, has long captured the attention of scholars and public policy makers alike.

Over the past several decades here in Oregon, migration has undoubtedly shaped the state socially and culturally, but equally pronounced and perhaps more tangible have been the long-term challenges and prospects linked to the reciprocal relationship between migration and economics.

The Beaver State grew by more than 400,000 people during the first decade of this century, and nearly half of the growth was concentrated in Deschutes, Multnomah and Washington counties.

While roughly two-thirds of the state’s population growth was attributable to net in-migration, much of the migration-related growth occurred during the early 2000s. Due to the migration slow-down accompanying the economic recession that began in late 2007, natural increase accounted for a greater share of Oregon’s population growth.

In addition to Deschutes, Multnomah and Washington counties, other counties with significant net in-migration included Clackamas (Portland metro), Lane (Eugene metro), and Jackson (Medford metro). Counties with net out-migration included, from lowest to highest: Wheeler, Wallowa, Sherman, Baker, Harney, Grant, Morrow and Malheur (Figure 1).

Figure 1

Unemployment rates are a lagging indicator of economic performance, but the relationship between net migration and Oregon’s annual unemployment rate is clear: as the state’s unemployment rate climbs, there is a delayed, but pronounced decline in net migration.

Prior to the economic recession that began in December 2007, net migration in Oregon averaged 28,000 people per year. Indeed, Oregon gained the majority of the decade’s 250,000 net in-migrants during this period. From 2008 through the end of the decade, however, net migration averaged slightly more than 16,000 people annually—half of what it was during the beginning-to-middle part of the decade (Figure 2).

Figure 2

Analyzing net migration and other metrics of migration is important for gauging the relative importance of migration, along with fertility and mortality, as components of population change. However, because counties with larger population (often urban counties) will often send and receive greater volumes of migrants compared with their rural counterparts, it is important to consider a migration statistic that summarizes the relative effect of migration across all counties. While it has some pitfalls, the Net Migration Rate (NMR) (Table 1 and Figure 3) provides such comparability.

Table 1

Figure 3

While Washington County recorded the highest level of net in-migration (35,852) of any county in Oregon, the net gain of 35,434 migrants in Deschutes County represented the highest relative gain. Because Washington County is more than three times as large as Deschutes County, Washington County’s net gain of migrants translated to 7.2 net in-migrants per 100 residents while Deschutes County recorded a 25.1 net in-migration rate. Put another way, 84% of Deschutes County’s gain in population was the result of net in-migration, compared to 43% in Washington County.

Polk, Curry, and Josephine counties recorded the next highest net in-migration rates at 15.2, 13.0 and 11.7, respectively.

The largest net out-migration rates were in the Eastern Oregon counties of Sherman, Malheur and Morrow. Sherman County, for example, recorded a marginal net loss of 133 migrants. However, Sherman’s total population of fewer than 2,000 residents makes it one of the least-populated counties in Oregon. As such, the net loss of more than 100 persons translated into a pronounced relative loss.

Disaggregating domestic and international migrant flow data from the U.S. Census Bureau uncovers an interesting trend for Oregon and its counties.

According to U.S. Census Bureau estimates, during the 2000s, approximately two-thirds of Oregon’s net migrants were domestic migrants; one-third were international migrants. In terms of numbers, 165,000 people were net domestic migrants, leaving 85,000 net international migrants.

As the state average suggests, the net migration in the majority of Oregon counties was due to domestic migration flows. However, some counties had considerably different migration patterns.

In the Portland metro area, for example, Clackamas County reflected many counties across Oregon where net in-migration flows were due to domestic migration (80 percent). However, Multnomah and Washington counties were fundamentally different from the larger state pattern (Figure 4). In Washington County, half of net in-migration was due to domestic migrants and the other half was due to international migrants. In Multnomah County, 90 percent of the net migration gains were attributable to international migrants.

Other Oregon counties where international migration accounted for more than half of the gains in net migration included Benton, Marion and Wasco counties.

Figure 4

Figure 5 is illustrative of general age-articulated migration patterns across the life-course. Persons in their first five years of life have moderate mobility levels that decline through age 17, only to increase significantly at age 18 with moving to college, entering the military or moving for employment following high school. Mobility levels generally fall precipitously thereafter as people settle into jobs and communities.

Using the U.S. Census Bureau definition, which classifies a migrant as someone who crosses political (county) boundaries, Figure 5 illustrates a rather significant decline in the propensity to migrate, across almost all age cohorts, between 2005 and 2009. As explained in Figure 1, the economic recession and housing crisis helped to curb migration rates significantly across virtually all age cohorts.

Figure 5

American Community Survey (ACS) estimates for 2005-2007, the three-year period prior to the economic recession, and for the ensuing three years show a 3.3 percent (+/- 0.8 percent) decrease in the number of movers over the period. Comparing the two periods, the number of migrants declined for virtually all types of moves, but most pronounced were individuals migrating across state lines.

Figure 6 underscores a critically important point—the economic recession did not impede migration rates uniformly across the population. In fact, the opposite is true for Oregon interstate migrants. While there was a statistically significant (95%) decline in the number of migrants moving across Oregon county lines between the two periods, there was no statistically significant decline in the number of interstate migrants with at least a Bachelor’s degree during the period. In other words, mobility levels for migrants with higher levels of educational attainment seemed to be somewhat insulated from the economic recession compared to their less educated counterparts.

Figure 6

The largest sources of migrants to Oregon were from California and Washington. In 2005, 39.8percent (+/- 7.5 percent) and 14.1 (+/- 3.5 percent) of migrants were from California and Washington, respectively. In 2009, however, a decline in migrants from California (24.9 percent, +/- 4.8 percent) led to a corresponding increase in the percentage of migrants from Washington (20.2 percent, +/- 3.6 percent).

According to 2009 ACS estimates, fewer than half of Oregon’s residents were born in Oregon and approximately 1 out of every 7 Oregonians was born in California.

Figure 7

One of the seminal laws of migration, developed by Ravenstein (1885), is that for every migrant inflow, there is a counterstream. Migrant exchanges are often a result of physical distance—places in close proximity share a greater degree of interaction. Figures 7 and 8 underscore these principles of migration.

Where California and Washington were the two greatest sources of migrants to Oregon, these two states were also the top destinations for residents leaving Oregon. In both 2005 and 2009, approximately 40 percent of out-migrants left Oregon for these two states.

Figure 8

Figure 9 assesses the comparability of migration information from three primary sources: the ACS; the number of driver’s licenses surrendered at Oregon Department of Motor Vehicle offices; and, the number of exemptions listed on Internal Revenue Service tax returns for the period.

As Figure 9 illustrates, generally, the data sources confirm the top five origin states for Oregon in-migrants: California, Washington, Arizona, Idaho and Colorado.

Figure 9

Table 2 reports county-specific net migration figures for the three-county Portland region (Multnomah, Washington, and Clackamas counties). The largest absolute flow of migrants out of the Portland region is to Clark County, WA. According to 2004 IRS tax return data, there were almost 4,000 more out-migrants to Clark County, WA compared to in-migrants to the Portland region. The largest net flows of in-migrants come from Los Angeles County, CA and Lane County, OR at 1,300 and 700, respectively.

Because counties with larger populations often send and receive greater volumes of migrants, migration streams are often analyzed in terms of relative flow by using a measure of Demographic Effectiveness, or how ‘effective’ migration streams are at redistributing population. Relying on this metric, the most effective county-specific out-migration streams are to Clark, WA and Deschutes, OR. Conversely, the most effective county-specific in-migration streams are all from California—Alameda, Los Angles, and Orange counties.

Table 2

With an increasingly globalized economy dependent on a highly skilled and well-educated workforce, the future may lead to demand for human capital exceeding the state’s supply from institutions of higher learning. If this is the case, Oregon will continue to be dependent on continued importation of human capital through in-migration. What is clear, with history as a guide, is an inextricable link between in and out migration streams and Oregon’s economy—as demography will undoubtedly determine Oregon’s economic destiny.

Sheila Martin, Charles Rynerson, Risa Proehl and Michael Burnham contributed to this report.

The number of college-educated Oregonians grew steadily during the past two decades — especially in Central Oregon — but there were significant regional disparities across the state, census data show.

The proportion of Oregon residents age 25 and older with at least a bachelor’s degree grew from 20.6 percent in 1990 to 25.1 percent in 2000. The figure climbed to 28.3 percent during the 2005-2009 period, according to newly released American Community Survey (ACS) estimates from the U.S. Census Bureau. The margin of error for the five-year period was plus or minus 0.3 percent.

Average educational attainment in the Portland metropolitan area, which includes Multnomah, Clackamas and Washington counties, was consistently higher than the state average during the past two decades. What’s more, the proportion of college-educated people in the three counties increased more precipitously than any other part of Oregon.

In Multnomah, Clackamas and Washington counties combined, 25.2 percent of residents had at least a bachelor’s degree in 1990. The figure was 31.3 percent in 2000 and 36 percent (+/- 0.3 percent) during the 2005-2009 period. Here’s how the counties changed over time:

• College educational attainment in Washington and Clackamas counties increased by more than 4 percent, with a margin of error of plus or minus 0.3 percent, following Census 2000.
• Multnomah County experienced one of the state’s largest increases in educational attainment during the period, at 6.2 percent (+/-0.6 percent). Portland was a major factor: 25 percent of the city’s residents age 25 or older had at least a bachelor’s degree in 1990; the figure climbed to 43 percent during the 2005-2009 period.

In addition to general increases in educational attainment for residents already residing in the Portland metropolitan area, relatively young college-educated individuals moving to the metroplex contributed to the growth.

So, how did other parts of the state stack up?

Central Oregon, including Crook, Deschutes and Jefferson counties, was second to only the Portland metropolitan region in terms of educational attainment. Along with the Portland metropolitan area, Central Oregon had one of the state’s largest increases in educational attainment during the past 20 years (Figure 1, below).

In 1990, 16.9 percent of residents in Central Oregon had at least a bachelor’s degree. In 2000 and during the 2005-2009 period, the figure increased to 22.1 percent and 26 percent (+/- 0.9 percent), respectively. From 2000 to the 2005-2009 period, the region’s largest increases in educational attainment were in Deschutes and Crook counties, at 3.9 percent (+/- 1.1 percent) and 3.2 percent (+/- 2.2 percent), respectively. Population in Deschutes and Crook grew at 48 percent and 42 percent, respectively, during the period — the fastest relative population growth in the state. Ample recreational opportunities, real estate and rural amenities helped lure well-educated migrants.

The Upper Willamette Valley, including Benton, Lane, Linn, Marion, Polk and Yamhill counties, followed Central Oregon. In 1990, 20.6 percent of residents in Upper Willamette Valley counties had at least a bachelor’s degree. The proportion increased to 23.7 percent in 2000 and 24.8 percent (+/- 0.4 percent) in 2005-2009.

In 1990 and 2000, these counties exceeded or kept pace with Oregon’s average educational attainment levels. During the 2005-2009 period, however, buoyed by considerable increases in educational attainment in the Portland metro area and Central Oregon, levels dipped below the state average.

While the Willamette Valley region is the home of several major educational institutions, including the University of Oregon (Lane County), Oregon State University (Benton County) and Willamette University (Marion County), many college-educated individuals seeking employment after completing their degree leave the region, the data show.

Increases in educational attainment after 2000 were greatest in Yamhill and Linn counties, at 2.4 percent (+/- 1.2 percent) and 2.2 percent (+/- 1 percent), respectively. Benton County, with 47.7 percent (+/-1.8 percent) of residents possessing at least a bachelor’s degree, had the highest educational attainment of any county in Oregon.

Benton stood in stark contrast with the Northwest, Southwest, and Eastern counties, which had the lowest levels of educational attainment in Oregon. On average, 14 percent of residents in these counties possessed a bachelor’s degree or higher in 1990; the percentage increased to 17 percent in 2000 and 19.5 percent in 2005-2009. Here’s how the regions compared:

Northwest Oregon, including Clatsop, Columbia, Lincoln and Tillamook counties, experienced a modest increase in educational attainment, from 14.5 percent in 1990 to 20.5 percent (+/- 0.8 percent) in 2005-2009. The largest increases were in Clatsop and Columbia counties, both averaging a 3 percent (+/- 1.5 percent) increase in educational attainment.

Southwest Oregon, which includes Douglas, Jackson, Josephine, Coos and Curry counties, had a modest increase in educational attainment allowing the region to keep pace with Northwest Oregon, albeit at slightly lower levels.,Largely boosted by Coos, Jackson and Josephine counties, 14.1 percent of Southwest Oregon residents had at least a bachelor’s degree in 1990, 17.4 percent in 2000, and 19.7 percent (+/- 0.5 percent) in 2005-2009.

Sparsely populated Eastern Oregon was last among regions in terms of educational attainment. Fourteen percent of the region’s residents had at least a bachelor’s degree in 1990; the figure climbed to 17.9 percent (+/- 0.6 percent) during the 2005-2009 period.

While counties east of the Cascades — excluding Central Oregon — had the lowest levels of educational attainment in Oregon in the latest ACS period, educational attainment rose significantly in some Columbia River counties. Since 2000, educational attainment increased 4.1 percent (+/- 2.7 percent) and 5.5 percent (+/-2 percent) in Hood River and Wasco counties, respectively. In addition to increased commuting from Hood River County to the Portland metropolitan area, recreation, tourism, retirement migration, and technical jobs contributed to increases in this area.

Figure 1: Educational Attainment By Region

Source: US Census Bureau, American Community Survey. 2005-2009.

Interactive Map: Use zoom and pan to customize view, click on counties to highlight numbers in table. Click in table to highlight map entity.

We live in the Silicon Forest, but the landscape of educational attainment in the Portland region varies greatly from east to west and north to south.

Consider Orenco Station, a transit-oriented development midway between downtown Beaverton and downtown Hillsboro. According to newly released 2005-2009 American Community Survey (ACS) estimates from the U.S. Census Bureau, about 39 percent of residents of the census tract that includes Orenco Station had at least a bachelor’s degree. Not bad — when you consider that just 27.5 percent of the U.S. population and 32.9 percent of the metropolitan area had a bachelor’s or better — but this corner of Washington County is Intel Corp. country, after all.

So where are the metroscape’s best and brightest? Go east of Hillsboro, but not too far.

Census tracts with the highest educational attainment — where upward of 75 percent of residents age 25 and older had a bachelor’s degree or higher — were exclusively on the City of Portland’s west side. This belt includes the West Hills neighborhoods, stretching from Forest Park to Hillsdale, as well as the southern edge of downtown near Riverplace/South Auditorium, and the Northwest District, west of NW 23rd Avenue.

Other census tracts with relatively high educational attainment — where 65 to 75 percent of residents had a bachelor’s degree or higher — were found in Southwest and Northwest Portland, including the Pearl District. Similar percentages were found amid the Raleigh Hills/West Slope area of Washington County, Lake Oswego, and several Portland neighborhoods within one-to-three miles east of the Willamette River.

In Clark County, census tracts with the highest educational attainment were east of Interstate 205 in Fishers Landing and Camas. Between 50 and 55 percent of residents here had a bachelor’s or better, according to ACS population and housing data collected between Jan. 1, 2005, and Dec. 31, 2009. Keep in mind that these survey data are estimates and carry margins of error (MOEs) that vary by census tract, with most MOEs within the range of 4 to 8 percent.

Washington County was a land of sharp contrasts. In the tract south of the Max line that passes Orenco Station in Hillsboro, just 11.9 percent of residents had a bachelor’s degree or higher. North of Orenco, the percentage increased to 51.7. Head a few miles west of Hillsboro to Forest Grove, and the number dipped to 10.2 percent.

Clickable Map of Census Tracts

Source: US Census Bureau, American Community Survey. 2005-2009.

The census data also show major change over time in some tracts.

Several census tracts with the largest increases of educational attainment are in North Portland, including Central St. Johns, where the share of residents with a bachelor’s degree or higher increased from 5 percent in 1990, to 15 percent in 2000, to 36 percent within the five-year ACS period. In four contiguous tracts along I-5 and the Interstate Avenue MAX line in the Overlook, Arbor Lodge and Kenton neighborhoods, the population with a bachelor’s degree or higher did not exceed 13 percent in 1990 and 19 percent in 2000. But according to 2005-2009 ACS estimates, the ranks of the college-educated living within this area is now about 40 percent.

Though not as dramatic as the growth in North Portland, several census tracts in Southeast Portland — areas between 39th and 82nd Avenues that once had relatively low levels of educational attainment — have recently made significant gains. Amid the Mt. Scott-Arleta, Foster-Powell, and Creston-Kenilworth neighborhood tracts, between 8 and 12 percent of residents age 25 and older had a bachelor’s degree or higher in 1990. The figure surpassed 30 percent during the ACS period.

Large increases in educational attainment also occurred amid census tracts that include Portland’s Pearl District and Vancouver’s Esther Short Park. Both of these areas have benefited from high-density residential development since the 2000 census.

Most census tracts with the lowest levels of educational attainment — where the share of residents age 25 and older with a bachelor’s degree or higher was 12 percent or less — were clustered in four distinct areas: a crescent-shaped area in outer Southeast Portland, including portions of Portland’s Brentwood-Darlington, Lents and Powellhurst-Gilbert neighborhoods, as well as Clackamas County’s unincorporated Southgate community; a roughly four-square-mile-area east of 162nd Avenue that encompasses Gresham’s Rockwood and North Gresham neighborhoods and portions of the Centennial neighborhoods along the Portland-Gresham border; the Central Vancouver neighborhoods of Harney Heights and Bagley Downs; and portions of East Vancouver and Orchards near Fourth Plain Road east of I-205. The share of residents in each of these areas with a bachelor degree or higher has changed very little since the 1990 and 2000 censuses.

Other census tracts with similarly low educational attainment — where the share of residents age 25 and older with a bachelor’s degree or higher was 12 percent or less — include portions of Battle Ground (Clark County), Cornelius and Hillsboro (Washington County), St. Helens and Goble (Columbia County), Molalla (Clackamas County), Willamina/Sheridan (Yamhill County), Wood Village (Multnomah County), and in a downtown Portland census tract where a majority of “residents” are inmates of the Multnomah County Detention Center.

Educational attainment correlates with income, and it’s interesting to view the relationships between educational attainment and poverty. To view the latest census data on poverty rates for the region, refer to the related article here.

Poverty is not only the province of Portland — Oregon’s largest city and cultural capital.

An estimated 30 percent or more of the population lives at or below the federal poverty level in 20 metropolitan Portland census tracts, including some amid outlying suburbs, according to recently published U.S. Census Bureau data.

Several of the tracts are clustered in downtown Portland, as well as North and Northeast Portland neighborhoods with comparatively high minority populations. Perhaps less predictably, the cities of Forest Grove and McMinnville, as well as North Portland’s University Park neighborhood, include tracts with similarly high poverty rates. These tracts include concentrations of college students, who typically have low incomes.

The American Community Survey (ACS) population and housing data were collected by the U.S. Census Bureau between Jan. 1, 2005, and Dec. 31, 2009. The estimated poverty rate for the Portland metro area — which includes Washington, Multnomah, Yamhill, Clackamas and Columbia counties in Oregon and Clark and Skamania counties in Washington — was 11.7 percent, with a margin of error of plus or minus 0.3 percent. For these 20 high-poverty tracts, the margins of error ranged from 5 to 15 percentage points.

The federal poverty level for a family of four is income of $22,050 annually for all states, except Alaska and Hawaii, according to U.S. Department of Health and Human Services guidelines. The federal poverty rate for an individual is $10,830 annually.

Tracts 33.01 and 34.01, which overlay parts of Portland’s Humboldt and King neighborhoods, marked a notable concentration of poverty. More than a third of individuals within these tracts lived at or below the poverty line. Non-whites constituted about half of the population, including persons identifying as African-American, who represented about one third of the population of each tract; roughly half of the households in each tract were renters.

Tract 76, which is part of Northeast Portland’s Cully neighborhood, was another notable poverty pocket. More than 38 percent of individuals here lived at or below the federal poverty level. Nearly half of the tract’s residents were non-white; about 40 percent of the tract’s residents were renters. The data underscore that poverty is not just an urban problem. Indeed, more than a third of individuals lived at or below the poverty line in four census tracts clustered around Portland’s eastern border with Gresham. These tracts also had a comparatively high percentage of renters.

Census tract 40.02 in North Portland’s University Park neighborhood, which includes the University of Portland, also had a high poverty rate compared to other parts of the metropolitan area. Nearly 34 percent of individuals lived at or below the federal poverty level. The tract had slightly higher shares of white residents and homeowners than the metro area overall, but about 41 percent of its residents were college students.

The outlying suburbs of McMinnville and Forest Grove, which include higher-education institutions, also contained census tracts with comparatively high poverty rates. Nearly 42 percent of individuals in Yamhill County tract 308.02, which includes McMinnville’s Linfield College, lived at or below the poverty line. Thirty-five percent of the tract’s residents were students.

In Washington County’s census tract 332, the home of Pacific University in Forest Grove, more than 32 percent of individuals lived at or below the poverty line. The tract is notable for its 31 percent Latino population share and more than 1,000 college students, 17 percent of all residents. Seventy-two percent of households are renters.

Clickable Map of Census Tracts

Source: US Census Bureau, American Community Survey. 2005-2009.

“The world is complex, dynamic, multidimensional; the paper is static, flat. How are we to represent the rich visual world of experience and measurement on mere flatland?”

- Edward R. Tufte

About the Metropolitan Knowledge Network

The Metropolitan Knowledge Network was created as a collaboration between Portland State University’s Institute of Portland Metropolitan Studies, Population Research Center, and a group of expert advisors as a way to transform regional data into regional action. As the leaders and citizens of the Portland region work together to improve our quality of life, we need to assess the condition of this place and understand how it is changing. For example, we might ask the following questions:

1. What kind of people live in the Portland Metropolitan Region?
2. How is our population changing?
3. How do we measure our economical prosperity?
4. Is our human capital expanding?
5. Are we good stewards of our natural resources?
6. Is our population healthy?
7. Are we investing in local cultural institutions?
8. What issues worry us?
9. What issues should worry us?

We created the Metropolitan Knowledge Network to provide a forum for exploring these issues while making clear information available to the general public. Citizens and leaders can turn to the MKN for information that is useful because it helps us understand the region we live in from a variety of perspectives. The MKN includes data about these important issues explained in a way that helps you understand how trends shape our region.

We will introduce the MKN in the style of a “Frequently Asked Questions” document.

Metro Knowledge Network “FAQ”

Who should use the Metropolitan Knowledge Network?

1. Policy makers trying to understand the facts behind important economic and social issues;
2. Researchers accessing datasets formatted in a consistent fashion;
3. Nonprofits needing to articulate the need for and impact of their work in our community; and
4. The Public trying to understand how trends are affecting their daily lives.

How do we define the Portland region?

We define the Portland Metropolitan region just as it is defined by the United States Office of Management and Budget (OMB). The Standard Metropolitan Statistical Area (SMSA) for the Portland region includes seven counties: Clackamas, Columbia, Multnomah, Washington, and Yamhill counties in Oregon, and Clark and Skamania counties in Washington. This definition has changed over time, generally by adding counties as the region became more populous and economically integrated.

Don’t we already have access to more data than we can use?

Though there are many web sites that offer data developed for a specific use, accurate understanding of these data requires a significant investment of time and effort because these sites can’t provide narratives that put the data in context. Data should be viewed in the context of a geography, or in the context of an important trend, or in the context of efforts to help our communities. In short, data are only useful if they guide decisions about policies and programs that affect our future. At MKN, we try to provide the important context that promotes understanding of the issues illuminated by the data.

What geographical data is used in MKN?

The data available on MKN uses several different geographies: when contextualizing Portland within the United States, we use the MSA (“Metropolitan Statistical Area”), which includes both larger “Metrpolitan” and smaller “Micropolitan” areas. Excellent data is available at the level of metropolitan regions. Whenever possible, MKN includes data for Skamania county in Washington State to reflect the wider influence of the urban area and the greater integration in terms of commuting and employment.

(One should note that MSA definitions are regularly changed to reflect changes in the US population. From 1950 to 1983, the Portland Metropolitan area was defined as Clackamas, Multnomah, Clark, and Washington Counties. In 1983, Yahmhill county was added. In 1993, the PMSA of Portland gained Columbia county, and a greater “core-based MSA” (CMSA) was created that includes Marion and Polk counties. For a discussion of Census definitions of Metropolitan areas, see this page.)

When making comparisons within the local metropolitan region, MKN generally uses county level analysis. Counties are a convenient nexus of administrative data and reporting for public health records, vital records, building permits, business licenses, and the like. Counties also are the regional focus of state and federal employment offices and statistical reporting. Unfortunately, county boundaries do not reflect homogeneous populations and thus can give a false picture of local dynamics, since they were designed only with political needs in mind. However, their ubiquity in governmental organization makes them very useful for data analysis.

MKN also uses smaller geographies, especially the “Census Tract”, to give a sense of how certain measures differ across the region on neighborhood scales. This geographic level is especially interesting when comparing different parts of the Portland metropolitan area in terms of poverty, racial composition, and related measures.

How do I access the images?

We keep images and maps small when they are embedded in an article, but if you want to see larger versions of the graphs and maps on the site, simply click on them and a larger version will pop up on your screen; this image will disappear if you click on it. This effect is achieved through “Lightbox” technology, an open-source software library. These lightbox images can be copied to the clipboard and pasted just like regular images.

How do I access the data?

You can just look at the tables as presented in the article, but if you want to dig deeper to the full dataset behind the table, click on the table and a dialog will open. This dialog allows you to select particular views of the data, choosing which rows and columns to display, as well as choosing how to aggregate data.

Roughly, the process is as follows. The user selects a variable to use for choosing which rows display in the leftmost pull-down menu. The user chooses which column to sort the result table using the “Order by” drop-down, and which sort order to use with the “sort order” drop down (either ascending or descending). Then the user chooses whether to display the data as a table or one of many types of graphic (currently only “xy plots”). Click on “close” to close the widget.

This interface, called the “MQT” for Metropolitan Query Tool, may take a little bit of work to get used to, but it is extremely powerful, and we think somewhat revolutionary.

All the output from the MQT or the images can be cut and pasted into your documents however you see fit, though we ask you credit us.

How does the query tool work?

MKN uses a new approach to data interactive display based on Ajax technologies (the same technology behind Gmail) to make viewing images more dynamic and to allow users to customize the output. The articles themselves are fairly high-level analyses of social and economic dynamics. However, whenever there is more detail that supports this analysis, or views it from a different angle, or makes it more widely applicable, we make that data available through the MQT. This approach to disseminating data is different than other data providers, in that we embed the data in a narrative, explaining what the data might mean, whereas other providers allow you to browse data without any information about its importance or its implications.

The Query Tool also allows the user to graph data of interest, not just inspect tables. It uses an advanced style of statistical graphics called Trellis, developed at Bell Labs, that allows the user to quickly see trends, as well as efficiently compare many different subsets of interest. Trellis graphics was developed largely by William Cleveland, and his work on statistical graphics perception has inspired the graphics on the New York Times, among many others.

What kind of metadata do you store?

We store extensive metadata (“data about data”), including the source for the data, URLs and contact information, a description of what each column stores, any in-house formulas used to derive the data (for example, we might calculate poverty percentages by dividing the under-poverty population by the total population), and expiration dates for the data. This metadata is always available through the MQT, and it is also used to create “tooltips” and automatic captions in the articles. This system is expandable, so as more metadata becomes important we can easily include it.

Can you tell me about the database driving the Metropolitan Knowledge Network?

There is a lot going on behind each table displayed on the site, which is what makes the Metropolitan Knowledge Network so powerful.

The data on the MKN is stored in a database driven by the open source PostgreSQL database server, using extensions which allow it to run the R statistical package and the PostGIS package for geographic data. Each data table has a column which describes the time unit in which the measurement was taken and the spatial unit for which the measurement applies; typically these columns use years for time and FIPS codes for geography, but other units are possible. (For more on geography codes, see this page.)

When appropriate, we store supplementary information (like confidence intervals for the American Community Survey) along with the data. This way, they can be tabulated and graphed along with the more typical measures of central tendency.

Where does the MKN get its data?

The MKN gets the latest data available from a myriad of federal, state, local, and private sources.

Official statistics tabulations: most data comes from tabulations created by statistical agencies like the Bureau of Economic Analysis, county public health departments, and the US Census Bureau. We download these, adjust the formatting if necessary, calculate any additional columns, create metadata for them, and put them online.

Less official statistics tabulations: Sometimes MKN will distribute statistics that have been tabulated by non-governmental researchers, including private companies and independent researchers. Although we can’t validate the methodologies used to collect these data, we ensure that the data is consistently formatted and useable by lay-people.

Administrative records: sometimes, MKN will do its own tabulations from records, for instance lists of building permits, birth and death records, etc. When we do these custom tabulations, we will note our methodologies in the metadata as well as in whatever article they are appearing.

Who are the Metropolitan Knowledge Network advisors?

The MKN depends on a wide range of community and business people for testing and ideas. These include:

Will Garrick, PSU Office of Information Technology
Seth Hudson, Portland Development Commission
Paula Kinney, Park Academy
Pam Lesh, Portland General Electric
Megan McCarthy, Portland Development Commission
Dick Sadler, Dundee Fruit Company
Scott Stewart, Portland Multnomah Progress Board
Ray Teasley, Mid-Willamette Valley Council of Governments
Bob Vieira, Oregon Health Sciences University
Adriana Prata, Research Director, Clark County Budget Office
Mark Bosworth, Metro Data Resources Center
Christian Kaylor, Oregon Employment Department
Joe Cortright, Impresa, Inc.
Lynn St. Jean, Worksystems, Inc.

Who do I talk to for more information?

Sheila Martin
Director
Institute of Portland Metropolitan Studies
503-725-5170
Sheilam@pdx.edu

1. Population Growth over Time

Metropolitan Portland-Vancouver has grown at least as fast as the United States since 1930, sometimes growing at double the fspeed of the nation. Since the 19fig^th century, Oregon, like much of the West Coast, has been a destination for migrants seeking economic opportunity and lifestyle amenities. The Portland region’s growth rates tend to follow the state of Oregon’s growth rates, partly because the region is home to a large share of the state population. Figure 2 compares the rate of growth of the Portland-Vancouver region, the state of Oregon, and the USA. Figure 3 present the decade-by-decade population individual counties within the Portland-Vancouver MSA since 1930. Table 1 in the appendix gives numerical detail by county.

Figure 2. Population Change in Portland-Vancouver MSA, Oregon, and the USA 1930-2008

Source: Portland State Population Research Center, US Census Bureau, and Washington State Office of Financial Management.

Figure 3. Population in the Portland-Vancouver MSA by County

Source: Portland State Population Research Center, US Census Bureau, and Washington State Office of Financial Management.

In the 1980s, the economic downturn affected the state of Oregon’s population, which grew at a slower pace over the decade (7.9%) than the population of the USA as a whole (10.4%). However, the Portland-Vancouver region’s population still grew faster than the overall US population during the 1980s.

Between 1990 and 2000, Oregon’s population grew by 20% and the Portland-Vancouver region’s population grew by 27%, which greatly outpaced US population growth of 13% over the same decade. During the 1990s, Clark and Washington counties experienced the greatest population increase among the regional counties, at 45% and 43% respectively, outpacing the region dramatically.

Clark and Washington counties continued to outpace the rest of the Portland-Vancouver region from 2000 to 2008. Since 2000, population growth has slowed in all of metropolitan Portland-Vancouver to 1.6% per year from 2000 to 2008, in contrast to the annualized rate of 2.1% per year from 1990 to 2000. One of the most important drivers of migration for adults is job availability. Thus, given the current economic climate in Oregon, population growth is not likely to increase before the next census in 2010. Population will likely continue to grow, but at a lower rate per year. Official State of Oregon population forecasts reflect the new assumptions about a decline in migration due to lower employment.

The share of population growth in the Portland-Vancouver MSA has shifted to different counties over the course of the region’s history. The population within the Portland-Vancouver MSA has spread from the urban core. Figure 4 shows each county’s proportion of the total Portland-Vancouver regional population. Suburban counties within the Portland-Vancouver MSA have taken a greater proportion of the entire population. Table 2 in the appendix gives more detail about population growth rates by county.

Figure 4. Proportion of Population in the Portland-Vancouver MSA by County, 1930-2008

Source: Portland State Population Research Center, US Census Bureau, and Washington State Office of Financial Management.

A large percentage of the Portland-Vancouver MSA’s population has gradually shifted from Multnomah County to the outlying counties in the past 80 years. In 2008, only about 33% of the population lived in Multnomah County, down from 59% in 1960. Washington County has experienced dramatic growth over the past few decades, growing from 10% of the region’s population in 1960 to 24% of the population of the Portland-Vancouver MSA in 2008.

2. Components of Population Growth

Population growth includes two components: natural increase (births minus deaths), and net migration (in-migration minus out-migration). In Oregon and the Portland-Vancouver region, migration has been at least as important as natural increase through births since the region’s settlement in the 19^th century. However, natural increase can become more important than migration whenever the economy experiences a downturn, since fewer people are inclined to migrate to a destination with high unemployment. Oregon’s economy faltered in the 1980s, leading to a corresponding decrease in total growth and a shift to natural increase. In the 1990s, the Portland region experienced an economic boom, attracting new residents through migration. In the 2000s, a middle ground emerged between these two extremes as the economy slowed in the early part of the decade and then subsequently recovered.

Mortality and life expectancy levels have remained steady in the metropolitan Portland-Vancouver area for the past two decades. The crude death rate, the number of deaths per 1,000 residents for a single year, has remained at about 8 per 1,000 people since 1980. In 2005, Life expectancy at birth in Oregon was 75.7 years for men and 80.2 years for women, which is slightly higher than the U.S. average.

Fertility in the Portland-Vancouver MSA is relatively constant, though less stable than mortality. In 2006, Oregon had a total fertility rate (TFR) of 1.98 children per woman over a lifetime—a little bit lower than the “replacement rate” of about 2.1. If women have children at current rates in the long term, the population will decrease slowly unless it is augmented by in-migration. In the long term, only regions with low in-migration rates feel the effects of increases in the total fertility rate over the long term. The crude birth rate varies by county partly due to varying social characteristics such as education levels and age structure.

Table 4 uses these data to calculate demographic rates (in terms of events per 1000 persons per year). As an example of natural increase and migration dynamics, consider Clark and Washington counties, both counties with high population growth rates. In Clark County, migration greatly effects population change, since 17.9 people move in each year for every 1000 people already living in Clark County, versus 8.9 moving into Washington County. In Washington County, natural increase contributes more to population growth than migration, with 10.4 net births per 1000 in Washington County compared with 7.8 net births per 1000 in Clark County. The data show that more established communities have a higher proportion of natural increase because they typically have less room for new housing and new migrants. Figure 4 compares the crude rate of natural increase and the crude migration rate by county. Table 3 in the appendix gives more detail about crude births, deaths, natural increase, and migration for each county between 2000-2008. Table 4 in the appendix provides numerical detail for the factors driving migration by county between 2000 and 2008.

Figure 5. Crude Rates of Natural Increase and Migration in Portland-Vancouver MSA by County, per 1000 People, 2000-2008

Source: Portland State Population Research Center, US Census Bureau, and Washington State Office of Financial Management.

Although migration plays an important role throughout the metropolitan region, its importance varies by county. Natural increase drives growth in Multnomah and Washington counties whereas migration primarily drives growth in Clark, Clackamas, and Columbia counties. Migration is more complex than natural increase, because it is driven by political, cultural, and economic factors. Typically, rates of natural increase and their components change slowly, while migration rates are more volatile. Figure 6 shows the percent of population growth between 2000 and 2008 from natural increase and net migration.

Figure 6: Percent of Population Growth Due to Natural Increase vs. Net Migration by County, 2000 to 2008

Source: Portland State Population Research Center, US Census Bureau, and Washington State Office of Financial Management.

The percent of population growth driven by net migration has fluctuated dramatically in the Portland-Vancouver MSA and Oregon as a whole since 1980, as shown in Figure 7. In the appendix, Tables 5 and 6 provide the detailed population dynamic data for the decade from 1990 to 1999, and Tables 7 and 8 provide data for the decade from 1980 to 1989.

Figure 7. Natural Increase vs. Net Migration in Portland-Vancouver MSA, Oregon, and Washington State, 1980-2008

Source: Portland State Population Research Center, US Census Bureau, and Washington State Office of Financial Management.

3. Influence of International and Domestic Immigration

Some Portland-Vancouver counties have large numbers of people moving to the county from within the United States (“domestic migration”), while some counties have large numbers of people settling in the county after relocating from outside the USA (“international migration”). Multnomah County lost population due to domestic migration—people moving from Multnomah to other counties—while it gained international population. Washington County has large numbers of international migrants, while most of the people moving into Clackamas County were moving from somewhere in the USA. Table 9 in the appendix and Figure 8 summarize the migration dynamics in the seven-county region from 2000 to 2009.

Figure 8. International and Domestic Migration in the Portland-Vancouver MSA by County, 2000-2008

Source: US Census Bureau, American Community Survey. 2005-2007 PUMS data.

Foreign-born residents represent a significant portion of the Portland-Vancouver MSA’s population. As shown in Table 20, the 2005-2007 data from the ACS show that foreign-born residents represent about 13.6% of the population of the region, compared to 10.5% of the population of Oregon. Within Oregon, approximately 105,000 were unauthorized, with over 95% of the undocumented coming from Mexico and other Central and South American countries (Warren, 2009).

The majority of people living in the Portland-Vancouver MSA were not born in Oregon. Table 10 shows estimates of migration from various states and countries, at least for the PUMAs that are within the Portland-Vancouver region. For example, 40% of all the residents of the region were born in Oregon, while 11% were born in Washington State.). Notably, 1.2% of the region’s residents were born in Minnesota, nearly the same percentage as were born in New York, a much more populous state.

Table 10. Top 20 Places of Birth for Residents of the Portland-Vancouver MSA 2005-2007

Source: US Census Bureau, American Community Survey. 2005-2007 PUMS data

4. Effects of Employment on Population Change

Prolonged periods of high unemployment have driven regional population growth rates. Figure 10 shows unemployment in the USA, Oregon, and the Portland-Vancouver MSA. There are three spikes in unemployment—around 1982, 1993, and 2002. The years with highest unemployment correspond to the decade with the lowest population growth—the 1980s. The 1990s have the lowest unemployment overall, reflected in rapid population growth. Oregon, and especially the seven-county region, felt the effects of the dot-com bust in 2002 and 2003 more strongly than the 1991 recession. The moderate population growth from 2000 to 2008 reflects the recession of the early part of the decade. We can see a slight uptick in unemployment in 2008, and the unemployment for 2009 soars to 1983 levels, so we can expect there to be correspondingly much slower population growth in the next few years. However, the seven-county region remains a destination for many migrants for its many amenities, dampening the effect of the local economy on population change.

Figure 9. U.S., Oregon, and Metropolitan Portland-Vancouver Unemployment Rates

Source: State of Oregon, Oregon Employment Department.

5. Racial and Ethnic Composition of the Portland-Vancouver MSA

The metropolitan Portland-Vancouver area population has a less ethnically diverse population than many other major metropolitan areas in the United States. However, the minority population has increased in every county in the Portland-Vancouver MSA in the last 30 years. The overall minority population increased from 360,000 people in 2000 to 507,202 people in 2008, an increase of 40.7%. This figure includes Asian Americans, Hawaiians and Pacific Islanders, Hispanics, African Americans, American Indians, and persons reporting two or more races. During this period, minority populations grew more than seven times faster than the overall population of the Portland-Vancouver MSA, which grew by 5.4% during the same period.

The minority population in the Portland-Vancouver MSA is quite heterogeneous. Almost all the African American and American Indian residents in metropolitan Portland-Vancouver were born within the United States. Many Asian American and Hispanic residents, however, are foreign-born. Table 11 in the appendix shows the racial composition of the Portland-Vancouver MSA.

Latinos are the fastest growing minority population because of domestic and international migration, as well as fertility levels above the Oregon state average. The Hispanic population grew by 10.9% between 1990 and 2000 and 6.0% between 2000 and 2008. Figure 10 shows the population growth rates of different race and ethnic groups between 1990, 2000 and 2008. Asian Americans are the second-largest minority population in the metropolitan area. The region receives immigrants from Vietnam, Hong Kong, Taiwan, Korea, Philippines, and Japan as well as Asian Americans who move here from other states. American Indians have remained a small but important minority in the Portland-Vancouver region. African Americans are the third largest minority population in the metropolitan area, numbering 53,892 in 2000, increasing from 42,785 in 1990.

Prior to the 2000 Census, there were no accurate estimates for the number of Oregonians who identify with two or more racial or ethnic groups. In the 2000 Census, 80% of residents in Portland and Oregon reported themselves as white. However, 3.3% of the population, or 53,480 people in the metropolitan Portland-Vancouver area, identified themselves as having two or more races in the 2000 census.

Figure 10. Percent Non-White by Year in the Portland-Vancouver MSA, 1990-2008

Source: Portland State Population Research Center, US Census Bureau, and Washington State Office of Financial Management.

6. Future Population Trends

Due to the recent economic downturn, the State of Oregon is currently revising the official population forecast and forecasts are currently unavailable at the regional level. Preliminary results project that Oregon as a whole will grow 1.0% in 2010, 1.2% in 2011, and then stabilize at a yearly rate of about 1.2% per year until 2015. If we assume that the Portland-Vancouver Metro region will have similar growth levels, we can forecast a total population of 2,369,578 in 2015 for the region.

Data Sources

In this report, we use official state population estimates from Washington and Oregon and U.S. Census data from the decennial Census and the yearly county estimates program. This article does not cover Yamhill, Columbia, or Skamania counties, because these small-population counties belong to PUMAs that also include large areas outside of the Portland-Vancouver region.

Glossary

Annualized Growth. “Annualized growth” is the percentage growth that occurs each year during a period, calculated from two points which are more than a single year apart using the geometric average. For example, above we see that the annualized growth for the region between 1980 and 1990 was 1.3% – this means that in each year during that decade, the region added 1.3% more people than the previous year. Obviously, growth rates change from year to year, with some years higher and some lower; these are averaged together in “annualized growth” numbers.

Natural Increase. “Natural increase” is the amount by which the population changes that is NOT due to migration (people entering the population after they are born somewhere else). It is calculated by subtracting the number of deaths in a year from the number of births in the same year.

PUMA. A PUMA (Public Use Microdata Area) is a region designed to contain about 100,000 people at the 2000 Census, and is the level of geography specified in the one and three year pooled PUMS data. The Census Bureau withholds finer grained detail regarding location in order to protect the confidentiality of the survey respondents; otherwise, an analyst might be able to learn private details about a person and find that person through the PUMS data.

PUMS Data. PUMS (Public Use Microdata Sample) data are a copy of about half of the individual responses to the ACS questionnaire, along with some other variables added by the Census Bureau. These data allow us to create custom analyses, rather than rely on tabulations made by the Census Bureau.

Rate. A “rate” is a measure of how often an even happens for each unit of “exposure.” Exposure, in turn, refers to how many people are alive for a year. In order to calculate the mortality rate in 2006, for example, we would divide the number of deaths in the year by the number of people alive in the same year. We can calculate rates for many different events, including migration, fertility, marriage, etc.

Acknowledgments

We would like to thank Vivian Siu, Nancy Hales, Charles Rynerson, Sarah Iannarone, Emily Renfrow, and Lisa Yarbrough for their many helpful comments. Any errors are the responsibility of the author.

Appendix

Table 1. Population in the Portland-Vancouver MSA by County, 1930-2008 (Jump Back)

Source: Portland State Population Research Center, US Census Bureau, and Washington State Office of Financial Management.

Table 2. Population Change per Decade in Portland-Vancouver MSA, Oregon, USA and Portland-Vancouver MSA counties, 1930-2008 (Jump Back)

Source: Portland State Population Research Center, US Census Bureau, and Washington State Office of Financial Management.

Table 3. Crude Rates in Portland-Vancouver MSA by County (Events per 1000 per Year), 2000-2008 (Jump Back)

Source: Portland State Population Research Center, US Census Bureau, and Washington State Office of Financial Management.

Table 4. Components of Population Change, 2000-2008 (Jump Back)

Source: Portland State Population Research Center, US Census Bureau, and Washington State Office of Financial Management.

Table 5. Components of Population Change, 1990-1999 (Jump Back)

Source: Portland State Population Research Center, US Census Bureau, and Washington State Office of Financial Management.

Table 6. Components of Population Change, 1980-1989

Source: Portland State Population Research Center, US Census Bureau, and Washington State Office of Financial Management.

Table 7. Crude rates by County 1990-1999 (Jump Back)

Source: Portland State Population Research Center, US Census Bureau, and Washington State Office of Financial Management.

Table 8. Crude rates by County 1980-1989

Source: Portland State Population Research Center, US Census Bureau, and Washington State Office of Financial Management.

Table 9. International and Domestic Migration in the Portland-Vancouver MSA by County, 2000-2008 (Jump Back)

Source: US Census Bureau.

Table 11. Racial Composition of the Portland-Vancouver MSA (Jump Back)

Source: Portland State Population Research Center, US Census Bureau, and Washington State Office of Financial Management.