Table of Contents

1. Introduction

The methodology used to determine annual federal poverty thresholds was developed in 1964 as a measure of the adequacy of a household’s income for providing its most basic needs. Developed by Mollie Oshransky of the Social Security Administration, the methodology was based on her analysis of consumption data that showed that families of three or more persons spent about one third of their after-tax money income on food in 1955. She developed the thresholds based on this assumption and the cost of the Department of Agriculture’s Economy Food Plan. The thresholds vary by size of household and number of related children below 18 and they are adjusted over time for inflation. However, the methodology does not account for differences in the cost of living due to location, age of children, or other factors. Furthermore, the spending assumptions on which the methodology was based are outdated. According to the 2007 Consumer Expenditure Survey, U.S. households now spend an average of 12.4 percent of all their spending on food. Even for very low-income households, this percentage is about 15 percent, much lower than the one-third assumed in the methodology for calculating the federal poverty thresholds.

Many researchers and policy analysts have criticized the FPL as out of date and an inaccurate measure of poverty. They argue that the FPL overlooks a number of families who are experiencing economic distress. Because many federal and state safety net programs are based on the FPL, this means that many households who are in economic distress might not receive assistance.

Dr. Diana Pearce, Director of the Center for Women’s Welfare at the University of Washington, has developed an alternative measure of income adequacy called the Self-Sufficiency Standard. This measure considers many factors ignored by the federal poverty thresholds. For example, the Self-Sufficiency Standard includes the cost of housing, childcare, food, health care, transportation, and reflects differences in these items by geography. It also varies by the ages of children in a household to reflect how a household budget varies as needs for child care, health care, and food vary with the age of children. Finally, the standard also includes the effect of taxes and tax credits. Dr. Pearce’s work was funded by WorkSystems Inc.

Dr. Pearce has calculated the Self-Sufficiency Standard for each of Oregon’s counties, and this is reflected in Table 1. Table 1 also includes the median household income for each county and the federal poverty standard for each household type.

Table 1: Self-sufficiency Wages for All Counties in Oregon State and Federal Poverty Level, 2008

Sources: The Self-Sufficiency Standard for Oregon State by Diana Pearce at University of Washington. The data are also available here. Note: *, Median Household income obtained from the American Community Survey for the period of 2005 to 2007. All values in US Dollars.

Table 1 shows that for a single adult, the most expensive county in Oregon is Washington County, with a Self-Sufficiency Standard of $22,646. The least expensive county in Oregon is Baker County, with a Self-Sufficiency Standard of $15,927 for a single adult. The table also shows that the federal poverty threshold for a single adult, $11,201, is inadequate income for any of Oregon’s counties.

Table 1 also reflects changes in the Self-Sufficiency Standard as household size and composition changes. In Clackamas County, for example, an adult with an infant must make $41,894 to meet the Self-Sufficiency Standard, while an adult with a preschooler only needs $39,663. This reflects the standard’s sensitivity to differences in the cost of childcare from an infant to an older child.

Finally, Table 1 shows how the Self-Sufficiency Standard compares to the median household income in each county for 2005-2007. In most counties, the median household income is sufficient to meet the Self-Sufficiency Standard. However, keep in mind that this is the median, or middle income level. That means that half of the households earn less, and half earn more, than the median income.

The Institute of Portland Metropolitan Studies has used Dr. Pearce’s calculations and information from the American Community Survey to calculate the percentage of households earning sufficient income to meet their basic needs. Our objective for the analysis was to further our understanding of the extent of poverty in Oregon, the geographic areas and households types most affected, and the extent to which the existing federal poverty standard disregards households failing to make ends meet. A detailed description of the methodology and assumptions used in the analysis is provided at the end of this article.

This policy brief offers a quick glance of the results of our analysis. A more thorough analysis and report is forthcoming and will be available by the end of June 2009 on the IMS web site. The data developed for the analysis are available for download by exploring the tables and charts contained in this policy brief.

2. Self-Sufficiency in Oregon’s Counties

Figures 1 and 2 offer an overview of the percentage of households not meeting the Self-Sufficiency standard and federal poverty level by county in Oregon. Statewide, 27.1 percent of all households do not earn enough money to meet the Self-Sufficiency standard for their county and household type. The map shows that the counties with the highest percentage of households with inadequate income include Benton, Coos, Curry, Josephine, Lane, and Linn Counties. Within these counties, at least 30 percent of households do not earn enough to meet their basic needs as defined by the Self-Sufficiency Standard. Counties with the lowest percentage of households not meeting the standard include Clackamas, Multnomah, and Douglas Counties.

Figure 1: Percent of Population below Federal Poverty Level, by County

Figure 2: Percent of Population below the Self-Sufficiency Standard, by County

A closer look at the data in Table 2 offers additional insights about the Self-Sufficiency Standard and its relationship to the federal poverty standard. For example, while only 7.8 percent of households in Lincoln County don’t earn enough income to meet the federal poverty standard for their household type, an additional 21.8 percent are above the federal poverty standard but below the Self-Sufficiency Standard. A policymaker examining poverty in Lincoln County using only the federal poverty standard might not realize that there is such a large number that fall in between the two standards. These households may be forgotten as targets of prosperity policy.

Note that when reading this table and most of the tables that follow, the percentages sum to 100% by row; for example, in Baker County, 13.2% + 14.7% = 27.9% (the subtotal percentage below self-sufficiency in Baker County), and  27.9%+72.1% = 100% (all people in Baker County).  This allows us to examine the effect that membership in a row category  has on the self-sufficiency outcome.  For example, we see that if a person lives in Benton County, they have a 31.0% chance of falling below the self-sufficiency standard, but if they live in Clackamas County, their chances fall to 24.8%, all other things being equal.

Note also that all totals and subtotals will be bolded in the tables.  So in Table 2, “Total Percent Below Self-Sufficiency Standard” is a subtotal of “Percent Below Poverty” and “Percent Above Poverty [But Still Below Self-Sufficiency Standard]“, so it is bolded.

Table 2: Self Sufficiency Standard and Federal Poverty Level for Households by County in Oregon: 2005-2007

Source: American Community Survey, 1% PUMS data 2005-2007

3. Self-Sufficiency and Ethnic Origin

It is widely understood that poverty falls disproportionately on households headed by non-whites. Table 3 reinforces our understanding by showing that while only 23.7 percent of white Oregon households earn income insufficient to meet the Self-Sufficiency Standard, the percentage is 56.2 percent for Latinos (of any race), 42.3 percent for black households, 37.9 percent for Native Americans, and 32 percent for Asian and Pacific Islanders.

Table 3-A: The Self-Sufficiency Standard, and Federal Poverty Level by Race of Householder by Household Income: Oregon 2005-2007

Source: American Community Survey, 1% PUMS data 2005-2007. * Latino may be of any race

Table 3-B: Percentage Households by Race: Oregon 2005-2007

Source: American Community Survey, 1% PUMS data 2005-2007. * Latino may be of any race

4. Women and Self-Sufficiency

Women head of households are also less likely to meet the Self-Sufficiency standard than are men. In Table 4-A we see that female headed households have a higher chance of falling below the self sufficiency standard than male-headed households, with 32% of all female headed households in Oregon below the standard and 23% of male households below. We also see from Table 4-B that this percentage increases to 61% of married family households headed by a female with children but without a spouse.

Note that unit of measurement here is the household, rather than population; so we are counting groups of people that live together at a single address. We are not counting “group quarters” population (for example, prisoners or military servicepeople housed in barracks); we are also not counting households headed by either the disabled or someone outside the ages 18-64. Note also that like above the percentages are calculated by row; for example on the first row we read across, and see that 23% of males are below the SSS, 73% are above it, and the total (in the far left column) is 100%, accounting for all the male headed households in Oregon able to work (a total of 567,776 households).

Table 4-A: The Self-Sufficiency Standard by Sex of Householder

Table 4-B: The Self-Sufficiency Standard by Household Type

Table 4-C: Percentages of household types within Oregon

5. Self-Sufficiency and Education

We also know that education is tied to income. Figure 3 and Table 5 demonstrate that relationship by showing that for both white and nonwhite female and males, the percentage of households not meeting the Self-Sufficiency Standard falls as the level of education rises. Among households headed by nonwhite females with less than a high school education, 75.9 percent do not meet the Self-Sufficiency Standard. For those households, the percentage falls to 28.1 percent if the female head of household has a bachelor’s degree or higher. Similarly, for households headed by a white male, the percentage falls from 36 percent for those with less than a high school education to 11 percent for those with a bachelor’s degree or higher.

Figure 3: Households Below the Self-Sufficiency Standard, by Education, Race/Ethnicity, and Gender: Oregon 2005-2007

Table 5-A: The Self-Sufficiency Standard by Education Level, Gender, and Race: Oregon 2005-2007

Table 5-B: Household distribution by educational attainment, sex, white or non-white.

Source: American Community Survey, 1% PUMS data 2005-2007. Note: The householder is the person (or one of the persons) in whose name the housing unit is owned or rented or , if there is no such person, the householder is any adult member, excluding roomers, boarders, or paid employees.

6. Self-Sufficiency and Work

Having a steady job does not guarantee the ability to meet basic needs, as shown in Table 6. In Oregon many households with two workers still do not meet the Self-Sufficiency Standard. Even for households without children and two working adults, 11.9 percent do not meet the Self-Sufficiency standard. Among households with children and two workers, 20.4 percent of those headed by a male or part of a married couple don’t make the standard, while 44.7 percent of those headed by a female don’t meet the standard, despite the presence of two workers.

Table 6-A: The Self-Sufficiency Standard and Federal Poverty Level by Work Status of Adults: Oregon 2005-2007

Source: American Community Survey, 1% PUMS data 2005-2007

Table 6-B: The Distribution of Households by Work Status of Adults: Oregon 2005-2007

Source: American Community Survey, 1% PUMS data 2005-2007

7. Conclusion

The Self-Sufficiency Standard developed by Dr. Diana Pearce offers a more realistic view of what it takes to get by in Oregon and shows us who is getting by and who is not. Although only 9.7 percent of Oregon’s households earn incomes below the federal poverty standard, 27.1 percent do not make enough to meet basic needs without assistance. Many households with a good education and two workers still cannot meet the Self-Sufficiency Standard. This information sheds additional light on the economic realities facing many of Oregon’s households.

To read about Metholodology and Assumptions for this analysis, click here.

]]> http://mkn.research.pdx.edu/2009/05/getting-by-in-oregon/feed/ 0 http://mkn.research.pdx.edu/2009/05/mkn-survey-posted/ http://mkn.research.pdx.edu/2009/05/mkn-survey-posted/#comments Thu, 28 May 2009 19:18:10 +0000 epicha http://mkn.research.pdx.edu/?p=904 ]]>

Travel time (minutes) for Interstate 5, from junction with 205 to the Columbia River Bridge (NB: 23 miles; SB: 21 miles):

Travel time (minutes) for Highway 84, from junction with 205 to Downtown Portland (EB: 3.7 miles; WB: 3.5 miles):

Travel time (minutes) for Highway 26, from Hillsboro to Downtown Portland (EB: 13.3 miles; WB 11.7 miles):

Data provided by PORTAL — the “Portland Transportation Archive Listing”:

The unemployment rate in the Portland Metro area continues to increase at record speed – see Figure 1. March statistics indicate that Oregon, at 12.1%, has the second highest unemployment rate in the US, just a half point behind Michigan.

The Metro area lost almost 44,000 jobs in the 12 months leading up to March 2009, representing 4.4% of all jobs. For comparison, the US lost 3.5% of all jobs. See Figure 2. Certain industries were hit harder than others. While the Health Care and Private Education industries continue to grow, every other industry has lost jobs over the last 12 months. Particularly hard hit was manufacturing, which has lost almost one in twelve jobs in the last 12 months.  See Figure 3.

The industry with the most job loss is undoubtedly the Construction industry, which has lost more than one in seven jobs year over year. The real estate bubble burst in late 2007, and the value of Portland homes continues to drop. Every month, Standard & Poor’s publishing an estimate of the value of houses in the US Metro areas. Since the summer of 2007, the value of the average Portland area home has declined by almost 20 percent. Of greater concern, the value of homes is continuing to fall at an increasing rate.  See Figure 4.

No one knows when housing values, or the job market, will stop dropping. A popular economic indicator is housing permit data. Before a construction company can hire people to work on building a new house, they must apply for a housing permit. In the first quarter of 2009, 741 permits were issued, that’s less than one fifth of the number of permits issued in the first quarter of 2007, during the height of the housing boom. This suggests that jobs, at least in the construction industry will continue to disappear through 2009. That’s bad news for an industry that’s already lost about 25 percent of all jobs since the summer of 2007.  See Figure 5.

Portland’s economic troubles are severe, and few metro areas in the US have posted higher unemployment rates. However, the region’s high unemployment rate is not simply a function of the jobs lost. As we’ve lost jobs, Portland’s workforce has continued to grow, primarily from migrants coming from other US states. In the 12 months ending in June 2008, the Portland Metro population grew by more than 30,000 people – as the weakening job market created only about 6,000 new jobs. See Figure 6.

This is not a new situation for us. Every month in 2002, during the last recession, Oregon had the highest unemployment rate in the US.  Oregon, and Portland’s, population continued to grow as we lost jobs. This unusual trend pushed Oregon’s unemployment rate to the highest in the nation in the last recession, and may do so again in 2009.

Table of Contents

0. Introduction

Are the businesses in the Portland metropolitan region prospering?

When we think of business prosperity, we picture a company with growing revenues hiring new employees and opening new plants and offices. Given our persistently negative recent economic news, we might immediately jump to the conclusion that our businesses are on the decline.

But what evidence should we use to determine whether our businesses are thriving?

The Brookings Institution’s Blueprint for Regional Prosperity identifies three types of growth necessary for regional prosperity: productive growth, inclusive growth, and sustainable growth. Although all three play important roles in metropolitan prosperity, this article focuses on productive growth, because businesses are the primary drivers of productive growth.

Productive growth requires innovation and entrepreneurship and leads to income and job growth. We examine data that point to productivity, entrepreneurship, and the ingredients of innovation: venture capital investment, patent activity, and educational attainment.

Finally, we assess the region’s job growth to determine which economic sectors have the most robust growth.

Like other articles on the Metropolitan Knowledge Network, we examine our region’s prosperity in comparison to other regions comparable to the Portland MSA and present the data in order of their 2008 population estimates. Figure 1 shows Portland and the 10 comparator metropolitan areas in descending order by 2008 population.

1. Productivity

Productivity growth is a key ingredient to a growing and vibrant economy. Productivity growth, usually measured as output per unit of labor, is important because it leads to a rising standards of living. Productivity growth usually coincides with rising wages, and companies, industries, and nations with rising productivity are generally considered more competitive and profitable than other companies, industries, and nations. And although the wage/productivity link is currently being debated, rising productivity is generally a sign that workers and company shareholders will eventually benefit.

Productivity is usually measured as output or value added per unit of labor. For the United States and for individual business sectors, the Bureau of Labor Statistics calculates both labor productivity and multifactor productivity, which takes into account not only labor, but also capital and intermediate inputs. It does not publish productivity statistics for states or metropolitan areas.

In an attempt to fill the gap in metropolitan level productivity statistics, we calculate productivity measures for the Portland region and its competitor MSAs by taking the ratio of Gross Metropolitan Product (GMP), published by the Bureau of Economic Analysis (BEA), to total non-farm workers, also published by BEA. Please note that the GMP estimates are experimental. See Figure 2. Therefore, the same caveats that apply to these estimates apply to these productivity measures as well.

Regional Comparisons

Portland ranks low relative to the comparator metropolitan areas in GMP per worker. For the Portland MSA, GMP per worker rose from $62,298 in 2001 to $76,803 in 2006 (see Figure 3). This 23.3 percent increase places the Portland region 5th in terms of productivity growth among its peer regions (see Figure 2). GMP per worker for Portland in 2006 was lower than seven of the peer regions.

The San Jose and Charlotte MSAs had the highest GMP per worker at $118,022 and $109,096, respectively, and the Austin and Salt Lake City regions had the lowest.

Table 1: GDP per Worker for Portland and Comparator MSAs, 2001-2006.

Source: Bureau of Economic Analysis, “Gross Domestic Product by Metropolitan Area” and “Local Area Income and Employment” data sets from 2006.

]]> http://mkn.research.pdx.edu/2009/03/business-prosperity-in-the-portland-metro-region/feed/ 0 http://mkn.research.pdx.edu/2009/02/mkn-introduction/ http://mkn.research.pdx.edu/2009/02/mkn-introduction/#comments Wed, 18 Feb 2009 22:07:31 +0000 wsprague http://mkn.research.pdx.edu/wordpress/?p=377 ]]>

“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

Webb Sprague
Project Director, Metropolitan Knowledge Network
503-725-5132
wsprague@pdx.edu

The Portland-Beaverton-Vancouver Primary Metropolitan Statistical Area includes Multnomah, Clackamas, Washington, Columbia and Yamhill counties in Oregon, and Clark and Skamania counties in Washington.

Table of Contents

Executive Summary

Prosperity refers to the economic success of the regional economy and can be measured using income data. In order to assess regional prosperity in the Portland Metropolitan region, we will consider two measures of income: aggregate regional income (Metropolitan GDP) and income for individuals and households. In addition, we will discuss poverty levels in the Portland Metropolitan region according to the federal poverty standard. To ascertain the significant income variations both within the region and between comparable regions, this paper will compare counties within the Portland Metropolitan region and discuss how Portland measures up to ten “comparator regions” in the United States.

How quickly has income grown?

In the past several years, both aggregate regional income and personal income rose in the Portland Metropolitan region. Between 2001 and 2007, Portland’s per capita personal income grew 19 percent from $32,338 to $38,511. Between 2001 and 2006, the Portland Metropolitan region’s GDP grew 34 percent from $77 billion to $103 billion.

How does the Portland Metropolitan region’s income compare to other metropolitan regions?

Between 2001 and 2006, the Portland region and its comparator regions have seen vastly different rates of Metropolitan GDP growth. The Portland region’s Metropolitan GDP grew at a rate of 34 percent compared to 12 percent for the San Jose, CA region and 67 percent for the Las Vegas, NV region. The Portland Metropolitan region has a similar Metropolitan GDP to Austin and Salt Lake City. Portland’s per capita personal income in 2006 was $38,511, which was on the lower end of the scale in terms of the ten comparator regions but still comparable to the Austin, Charlotte, and Salt Lake City regions. According to the U.S. Census American Community Survey, Portland’s median household income is $52,480—just below Austin, but higher than both Phoenix and Charlotte. The Portland Metropolitan region’s level of poverty is at the median of the comparator region group with 11.5 percent of individuals earning incomes below the federal poverty line.

How is income within the Portland Metropolitan region distributed among counties?
Income varies greatly between the seven counties in the Portland Metropolitan region. Clackamas County has the highest level of per-capita personal income at $41,378, followed by Multnomah County with $38,529. Skamania County has the lowest level of per-capita income at $28,265, while Washington County is very close to the average for the metropolitan area at $36,259.

1. How Should We Gauge Our Region’s Prosperity?

How do we know whether our region is prosperous? Although prosperity probably means different things to different people, we usually think of prosperity as economic success or vibrancy. With respect to the Portland-Vancouver region, then, prosperity refers to economic success or the vibrancy of the regional economy. Does the region’s economy provide the income, goods, and services that people need to feel satisfied with their lives? Do the region’s inhabitants feel economically secure and confident that they can live in a reasonably comfortable fashion? Are they able to enjoy some of the non-economic pleasures that contribute to quality of life? These are some of the questions we might ask as we investigate whether our region is economically prosperous.

This Metropolitan Knowledge Network Journal issue paper presents a variety of data that paint a picture of the prosperity of our region. In particular, we focus on the economic prosperity of individuals. The financial status and viability of business is certainly important to the notion of regional prosperity because businesses create value, earn income from outside the region and offer economic opportunities to individuals. We provide a discussion of business vitality and the data that describe it in a future article entitled “How Prosperous are our Region’s businesses?” This paper focuses specifically on outcome measures of prosperity, including the Gross Domestic Product of the region, personal income, money income, and poverty. A discussion of prosperity should also consider whether we are investing in the drivers or inputs to that prosperity. These drivers include innovation, human capital, infrastructure, and quality places. These indicators of assets for prosperity will be explored in future articles on this site.

1.1 What Measures Are Normally Used to Determine Whether a Region Is Doing Well

Most people gauge the state of their economic well-being, at least in part, by how much income they receive. Income determines, in large part, a person’s or household’s standard of living. It determines whether they can afford to meet the basic needs of their family and whether they can purchase other goods and services that enrich their lives. However, income is only part of the prosperity equation. It is only relevant relative to cost. Thus, factors that affect a family’s cost of living, such as household structure (number of income earners, number and age of children) and location (which affects the cost of housing and transportation) also determine economic well-being.

A new set of data recently developed by the University of Washington estimates the level of earnings required for a household to meet its basic needs without government assistance. This income level, called the Self-sufficiency Standard, varies by county and household type. We must also consider the amount of time a person devotes to earning income. A person earning $40,000 per year working 40 hours per week might feel much better off than someone earning the same annual income but working one full-time and two part-time jobs in order to achieve that income. Thus, an earner’s hourly wage and the activities that a person must give up to earn an income might also enter into a person’s sense of their own prosperity.

While we consider the income of individuals, households, and families in the metropolitan region, we might also look at the region’s income in the aggregate. Regional measures of income allow us to consider the prosperity of the region as a whole, or on a per capita basis, regardless of how it is distributed. We will consider both measures of income—aggregate regional income and income for individuals and households—in discussing regional prosperity. Finally, regional income is determined, in large part, by the level and value of economic activity in the region. The Gross Domestic Product (GDP) for metropolitan regions is the total value of goods and services produced in the region. Akin to the national measure of GDP, metropolitan level GDP can be interpreted as a comprehensive measure of economic activity. At the national level, GDP is the most widely used measure of the state of the national economy.

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1.2 Measures of Income

There are generally three sources of publicly available income data:

1. Personal income data is collected and distributed by the Bureau of Economic Analysis (BEA).
2. Money income data is collected and distributed by the Census Bureau.
3. The Internal Revenue Service publishes aggregated measures of adjusted gross income of individuals.

The BEA produces annual estimates of personal income for local areas, including counties, metropolitan areas, and BEA economic areas. These estimates are designed to be consistent with the national income and product accounts, which are used to estimate Gross National Product and other national economic data. The BEA’s personal income measure is a more comprehensive measure of income than the money income measure used by the Census Bureau. As described below, personal income is the current income that is received by, or on behalf of, the residents of that area from all sources, minus their contributions for social insurance (BEA 2008).

The Census Bureau derives income information from the Decennial Census, the American Community Survey, and the March supplement of the Current Population Survey. Money income includes only money income received by individuals and excludes non-cash benefits. Poverty rates reported by the Census Bureau are based on money income. The Internal Revenue Service Adjusted Gross Income measure consists of the taxable income of individuals who filed a federal income tax return. In general, BEA estimates of personal income are higher than both the money income estimates provided by the Census Bureau and the adjusted gross income measure offered by the IRS. For more detail about these three definitions of income, see the inset below.

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1.3 Sources of Income

Income reported by the BEA has three sources: earnings from work; income from investment; and transfer payments, which include social security, pensions, and welfare. For most people, the largest part of their income is derived from their earnings from employment. However, some regions may include a larger than average number of people whose main source of income is from transfer payments. This information is important because the economic structure of such regions can be fundamentally different than those with higher percentage of income from earnings. Thus, they may react differently than other regions to national economic trends and to economic policy.

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1.4 Metro Level GDP Per Capita

The Bureau of Economic Analysis recently began calculating a gross domestic product (GDP) measure for metropolitan regions. Akin to the GDP for the nation, the metropolitan level GDP estimates the market value of all the goods and services produced in the metropolitan region. In the first release of these statistics in September 2007, these data were described as prototype statistics being released “for evaluation and comment by data users.” The methodology used to create these estimates relies heavily on industry earnings, which causes some problems that are explained in the inset below.

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