PLACES IN NEED THE CHANGING GEOGRAPHY OF POVERTY Scott W Allard Russell Sage Foundation NEW YORK THE RUSSELL SAGE FOUNDATION The Russell Sage Foundation, one of the oldest of America’s general purpose foundations, was established in 1907 by Mrs Margaret Olivia Sage for “the improvement of social and living conditions in the United States.” The foundation seeks to fulfill this mandate by fostering the development and dissemination of knowledge about the country’s political, social, and economic problems While the foundation endeavors to assure the accuracy and objectivity of each book it publishes, the conclusions and interpretations in Russell Sage Foundation publications are those of the authors and not of the foundation, its trustees, or its staff Publication by Russell Sage, therefore, does not imply foundation endorsement LIBRARY OF CONGRESS CATALOGING-IN-PUBLICATION DATA Names: Allard, Scott W., author Title: Places in need : the changing geography of poverty / Scott W Allard Description: New York : Russell Sage Foundation, [2017] | Includes bibliographical references and index | Description based on print version record and CIP data provided by publisher; resource not viewed Identifiers: LCCN 2016052524 (print) | LCCN 2017011615 (ebook) | ISBN 9781610448659 (ebook) | ISBN 9780871545190 (pbk : alk paper) Subjects: LCSH: Poverty—United States | Population geography—United States Classification: LCC HC110.P6 (ebook) | LCC HC110.P6 A675 2017 (print) | DDC 362.50973—dc23 LC record available at https://lccn.loc.gov/2016052524 Copyright © 2017 by Russell Sage Foundation All rights reserved Printed in the United States of America No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the publisher Reproduction by the United States Government in whole or in part is permitted for any purpose The paper used in this publication meets the minimum requirements of American National Standard for Information Sciences— Permanence of Paper for Printed Library Materials ANSI Z39.48-1992 Text design by Matthew T Avery RUSSELL SAGE FOUNDATION 112 East 64th Street New York, NY 10065 For my son, August William Allard-Hill— To the hope and work that will make our communities better places for all people CONTENTS LIST OF ILLUSTRATIONS ABOUT THE AUTHOR PREFACE Chapter Introduction Chapter (Re)Considering Poverty and Place in the United States Chapter The Changing Geography of Poverty in the United States Chapter The Local Safety Net Response Chapter Understanding Metropolitan Social Service Safety Nets Chapter Rethinking Poverty, Rethinking Policy TECHNICAL APPENDIX NOTES REFERENCES INDEX LIST OF ILLUSTRATIONS Figures Figure 1.1 Number of Poor People in Urban and Suburban Census Tracts, 1990 and 2014 Figure 1.2 Mean Poverty Rates for Urban and Suburban Census Tracts, 1990 and 2014 Figure 1.3 Number of Poor Urban and Suburban Residents in 2014, by Data Source Figure 1.4 Mentions of Place and Poverty in Newspaper and Magazine Articles, 1990–2010 Figure 1.5 Mentions of Place and Poverty in Academic Journal Articles, 1990–2010 Figure 3.1 Number of People with Income Near or Below the Federal Poverty Line in Urban and Suburban Census Tracts, 1990–2014 Figure 3.2 Average Poverty Rates in Urban and Suburban Census Tracts, 1990–2014 Figure 3.3 Number of Poor People Across Mature and Newer Suburban Census Tracts, 1990–2014 Figure 3.4 Percentage Change in the Number of Poor People in Metropolitan Chicago, 1990–2014 Figure 3.5 Racial and Ethnic Composition of the Poor in Urban and Suburban Locations, 2014 Figure 3.6 Demographic Characteristics and Mobility Status of Poor People in Urban and Suburban Locations, 2014 Figure 3.7 High-Poverty Urban and Suburban Census Tracts in the One Hundred Largest Metropolitan Areas, 1990–2014 Figure 3.8 Demographic Characteristics of High-Poverty Urban and Suburban Census Tracts, 2014 Figure 3.9 Change in Total Number of Jobs by Change in Poverty Across Urban and Suburban Locations, 2002–2010 Figure 3.10 Change in Total Number of Workers by Earnings Across Urban and Suburban Locations, 2002–2010 Figure 4.1 Urban and Suburban Trends in Public Safety Net Program Participation, 2000–2010 Figure 4.2 Urban and Suburban Trends in Nonprofit Human Service Expenditures, 2000–2010 Figure 4.3 Median Annual Nonprofit Human Service Expenditure per Person at or Below 150 Percent of the FPL, by Urban and Suburban Location, 2000–2010 Figure A.1 Defining Urban and Suburban Places in Metropolitan Chicago Figure A.2 Percentage Change in Number of Poor People in Metropolitan Los Angeles, 1990–2014 Figure A.3 Percentage Change in Number of Poor People in Metropolitan Washington, D.C., 1990– 2014 Figure A.4 Percentage-Point Change in Census Tract Poverty Rates in Metropolitan Chicago, 1990– 2014 Figure A.5 Percentage-Point Change in Census Tract Poverty Rates in Metropolitan Los Angeles, 1990–2014 Figure A.6 Percentage-Point Change in Census Tract Poverty Rates in Metropolitan Washington, D.C., 1990–2014 Figure A.7 Change in Nonprofit Human Service Expenditures Across Urban and Suburban Counties, 2000–2010 Figure A.8 Change in Nonprofit Human Service Expenditures of Organizations with Annual Revenues Under $10 Million, Across Urban and Suburban Counties, 2000–2010 Tables Table 3.1 Poverty and Demographic Change in Urban and Suburban Census Tracts, 1990–2014 Table A.1 Urban and Suburban Trends in Population and Poverty in the One Hundred Largest Metropolitan Areas, 1990–2014 Table A.2 Urban and Suburban Trends in People with Income Near or Below the Federal Poverty Line, 1990–2014 Table A.3 Average Poverty Rates in Urban and Suburban Census Tracts, 1990–2014 Table A.4 Poverty Trends in Metropolitan Chicago, Los Angeles, and Washington, D.C., 1990–2010 Table A.5 Demographic Characteristics of Older, High-Poverty Suburban Municipalities in Metropolitan Chicago, Los Angeles, and Washington, D.C., 1990–2014 Table A.6 Demographic Characteristics of Newer Suburban Municipalities in Metropolitan Chicago, Los Angeles, and Washington, D.C., 1990–2014 Table A.7 Poverty and Race in Urban and Suburban Census Tracts, 1990–2014 Table A.8 Poverty Rates, High-Poverty Census Tracts, and Race in Urban and Suburban Areas, 1990–2014 Table A.9 Poverty and Demographic Change in Urban and Suburban Areas, 1990–2014 Table A.10 High-Poverty Census Tracts in Urban and Suburban Areas, 1990–2014 Table A.11 Characteristics of High-Poverty Census Tracts in Urban and Suburban Areas, 1990–2014 Table A.12 Urban, Suburban, and Rural County Trends in Safety Net Caseloads, 2000–2010 Table A.13 Nonprofit Social Service Expenditures in Urban, Suburban, and Rural Counties, per Poor Person, 2000–2010 ABOUT THE AUTHOR SCOTT W ALLARD of Washington is professor at the Evans School of Public Policy and Governance at the University PREFACE My childhood in the 1970s and 80s was spent in the Diamond Lake neighborhood of Minneapolis, the southernmost neighborhood in the city During most of those years, my family rented a two-bedroom duplex on a busy through street largely composed of similar rental units for young adults, single parents, and retirees The side streets were a mix of modest-sized prewar starter homes on small lots Homes were nicer in some tucked-away areas, a bit more basic in others The neighborhood was predominantly white and middle-class, with relatively little residential turnover It had a few basic amenities: a couple of gas stations, a drugstore, an ice cream shop, a hardware store, and a small chain supermarket The most exciting features of this sleepy neighborhood were a record store and a fish-and-chips restaurant owned by a Minnesota North Stars hockey player Immediately bordering my neighborhood to the south was the suburb of Richfield The local historical society touts Richfield as “Proudly Suburban Since 1854.” South Minneapolis and Richfield are separated by Minnesota State Highway 62, built in the 1960s and locally known as “the Crosstown” freeway Apart from driving over a bridge spanning the Crosstown, it could be difficult to know when you had left Diamond Lake and entered Richfield As in all municipalities in the Twin Cities metropolitan area at that time, there was a large green sign on a main local thoroughfare stating that you had entered Richfield and providing you with the most recent decennial census population count The housing stock was built roughly at the same time as Diamond Lake’s and shared many of the same features Richfield too was a predominantly white, middle-class community Street signs and sidewalks were only negligibly different from Diamond Lake’s Both communities shared the ignominious distinction of being immediately underneath the landing flight path for much of the air traffic into Minneapolis–St Paul International Airport Planes flew over Diamond Lake and Richfield so close to the ground that most children growing up there in the 1970s would remember seeing the heads of passengers in the windows of noisy DC-10s roaring over the treetops of local parks Yet there were differences between Diamond Lake and Richfield that might not have caught the casual eye Richfield was zoned like a suburb, with postwar ranch homes on large lots Big-box stores and fast-food restaurants populated several strip malls My family did most of its shopping at “the Hub,” which was the largest shopping center in the Twin Cities when it opened in 1954 Richfield offered the typical teenager many more employment opportunities than Diamond Lake In high school, I took a minimum-wage job as a dishwasher at a hamburger grill in Richfield patterned after the TV show Happy Days Richfield’s school system did not appear to have budget and class size problems comparable to those in my South Minneapolis schools As an adolescent, I was acutely aware that Richfield’s high school sports teams were better than those of the local Minneapolis public high schools—particularly in hockey, the first sport of Minnesotans Richfield’s hockey history was rich with future college, Olympic, and professional hockey players About ten years ago, my brother and his wife began to look for a house back in our old neighborhood After much searching, they decided to buy a house in Richfield instead, just across the Crosstown Freeway from our childhood home Property values in Richfield were more affordable 68–69, 69, 72, 83–84, 85, 222–223, 252n26; poverty rates, 31, 34, 62–63, 70, 79, 224, 252n27; residential and settlement patterns, 67–68, 76; in suburbs, 36–37, 67–69, 69, 72, 84, 222–223, 252n26; in urban areas, 68–69, 69, 70, 83–84, 222–223, 252n24 Boston, Massachusetts, 22 Bowie, Maryland, 73, 220–221, 234 Briggs, Xavier de Sousa, 188 Brookings effect, 11 Brookings Institution: Metropolitan Policy Program, xviii, xviii–xix, 11, 38–39, 237 Brown, Michael, 72 Buffalo, New York, 56–57 Bungalow Heaven (Pasadena, CA), 83 Burgess, Ernest, 28–29 business leaders, 241 See also leadership California, 64–65 call centers, 197–198 Calumet City, Illinois, 72, 204, 218–219 Calvert County, Maryland, 206, 209 Cambridge, Massachusetts, 22, 231 Camden, New Jersey, 230–231 Cancian, Maria, 247n41 CAP See Community Action Program cash and in-kind programs, 122, 129–130, 141 Catholic Charities, 158, 238 Cayton, Horace, 29 CDBG See Community Development Block Grant CDFIs See community development financial institutions census: decennial, 9, 235–236; funding for, 183–184 census tract–level data, 38 census tracts, 9, 244n13, 246n21; high-poverty, 37, 45; number of poor urban and suburban residents, 7–9, Central America, 67 central cities, 248n59 Centreville, Virginia, 220–221 charitable giving, 109, 114–115, 120, 133, 162–163, 194–195, 258n56 Charles County, Maryland, 206, 209 Chetty, Raj, 188 Chicago, Illinois, xix, 16; demographic characteristics, 218–219, 220–221; East, 218–219; EITC receipts, 127–128; high-poverty areas, 82–83; institutional fragmentation, 159–163; metropolitan area, 232–235; minimum wage laws, 256n28; municipal boundaries, 22–23, 159; newer suburbs, 64–65, 220–221; nonprofit human service expenditures, 139–140; older, high-poverty suburbs, 218–219; population, 45–46, 51, 54–55, 217, 234–235; poverty, 4–5, 59–61, 60, 61–62, 215; poverty rates, 2, 4–5, 54–55, 207; public transportation, 171; South Side, 5, 61–62, 83; suburban areas, 3–5, 21–22, 24–25, 63, 171, 204, 217–230, 234–235; suburban poverty, 51–52, 64–65, 152–153; townships, 159; underclass neighborhoods, 4; urban areas, 21, 24–25, 204, 217–230; West Side, Chicago Heights, Illinois, 3–4, 61–62, 83, 218–219 Chicago Public Schools (CPS), child care subsidies, 143 children, 77–79, 225, 247n34 Children’s Health Insurance Program (CHIP), 141 chilling effect, 166–167 Chillum, Maryland, 218–219 CHIP See Children’s Health Insurance Program Cicero, Illinois, 233 cities: central, 248n59; institutional fragmentation, 159 See also urban areas; specific cities The City (Park and Burgess), 28–29 class differences, 12–13 class segregation, 79–88 Cleveland, Ohio, 56–57 Clinton, Maryland, 73 COGs See councils of governments collaboration, 198–201, 241 college education, 89–92, 91; in high-poverty tracts, 227; in newer suburb, 220–221; in older, high-poverty suburbs, 218–219 See also education College Park, Maryland, 63, 218–219, 234 Colorado, 119 Community Action Program (CAP), 101, 110 community attitudes, 241 community-based organizations, 136, 193–196, 255n5 community-based social service programs, 119–120, 143 community building, 198–201 Community Development Block Grant (CDBG), 185–188, 255n7, 262n31 community development financial institutions (CDFIs), 190–191 community leaders, 192–193 See also leadership commuting, 23 competition, 111–116, 142 Compton, California, 62–63, 218–219, 234 Concord, Massachusetts, 22 confidentiality concerns, Cook County, Illinois, 204, 217–230; nonprofit human service expenditures, 139–140; population growth, 234–235; poverty, 59–61, 60, 61–62, 207, 215; suburbs, 7; TANF caseload, 131 Council Bluffs, Iowa, 231 councils of governments (COGs), 190 county-level data, 7–9, See also specific counties CPS See Chicago Public Schools; Current Population Survey crime, 13–14 criminal justice, 33 culture, 34 Current Population Survey (CPS), 7, 38, 76, 246n21, 251n20; data for metropolitan areas, 7–9, 8; funding for, 183–184 Cytron, Naomi, 190 Daley, Richard M., 173 Dallas, Texas, 55 data limitations, 7–9, 8, 238–239, 248n81, 250n10, 254n66 See also specific data sources data sources, 16, 235; interviews, 239–242 See also specific sources data systems, 196–198 decennial census, 9, 235–236 deep poverty, 1–2, 45, 246n33; definition of, 32; suburban, 47–49, 48, 50, 99; trends in, 48, 50, 213; urban, 47–49, 48, 50 deindustrialization, 32–33 DeKalb County, Illinois, 204 demographics, xvi–xvii, 225; suburban, 65–66, 143–144; trends in, 212; urban, 61–62, 65–66 Detroit, Michigan, 55–57, 230 developing countries, 32 Diamond Lake (Minneapolis, MN), xv–xvi discourse: political, 13–14; spatial, 9–12, 14–15 discrimination See racial segregation distance challenges, 169–172 distress: indicators of, 83 distressed suburbs, 39 diverse suburbs, 36–37, 39, 78–79, 248n71 Dorn, David, 247n38 Drake, St Clair, 29 Du Bois, W E B., 28–29 DuPage County, Illinois, 204; nonprofit social services, 139–140; poor and near-poor, 125; poverty, 59–61, 60, 125, 140, 207, 215; SNAP caseload, 125 Earned Income Tax Credit (EITC), 104, 117–118, 126–128, 141, 143, 179; caseloads, 130, 259n75; county-level data, 236–237; “EITC Interactive” (Brookings), 237; expansion, 127–128, 185, 262n26; expenditures or spending, 257n37, 261n9; filings, 126–128, 228, 236–237; tax credit benefits, 117–118; trends in participation, 123; ways to strengthen, 186–187 EBT See electronic benefit transfer economic conditions, 88–89, 240; innovation economy, 33; metropolitan area, 32–33; urban poverty, 61–62 Economic Opportunity Act, 101, 108, 110 Edelman, Peter, 262n26 Edin, Kathryn, 32 education, 41, 75, 76–77; General Educational Development (GED), 2–3; in high-poverty tracts, 85, 86; schools, 13, 24–25, 76–77, 108; urban, 24–25 See also college education EITC See Earned Income Tax Credit elder poverty, 78, 225 electronic benefit transfer (EBT) cards, Elementary and Secondary Education Act, 108 El Monte, California, 62–63, 218–219, 234 Emanuel, Rahm, 173 employment, 41, 75, 76–77; low-paying jobs, 97; minimum wage laws, 256n28; opportunities for, 32–33, 52–53, 62, 79; private assistance, 164–165; in suburban areas, 98; in urban areas, 98 See also jobs; unemployment employment programs, 101 employment rates, 90 empowerment zones, 109 Englewood, Illinois, 61 enterprise zones, 109 Erickson, David, 190 ethnic diversity, 26, 66–73, 69; in high-poverty tracts, 88; poverty rates, 70; in suburbs, 36–37, 39, 78–79, 248n71 ethnic segregation, 28, 78–88 Europe, 243n4 Evanston, Illinois, 63 extreme poverty, 32, 231, 246n33, 247n34 See also deep poverty Fairfax, Virginia, 206, 209, 216 Fairland, Virginia, 218–219 faith-based organizations (FBOs), 195–196, 241 Falls Church, Virginia, 206, 209 FBOs See faith-based organizations federal funding, 108, 110, 141 federal poverty line (FPL), 30–32, 45, 231 federal programs, 116, 185–186; cash and in-kind, 122; grants, 190 See also specific programs female-headed households, 77, 247n41, 259n74; in high-poverty tracts, 86; in newer suburbs, 220–221; in older, high-poverty suburbs, 218–219; participation in public assistance programs, 122–124, 123, 129–131, 258n59; poverty rates, 247n42 Ferguson, Missouri, 72 Ferris Bueller’s Day Off, fertility, nonmarital, 76–77 financial assistance programs, 34, 256n28 See also specific programs food assistance, 117, 152, 250n6, 257n39 food banks, 198 food pantries, xix, xvii–xviii, 164–165 food stamps, 2, 101, 126, 157 Ford Heights, Illinois, 61–62, 233 Fort Lauderdale, Florida, 230–231 Fort Myers, Florida, 56, 230–231 Fort Worth, Texas, 230–231 FPL (federal poverty line), 30–32, 45, 231 Frederick County, Maryland, 209 Fredericksburg, Virginia, 218–219 Frey, William, 36–37 funding: challenges of securing, 158–164; charitable giving, 109, 114–115, 120, 133, 162–163, 194–195, 258n56; federal, 108, 110, 141; grants, 109; limitations on, 162; philanthropy, 109, 114–115, 120, 133, 162–163, 194–195, 258n56; private, 109, 258n56; for social services, 187–189 future directions, 180–184 Gaithersburg, Maryland, 220–221 Galloway, Ian, 190 Galster, George, 80 Gans, Herbert, 248n61 Gary, Indiana, 217 Gautreaux Assisted Housing Program, 109 General Educational Development (GED), 2–3 geography, 9–15, 19–43, 44–100; distance challenges, 169–172; and labor market, 93–99; and safety net policy, 105–107 Geverdt, Douglas, 37 ghetto poverty, 12–13, 19, 21; suburban, 26 See also urban poverty Gilens, Martin, 13, 244n25 government, 158; local, 111–115 grants, 109 Great Recession, 7, 15, 40 Greenbelt, Maryland, 63, 218–219 Grundy County, Illinois, 204, 207 Gurnee, Illinois, 220–221, 234–235 Harlem Children’s Zone (HCZ) Project, 110 Harrington, Michael, 29, 44–45 Harrison, New York, 231 Harvey, Illinois, 3–4, 204, 233; demographic characteristics of, 218–219; high-poverty areas, 83; poverty trends, 61–62 HCZ See Harlem Children’s Zone Head Start, 2–3, 101 help-seeking, 155–158 Hennepin County, Minnesota, 243n10 high-poverty areas, 37, 45, 79–82, 81, 177; characteristics of, 227; definition of, 232; demographic features of, 83–86, 85, 88, 218–219; lived experience in, 86–88; population changes, 227, 253n56, 254n58, 254n60; poverty rates, 224, 227; suburban tracts, 82–88, 85, 218–219, 226, 253n56, 254n58, 254n60; urban tracts, 80–88, 81, 85, 226, 253n56 Hirasuna, Donald, 128 Hispanics, 12–13, 25, 251n21; in high-poverty tracts, 83–84, 85, 227, 254n60; immigrant populations, 165; immigration into suburbs, 40–41; in newer suburb, 220–221; in older, high-poverty suburbs, 218–219; participation in public programs, 258n59; population growth, 67–69; in poverty, 68–69, 69, 71, 83–84, 85, 222–223; poverty rates, 31–32, 34, 62–63, 70, 79, 224, 246n24, 252n27; residential and settlement patterns, 67, 76; in suburbs, 36–37, 40–41, 68–69, 69, 71, 84, 222–223; in urban locations, 68–69, 69, 70, 83–84, 222–223 Holzer, Harry, 188 homelessness, xvii–xviii, 150 Household Food Consumption Survey, 245n18 household income, 41, 89–90, 91, 93, 177; in high-poverty tracts, 84–86, 227 households: composition of, 41, 76–77; near-poor, 122–124, 123 See also single-parent households Housing Acts, 255n8 housing assistance, 13, 249n94, 255n8 housing insecurity, 150 Houston, Texas, 22–23, 55 HUD See U.S Department of Housing and Urban Development Hudson County, New York, 230–231 Hughes, John, Hull-House, 28–29 Human Needs Index (Salvation Army), 198 human service programs See social service programs Humphrey, Hubert H., 30 IFF, 191 imagery, 25 immigrants: anti-immigrant sentiment, 166–169; challenges of serving, 164–169; Hispanic, 40–41, 165; isolation and marginalization of, 154–155; low-income, 164–165; Mexican, xvii, 67, 70–71, 154–155; public perception of, 165–166; services for, 164–166; suburban communities, 164–169 immigration, 28, 36–37, 74–76, 75, 241; into suburbs, 40–41 Immigration and Nationality Act, 154–155 income(s), 52, 93 income inequality, 84–86 income poverty, 32 information management, 196–198 Inland Empire (southern California), 23, 245n5 inner-city poverty, 26, 30 innovation, 33, 241 institutional fragmentation, 111–116, 158–164 integrated suburbs, 37, 78–79, 248n71 Internal Revenue Service (IRS), 16, 238–239 interviews, 239–242, 261n2 invisible Americans, 44 Jackson, Kenneth, 255n8 Jackson, Mississippi, 45–46 Jargowsky, Paul, 254n57 Jasper County, Illinois, 204 Jersey City, New Jersey, 230–231 job growth, 33, 62–63, 98–99, 254n66 job loss, 62–63, 98 jobs, 94–97, 95; affluent centers, 249n85; low-paying, 97; near-poverty, 97 See also employment; unemployment Johnson, Lyndon B., 19, 44, 101–102, 254n1 justice, criminal, 33 Kane County, Illinois, 204, 207, 215 Kansas City, Kansas, 231 Kearny, New York, 231 Kendall County, Illinois, 204, 207 Kenosha County, Illinois, 204 Kingsley, Thomas, 37 Kirby, Maria, 130 Kneebone, Elizabeth, xviii labor force participation, 41, 75, 77 See also employment labor markets, 32–33; changes in, 52–53, 93–99; suburban, 155–156 Lake County, Illinois, 1, 204, 217–230; data systems, 197; EITC receipts, 128; nonprofit human service expenditures, 139; poor and nearpoor, 125; poverty, 1–3, 59–61, 60, 215; poverty rates, 125, 207; SNAP caseload, 125 Lake Forest, Illinois, 233 Landover, Maryland, 63, 218–219, 234 Lansing, Illinois, 72–73 Latino Leadership Academy, 193 Latino Policy Forum, 193 Latinos, 24–25, 165–168 leadership, 168–169, 192–193, 241; suburban, 149–155 LEHD See Longitudinal Employment-Household Dynamics The Levittowners (Gans), 248n61 Lewis, Oscar, 34 Lexington, Massachusetts, 22 local food programs, 152 local governments, 111–115 local safety nets, 101–144; competitive pressures, 111–116; institutional fragmentation, 111–116; regional capacity, 189–192; ways to improve, 185; ways to strengthen, 189 LODES (LEHD Origin-Destination Employment Statistics), 236 Long Beach, California, 230–231 Longitudinal Employment-Household Dynamics (LEHD) program (Census Bureau), 94, 236 Longitudinal Employment-Household Dynamics (LEHD) Origin-Destination Employment Statistics (LODES), 236 Los Angeles, California, xvii–xviii, 16; demographic characteristics of, 218–219, 220–221; EITC filings, 128; geography, 234; highpoverty neighborhoods, 82; metropolitan area, 232–235; minimum wage laws, 256n28; newer suburbs, 64–65, 220–221; nonprofit human service expenditures, 139–140; older, high-poverty suburbs, 218–219; overcrowding, 151; population, 45–46, 54–55, 205, 234–235; poverty, 51, 54–55, 61–63, 205, 208, 215; public transportation, 171; South-Central, 62–63; suburban areas, 21–23, 25–26, 51, 151, 234–235; urban areas, 21, 25–26 Los Angeles County, California: nonprofit human service expenditures, 139; suburbs, 23, 234; TANF caseload, 131 Loudoun County, Virginia, 206, 209, 216 low-income immigrants, 164–165 low-income poverty, 45, 48, 50, 213; population rates, 47–49, 48; suburban, 47–49, 48, 50, 159; urban, 47–49, 48, 50 low-poverty, 89–90 Luce, Thomas, 37 Lucy, William, 37 Madden, Janice, 37 Manassas, Virginia: demographic characteristics, 65, 73, 220–221; poverty rates, 64–65, 234; unemployment rates, 64–65 manufacturing, 32–33 Maryland, 73, 140, 162–163, 169, 171–172 Massachusetts, 236 material status, 245n16 McHenry County, Illinois, 204, 207, 215 Mead, Lawrence, 34 media coverage, 10–11, 10, 13–14, 39–40 Medicaid, 101, 116, 141, 250n6; expansion, 185 Medicare, 256n36 metropolitan areas, 45–46; definition of, 45, 203, 250n4; economies of, 32–33; focal areas, 232–235; institutions of governance, 111–116; population trends, 67, 250n5; poverty rates, 7–10, 8, 31–32, 55–57, 228; social service safety nets, 145–176 See also specific areas Metropolitan Policy Program (Brookings Institution), xviii, xviii–xix, 11, 38–39; “EITC Interactive,” 237 metropolitan statistical areas (MSAs), 250n4 See also metropolitan areas Mexican immigrants, xvii, 67, 70–71, 154–155 Miami, Florida, 55 Michigan Recession and Recovery Study (MRRS), 184, 261n18 microdata, 7, 258n60 Middlesex County, Massachusetts, 22 migration, 28, 73–76, 75, 244n25; black, 28, 40–41, 244n25; into suburbs, 40–41 See also immigration Mikelbank, Brian, 39 minimum wage laws, 256n28 Minneapolis, Minnesota, xv, 203 Minnesota: Working Family Credit, 128 Mission Viejo, California, 65, 220–221 mobility, 74–76, 75 Model Cities Program, 108, 110 moderate-poverty areas, 80 Montgomery County, Maryland: demographic changes, 71; nonprofit human service expenditures, 140; poverty, 140, 206, 209, 216; TANF, 131 Moving to Opportunity (MTO) experiment, 42, 109 MSAs See metropolitan statistical areas MTO See Moving to Opportunity Multicultural Leadership Academy, 193 multi-ethnic suburban communities, 37 municipalities, 111–112; boundaries, 22–23, 45; urban, 217 See also specific municipalities Murphy, Alexandra, 9, 248n61 Myrdal, Gunnar, 29 Naperville, Illinois, 204, 233; demographic characteristics of, 65, 220–221; population growth, 234–235; poverty, 3, 65 Narducci, Chris, 248n76 National Academy, 246n20 National Center for Charitable Statistics (NCCS), 237–239 National School Lunch Program, 257n39 National Taxonomy of Exempt Entities (NTEE), 238, 257n52 Native Americans, 19 NCCS See National Center for Charitable Statistics near-poor households, 122–124, 123 near-poverty, 49, 97, 213 Neighborhood Centers (Houston, TX), 191 Newark, New Jersey, 230–231 newer suburbs, 57–65, 58, 94–97, 95, 96, 220–221 New Hampshire, 236 New Hope program, 189 “newly” poor, 155–158 “newly” unemployed, 156 New Orleans, Louisiana, 56 new poor, 39–40, 156–157 Newport Beach, California, 220–221 new poverty, 39–40, 74 news media, 10–11, 10, 13–14 Newton County, Illinois, 204 new urbanism, 24 New York City, New York, 44, 163; Harlem Children’s Zone (HCZ) Project, 110; metropolitan area, 203; Opportunity NYC experiments, 110 NIMBY-ism (Not In My Back Yard), 142, 153–154, 241 non-Hispanic whites See whites nonprofit sector, 241 nonprofit social service organizations, 120, 136, 146, 176, 179; challenges to cultivating, 148; community-based, 136; data limitations, 238–239; human service expenditures, 132–140, 134, 137, 210, 211, 229, 261n9; immigrant-serving, 164–165, 168; private contributions to, 133, 258n56; programs, 132–141; programs for low-income immigrants, 164–165; rural, 260n84; suburban, 148 Norfolk, Virginia, 230–231 Northeast, 251n12 Norwalk, Connecticut, 231 NTEE See National Taxonomy of Exempt Entities O’Connor, Alice, 245n7 Office of Management and Budget (OMB), 45, 203, 250n4 O’Hare airport, 236 older suburbs, 66–67, 218–219, 235 OMB See Office of Management and Budget Opportunity NYC experiments, 110 Orange County, California: demographic characteristics of, 64–65, 220–221; EITC filings, 128; population growth, 234–235; poverty, 205, 208, 215; suburbs, 23–24, 234 Orfield, Myron, 37 Orlando, Florida, 56 Orshansky, Mollie, 30, 35–36 The Other America (Harrington), 29, 44–45 “others,” 151, 156 Out of Reach: Place, Poverty, and the New American Welfare State (Allard), xvii overcrowding, 150–151 Palatine County, Illinois, 64–65, 73, 204, 220–221 Palmdale, California, 220–221 Park, Robert, 28–29 Park Forest, Illinois, 72, 218–219 Pasadena, California, 82–83 Pavetti, LaDonna, 187 Pendall, Rolf, 248n76 Personal Responsibility and Work Opportunity Reconciliation Act (PRWORA), 34 Pettit, Kathryn, 37 Philadelphia, Pennsylvania, 22–23 philanthropy, 109, 114–115, 120, 133, 162–163, 194–195, 258n56 Phillips, David, 37 Phoenix, Arizona, 22–23 Pittsburgh, Pennsylvania, 250–251n12 place: poverty and, 19–43 See also geography places in need, suburban, 3, policing tactics, 33 policy, 177–201 political discourse, 13–14 political fragmentation, 159 political leaders, 241 See also leadership political support, 174 Pomona, California, 63, 218–219 poor people: demographic characteristics, 74–76, 75; mobility, 74–76, 75; as “others,” 151, 156; participation in public assistance programs, 122–124, 123 Porter County, Illinois, 204 poverty, 77–79, 177–201; alternative measures of, 253n45; children in, 77–79, 225, 247n34; class differences in, 12–13; concentrated, 80, 232; cultural factors, 34; by data source, 7–9, 8; deep, 1–2, 32, 45, 99, 246n33; definition of, 231–232; in developing countries, 32; dimensions of, 240; distance challenges, 169–172; elder, 78, 225; ethnic differences in, 26; European, 243n4; extreme, 32, 231, 246n33, 247n34; federal line, 30–32, 45, 231; geography of, 14–15, 19–43, 44–100; ghetto, 12–13, 19, 21, 26; imagery around, 9; income, 32; income trends and, 93; indicators of, 45, 232; inner-city, 26, 30; low-income, 45, 47–49, 48; low-poverty, 89–90; manifestations of, 240; media coverage of, 10–11, 10, 13–14, 39–40; metropolitan, 9–10; metropolitan trends, 59–61, 60; migration and, 73–76; new, 39–40, 74, 156–157; perception of, 149–155; persistence of, 47–57; public awareness of, 149–155, 200; racial differences in, 12–14, 19, 26; relative measures of, 245n16; rural, 44, 212, 213, 214, 228; and safety net policy, 105–107; simulations, 200; spatial distribution of (see geography); suburbanization of, 47–57; supplemental measures of, 246n20, 253n45; trends in, 212, 213, 215; unemployment and, 92–93; War on Poverty, 19–20, 44, 101–103, 108–109, 254n1 See also suburban poverty; urban poverty poverty areas, 80, 253n55; definition of, 254n57; moderate-poverty areas, 80; population growth, 254n57 See also high-poverty areas poverty rates, 4, 45, 103, 177–178, 214, 224, 232; calculation of, 31; elderly, 225; in high-poverty tracts, 85, 224 poverty-related challenges, 2–3 poverty research, 11, 12, 28–42; future directions, 180–184 poverty trends, 57–59, 58; key findings, 46 Prince George’s County, Maryland, 63, 262n48; household income, 73; leadership, 192–193; nonprofit human service expenditures, 140; poverty, 140, 206, 209, 216 Prince William County, Virginia, 206, 209, 216 private gifts and donations, 109 Promise Neighborhoods Initiative, 110, 190 PRWORA See Personal Responsibility and Work Opportunity Reconciliation Act public assistance programs: food banks, 198; food pantries, xix, xvii–xviii, 164–165; food programs, 117, 152, 250n6, 257n39; food stamps, 2, 101, 126, 157; local safety nets, 101–144, 185, 189–192; metropolitan safety nets, 145–176; public housing, 13, 249n94, 255n8; trends in participation, 122–124, 123 See also specific programs public awareness, 149–155, 200; of immigrants, 165–166 public officials, 149–155 public transportation, 170–172, 199–200 race, 66–73, 69, 241 Race-to-the-Top grants, 190 racial diversity, 12–14, 19, 26; high-poverty tracts, 88; poverty rates, 70; suburban, 36–37, 39, 78–79, 248n71 racial segregation, 12–14, 28–29, 33–34, 36, 39, 78–88 racial stereotypes, 13–14, 29 Raleigh, North Carolina, 56 rapid-growth suburbs, 39, 249n85 Reed, Deborah, 247n41 regional councils of governments (COGs), 190 regional governments, 111–112 regional organizations, 191–192 research: future directions, 180–184; on poverty, 11, 12, 28–42; on suburban poverty, 35–42; on urban poverty, 29–35 residential mobility, 74–76, 75 Richfield, Minnesota, xv–xvii, xxii Richfield High School (Richfield, MN), xvi, xvii Riverside County, California, 232–233; EITC filings, 128; nonprofit human service expenditures, 140; poverty, 205, 208, 215 Romeoville, Illinois, 220–221, 234–235 Roth, Benjamin, xviii–xix Round Lake Beach, Illinois, 70–71, 220–221, 233–235 rural areas, 229, 260nn84–85 rural poverty, 44, 212, 213, 214, 228 Rust Belt, 56 safety net policy, 105–111, 256n36 safety nets, 47, 241; administrative challenges, 159; county-level data, 236–237; eligibility for services, 231, 257n42, 258n59; federal, 122, 185–186; improvements, 185; local, 101–144, 185, 189–192; metropolitan, 145–176; urban vs suburban, 172–175 See also specific programs Salvation Army, 158, 198 San Bernardino County, California, 232–233; EITC filings, 128; nonprofit human service expenditures, 140; poverty, 205, 208, 215 Santa Clarita, California, 64–65, 73, 220–221 Schaumburg, Illinois, 233 scholarly research: future directions, 180–184; on poverty, 11, 12, 28–42; on suburban poverty, 35–42; on urban poverty, 29–35 school lunch, 257n39 schools, 24–25, 76–77; federal funding for, 108; under-performing, 13 See also education Schott, Liz, 187 Scranton–Wilkes-Barre, Pennsylvania, 56 Secaucus, New York, 231 segregation, 12–14, 28, 79–88 ServicePoint, 197 settlement patterns, 40–41 Shaefer, Luke, 32 shame, 157–158 shared fate, 198–201 sidewalks, 170 Silver Spring, Maryland, 63, 83, 218–219 simulations, 200 single female-headed households, 247n41; poverty rates, 247n42; in public assistance programs, 122–124, 123, 129–131, 258n59 See also female-headed households; single-parent households single-mother families See single female-headed households; single-parent households single-parent households, 33, 41, 75, 76–77, 225, 258n59; female-headed households, 122–124, 123, 129–131, 247n42, 258n59; in highpoverty tracts, 85, 86, 227; poverty rates, 247n42; in public assistance programs, 122–124, 123, 129–131, 258n59 skeletal suburbs, 39 Skokie, Illinois, 63 slums, 28, 45 Small, Mario Luis, 247n48, 251n19 SNAP See Supplemental Nutrition Assistance Program social problems, 26 social science research, 11, 12, 28–29; future directions, 180–184; on suburban poverty, 35–42; on urban poverty, 29–35 See also research Social Security, 256n36 social service organizations: community-based, 193–196, 255n5; faith-based, 195–196, 241; fund-raising resources, 162; regional, 191–192 See also nonprofit social service organizations social service programs, 254n1; community-based, 119–120, 143; data limitations, 238–239; expansion of, 108–109; federal, 108–109, 116, 122, 141; federal funding for, 110; funding for, 108–110, 142–143, 187–189, 241; local safety nets, 101–144, 185, 189–192; for low-income immigrants, 164–165, 168; metropolitan safety nets, 145–176; nonprofit, 132–141, 137, 229; private, 141; safety net policy, 105–107; scaling, 187–189; urban vs suburban work, 172–175 Social Services Block Grant (SSBG): expenditures, 185–186; federal appropriations, 187–188, 262n31 social welfare policy, 142 socioeconomic differences, xvi, 26 solidarity: building, 198–201 Somerville, Massachusetts, 22 southern California, 64–65 See also specific areas South Holland, Illinois, 72–73 Southland (Chicago, IL), 3–4, 61–62, 192–193 South Laurel, Maryland, 73 spatial discourse, 9–15 Special Supplemental Nutrition Program for Women, Infants, and Children (WIC), 257n39 SPM See supplemental poverty measure SSBG See Social Services Block Grant SSI See Supplemental Security Income St Paul, Minnesota, 203, 230–231 St Petersburg, Florida, 231 Stamford, Connecticut, 231 start-ups, 241 state earned income credits, 118 stereotypes, racial, 13–14 stigma, 158 Stinson, Thomas, 128 struggling suburbs, 39 suburbanism, xv–xvi suburban near-poverty, 49 suburban poor, 64–66, 74–76, 75; children, 77–78; elderly, 78; population growth of, 47–51, 48, 56; racial and ethnic characteristics of, 37, 68–69, 69, 222–223 suburban poverty, xvii–xix, 3–7, 5, 15, 19–20, 27, 88–90, 91, 115, 225, 228; by data source, 7–9, 8; deep poverty, 47–49, 48, 50, 99; distance challenges, 169–172; European, 243n4; low-income poverty, 47–49, 48, 50; media coverage of, 10–11, 10; new poverty, 74; perception of, 157; public awareness of, 149–155; rates, 4, 6, 6, 37, 53–54, 54, 56, 59, 64, 70, 103, 136, 214, 224; research on, 11, 12, 35–42; trends, 46, 57–59, 58, 212, 213 suburban sprawl, 23 suburbs and suburban areas, 9–10, 19–20, 231; affluent job centers, 249n85; American ideal, 1; at-risk segregated suburbs, 39; bedroomdeveloping, 39, 249n85; categories of, 258n61; definition of, 20–28, 203–231; demographic features of, 36–37, 64–69, 69, 84, 143–144, 218–219, 225; development of, 245n3; distressed, 39; diverse or integrated, 37, 78–79, 248n71; EITC filings, 127; expansion of, 36–37; government in, 158; high-poverty tracts, 37, 64, 80–88, 81, 85, 218–219, 224, 226, 227, 253n56, 254n58, 254n60; history of, 22; household income, 93; immigrant communities, 164–169; immigration into, 40–41; inner-ring, 37, 64; institutional fragmentation, 161–163; jobs, 94–97, 95, 98, 155–156; leaders, 149–155; mature, 57–65, 58, 93–95, 95, 96, 97; multiethnic communities, 37; municipalities, 111–112; newer, 57–65, 58, 94–97, 95, 96; nonprofit social service expenditures, 133–140, 134, 137, 140, 210, 211, 229; nonprofit social service organizations, 136, 148; older suburbs, 66–67, 218–219, 235; overcrowding, 150–151; places in need, 5; population trends, 35, 45–46, 212, 235, 250n5, 252n34, 254n58, 254n60; postwar, 22; poverty areas, 38–39; public officials, 149–155; racial diversity, 37, 67–68, 71–72, 78–79; rapid-growth suburbs, 39, 249n85; safety net program participation, 122–124, 123, 172–175, 228; skeletal, 39; social service programs, 142–143; struggling, 39; symbiotic, 39; TANF participation, 129–131; transportation challenges, 171; unemployment rates, 4, 92–93; white bedroom suburbs, 39, 249n85; workers, 96, 97 Suitland, Maryland, 218–219 Supplemental Nutrition Assistance Program (SNAP), 104, 117, 122–126, 141, 179, 250n6; average monthly household benefits, 117; caseloads, 122–124, 123, 125, 130, 228, 236–237, 259n75, 259nn65–66; county-level data, 236–237; eligibility for services, 257n42; expansion, 185; expenditures, 257n37, 261n9; food assistance, 117; trends in participation, 122–124, 123; ways to strengthen, 186–187 supplemental poverty measure (SPM), 246n20 Supplemental Security Income (SSI), 257n37 symbiotic suburbs, 39 symbols, 13 TANF See Temporary Assistance for Needy Families tax credits: state earned income credits, 118; Working Family Credit (Minnesota), 128 See also Earned Income Tax Credit (EITC) tax rates, 113–114 Temporary Assistance for Needy Families (TANF), 105, 118–119, 129–132, 142–143, 179; administration of, 119, 121–122, 131; caseloads, 119, 129–131, 228, 236–237, 259nn73–74, 259n76, 259nn78–79; county-level data, 236–237; expenditures, 185–187, 257n37; monthly benefits, 119; responsiveness, 187; trends in participation, 123, 130–132; ways to improve, 187; work participation requirements, 262n27 Temporary Assistance for Needy Families–Unemployed Parent (TANF-UP), 237 terminology, 12–13 tools, 196–198 transportation, 169–172, 199–200, 231–232, 241 Trump, Donald, 13–14 Tulsa, Oklahoma, 56 2-1-1 call centers, 197–198 underclass neighborhoods, underemployment, 52–53 unemployment, 4, 13–14, 52–53, 89–90, 91, 92–93, 177; in high-poverty tracts, 85, 86, 227; in newer suburbs, 220–221; “newly” unemployed, 156; in older, high-poverty suburbs, 218–219; suburban rates, 64–65 United States: federal poverty line (FPL), 30–32, 45, 231; Kennedy administration, 44; metropolitan areas, 36–37, 45–46; Obama administration, 110, 141; population growth, 251n12; poverty in, 9–15, 19–43, 44–100; safety net policy, 105–107; suburban population, 45–46; suburbs, 22, 35–37; urban areas, 35 University of Chicago, 21 urban areas, 230–231; black-out migration from, 40–41; definition of, 20–28, 203–231; demographic characteristics of, 67–69, 69, 89–90, 91, 225; education, 24–25; EITC filings, 127; high-poverty tracts, 80–88, 81, 85, 224, 226–227, 253n56; hometowns, xxii; household income, 93; jobs, 94–97, 95, 98; municipal places, 217; near-poverty, 49; nonprofit social service expenditures, 132–140, 134, 137, 210, 211, 229; safety nets, 107–111, 122–124, 123, 172–175, 228; slums, 45; social problems, 26; TANF participation, 129–131; unemployment rates, 92–93; workers, 96, 97 See also specific areas urbanism, new, 24 urbanity, urbanization, 22–23 urban poor, 65–66, 68–69, 69; children, 77–78; demographics of, 61–62, 74–76, 75; elderly, 78; population trends, 47–50, 48, 212, 252n25; racial diversity of, 13–14, 67–68 urban poverty, xvii–xviii, 4–5, 5, 6, 6, 19, 24, 212, 213, 222–223, 225, 228; academic research, 11, 12; by data source, 7–9, 8; deep, 47–49, 48; economic changes, 61–62; history of, 21; inner-city poverty, 26, 30; low-income, 47–49, 48; media coverage of, 10–11, 10; persistence of, 47–57; rates, 35, 53–54, 54, 99–100, 103, 126–127, 214, 224; research on, 29–35; symbols of, 13; synonyms for, 12–13; trends in, 32–33, 46 urban underclass, 12–13 U.S Census Bureau, 16; data from, 38; definition of deep poverty, 32; Longitudinal Employment-Household Dynamics (LEHD) program, 94; Longitudinal Employment-Household Dynamics (LEHD) Origin-Destination Employment Statistics (LODES), 236 U.S Department of Housing and Urban Development (HUD): Community Development Block Grant (CDBG), 255n7 Vancouver, Washington, 231 Virginia, 128 vouchers See Moving to Opportunity (MTO) experiment War on Poverty, 19–20, 44, 101–103, 108–109, 254n1 War on Poverty (Humphrey), 30 Washington, D.C., 16; data sources, 236; demographics, 71, 218–219, 220–221; EITC filings, 128; as focal metropolitan area, 232–235; high-poverty neighborhoods, 82; high-poverty suburbs, 218–219; institutional fragmentation, 159, 162–163; low-income households, 128; newer suburbs, 64–65, 220–221; nonprofit human service expenditures, 139–140; older, high-poverty suburbs, 218–219; poor population, 206; population growth, 51, 54–55, 234–235; poverty, 51, 61–65, 216; poverty rates, 54–55, 64–65, 209; school-age population, 164; suburban areas, 21, 24, 51, 63–65, 172, 218–219, 220–221, 234–235; transportation challenges, 172; urban areas, 21 Waukegan, Illinois, 204, 233; demographic characteristics of, 218–219; high-poverty areas, 83; poverty, 2, 63; SNAP participation, 126 Weir, Margaret, 248n76 welfare See Temporary Assistance for Needy Families welfare reform, 34, 108–109, 118–119, 246n33 West Cook County Housing Collaborative (Chicago, IL), 191 Westmont, California, 62–63, 218–219 Wheaton, Maryland, 71, 218–219 white bedroom suburbs, 39, 249n85 White Plains, New York, 231 whites, 13–14, 251n21; in deep poverty, 246n33; in high-poverty tracts, 85, 218–219, 227; in newer suburbs, 220–221; non-Hispanic, 66–67; in older, high-poverty suburbs, 218–219; population growth, 66–67; in poverty, 68–69, 69, 84, 85, 222–223; poverty rates, 31–32, 70, 224, 252nn27–28; residential mobility, 76; in suburbs, 68–69, 69, 78–79, 84, 218–219, 220–221, 222–223; in urban locations, 68–69, 69, 70, 222–223 Wilkes-Barre, Pennsylvania, 56 Will County, Illinois, 140, 204, 207, 215, 233 Williams, Erica, 262n26 Wilmington, Delaware, 230–231 Wilson, William Julius, women See female-headed households work See employment workers: changes in number of, 96, 97–98 See also employment Working Family Credit (Minnesota), 128 World Bank, 32 YMCA, 120 Youngstown, Ohio, 45–46 Zion, Illinois, 2, 83 ... focused on increases in poverty in suburban communities, also point to persistently high rates of poverty in cities Moreover, any rethinking of the geography of poverty must look at the changing racial... close to the ground that most children growing up there in the 1970s would remember seeing the heads of passengers in the windows of noisy DC-10s roaring over the treetops of local parks Yet there... illuminate the diversity of suburban experiences with poverty Chapter assesses the geography of the contemporary antipoverty safety net and examines the response of the safety net to the changing geography