Equitable Growth Profile of the Triad Region Equitable Growth Profile of the Piedmont Triad Region PolicyLink and PERE 2 Summary

Communities of color are driving the Piedmont Triad’s population growth, and their ability to participate in the economy and thrive is central to the region’s success now and in the future. But slow growth in jobs and economic activity – along with rising inequality and wide racial gaps in income and opportunity – place the region’s economic future at risk.

Equitable growth is the path to sustained economic prosperity. By growing good jobs, investing in its increasingly diverse workforce, and infusing economic inclusion into its economic development and growth strategies, the region’s leaders can put all residents on the path toward reaching their full potential and secure a bright economic future for the Piedmont Triad. Equitable Growth Profile of the Piedmont Triad Region PolicyLink and PERE 3 List of indicators

DEMOGRAPHICS Median Hourly Wage by Race/Ethnicity, 2000 and 2010

Who lives in the region and how is this changing? Is the middle class expanding? Race/Ethnicity and Nativity, 2010 Households by Income Level, 1979 and 2010 Growth Rates of Major Racial/Ethnic Groups, 2000 to 2010 Is the middle class becoming more inclusive? Racial/Ethnic Composition, 1980 to 2040 Racial Composition of Middle-Class Households and All Percent People of Color by County, 1980 to 2040 Households, 1980 and 2010 Share of Population Growth Attributable to People of Color by County, FULL EMPLOYMENT 2000 to 2010 How close is the region to reaching full employment? Racial Generation Gap: Percent People of Color by Age Group, Unemployment Rate by County, March 2014 1980 to 2010 Labor Force Participation by Race/Ethnicity, 1990 and 2010 Median Age by Race/Ethnicity, 2010 Unemployment Rate by Race/Ethnicity, 2010 INCLUSIVE GROWTH Unemployment Rate by Educational Attainment and Race/Ethnicity, Is economic growth creating more jobs? 2010

Average Annual Growth in Jobs and GDP, 1990 to 2007 and 2009 to ACCESS TO GOOD JOBS 2012 Can workers access high-opportunity jobs? Is the region growing good jobs? Jobs by Opportunity Level by Race/Ethnicity and Nativity held by Growth in Jobs and Earnings by Industry Wage Level, 1990 to 2010 Workers with a Bachelor’s Degree or Higher, 2011 Is inequality low and decreasing? Can all workers earn a living wage? Level of Income Inequality, 1979 to 2010 Median Hourly Wage by Educational Attainment and Race/Ethnicity, Are incomes increasing for all workers? 2010 Real Earned-Income Growth for Full-Time Wage and Salary Workers, 1979 to 2010 Equitable Growth Profile of the Piedmont Triad Region PolicyLink and PERE 4 List of indicators

Do all residents have access to a car? PREPARED YOUTH

Percent of Households without a Vehicle by Race/Ethnicity, 2010 Are youth ready to enter the workforce? Do all residents earn enough to cover housing and transportation costs? Share of 16-24-Year-Olds Not Enrolled in School and without a High Average Housing and Transportation Costs as a Percent of Household School Diploma, 1990 to 2010 Income by County, 2009 Disconnected Youth: 16-24-Year-Olds Not in Work or School, ECONOMIC SECURITY 1980 to 2010 ECONOMIC BENEFITS OF EQUITY Is poverty low and decreasing? Poverty Rate by Race/Ethnicity, 2000 and 2010 How much higher would GDP be without racial economic inequities? Is working poverty low and decreasing? Actual GDP and Estimated GDP without Racial Gaps in Income, 2012 Working Poverty Rate by Race/Ethnicity, 2000 and 2010

STRONG INDUSTRIES AND OCCUPATIONS

What are the region’s strongest industries? Strong Industries Analysis, 2010

What are the region’s strongest occupations? Strong Occupations Analysis, 2011 SKILLED WORKFORCE

Do workers have the education and skills needed for the jobs of the future? Share of Working-Age Population with an Associate’s Degree or Higher by Race/Ethnicity and Nativity, 2010 and Projected Share of Jobs that Require an Associate's Degree or Higher, 2020 Equitable Growth Profile of the Piedmont Triad Region PolicyLink and PERE 5 Introduction Foreword

Equity and inclusion have become the guiding pillars of that all of our residents can access the resources and the Piedmont Together plan to build a resilient, opportunities they need so they, and all of us, can prosperous economy and a better quality of life for all thrive in the coming decades. We hope that you join us of the Piedmont Triad’s residents. in building this more equitable and sustainable future for all in the Triad. The Piedmont Together effort is led by a diverse team of community stakeholders working to find solutions to the region’s challenges. Broad resident engagement in the planning process has been a priority from the beginning. Our region has not included the voices of all Mark E. Kirstner residents, especially marginalized groups, in planning Director of Planning processes. We knew that deep, meaningful Piedmont Authority for Regional Transportation engagement was critical to build trust and create a plan that truly reflects the diverse communities of our region.

Yet, after the first rounds of engagement it became clear that seeking input from traditionally underserved groups was essential but insufficient. We also needed to build a shared narrative about why inclusion matters to the region’s future, and specific strategies to ensure that the plan steered regional growth and development toward truly equitable outcomes.

This Equitable Growth Profile represents that new approach. Developed in partnership with PolicyLink and the USC Program for Environmental and Regional Equity (PERE), it tells the story of how our region is growing and why equity is our path to broadly shared prosperity. It points to what we need to do to ensure Equitable Growth Profile of the Piedmont Triad Region PolicyLink and PERE 6 Introduction Overview

Across the country, regional planning This profile was developed to help Piedmont organizations, local governments, community Together implement its plan for equitable organizations and residents, funders, and growth. We hope that it is broadly used by policymakers are striving to put plans, advocacy groups, elected officials, planners, policies, and programs in place that build business leaders, funders, and others working healthier, more vibrant, more sustainable, and to build a stronger and more equitable more equitable regions. Piedmont Triad.

Equity – ensuring full inclusion of the entire The data in this profile are drawn from a region’s residents in the economic, social, and regional equity database that includes the political life of the region, regardless of race, largest 150 regions in the United States. This ethnicity, age, gender, neighborhood of database incorporates hundreds of data residence, or other characteristics – is an points from public and private data sources essential element of the plans. including the U.S. Census Bureau, the U.S. Bureau of Labor Statistics, the Behavioral Risk Knowing how a region stands in terms of Factor Surveillance System (BRFSS), and the equity is a critical first step in planning for Integrated Public Use Microdata Series equitable growth. To assist communities with (IPUMS). Note that while we disaggregate that process, PolicyLink and the Program for most indicators by major racial/ethnic group, Environmental and Regional Equity (PERE) figures for the Asian/Pacific Islander developed a framework to understand and population as a whole often mask wide track how regions perform on a series of variation. Also, there is often too little data to indicators of equitable growth. break out indicators for the Native American population. See the “Data and methods" section for a more detailed list of data sources. Equitable Growth Profile of the Piedmont Triad Region PolicyLink and PERE 7 Introduction Defining the region

For the purposes of this profile, we define the region as the 12 counties that are included in the Piedmont Together regional plan and shown on this map. All data presented in the profile use this regional boundary. Minor exceptions due to lack of data availability are noted in the “Data and methods” section. Equitable Growth Profile of the Piedmont Triad Region PolicyLink and PERE 8 Introduction Why equity matters now

1 Manuel Pastor, “Cohesion and Competitiveness: Business Leadership for The face of America is changing. • More equitable nations and regions Regional Growth and Social Equity,” OECD Territorial Reviews, Competitive Cities in the Global Economy, Organisation For Economic Co-Operation And Our country’s population is rapidly experience stronger, more sustained Development (OECD), 2006; Manuel Pastor and Chris Benner, “Been Down 1 So Long: Weak-Market Cities and Regional Equity” in Retooling for Growth: diversifying. Already, more than half of all growth. Building a 21st Century Economy in America’s Older Industrial Areas (New babies born in the United States are people of • Regions with less segregation (by race and York: American Assembly and Columbia University, 2008); Randall Eberts, George Erickcek, and Jack Kleinhenz, “Dashboard Indicators for the color. By 2030, the majority of young workers income) and lower income inequality have Economy: Prepared for the Fund for Our Economic Future” (Federal Reserve Bank of Cleveland: April 2006), will be people of color. And by 2043, the more upward mobility. 2 http://www.clevelandfed.org/Research/workpaper/2006/wp06-05.pdf. United States will be a majority people-of- • Companies with a diverse workforce achieve 2 Raj Chetty, Nathaniel Hendren, Patrick Kline, and Emmanuel Saez, “Where is the Land of Economic Opportunity? The Geography of Intergenerational color nation. a better bottom-line.3 Mobility in the U.S.” http://obs.rc.fas.harvard.edu/chetty/website/v2/Geography%20Executive% • A diverse population better connects to 20Summary%20and%20Memo%20January%202014.pdf Yet racial and income inequality is high and global markets.4 3 Cedric Herring. “Does Diversity Pay?: Race, Gender, and the Business Case for Diversity.” American Sociological Review, 74, no. 2 (2009): 208-22; Slater, persistent. Weigand and Zwirlein. “The Business Case for Commitment to Diversity.” Business Horizons 51 (2008): 201-209. Over the past several decades, long standing The way forward: an equity-driven 4 U.S. Census Bureau. “Ownership Characteristics of Classifiable U.S. Exporting inequities in income, wealth, health, and growth model. Firms: 2007” Survey of Business Owners Special Report, June 2012, opportunity have reached unprecedented To secure America’s prosperity, the nation http://www.census.gov/econ/sbo/export07/index.html. levels. And while most have been affected by must implement a new economic model growing inequality, communities of color have based on equity, fairness, and opportunity. felt the greatest pains as the economy has shifted and stagnated. Metropolitan regions are where this new growth model will be created. Strong communities of color are necessary Regions are the key competitive unit in the for the nation’s economic growth and global economy. Metros are also where prosperity. strategies are being incubated that foster Equity is an economic imperative as well as a equitable growth: growing good jobs and new moral one. Research shows that equity and businesses while ensuring that all – including diversity are win-win propositions for nations, low-income people and people of color – can regions, communities, and firms. For example: fully participate and prosper. Equitable Growth Profile of the Piedmont Triad Region PolicyLink and PERE 9 Introduction What is an equitable region?

Regions are equitable when all residents – regardless of race/ethnicity, nativity, neighborhood of residence, age, gender, or other characteristics – are fully able to participate in the region’s economic vitality, contribute to its readiness for the future, and connect to its assets and resources.

Strong, equitable regions:

• Possess economic vitality, providing high- • Are places of connection, where residents quality jobs to their residents and producing can access the essential ingredients to live new ideas, products, businesses, and healthy and productive lives in their own economic activity so the region remains neighborhoods, reach opportunities located sustainable and competitive. throughout the region (and beyond) via transportation or technology, participate in • Are ready for the future, with a skilled, political processes, and interact with other ready workforce, and a healthy population. diverse residents. Equitable Growth Profile of the Piedmont Triad Region PolicyLink and PERE 10 Demographics Who lives in the region and how is this changing?

The Piedmont Triad is a moderately diverse region. In 2010, one-third (33 percent) of the region’s residents were people of color, compared to 36 percent nationwide.

1.4% 0.6% 0.4% 2% Race/Ethnicity and Nativity, 5% 2010 4% White Black Latino, U.S.-born 20% Latino, Immigrant API, U.S.-born API, Immigrant Native American and Alaska Native Other or mixed race 67%

Source: IPUMS; U.S. Census Bureau. Note: Graph based on 2006 through 2010 IPUMS data on people by race/ethnicity and nativity, adjusted to match 2010 census results on people by race/ethnicity. Equitable Growth Profile of the Piedmont Triad Region PolicyLink and PERE 11 Demographics Who lives in the region and how is this changing?

Communities of color are driving population growth. Between 2000 and 2010, the Latino population grew 96 percent, adding 70,000 residents. The Asian population also grew 81 percent, adding about 15,000 residents.

Growth Rates of Major White 3% Racial/Ethnic Groups, 2000 to 2010 Black 18%

Latino 96%

Asian/Pacific Islander 81%

Native American 23%

Other 80%

Source: U.S. Census Bureau. Equitable Growth Profile of the Piedmont Triad Region PolicyLink and PERE 12 Demographics Who lives in the region and how is this changing?

People of color are quickly becoming the majority. Latinos will continue to drive growth, increasing from 9 to 26 percent of the population by 2040. When the nation becomes majority people of color around 2043, the region will already be 53 percent people of color.

1% 1% 1% 1% 2% 2% 3% 3% 0.2% 1% 2% Racial/Ethnic Composition, 19% 19% 5% 3% 3% 4% 9% 12% 19% 18% 26% 1980 to 2040 20% 21% 80% 79% U.S. % White 21% Other 73% 67% 20% Native American 62% Asian/Pacific Islander 54% Latino 47% Black White

1980 1990 2000 2010 2020 2030 2040

Projected

Source: U.S. Census Bureau; Woods & Poole Economics, Inc. Equitable Growth Profile of the Piedmont Triad Region PolicyLink and PERE 13 Demographics Who lives in the region and how is this changing?

The share of people of color is growing throughout the region. Forsyth and Guilford counties (home to Winston-Salem and Greensboro, respectively) are nearly majority of color today. By 2040, four of the Triad’s 12 counties will be majority people of color.

Percent People of Color by County, 1980 to 2040

Source: U.S. Census Bureau; Woods & Poole Economics, Inc.. Equitable Growth Profile of the Piedmont Triad Region PolicyLink and PERE 14 Demographics Who lives in the region and how is this changing?

In the past decade, people of color contributed the vast majority of net population growth (84 percent). Communities of color drove growth in all Piedmont Triad counties except Caswell, Stokes, and Davie. Caswell is the only county where the people of color population is declining and the white population is growing.

Share of Population Growth Piedmont Triad 84% Attributable to People of Color Guilford 99% by County, 2000 to 2010 Forsyth 92% Randolph 74% Alamance 66% Davidson 53% Surry 100% Rockingham 100% Yadkin 72% Montgomery 63% Rural Urban Davie 31% Stokes 23% Caswell 0%

Source: U.S. Census Bureau. Note: Caswell county saw a decline in both the white and people of color populations over the decade. Equitable Growth Profile of the Piedmont Triad Region PolicyLink and PERE 15 Demographics Who lives in the region and how is this changing?

There is a growing racial generation gap. Today, 44 percent of youth are people of color, compared to 17 percent of seniors. This 27-percentage point gap has tripled since 1980 and is above the national average (26 percentage points).

Racial Generation Gap: Percent People of Color by 44% Age Group, 1980 to 2010 Percent of seniors who are POC Percent of youth who are POC 27 percentage point gap 25% 9 percentage point gap 16% 17%

1980 1990 2000 2010

Source: U.S. Census Bureau. Note: Youth include persons under age 18 and seniors include those age 65 or older. Equitable Growth Profile of the Piedmont Triad Region PolicyLink and PERE 16 Demographics Who lives in the region and how is this changing?

The region’s fastest-growing demographic groups are also comparatively young. The region’s Latino and mixed race populations, for example, have median ages of 23 and 17 years, compared to the white population’s median age of 42 years.

Median Age by Race/Ethnicity, All 2010 38 White 42

Black 33

Latino 23

Asian/Pacific Islander 31

Native American 35

Other or mixed race 17

Source: IPUMS. Note: Data represents a 2006 through 2010 median. Equitable Growth Profile of the Piedmont Triad Region PolicyLink and PERE 17 Inclusive growth Is economic growth creating more jobs?

The region is struggling to recover from the Great Recession. Since 2009, the Piedmont Triad has experienced slower growth in both jobs and GDP than the United States overall. Job growth has been half the national average; GDP growth has lagged even further behind.

Average Annual Growth in Jobs and GDP, 1990 to 2007 2.6% and 2009 to 2012

Jobs 1.8% 1.6% 1.6% GDP 1.1% 1.0%

0.5% 0.3%

Piedmont Triad All U.S. Piedmont Triad All U.S. 1990-2007 2009-2012

Source: U.S. Bureau of Economic Analysis. Equitable Growth Profile of the Piedmont Triad Region PolicyLink and PERE 18 Inclusive growth Is the region growing good jobs?

There is a hollowing out of middle-wage jobs in the Piedmont Triad. Over the past two decades, the region only grew low- and high-wage jobs, while middle-wage jobs in sectors such as manufacturing declined. While the low-wage sector is growing, earnings for these workers have stagnated.

Growth in Jobs and Earnings by Industry Wage Level, 1990 25% to 2010 19% 16% Low-wage 15% Middle-wage High-wage 1%

Jobs Earnings per worker

-11%

Source: U.S. Bureau of Labor Statistics; Woods & Poole Economics, Inc.. Universe includes all jobs covered by the federal Unemployment Insurance (UI) program. Equitable Growth Profile of the Piedmont Triad Region PolicyLink and PERE 19 Inclusive growth Is inequality low and decreasing?

Income inequality is on the rise. Income inequality in the region is lower than the national average, but has steadily increased over the past three decades.

Level of Income Inequality, 0.55 1979 to 2010

Piedmont Triad 0.50 United States 0.46 0.47 0.45 0.46 0.43 Inequality is measured here by the Gini 0.44 coefficient, which ranges from 0 (perfect 0.40 Level of Inequality of Level 0.42 equality) to 1 (perfect inequality: one person 0.40 has all of the income). 0.39

0.35 1979 1989 1999 2010

Source: IPUMS. Note: Data for 2010 represents a 2006 through 2010 average. Equitable Growth Profile of the Piedmont Triad Region PolicyLink and PERE 20 Inclusive growth Are incomes increasing for all workers?

Wage growth is uneven across the income spectrum, and shows much higher gains for the top earners. The median worker in the region only experienced a 7 percent income increase over the past three decades, compared with an 18 percent increase for the worker at the 90th percentile.

Real Earned-Income Growth for Full-Time Wage and Salary 22% Workers Ages 25-64, 18% 1979 to 2010 14% Piedmont Triad 9% United States 7% 5% 5%

10th Percentile 20th Percentile 50th Percentile 80th Percentile 90th Percentile -3% -4% -6%

Source: IPUMS. Universe includes civilian noninstitutional full-time wage and salary workers ages 25 through 64. Note: Data for 2010 represents a 2006 through 2010 average. Equitable Growth Profile of the Piedmont Triad Region PolicyLink and PERE 21 Inclusive growth Are incomes increasing for all workers?

Wages have declined for most workers of color over the past decade. Wage growth has been uneven across racial/ethnic groups. Since 2000, median hourly wages have declined for Asians, Latinos, and blacks, while wages have increased for whites and people of other/mixed race.

Median Hourly Wage by $18.90 Race/Ethnicity, 2000 and 2010 $18.50 $15.90 $15.10 $15.90 2000 $14.90 $14.90 $15.20 2010 $11.40 $11.00

White Black Latino Asian/Pacific Other Islander

Source: IPUMS. Universe includes the civilian noninstitutional population ages 25 through 64. Note: Data for 2010 represents a 2006 through 2010 average. Dollar values are in 2010 dollars. Equitable Growth Profile of the Piedmont Triad Region PolicyLink and PERE 22 Inclusive growth Is the middle class expanding?

The middle class is shrinking. Since 1979, the share of households with middle-class incomes dropped from 40 percent to 36 percent, while the share of lower-income households grew from 30 to 37 percent.

Households by Income Level, 1979 and 2010 30% Upper 27% $74,378 $66,934

36% 40% Middle

$32,975 $29,675

Lower 37% 30%

1979 1989 1999 2010

Source: IPUMS. Universe includes all households (no group quarters). Note: Data for 2010 represents a 2006 through 2010 average. Dollar values are in 2010 dollars. Equitable Growth Profile of the Piedmont Triad Region PolicyLink and PERE 23 Inclusive growth Is the middle class becoming more inclusive?

The demographics of the middle class reflect the region’s changing demographics. While the share of households with middle-class incomes has declined since 1979, middle-class households have become more racially and ethnically diverse as the population has become more diverse.

Racial Composition of Middle- 1% 0.5% Class Households and All 0.4% 0.5% 3% 3% 15% 16% 5% 5% Households, 1980 and 2010 20% 21% 84% 83% Asian, Native American or Other 72% 71% Latino Black White

Middle-Class All Households Middle-Class All Households Households Households 1979 2010

Source: IPUMS. Universe includes all households (no group quarters). Note: Data for 2010 represents a 2006 through 2010 average. Equitable Growth Profile of the Piedmont Triad Region PolicyLink and PERE 24 Full employment How close is the region to reaching full employment?

Unemployment rates across the region are generally on par with the national average. In March 2014, the regional unemployment rate was 6.6 percent, compared to the U.S. rate of 6.7 percent. Rockingham County had a particularly high unemployment rate, while Yadkin County had a particularly low rate.

Unemployment Rate by Piedmont Triad 6.6% County, March 2014 Davidson 6.8% Guilford 6.7% Alamance 6.4% Randolph 6.3% Forsyth 6.2% Rockingham 8.3% Montgomery 7.1% Surry 7.0% Caswell 6.9% Rural Urban Davie 6.1% Stokes 6.0% Yadkin 5.5%

Source: U.S. Bureau of Labor Statistics. Universe includes the civilian noninstitutional population ages 16 and older. Equitable Growth Profile of the Piedmont Triad Region PolicyLink and PERE 25 Full employment How close is the region to reaching full employment?

People of color are less attached to the labor force. Since 1990, labor force participation rates have declined more among people of color than among whites.

Labor Force Participation Rate by Race/Ethnicity, White 83% 1990 and 2010 80%

1990 83% Black 79% 2010

86% Latino 82%

79% Asian/Pacific Islander 76%

87% Native American 71%

Source: IPUMS. Universe includes the civilian non-institutional population ages 25 through 64. Note: Data for 2010 represents a 2006 through 2010 average. One result of this is that the full impact of the Great Recession is not reflected, and the apparent trends may change as new data become available. Equitable Growth Profile of the Piedmont Triad Region PolicyLink and PERE 26 Full employment How close is the region to reaching full employment?

People of color are much more likely to be jobless than white residents. The unemployment rate of blacks and people of other/mixed race is double the rate of white unemployment. The unemployment rate of Asians in the region is also high, at 9.6 percent.

Unemployment Rate by Race/Ethnicity, 2010 All 7.1%

White 5.7%

Black 11.1%

Latino 7.9%

Asian/Pacific Islander 9.6%

Native American 7.1%

Other 11.1%

Source: IPUMS. Universe includes the civilian non-institutional population ages 25 through 64. Note: Data for 2010 represents a 2006 through 2010 average. One result of this is that the full impact of the Great Recession is not reflected, and the apparent trends may change as new data become available. Equitable Growth Profile of the Piedmont Triad Region PolicyLink and PERE 27 Full employment How close is the region to reaching full employment?

Unemployment decreases as education levels rise, but racial gaps persist. At every level of educational attainment, blacks have higher unemployment than whites. College-educated Asians, blacks, and Latinos are all more likely to be unemployed than their white counterparts.

Unemployment Rate by 24% Educational Attainment and Race/Ethnicity, 2010 18%

White Black 12% Latino Asian/Pacific Islander 6%

0% Less than a HS Diploma, More than HS BA Degree HS Diploma no College Diploma but less or higher than BA Degree

Source: IPUMS. Universe includes the civilian noninstitutional population ages 25 through 64. Note: Data represents a 2006 through 2010 average. Equitable Growth Profile of the Piedmont Triad Region PolicyLink and PERE 28 Access to good jobs Can workers access high-opportunity jobs?

Latino immigrants, Asian immigrants, and blacks with college degrees are less likely to hold high-opportunity jobs. Among college-educated workers, four-fifths of whites (80 percent) are likely to hold high-opportunity jobs, compared with just over half (51 percent) of Latino immigrants.

Jobs by Opportunity Level by 80% 71% 51% 76% Race/Ethnicity and Nativity held by Workers with a Bachelor’s Degree or Higher, 2011 High-opportunity Middle-opportunity 26% Low-opportunity

17% 9% 12% 23% 15% 8% 12% White Black Latino, API, Immigrant Immigrant

Source: U.S. Bureau of Labor Statistics; IPUMS. Universe includes the employed civilian noninstitutional population ages 25 through 64. While data on workers is from the Piedmont Triad region, the opportunity ranking for each worker’s occupation is based on analysis of the Greensboro-High Point, Winston-Salem, and Burlington Core Based Statistical Area as defined by the U.S. Office of Management and Budget. Note: High-opportunity jobs are those that rank among the top third of jobs on an “occupation opportunity index,” based on five measures of job quality and growth. See the “data and methods” section for a description of the index. Equitable Growth Profile of the Piedmont Triad Region PolicyLink and PERE 29 Access to good jobs Can all workers earn a living wage?

Blacks and Latinos earn lower wages than whites at every education level. People of color with four-year college degrees still earn $6/hour less than their white counterparts.

Median Hourly Wage by $30 Educational Attainment and $25 Race/Ethnicity, 2010 $20 White Black $15 Latino $10

$5

$- Less than a HS Diploma, More than HS BA Degree HS Diploma no College Diploma but less or higher than BA Degree

Source: IPUMS. Universe includes civilian noninstitutional full-time wage and salary workers ages 25 through 64. Note: Data represents a 2006 through 2010 average. Dollar values are in 2010 dollars. Equitable Growth Profile of the Piedmont Triad Region PolicyLink and PERE 30 Access to good jobs Do all residents have access to a car?

People of color are more likely to be carless. African Americans are three times less likely than whites to have a vehicle at home.

Percent of Households without a Vehicle by All 6.5% Race/Ethnicity, 2010 White 4.3%

Black 14.2%

Latino 5.0%

Asian/Pacific Islander 6.7%

Native American 7.8%

Other 7.7%

Source: IPUMS. Note: Data represents a 2006 through 2010 average. Equitable Growth Profile of the Piedmont Triad Region PolicyLink and PERE 31 Access to good jobs Do all residents earn enough to cover housing and transportation costs?

Regionwide, people are paying too much for housing and transportation. In all 12 counties, housing and transportation costs typically exceed 45 percent of household income, which is considered an affordability threshold. These expenses are highest in Surry County, at 60 percent of income.

45%

Average Housing and Forsyth 24% 30% Transportation Costs as a Percent of Household Income Davidson 21% 34% by County, 2009 Randolph 20% 36% Alamance Housing 23% 33% Transportation Guilford 27% 31% Yadkin 16% 36% Stokes 17% 35% Rockingham 18% 35% Davie 22% 35% Rural Urban Surry 18% 42% Caswell No data available Montgomery No data available

Source: Center for Neighborhood Technology, H+T Affordability Index. Equitable Growth Profile of the Piedmont Triad Region PolicyLink and PERE 32 Economic security Is poverty low and decreasing?

Poverty is on the rise, and it is higher for all groups of color. One out of every three Latinos and one out of every four African Americans and those of other or mixed race currently live in poverty.

35% 35% Poverty Rate by 33.4% Race/Ethnicity, 30% 30% 2000 and 2010 25.8% 25% 24.3% 25% 25.3% All 19.7% 20.5% White 20% 20.2% 20% Black 17.0% Latino 15% 15.4% 15% 15.5% Asian/Pacific Islander 11.0% Native American 10% 10.8% 10% 10.0% Other 7.1% 5% 5%

0% 0% 2000 2010

Source: IPUMS. Universe includes all persons not in group quarters. Note: Data for 2010 represents a 2006 through 2010 average. Equitable Growth Profile of the Piedmont Triad Region PolicyLink and PERE 33 Economic security Is working poverty low and decreasing?

Working poverty is also on the rise, and is particularly high for Latinos and Asians. Latinos and Asians are much more likely to be working poor (working full-time for an income below 150 percent of the poverty level) compared to other groups.

Working Poverty Rate by 20% 20% 19.6% Race/Ethnicity, 2000 and 2010 15% 15% 14.4% All White 10.5% Black 10% 10% Latino Asian/Pacific Islander 7.4% 6.8% Native American 6.4% 4.6% Other 5% 5% 4.3% 3.7% 4.1% 3.4% 2.2% 2.5% 2.2% 0% 0% 2000 2010

Source: IPUMS. Universe includes the civilian noninstitutional population ages 25 through 64 not in group quarters. Note: Data for 2010 represents a 2006 through 2010 average. Equitable Growth Profile of the Piedmont Triad Region PolicyLink and PERE 34 Strong industries and occupations What are the region’s strongest industries?

Health care, education, and management are strong and growing. Manufacturing – which traditionally provides many good, middle-skill jobs for people without college degrees – is highly concentrated in the region and employs many people, but is experiencing severe job losses.

Size Concentration Job Quality Growth Change in % Change in Real wage Total employment Location Quotient Average annual wage employment employment growth Industry (2010) (2010) (2010) (2000-10) (2000-10) (2000-10) Manufacturing 98,120 1.7 $47,159 -81,583 -45% 10% Health Care and Social Assistance 92,654 1.1 $40,017 24,189 35% -2% Retail Trade 73,343 1.0 $24,658 -9,520 -11% -5% Accommodation and Food Services 55,179 1.0 $13,858 8,584 18% -7% Administrative and Support and Waste Management and Remediation Services 45,262 1.2 $24,182 -1,809 -4% 8% Wholesale Trade 29,449 1.0 $49,949 -1,556 -5% 2% Finance and Insurance 28,870 1.0 $59,081 -186 -1% 0% Construction 28,093 1.0 $38,864 -12,133 -30% -3% Transportation and Warehousing 24,921 1.2 $39,701 -9,952 -29% -4% Professional, Scientific, and Technical Services 22,127 0.6 $55,419 -1,130 -5% 0% Other Services (except Public Administration) 15,937 0.7 $25,617 -2,429 -13% -3% Education Services 14,449 1.1 $54,412 3,461 31% 11% Management of Companies and Enterprises 14,330 1.5 $71,991 1,309 10% 9% Information 8,506 0.6 $50,000 -4,857 -36% -3% Arts, Entertainment, and Recreation 7,823 0.8 $19,356 1,055 16% -6% Real Estate and Rental and Leasing 6,766 0.7 $36,971 -1,402 -17% 9% Agriculture, Forestry, Fishing and Hunting 1,515 0.3 $28,337 -267 -15% -22% Utilities 1,396 0.5 $61,072 -915 -40% -11% Mining 485 0.1 $47,999 -125 -20% 2%

Source: U.S. Bureau of Labor Statistics; Woods & Poole Economics, Inc.. Universe includes all jobs covered by the federal Unemployment Insurance (UI) program. Equitable Growth Profile of the Piedmont Triad Region PolicyLink and PERE 35 Strong industries and occupations What are the region’s strongest occupations?

Health diagnostics, postsecondary teaching, business operations, and computer operations are strong and growing occupations. These job categories all pay good wages, employ many people, and have experienced strong growth in recent years.

Job Quality Growth Employment Change in % Change in Median Annual Wage Real Wage Growth Median Age Employment Employment Occupation (2011) (2011) (2011) (2005-11) (2005-11) (2006-10 avg) Health Diagnosing and Treating Practitioners 21,280 $75,781 5% 4,880 30% 44 Preschool, Primary, Secondary, and Special Education School Teachers 17,070 $41,612 8% 2,130 14% 41 Business Operations Specialists 14,890 $56,258 9% 4,040 37% 44 Health Technologists and Technicians 13,250 $41,309 0% 4,340 49% 38 Computer Occupations 11,350 $70,335 -1% 2,130 23% 40 Financial Specialists 8,650 $57,861 2% 360 4% 45 Top Executives 8,380 $110,550 13% -1,930 -19% 49 Other Management Occupations 7,640 $79,138 9% 350 5% 46 Supervisors of Office and Administrative Support Workers 6,370 $46,751 0% -410 -6% 45 Operations Specialties Managers 6,330 $96,246 11% -2,040 -24% 44 Postsecondary Teachers 5,100 $61,933 6% 1,700 50% 45 Sales Representatives, Services 5,090 $54,609 12% 670 15% 44 Supervisors of Production Workers 4,110 $49,360 -2% -1,490 -27% 45 Engineers 3,550 $76,469 -2% -180 -5% 42 Supervisors of Construction and Extraction Workers 2,700 $50,405 -1% -680 -20% 44 Supervisors of Installation, Maintenance, and Repair Workers 2,630 $55,895 -1% -340 -11% 47 Other Sales and Related Workers 2,530 $37,296 16% 50 2% 48 Advertising, Marketing, Promotions, Public Relations, and Sales Managers 2,230 $109,309 12% -1,380 -38% 43 Lawyers, Judges, and Related Workers 1,390 $110,200 -7% 40 3% 47 Librarians, Curators, and Archivists 1,260 $43,311 -5% 470 59% 51 Supervisors of Protective Service Workers 1,170 $57,891 10% 640 121% 46 Physical Scientists 800 $58,021 -3% 240 43% 40

Source: U.S. Bureau of Labor Statistics; IPUMS. Universe includes all nonfarm wage and salary jobs. Analysis reflects the Greensboro-High Point, Winston-Salem, and Burlington Core Based Statistical Area as defined by the U.S. Office of Management and Budget. Equitable Growth Profile of the Piedmont Triad Region PolicyLink and PERE 36 Skilled workforce Do workers have the education and skills needed for the jobs of the future?

The region will face a skills gap unless education levels increase. By 2020, 42 percent of the state's jobs will require an associate’s degree or above. Only 27 percent of U.S.-born Latinos and blacks, 10 percent of Latino immigrants, and 19 percent of Native Americans have that level of education.

Share of Working-Age 46% Population with an Associate’s 42% Degree or Higher by 38% 39% Race/Ethnicity and Nativity, 2010 and 27% Projected Share of Jobs that 27% Require an Associate’s Degree 19% or Higher, 2020 10%

Latino, Native Latino, U.S.- Black Other White API, Jobs in Immigrant American born Immigrant 2020

Source: Georgetown Center for Education and the Workforce; IPUMS. Universe for education levels of workers includes all persons ages 25 through 64. Note: Data for 2010 by race/ethnicity/nativity represents a 2006 through 2010 average and is at the regional level; data on jobs in 2020 is at the state level for . Equitable Growth Profile of the Piedmont Triad Region PolicyLink and PERE 37 Prepared youth Are youth ready to enter the workforce?

More of the region’s youth are getting high school degrees, but Latino immigrants are more likely to be behind. Overall, the region’s Latino and Asian youth are still much less likely to finish high school than its white and black youth.

Share of 16-24-Year-Olds Not Enrolled in School and without 64% a High School Diploma by Race/Ethnicity and Nativity, 1990 to 2010 45% 1990 33% 2000 2010

15% 15% 12% 12% 12% 12% 7% 8%

White Black Latino, U.S.-born Latino, API, Immigrant Immigrant

Source: IPUMS. Note: Data for 2010 represents a 2006 through 2010 average. Equitable Growth Profile of the Piedmont Triad Region PolicyLink and PERE 38 Prepared youth Are youth ready to enter the workforce?

A growing number of youth are disconnected from work and school. Youth of color are more likely to be disconnected. Among the region’s 27,000 disconnected youth, 54 percent are of color, compared with 44 percent of youth overall.

Disconnected Youth: 16-24- 40,000 Year-Olds Not in School or Work, 1980 to 2010 Asian, Native American or Other Latino

Black 20,000 White

0 1980 1990 2000 2010

Source: IPUMS. Note: In 1980 and 1990, Latino youth are included with Asian/Pacific Islander, Native American, and Other. Data for 2010 represents a 2006 through 2010 average. Equitable Growth Profile of the Piedmont Triad Region PolicyLink and PERE 39 Economic benefits of equity How much higher would GDP be without racial economic inequalities?

The Piedmont Triad Region’s GDP would have been $9.5 billion higher in 2012 if there were no racial gaps in income.

$90 $81.6 Equity $80 Dividend: Actual GDP and Estimated $72.2 $9.5 billion GDP without Racial Gaps in $70 Income, 2012 $60

GDP in 2012 (billions) $50

GDP if racial gaps in income $40 were eliminated (billions) $30

$20

$10

$0

Source: Bureau of Economic Analysis; IPUMS. Note: “Equity dividend” may not match difference in values reported in chart due to rounding. Dollar values are in 2012 dollars. Equitable Growth Profile of the Piedmont Triad Region PolicyLink and PERE 40 Data and methods

Data source summary and regional geography Assembling a complete dataset on employment and wages by industry Selected terms and general notes Broad racial/ethnic origin Growth in jobs and earnings by industry wage level, 1990 to 2010 Nativity Other selected terms Analysis of occupations by opportunity level General notes on analyses Estimates of GDP without racial gaps in income Summary measures from IPUMS microdata

Adjustments made to census summary data on race/ ethnicity by age

Adjustments made to demographic projections National projections County and regional projections

Estimates and adjustments made to BEA data on GDP Adjustments at the state and national levels County and metropolitan area estimates

Middle class analysis Equitable Growth Profile of the Piedmont Triad Region PolicyLink and PERE 41 Data and methods Data source summary and regional geography

Source Dataset Unless otherwise noted, all of the data and Integrated Public Use Microdata Series 1980 5% State Sample analyses presented in this equity profile are (IPUMS) 1990 5% Sample the product of PolicyLink and the USC 2000 5% Sample Program for Environmental and Regional 2010 American Community Survey, 5-year microdata sample Equity (PERE). 2012 American Community Survey, 5-year microdata sample The specific data sources are listed in the U.S. Census Bureau 1980 Summary Tape File 1 (STF1) table on the right. Unless otherwise noted, 1980 Summary Tape File 2 (STF2) the data used to represent the region were 1980 Summary Tape File 3 (STF3) assembled to match the 12 counties that are 1990 Summary Tape File 2A (STF2A) served by the Piedmont Authority for 1990 Modified Age/Race, Sex and Hispanic Origin Regional Transportation. File (MARS) 1990 Summary Tape File 4 (STF4) 2000 Summary File 1 (SF1) While much of the data and analyses 2010 Summary File 1 (SF1) presented in this equity profile are fairly 2008 National Population Projections intuitive, in the following pages we describe 2010 TIGER/Line Shapefiles, 2010 Counties some of the estimation techniques and Woods & Poole Economics, Inc. 2011 Complete Economic and Demographic Data adjustments made in creating the underlying Source database, and provide more detail on terms U.S. Bureau of Economic Analysis Gross Domestic Product by State and methodology used. Finally, the reader Gross Domestic Product by Metropolitan Area should bear in mind that while only a single Local Area Personal Income Accounts, CA30: region is profiled here, many of the analytical regional economic profile choices in generating the underlying data and U.S. Bureau of Labor Statistics Quarterly Census of Employment and Wages Occupational Employment Statistics analyses were made with an eye toward Center for Neighborhood Technology H+T Affordability Index replicating the analyses in other regions and Georgetown University Center on Recovery: Job Growth And Education Requirements the ability to update them over time. Thus, Education and the Workforce Through 2020; State Report Equitable Growth Profile of the Piedmont Triad Region PolicyLink and PERE 42 Data and methods Data source summary and regional geography (continued)

while more regionally-specific data may be available for some indicators, the data in this profile draws from our regional equity indicators database that provides data that are comparable and replicable over time. At times, we cite local data sources in the Summary document. Equitable Growth Profile of the Piedmont Triad Region PolicyLink and PERE 43 Data and methods Selected terms and general notes

Broad racial/ethnic origin • “Native American” and “Native American Other selected terms In all of the analyses presented, all and Alaska Native” are used to refer to all Below we provide some definitions and categorization of people by race/ethnicity and people who identify as Native American or clarification around some of the terms used in nativity is based on individual responses to Alaskan Native alone and do not identify as the equity profile: various census surveys. All people included in being of Hispanic origin. • The terms “region,” “metropolitan area,” our analysis were first assigned to one of six • “Other” and “other or mixed race” are used “metro area,” and “metro,” are used mutually exclusive racial/ethnic categories, to refer to all people who identify with a interchangeably to refer to the geographic depending on their response to two separate single racial category not included above, or areas defined as Metropolitan Statistical questions on race and Hispanic origin as identify with multiple racial categories, and Areas by the U.S. Office of Management and follows: do not identify as being of Hispanic origin. Budget, as well as to the region that is the • “White” and “non-Hispanic white” are used • “People of color” or “POC” is used to refer subject of this profile as defined previously. to refer to all people who identify as white to all people who do not identify as non- • The term “communities of color” generally alone and do not identify as being of Hispanic white. refers to distinct groups defined by Hispanic origin. race/ethnicity among people of color. • “Black” and “African American” are used to Nativity • The term “full-time” workers refers to all refer to all people who identify as black or The term “U.S.-born” refers to all people who persons in the IPUMS microdata who African American alone and do not identify identify as being born in the United States reported working at least 45 or 50 weeks as being of Hispanic origin. (including U.S. territories and outlying areas), (depending on the year of the data) and • “Latino” refers to all people who identify as or born abroad of American parents. The term usually worked at least 35 hours per week being of Hispanic origin, regardless of racial “immigrant” refers to all people who identify during the year prior to the survey. A change identification. as being born abroad, outside of the United in the “weeks worked” question in the 2008 • “Asian,” “Asian/Pacific Islander,” and “API” States, of non-American parents. American Community Survey (ACS), as are used to refer to all people who identify compared with prior years of the ACS and as Asian or Pacific Islander alone and do not the long form of the decennial census, identify as being of Hispanic origin. caused a dramatic rise in the share of respondents indicating that they worked at Equitable Growth Profile of the Piedmont Triad Region PolicyLink and PERE 44 Data and methods Selected terms and general notes (continued)

least 50 weeks during the year prior to the General notes on analyses • In regard to monetary measures (income, survey. To make our data on full-time workers Below we provide some general notes about earnings, wages, etc.) the term “real” more comparable over time, we applied a the analyses conducted: indicates the data have been adjusted for slightly different definition in 2008 and later • In the summary document that inflation and, unless otherwise noted, all than in earlier years: in 2008 and later, the accompanies this profile, we may discuss dollar values are in 2010 dollars. All “weeks worked” cutoff is at least 50 weeks rankings comparing the profiled region to inflation adjustments are based on the while in 2007 and earlier it is 45 weeks. The the largest 150 metros. In all such instances, Consumer Price Index for all Urban 45-week cutoff was found produce a national we are referring to the largest 150 Consumers (CPI-U) from the U.S. Bureau of trend in the incidence of full-time work over metropolitan statistical areas in terms of Labor Statistics, available at the 2005-2010 period that was most 2010 population. If the geography of the ftp://ftp.bls.gov/pub/special.requests/cpi/c consistent with that found using data from profiled region does not conform to the piai.txt. the March Supplement of the Current “official” metro area definitions used by the • Note that income information in the Population Survey, which did not experience a U.S. Office of Management and Budget, decennial censuses for 1980, 1990, and change to the relevant survey questions. For then we substitute the “custom” profiled 2000 is reported for the year prior to the more information, see region in place of the best corresponding survey. http://www.census.gov/acs/www/Downloads official metro area(s). For example, for the /methodology/content_test/P6b_Weeks_Wor profile created for the 12-county area ked_Final_Report.pdf. served by the Piedmont Authority for Regional Transportation, we substitute the 12-county region in for the official Winston- Salem and Greensboro-High Point metropolitan areas. Equitable Growth Profile of the Piedmont Triad Region PolicyLink and PERE 45 Data and methods Summary measures from IPUMS microdata

Although a variety of data sources were used, race/ethnicity, and nativity in each region of more counties contained in a single PUMA. much of our analyses are based on a unique the United States. dataset created using microdata samples (i.e., Because PUMAs do not neatly align with the “individual-level” data) from the Integrated The IPUMS microdata allows for the boundaries of metropolitan areas, we created Public Use Microdata Series (IPUMS), for four tabulation of detailed population a geographic crosswalk between PUMAs and points in time: 1980, 1990, 2000, and 2006 characteristics, but because such tabulations the region for the 1980, 1990, 2000, and through 2010 “pooled” together. While the are based on samples, they are subject to a 2006-2010 microdata. This involved 1980 through 2000 files are based on the margin of error and should be regarded as estimating the share of each PUMA’s decennial census and cover about 5 percent estimates – particularly in smaller regions and population that falls inside the region using of the U.S. population each, the 2006 through for smaller demographic subgroups. In an population information from Geolytics for 2010 files are from the ACS and cover only effort to avoid reporting highly unreliable 2000 census block groups (2010 population about 1 percent of the U.S. population each. estimates, we do not report any estimates information was used for the 2006-2010 The five-year ACS microdata file was used to that are based on a universe of fewer than geographic crosswalk). If the share was at improve the statistical reliability and to 100 individual survey respondents (i.e., least 50 percent, the PUMAs were assigned to achieve a sample size that is comparable to unweighted N<100). the region and included in generating regional that available in previous years. summary measures. For the remaining A key limitation of the IPUMS microdata is PUMAs, the share was somewhere between Compared with the more commonly used geographic detail: each year of the data has a 50 and 100 percent, and this share was used census “summary files,” which include a particular “lowest-level” of geography as the “PUMA adjustment factor” to adjust limited set of summary tabulations of associated with the individuals included, downward the survey weights for individuals population and housing characteristics, use of known as the Public Use Microdata Area included in such PUMAs in the microdata the microdata samples allows for the (PUMA) or “County Groups.” PUMAs are when estimating regional summary measures. flexibility to create more illuminating metrics drawn to contain a population of about of equity and inclusion, and provides a more 100,000, and vary greatly in size from being nuanced view of groups defined by age, fairly small in densely populated urban areas, to very large in rural areas, often with one or Equitable Growth Profile of the Piedmont Triad Region PolicyLink and PERE 46 Data and methods Adjustments made to census summary data on race/ethnicity by age

For the racial generation gap indicator, we Hispanic white, non-Hispanic black, Hispanic, ages combined) by subtracting the number generated consistent estimates of and the remainder of the population. To that is reported in STF2A for the populations by race/ethnicity and age group estimate the number of non-Hispanic Asian corresponding group. We then derived the (under 18, 18-64, and over 64 years of age) and Pacific Islanders, non-Hispanic Native share of each racial/ethnic group in the MARS for the years 1980, 1990, 2000, and 2010, at Americans/Alaskan Natives, and non-Hispanic file that was made up of “other race” or the county level, which was then aggregated others among the remainder for each age multiracial people and applied this share to to the regional level and higher. The group, we applied the distribution of these estimate the number of people by racial/ethnic groups include non-Hispanic three groups from the overall county race/ethnicity and age group exclusive of the white, non-Hispanic black, Hispanic/Latino, population (of all ages) from STF1. “other race” and multiracial, and finally non-Hispanic Asian and Pacific Islander, non- number of the “other race” and multiracial by Hispanic Native American/Alaskan Native, For 1990, population by race/ethnicity at the age group. and non-Hispanic other (including other county level was taken from STF2A, while single race alone and those identifying as population by race/ethnicity was taken from multiracial). While for 2000 and 2010, this the 1990 Modified Age Race Sex (MARS) file information is readily available in SF1 of each – special tabulation of people by age, race, year, for 1980 and 1990, estimates had to be sex, and Hispanic origin. However, to be made to ensure consistency over time, consistent with the way race is categorized by drawing on two different summary files for the Office of Management and Budget’s each year. (OMB) Directive 15, the MARS file allocates all persons identifying as “other race” or For 1980, while information on total multiracial to a specific race. After confirming population by race/ethnicity for all ages that population totals by county were combined was available at the county level for consistent between the MARS file and STF2A, all the requisite groups in STF1, for we calculated the number of “other race” or race/ethnicity by age group we had to look to multiracial that had been added to each STF2, where it was only available for non- racial/ethnic group in each county (for all Equitable Growth Profile of the Piedmont Triad Region PolicyLink and PERE 47 Data and methods Adjustments made to demographic projections

National projections for 2010 to the actual percentage reported by historical analysis of demographic change. National projections of the non-Hispanic the 2010 census. We subtracted the projected Once all adjustments were made at the white share of the population are based on percentage from the actual percentage for county level, the results were aggregated to national projections from the U.S. Census each group to derive an adjustment factor, produce a final set of projections at the Bureau of the population by race/ethnicity and carried this adjustment factor forward by regional and state levels. (the 2008 National Population Projections). adding it to the projected percentage for each However, because those projections are based group in each projection year. Similar to the 1990 MARS file described on the 2000 census and the 2010 census has above, the Woods & Poole projection follows since been released, we made some minor Finally, we applied the adjusted population the OMB Directive 15 race categorization, adjustments to incorporate the recently distribution by race/ethnicity to the total assigning all persons identifying as “other released 2010 census results and to ensure projected population from the 2008 National race” or multiracial to one of the five mutually consistency in the racial/ethnic categories Population Projections to get the projected exclusive race categories: white, black, Latino, included in our historical analysis of number of people by race/ethnicity. Asian/Pacific Islander, or Native American. demographic change. Thus, we first generated an adjusted version County and regional projections of the county-level Woods & Poole While our categorization of race/ethnicity Projections of the racial/ethnic composition projections that removed the other and includes a non-Hispanic other category by region and county are also presented. multiracial group from each of these five (including other single race alone and those These are based on initial county-level categories. This was done by comparing the identifying as multiracial), the 2008 National projections from Woods & Poole Economics, Woods & Poole projections for 2010 to the Population Projections follow OMB 1997 Inc. However, given that they were made prior actual 2010 census results, figuring out the guidelines and essentially distribute the non- to the release of the 2010 Census, and they share of each racial ethnic group in the Hispanic other single race alone group across use a different categorization of race than we Woods & Poole data that was composed of the other defined racial ethnic categories. use, a careful set of adjustments were made others and multiracials in 2010, and applying Specifically, we compared the percentage of to incorporate the recently released 2010 it forward to later projection years. From the total population composed of each Census results and to ensure consistency with these projections we calculated the county- racial/ethnic group in the projected data the racial/ethnic categories included in our level distribution by race/ethnicity in each Equitable Growth Profile of the Piedmont Triad Region PolicyLink and PERE 48 Data and methods Adjustments made to demographic projections (continued)

projection year for the five groups (white, adjusted national projections by black, Latino, Asian/Pacific Islander, and race/ethnicity using a simple iterative Native American), exclusive of others and proportional fitting (IPF) procedure. multiracials.

To estimate the county-level “other” and multiracial share of the population in each projection year, we then generated a simple straight-line projection of this share using information from SF1 of the 2000 and 2010 Census. Keeping the projected other and multiracial share fixed, we allocated the remaining population share to each of the other five racial/ethnic groups by applying the racial/ethnic distribution implied by our adjusted Woods & Poole projections for each county and projection year.

The result was a set of adjusted projections for the six-group racial/ethnic distribution in each county, which was then applied to projections of the total population by county from Woods & Poole to get projections of the number of people for each of the six racial/ethnic groups. Finally, these county- level projections were adjusted to match our Equitable Growth Profile of the Piedmont Triad Region PolicyLink and PERE 49 Data and methods Estimates and adjustments made to BEA data on GDP

The data on national Gross Domestic Product product at the county level for all years and at county estimates for each year was generated (GDP) and its analogous regional measure, the regional level prior to 2001, so we applied by taking our final state-level estimates and Gross Regional Product (GRP) – both referred the same adjustments to the data that were allocating gross product to the counties in to as GDP in the text – are based on data from applied to the national GDP data. Given a each state in proportion to total earnings of the U.S. Bureau of Economic Analysis (BEA). change in BEA’s estimation of gross product at employees working in each county – a BEA However, due to changes in the estimation the state and national levels from a Standard variable that is available for all counties and procedure used for the national (and state- Industrial Classification (SIC) basis to a North years. Next, the initial county estimates were level) data in 1997, a lack of metropolitan American Industry Classification System aggregated to metropolitan area level, and area estimates prior to 2001, and no available (NAICS) basis in 1997, data prior to 1997 were compared with BEA’s official county-level estimates for any year, a variety were adjusted to avoid any erratic shifts in metropolitan area estimates for 2001 and of adjustments and estimates were made to gross product in that year. While the change later. They were found to be very close, with a produce a consistent series at the national, to a NAICS basis occurred in 1997, BEA also correlation coefficient very close to one state, metropolitan area, and county levels provides estimates under an SIC basis in that (0.9997). Despite the near-perfect from 1969 to 2012. Because the regional year. Our adjustment involved figuring the correlation, we still used the official BEA definition used for this particular equity 1997 ratio of NAICS-based gross product to estimates in our final data series for 2001 and profile does not match the official SIC-based gross product for each state and later. However, to avoid any erratic shifts in metropolitan area definition used by BEA, the the nation, and multiplying it by the SIC- gross product during the years up until 2001, GRP data reported are an aggregation of our based gross product in all years prior to 1997 we made the same sort of adjustment to our final county-level estimate of gross product to get our final estimate of gross product at estimates of gross product at the across the counties contained in the region. the state and national levels. metropolitan-area level that was made to the state and national data – we figured the 2001 Adjustments at the state and national levels County and metropolitan area estimates ratio of the official BEA estimate to our initial While data on Gross State Product (GSP) are To generate county-level estimates for all estimate, and multiplied it by our initial not reported directly in the equity profile, years, and metropolitan-area estimates prior estimates for 2000 and earlier to get our final they were used in making estimates of gross to 2001, a more complicated estimation estimate of gross product at the metropolitan procedure was followed. First, an initial set of area level. Equitable Growth Profile of the Piedmont Triad Region PolicyLink and PERE 50 Data and methods Estimates and adjustments made to BEA data on GDP (continued)

We then generated a second iteration of our final estimate of gross product by state. county-level estimates – just for counties This was done using a simple IPF procedure. included in metropolitan areas – by taking the final metropolitan-area-level estimates and allocating gross product to the counties in each metropolitan area in proportion to total earnings of employees working in each county. Next, we calculated the difference between our final estimate of gross product for each state and the sum of our second- iteration county-level gross product estimates for metropolitan counties contained in the state (that is, counties contained in metropolitan areas). This difference, total nonmetropolitan gross product by state, was then allocated to the nonmetropolitan counties in each state, once again using total earnings of employees working in each county as the basis for allocation. Finally, one last set of adjustments was made to the county-level estimates to ensure that the sum of gross product across the counties contained in each metropolitan area agreed with our final estimate of gross product by metropolitan area, and that the sum of gross product across the counties contained in state agreed with Equitable Growth Profile of the Piedmont Triad Region PolicyLink and PERE 51 Data and methods Middle class analysis

To analyze middle class decline over the past four decades, we began with the regional household income distribution in 1979 – the year for which income is reported in the 1980 Census (and the 1980 IPUMS microdata). The middle 40 percent of households were defined as “middle class,” and the upper and lower bounds in terms of household income (adjusted for inflation to be in 2010 dollars) that contained the middle 40 percent of households were identified. We then adjusted these bounds over time to increase (or decrease) at the same rate as real average household income growth, identifying the share of households falling above, below, and in between the adjusted bounds as the upper, lower, and middle class, respectively, for each year shown. Thus, the analysis of the size of the middle class examined the share of households enjoying the same relative standard of living in each year as the middle 40 percent of households did in 1979. Equitable Growth Profile of the Piedmont Triad Region PolicyLink and PERE 52 Data and methods Assembling a complete dataset on employment and wages by industry

Analysis of jobs and wages by industry, Therefore, our approach was to first calculate straight-line approach. Finally, we reported on pages 18 and 34, is based on an the number of jobs and total wages from standardized the Woods & Poole industry industry-level dataset constructed using two- nondisclosed industries in each county, and codes to match the NAICS codes used in the digit NAICS industries from the Bureau of then distribute those amounts across the QCEW. Labor Statistics’ Quarterly Census of nondisclosed industries in proportion to their Employment and Wages (QCEW). Due to reported numbers in the Woods & Poole data. It is important to note that not all counties some missing (or nondisclosed) data at the and regions were missing data at the two- county and regional levels, we supplemented To make for a more accurate application of digit NAICS level in the QCEW, and the our dataset using information from Woods & the Woods & Poole data, we made some majority of larger counties and regions with Poole Economics, Inc., which contains adjustments to it to better align it with the missing data were only missing data for a complete jobs and wages data for broad, two- QCEW. One of the challenges of using Woods small number of industries and only in certain digit NAICS industries at multiple geographic & Poole data as a “filler dataset” is that it years. Moreover, when data are missing it is levels. (Proprietary issues barred us from includes all workers, while QCEW includes often for smaller industries. Thus, the using Woods & Poole data directly, so we only wage and salary workers. To normalize estimation procedure described is not likely instead used it to complete the QCEW the Woods & Poole data universe, we applied to greatly affect our analysis of industries, dataset.) While we refer to counties in both a national and regional wage and salary particularly for larger counties and regions. describing the process for “filling in” missing adjustment factor; given the strong regional QCEW data below, the same process was used variation in the share of workers who are for the regional and state levels of geography. wage and salary, both adjustments were necessary. Second, while the QCEW data are Given differences in the methodology available on an annual basis, the Woods & underlying the two data sources (in addition Poole data are available on a decadal basis to the proprietary issue), it would not be until 1995, at which point they become appropriate to simply “plug in” corresponding available on an annual basis. For the 1990- Woods & Poole data directly to fill in the 1995 period, we estimated the Woods & QCEW data for nondisclosed industries. Poole annual jobs and wages figures using a Equitable Growth Profile of the Piedmont Triad Region PolicyLink and PERE 53 Data and methods Growth in jobs and earnings by industry wage level, 1990 to 2010

The analysis on page 18 uses our filled-in This approach was adapted from a method QCEW dataset (see the previous page) and used in a Brookings Institution report, seeks to track shifts in regional job Building From Strength: Creating Opportunity composition and wage growth by industry in Greater 's Next Economy. For more wage level. information, see: http://www.brookings.edu/~/media/research/ Using 1990 as the base year, we classified files/reports/2012/4/26%20baltimore%20ec broad industries (at the two-digit NAICS level) onomy%20vey/0426_baltimore_economy_vey into three wage categories: low, middle, and .pdf. high wage. An industry’s wage category was based on its average annual wage, and each of While we initially sought to conduct the the three categories contained approximately analysis at a more detailed NAICS level, the one-third of all private industries in the large amount of missing data at the three-to region. six-digit NAICS levels (which could not be resolved with the method that was applied to We applied the 1990 industry wage category generate our filled-in two-digit QCEW classification across all the years in the dataset) prevented us from doing so. dataset, so that the industries within each category remained the same over time. This way, we could track the broad trajectory of jobs and wages in low-, middle-, and high- wage industries. Equitable Growth Profile of the Piedmont Triad Region PolicyLink and PERE 54 Data and methods Analysis of occupations by opportunity level

The analysis of strong occupations on page 35 “Occupation Opportunity Index” that is to replacements as older workers retire, is and jobs by opportunity level on page 28 are constructed is based on a measure of job estimated for each occupation from the same related and based on an analysis that seeks to quality and set of growth measures, with the 2010 5-year IPUMS American Community classify occupations in the region by job quality measure weighted twice as much Survey microdata file that is used for many opportunity level. as all of the growth measures combined. This other analyses (for the employed civilian weighting scheme was applied both because noninstitutional population ages 16 and To identify “high-opportunity” occupations in we believe pay is a more direct measure of older). The median age measure is also based the region, we developed an “Occupation “opportunity” than the other available on data for Metropolitan Statistical Areas (to Opportunity Index” based on measures of job measures, and because it is more stable than be consistent with the geography of the OES quality and growth, including median annual most of the other growth measures, which are data), except in cases for which there were wage, wage growth, job growth (in number calculated over a relatively short period fewer than 30 individual survey respondents and share), and median age of workers. (2005-2011). For example, an increase from in an occupation; in these cases, the median $6 per hour to $12 per hour is fantastic wage age estimate is based on national data. Once the “Occupation Opportunity Index” growth (100 percent), but most would not score was calculated for each occupation, consider a $12-per-hour job as a “high- Third, the level of occupational detail at which occupations were sorted into three categories opportunity” occupation. the analysis was conducted, and at which the (high, middle, and low opportunity). lists of occupations are reported, is the three- Occupations were evenly distributed into the Second, all measures used to calculate the digit Standard Occupational Classification categories based on employment. The strong “Occupation Opportunity Index” are based on (SOC) level. While considerably more detailed occupations shown on page 35 are restricted data for Metropolitan Statistical Areas from data is available in the OES, it was necessary to the top high-opportunity occupations the Occupational Employment Statistics to aggregate to the three-digit SOC level in above a cutoff drawn at a natural break in the (OES) program of the U.S. Bureau of Labor order to align closely with the occupation “Occupation Opportunity Index” score. Statistics (BLS), with one exception: median codes reported for workers in the American age by occupation. This measure, included Community Survey microdata, making the There are some aspects of this analysis that among the growth metrics because it analysis reported on page 28 possible. warrant further clarification. First, the indicates the potential for job openings due Equitable Growth Profile of the Piedmont Triad Region PolicyLink and PERE 55 Data and methods Estimates of GDP without racial gaps in income

Estimates of the gains in average annual income estimates due to small sample sizes, each group as a whole, and for all groups income and GDP under a hypothetical we added the restriction that a subgroup had combined. scenario in which there is no income to have at least 100 individual survey inequality by race/ethnicity are based on the respondents in order to be excluded. The key difference between our approach and IPUMS 2012 5-Year American Community that of Lynch and Oakford is that we include Survey (ACS) microdata. We applied a We then assumed that all racial/ethnic groups in our sample all individuals ages 16 years and methodology similar to that used by Robert had the same average annual income and older, rather than just those with positive Lynch and Patrick Oakford in Chapter Two of hours of work, by income percentile and age income values. Those with income values of All-in Nation: An America that Works for All group, as non-Hispanic whites, and took zero are largely non-working, and they were with some modification to include income those values as the new “projected” income included so that income gains attributable to gains from increased employment (rather and hours of work for each individual. For increases in average annual hours of work than only those from increased wages). example, a 54-year-old non-Hispanic black would reflect both an expansion of work person falling between the 85th and 86th hours for those currently working and an We first organized individuals aged 16 or percentiles of the non-Hispanic black income increase in the share of workers—an older in the IPUMS ACS into six mutually distribution was assigned the average annual important factor to consider given exclusive racial/ethnic groups: non-Hispanic income and hours of work values found for measurable differences in employment rates white, non-Hispanic black, Latino, non- non-Hispanic white persons in the by race/ethnicity. One result of this choice is Hispanic Asian/Pacific Islander, non-Hispanic corresponding age bracket (51 to 55 years that the average annual income values we Native American, and non-Hispanic Other or old) and “slice” of the non-Hispanic white estimate are analogous to measures of per multiracial. Following the approach of Lynch income distribution (between the 85th and capita income for the age 16 and older and Oakford in All-In Nation, we excluded 86th percentiles), regardless of whether that population and are notably lower than those from the non-Hispanic Asian/Pacific Islander individual was working or not. The projected reported in Lynch and Oakford; another is category subgroups whose average incomes individual annual incomes and work hours that our estimated income gains are were higher than the average for non- were then averaged for each racial/ethnic relatively larger as they presume increased Hispanic whites. Also, to avoid excluding group (other than non-Hispanic whites) to get employment rates. subgroups based on unreliable average projected average incomes and work hours for PolicyLink is a national research and action The USC Program for Environmental and institute advancing economic and social Regional Equity (PERE) conducts research and equity by Lifting Up What Works®. facilitates discussions on issues of environmental justice, regional inclusion, and social movement building.

Headquarters: University of Southern California 1438 Webster Street 950 W. Jefferson Boulevard Suite 303 JEF 102 Oakland, CA 94612 Los Angeles, CA 90089 t 510 663-2333 t 213 821-1325 f 510 663-9684 f 213 740-5680 Equitable Growth Profiles are products of a partnership between PolicyLink and PERE, the Program for Environmental and Regional Equity at the University of Communications: http://dornsife.usc.edu/pere Southern California. 55 West 39th Street 11th Floor The views expressed in this document are those of PolicyLink and PERE, and do not necessarily represent New York, NY 10018 those of Piedmont Together. t 212 629-9570 f 212 763-2350 ©2014 by PolicyLink and PERE. All rights reserved. http://www.policylink.org