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SEM-2, 2013 Final Examination Page 1 of 3 BIS243 - Organisational Security

EXAMINATIONS PAPERS ARE NOT PERMITTED TO BE REMOVED FROM THE EXAMINATION VENUE

Semester/Year: Semester 2, 2013

FINAL EXAMINATION COVER SHEET

Faculty / School: Engineering, Health, Science and the Environment / Engineering and Information Technology

Unit: BIS243 - Organisational Security Lecturer: Krishnan Kannoorpatti Examination Duration:

Reading: 10 minutes Writing: 120 minutes

1. INSTRUCTIONS TO CANDIDATES:

1.1 The examination has 2 sections. Both sections must be answered. The exam is worth 50 marks.

Section A Suggested time: 25 minutes

The section has one question. The section is worth 10 marks You must answer this question.

Section B Suggested time: 95 minutes

Answer any 5 of 7 questions. Each question is worth 8 marks. The section is worth 40 marks. In each question part A is worth 4 marks and Part B is worth 4 marks.

1.2 Note that questions ARE NOT of equal value. 1.3 Read ALL questions carefully. 1.4 Do not commence writing until instructed to do so.

2. ATTACHED MATERIALS: None

3. PERMITTED MATERIALS: No materials are permitted for this examination

4. EXAMINATION BOOKLETS: 1 20-pages Booklet(s) Answers on Exam Paper: NO

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SEM-2, 2013 Final Examination Page 2 of 3 BIS243 - Organisational Security

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Section A

The section has one question. The section is worth 10 marks. You must answer this question.

Question A1

Assume you are the CISO of your organisation. You are asked by the CEO to plan for the security of your organisation’s ICT network. What technologies and concepts will you use to defend the network? Explain this using a schematic diagram of your network. Identify other aspects of security planning that you need to take in to account.

Section B

Answer any 5 of 7 questions. Each question is worth 8 marks. The section is worth 40 marks. In each question part A is worth 4 marks and Part B is worth 4 marks.

Question B1

A. Passwords are important as a defence against unauthorised access. This is usually done by making the users comply with a password policy. What are the key ingredients of a best practice password policy? Explain why these are considered best practice. What are the effects of changing some key aspects of the password policy?

B. Name four 2nd factor authentication methods. Compare in a tabular form the strengths and weaknesses of each of the methods.

Question B2

A. Describe the Australian Government’s Information classification system. What are the consequences of compromise of information for each classification?

B. Explain what actions you will take in your organisation to cover 85% of the problems faced due to the OS and application bugs on servers and workstations.

Question B3

A. Describe the need for IDPSs on your organisation network.

B. What considerations will you take into account before acquiring IDPSs for your organisation?

SEM-2, 2013 Final Examination Page 3 of 3 BIS243 - Organisational Security

EXAMINATIONS PAPERS ARE NOT PERMITTED TO BE REMOVED FROM THE EXAMINATION VENUE

Question B4

A. Explain with a schematic diagram a DoS attack.

B. Why is it difficult to stop a DDoS attack?

Question B5

A. Name some key policies an organisation needs to control actions of users on a network to ensure security.

B. Your organisation has asked you to write a policy to stop users from plugging USB and other peripheral devices in to the organisation’s computers. What considerations will you take into account before writing this policy?

Question B6

A. Many free services available on the internet for email and social networking use a single sign-on to provide their services. While providing the services, it has been found that these services make use of usage patterns and email content to target advertisements to users. What is your assessment of this situation?

B. Name four information privacy principles used for regulating information privacy in Australia.

Question B7

A. What are the steps in setting up PGP to encrypt emails? Use schematic diagrams to present your answers.

B. What are the issues to be considered in setting up PGP for your organisation?

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America’s Racially Diverse Suburbs: Opportunities and Challenges

Myron Orfield and Thomas Luce

July 20, 2012

Acknowledgments The authors would like to thank a number of people who provided comments on drafts of this paper including Alan Berube, Douglas Massey, Christopher Niedt, Lawrence Levy, Alexander Polikoff, Gregory Squires, Erica Frankenberg, Gary Orfield, Genevieve Siegel-Hawley, Jonathan Rothwell, David Mahoney, and Bruce Katz. Eric Myott provided superior support with the data, mapping and editing, as usual. Thanks also to Cynthia Huff and Valerie Figlmiller of the University of Minnesota Law School and Kathy Graves for their excellent work on the press releases and their comments on the paper. All opinions and any remaining errors in the paper, of course, are the responsibility of the authors alone. Finally, we want to thank the Ford Foundation and the McKnight Foundation for their on-going support of our work. Copyright © 2012 Myron Orfield and Thomas Luce.

I. Overview

Still perceived as prosperous white enclaves, suburban communities are now at the cutting edge of racial, ethnic, and even political change in America. Racially diverse suburbs are growing faster than their predominantly white counterparts. Diverse suburban neighborhoods now outnumber those in their central cities by more than two to one.1 44 percent of suburban residents in the 50 largest U.S. metropolitan areas live in racially integrated communities, which are defined as places between 20 and 60 percent non-white. Integrated suburbs represent some of the nation’s greatest hopes and its gravest challenges. The rapidly growing diversity of the United States, which is reflected in the rapid changes seen in suburban communities, suggests a degree of declining racial bias and at least the partial success of fair housing laws. Yet the fragile demographic stability in these newly integrated suburbs, as well as the rise of poor virtually non- white suburbs, presents serious challenges for local, state, and federal governments.

By mid-century, the increasingly metropolitan nation that is the United States will have no racial majority. Last year a majority of the children born in the United States and nearly half of students in U.S. public schools were non-white.2 Almost 60 percent of U.S population lives in the 50 largest regions, 80 percent in its metropolitan areas. At the same time, a growing number of central-city blacks and Latinos experience apartheid levels of segregation and civic dysfunction. In comparison, integrated suburbs, despite challenges, are gaining in population and prosperity. Given these trends, ensuring successful racially integrated communities represents the best policy path for the nation’s educational, economic, and political success.

Stably integrated suburbs are places where whites and non-whites can grow up, study, work, and govern together effectively. Integrated communities have the greatest success eliminating racial disparities in education and economic opportunity. While non-whites in integrated communities have seen improvements in education and employment, non-white residents of segregated urban communities are further behind than ever. In integrated communities, whites and non-whites have the most positive perceptions of one another. Integrated suburbs are much more likely to be politically balanced and functional places that provide high-quality government services at affordable tax rates than high-poverty, segregated areas. In environmental terms, they are denser, more walkable, more energy-efficient, and otherwise more sustainable than outer suburbs. They also benefit from their proximity both to central cities and outer suburban destinations.

1 The terms “integrated” and “racially diverse” will both be used to describe municipalities and neighborhoods with non-white population shares between 20 and 60 percent. At the municipal scale, this broad measure may mask segregation at smaller scales, undermining the use of the term “integrated.” However, the municipality is also the dominant scale for the local housing and land-use policymaking that is most likely to affect integration and segregation rates. School policy (through school districts) is also often pursued at roughly this scale. Thus, while many of these municipalities are likely to be segregated at neighborhood scales, policy-making institutions—city councils or school boards, for instance—are much more likely to be integrated. In addition, if a municipality meets the criterion, this means that local policy institutions exist at scale large enough to fruitfully pursue integrative policies. The use of the term at the neighborhood scale, defined as a census tract for the purposes of this work, is much less problematic, as census tracts are generally much smaller than municipalities. 2 Sabrina Tavernise, “Whites Account for Under Half of Births in U.S.,” New York Times, May 17, 2012.

These communities also reflect America’s political diversity. On average, they are evenly split between Democrats and Republicans, and are often the political battlegrounds that determine elections. They are more likely than other suburbs to switch parties from one election to another and, as a result, often decide the balance of state legislatures and Congress as well as the outcomes of gubernatorial and presidential elections.3 Policy makers could pay a political price for failing to connect with “swing” voters in these integrated suburban communities.

Yet, while integrated suburbs represent great hope, they face serious challenges to their prosperity and stability. Integrated communities have a hard time staying integrated for extended periods. Neighborhoods that were more than 23 percent non-white in 1980 were more likely to be predominately non-white4 by 2005 than to remain integrated. Illegal discrimination, in the form of steering by real estate agents, mortgage lending and insurance discrimination,5 subsidized housing placement, and racial gerrymandering of school attendance boundaries, is causing rapid racial change and economic decline. By 2010, 17 percent of suburbanites lived in predominantly non-white suburbs, communities that were once integrated but are now more troubled and have fewer prospects for renewal than their central cities. Tipping or resegregation (moving from a once all-white or stably integrated neighborhood to an all non-white neighborhood), while common, is not inevitable. Stable integration is possible but, it does not happen by accident. It is the product of clear race-conscious strategies, hard work, and political collaboration among local governments. Critical to stabilizing these suburbs is a renewed commitment to fair-housing enforcement, including local stable-integration plans, equitable education policies and incentives that encourage newer, whiter and richer suburbs to build their fair share of affordable units.

If racially diverse suburbs can become politically organized and exercise the power in their numbers, they can ensure both the stability of their communities and the future opportunity and prosperity of a multi-racial metropolitan America.

II. The Pattern of Diversity

A. Residential and School Segregation

America is one of the most racially, ethnically, and economically diverse nations on earth. According to the Bureau of the Census, America will have no single racial majority in its 3 Myron Orfield, American Metropolitics: The New Suburban Reality (Washington D.C.: Brookings Institution, 2002), 155-72; Myron Orfield and Thomas Luce, Region: Planning the Future of the Twin Cities (Minnesota: University of Minnesota Press, 2010), 273-92; John B. Judis and Ruy Teixeira, The Emerging Democratic Majority (New York: Scribner, 2002), 69-117; David Brooks, On Paradise Drive (New York: Simon and Schuster, 2004), 1- 15. 4 For the purpose of this study predominately non-white is defined as more than 60 percent non-white. 5 In part because there is no equivalent to HMDA data for insurance, far less is known about insurance than mortgage lending. See Greg Squires and Sally O’Connor, “The Unavailability of Information on Insurance Unavailability and the Absence of Geocoded Disclosure Data,” Housing Policy Debate 12, no. 2 (2001): 247.

general population by 2042. While this diversity has been a source of great strength, poor race relations have often challenged America’s stability and cohesiveness.

Black-white residential segregation remains intense and most of the glacially paced improvement has come in areas with the smallest percentage of blacks. In the metropolitan areas where blacks form the largest percentage of population, particularly in the Northeast and Midwest where local government is highly fragmented, segregation remains virtually unchanged at apartheid levels. For Latinos, America’s largest and fastest growing non-white community, residential segregation is both high and ominously constant. In areas like California and Texas, where Latinos form a large part of the population, segregation between whites and Latinos is now greater than black-white segregation.6

Public-school segregation, after dramatically improving in the era of civil rights enforcement (1968-90), has significantly eroded. Blacks are now almost as racially isolated from whites as they were at the time of the passage of the 1964 Civil Rights Act. For Latino students, segregation is worse than ever.7 Like housing segregation, school segregation is most pronounced in the Northeast and Midwest.

B. An Expanding Ring of Diversity

In the large metro areas, time-series maps of integrated neighborhoods reveal expanding rings of racial integration emanating outward, ahead of similarly expanding non-white core areas. (See Maps 3 – 8 for examples.) Each decade, the ring of integration moves farther outward into inner and (sometimes) middle suburbs, and the expanding core of non-white segregated areas grows to include larger portions of the central city and/or large parts of older suburbs, overtaking neighborhoods that were once integrated.8 Integrated areas in turn are surrounded by an expanding, largely white peripheral ring at the edge of metropolitan settlement.

The core non-white neighborhoods, particularly those that have been non-white for the longest time, are isolated from educational and economic opportunities. In these neighborhoods, many schools—public and charter—are failing. Their students are much more likely to spend time behind bars than to go on to higher education or find living wage employment. Banks don’t lend, businesses don’t prosper, and economic conditions worsen even in periods of national economic growth. Property values and tax capacity9 decline as needs for services intensify. Tax rates go up and services decline because the city has to tax the lower-valued real estate more intensively. Businesses and individuals with economic choices choose not to locate there, and as conditions worsen, existing businesses and individuals leave.

6 John R. Logan and Brian J. Stults, “The Persistence of Racial Segregation in the Metropolis: New Findings from the 2010 Census,” http://www.s4.brown.edu/us2010/Data/Report/report2.pdf 7 Gary Orfield and Chugmei Lee, “Historic Reversals, Accelerating Resegregation and the Need for New Strategies” (UCLA Civil Rights Project/Proyecto Derechos Civiles, August 2007) http://civilrightsproject.ucla.edu/research/k- 12-education/integration-and-diversity/historic-reversals-accelerating-resegregation-and-the-need-for-new- integration-strategies-1 8 Ibid. 9 For a definition of tax capacity or tax base, see footnote 13.

As the core declines, new land is developed for predominantly white communities at the periphery, even in metropolitan areas with stagnant populations. Detroit provides a particularly clear example of this pattern. In the last fifty years it has not grown in population at all, but has expanded more than 60 percent in urbanized land area. Essentially, Detroit taxed itself to build new rings of predominantly white, exurban communities of escape, while causing its central city to become one of the most segregated and dysfunctional municipalities in the United States. (Map 1.) On the other hand, Portland, Oregon has maintained a strong, stably integrated core with coordinated regional housing, land use, and transportation policies. As a result, developed land area and population have grown at roughly the same rate. (Map 2.)

TUSCOLA SAGINAW

JACKSON

GENESEE

INGHAM

SANILAC

LAPEER

ST CLAIRMACOMB

OAKLANDLIVINGSTON

WASHTENAW WAYNE

MONROELENAWEE

Detroit

Clay

Ray

Rich

Elba

Riley

Troy

Rose

Lynn

NoviLyon

Holly (t)

Clyde

Orion

Iosco

Casco

Berlin

Attica

Imlay

Wales

Bruce

China

Burnside

Lenox

Huron

Genoa

Ira

Handy Oceola

Grant

Tyrone

Marion

Hadley

Shelby

Oxford (t)

Almont (t)

Canton

Mil- ford (t)

Dryden

St. Clair

Kimball

Livonia

Warren

Mussey

Oregon

Conway

Arcadia

Lapeer (t)

Armada

Howell (t)

Addison

Putnam

Oakland

Sumpter

Macomb

Emmett (t)

Hartland

Unadilla

Mayfield

Deerfield

Brandon

Romulus

Cohoctah

Highland

Brighton (t) Clinton

Deerfield

Hamburg

Columbus

Goodland

Taylor

Richmond

Kenockee

Brockway

Van Buren

Groveland

Green Oak

Metamora (v)

Waterford

Marathon

Springfield

White Lake

Burlington

Green- wood

Washing- ton

North Branch

(t)

Pontiac

Southfield

Commerce

Dearborn

Inde- pendence

Bloomfield

Chester- field

Sterling Heights

Rochester Hills

Westland

Farmington Hills

Cottrell- ville

Plymouth (c)

West Bloomfield

Grosse Ile

Harrison

Northville

Burtchville

Red- ford

Fort Gratiot

Wix- om

Auburn Hills

Browns- town

Port Huron

(t)

RO Rsv

Trenton

Wayne I

Port Huron

Lapeer (c)

Marysville

St. Clair Shores

East China

Fraser

Wyandotte

Howell (c)

Holly (v)

Ecorse

Gibraltar

Riv

St. Clair (c)

Brighton (c) Bir

Rochester

Milford (v) Utica

Frk

New Baltimore

South Lyon

Yale

Richmond (c)

Romeo

River Rouge

Capac

Clawson

HzP

Rockwood

Plymouth (t)

Frm

Marine City

Imlay City

Fowlerville

Almont (v)

New Haven

Oxford (v)

Northville (c)

Clifford

OLV

Emmett (v)

H HW

Algonac

Pinckney

Dryden (v)

Grosse Pointe Woods

Grosse Pointe Farms

Memphis

Belleville

Lake Angelus

Lake Orion

Leonard

Grosse Pointe Park

WvL

Ortonville

North Branch (v)

Columbiaville

Armada (v)

Otter Lake

Metamora

Royal Oak Grosse Pointe Shores

Novi (c)

Village of Clarkston

Grosse Pointe

CL By

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Lake Erie

£¤23

Lake St. Clair

¡¢94

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£¤12

¡¢96

¡¢75

£¤23

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¡¢96

Data Source: U.S. Census Bureau

¡¢75

CANADA

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Map 1: DETROIT REGION: Urban Land by Census Tract, 1970 to 2007*

$ Miles

0 20

¡¢75

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

Allen Park Birmingham Bingham Farms Bloomfield Hills Berkley Center Line Dearborn Heights Eastpointe Ferndale Flat Rock Franklin Farmington Garden City Hamtramck Highland Park Harper Woods Hazel Park Inkster Keego Harbor Lincoln Park Lathrup Village Melvindale Mount Clemens Madison Heights Orchard Lake Village Oak Park Riverview Royal Oak (c) Roseville Southgate Sylvan Lake Woodhaven Walled Lake Wolverine Lake

AP Bir BF BH By CL DH E F FR Frk Frm GC H HP HW HzP I KH LP LV M MC MH OLV OP Riv RO Rsv Sg SL Wd WL WvL

*2005-2009 American Community Survey data denotes the end year 2007 for the urban land map.

Legend

Note: A developed tract was defined as a tract with an average density of at least 1 housing unit per 4 acres.

Developed before 1970 Developed 1970-1980 Developed 1980-1990 Developed 1990-2000 Developed 2000-2007*

-20

1970 1980 1990 2000 2007

% Change

from 1970

Detroit

Population Urban Land

myott001

Text Box

CLACKAMAS

MARION

POLK

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Vancouver

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McMinnville

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West Linn

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ville

Newberg

Happy Valley

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Troutdale

St. Helens

Sandy

Ridgefield

Battle Ground

Washougal

Mil- waukie

Fair- view

Forest Grove

Sherwood

Rainier

Molalla

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Gladstone

Estacada

Sheridan

Cornelius

Vernonia

Dundee

Stevenson

North Bonneville

La Center

Carlton

Dayton

Clatskanie

AmityWillamina

Lafayette

Yacolt Columbia City

North Plains

King City

Wood Village

Yamhill

Banks

Durham Gaston

Maywood Park

Barlow

Prescott

Johnson City

River- grove

Map 2: PORTLAND REGION: Urban Land by Census Tract, 1970 to 2007*

OR W

OR WA

Data Source: U.S. Census Bureau.

$ Miles

0 10 0

100

120

1970 1980 1990 2000 2007

% Change

from 1970

Portland

Population Urban Land

*2005-2009 American Community Survey data denotes the end year 2007 for the urban land map.

Legend

Note: A developed tract was defined as a tract with an average density of at least 1 housing unit per 4 acres.

Developed before 1970 Developed 1970-1980 Developed 1980-1990 Developed 1990-2000 Developed 2000-2007*

myott001

Text Box

III. Racial Diversity in the Suburbs—the Suburban Typology

A. Overview

This study, which focuses on the 50 largest metropolitan areas of the United States, identifies four suburban types.10 1) Diverse suburbs are defined as communities where non-white residents represented between 20 and 60 percent of the population in 2010. 2) Predominantly non-white suburbs are areas where more than 60 percent of the population was non-white. 3) Predominantly white suburbs are areas that were more than 80 percent white. 4) Finally, exurbs are areas where less than 10 percent of the land area was categorized as urban in 2000 (regardless of the racial makeup).

Chart 1 shows the community-type distributions of the municipalities and residents in the 50 largest metropolitan areas in 2000 and 2010. By 2010, nearly 53 million people—almost a third of the total population and 44 percent of the suburban population of these large metros— lived in 1,376 diverse suburbs. These numbers represent substantial increases from 2000, when 42 million people lived in 1,006 diverse communities. Another 20 million (12 percent of total population and 17 percent of suburban residents) lived in 478 predominantly non-white suburbs, up from 11 million in 2000.

By 2010 just 28 percent of metropolitan residents (or 47 million people) lived in “traditional” suburbs—predominantly white communities or developing exurban areas (nearly all of which were also predominantly white). This is much lower than in 2000 when 35 percent (54 million people) lived in these types of suburbs. Put another way, in just 10 years, the percentage of suburbanites living in the 20th-century stereotype of the suburbs—largely white, rapidly developing places removed from the racial and economic diversity of the large central cities that they surround—fell from more than half (51 percent) to just 39 percent.

Many predominantly non-white and diverse suburbs border central cities, though some are in second-ring suburbs or are even further out along major highways. They are often fully developed. Predominately white suburbs and exurbs tend to be in still-developing parts of middle and outer suburban areas.

The predominantly white suburbs show the least socioeconomic stress. They have the highest incomes and local tax wealth, as well as the lowest poverty rates. Integrated suburbs, the second-most-prosperous group, follow in each category (sometimes very closely). The exurbs have lower incomes and property wealth than the integrated suburbs and more poverty, but are better off on these dimensions than the predominately non-white suburbs. Predominantly non-

10 Residents of these metro areas comprised 55 percent of the nation’s population in 2010. The large metros were 44 percent non-white, somewhat more diverse than the United States as a whole, which was 36 percent non-white. The suburbs of the large metros closely mirrored the racial diversity of the nation, with 11 percent non-Hispanic blacks (compared to 12 percent nationwide), and 17 percent Hispanics (compared to 16 percent). Population growth patterns were also similar. The large metros grew by 8 percent from 2000 to 2010, compared to 10 percent in the country as a whole, and the non-white population in the metros grew by 7 percentage points compared to 5 points nationally. Unincorporated areas were included in the typology, with the unincorporated area of a single county treated as a municipality. See note 1.

white suburbs show by far the highest degrees of stress—in fact, greater in most dimensions than central cities.

In order to understand the possibility of policy reform, it is important to understand the political makeup of the community types. Predominantly non-white suburbs are largely Democratic and exurbs are mostly Republican. Predominantly white suburbs lean Republican but are politically mixed. Diverse suburbs are the most evenly divided, showing an almost perfect split between Democratic and Republican voters. In close elections, such places could command disproportionate attention from competing political parties.

Chart 1: Distribution of Municipalities and Residents Across the Community Types

Municipalities 2000 2010

Population 2000 2010

Central Cities 68 1%

Diverse Suburbs 1,376 21%

Predominantly Non-white

Suburbs 478 7%

Predominantly White

Suburbs 2,478 38%

Exurbs 2,176 33%

Central Cities 68 1%

Diverse Suburbs 1,006 15%

Predominantly Non-white

Suburbs 312 5%

Predominantly White

Suburbs 2,984 46%

Exurbs 2,147 33%

Central Cities, 49,199,197,

29%

Diverse Suburbs,

52,748,396, 31%

Predominantly Non-white Suburbs,

20,122,337, 12%

Predominantly White

Suburbs, 30,180,578,

18%

Exurbs, 16,983,337,

10%Central Cities, 47,406,687,

31%

Diverse Suburbs,

40,350,901, 26%

Predominantly Non-white Suburbs,

11,711,327, 8%

Predominantly White

Suburbs, 39,333,003,

26%

Exurbs, 14,533,326,

B. Social, Economic and Political Characteristics of the Suburban Types

1. The Diverse Suburbs

Diverse suburbs, communities where 20 to 60 percent of the residents are non-white, represent the largest single suburban segment—53 million people in 2010, up from 40 million in 2000. Once a destination for whites avoiding city neighborhoods, many of these areas now struggle to maintain racial and economic diversity while competing against newer, whiter, and richer suburban communities that are often resistant to affordable housing and racial diversity.

However, diverse communities have many strengths. They are growing. Population in suburbs that were diverse in 2010 grew by 15 percent between 2000 and 2010—more than any other community type except the sparsely settled exurban group (Table 1).11 In fact, suburbs that were diverse in 2010 added more population in the previous 10 years (6.8 million people) than predominantly white areas (3.1 million) and exurbs (2.5 million) combined. They also contain more jobs per capita than any of the other groups except central cities, and show the greatest job growth of any group except exurbs (which started with a very small base of jobs). Many suburban job centers—the most important source of job growth in modern American metropolitan areas—are located in diverse suburbs because those diverse suburbs are often located near core areas and along interstate highways. Reflecting this, they are largely fully developed—about two-thirds of them are more than 80 percent urbanized and less than five percent of them are less than 20 percent urbanized.

Other common measures of social and economic welfare indicate that diverse suburbs are less stressed than central cities and predominantly non-white suburbs but lag behind predominantly white areas (Table 2).12 A typical diverse suburb had a local tax base roughly equal to its region’s average in 2008.13 In this regard, diverse suburbs trailed predominantly white suburbs by several percentage points, but fared far better than the non-white suburbs or the exurbs.

11 Note that this calculation differs from the changes shown in Chart 1. Changes in Chart 1 reflect changes in the number and composition of each community type in the two years—diverse suburbs in 2000 represent a different group of places than diverse suburbs in 2010. The calculation in Table 1 on the other hand isolates the change in the places that were a particular group in 2010, to highlight the relative strengths or weaknesses of the community types as they were composed in 2010. Job growth rates in Table 1 were calculated in the same way. 12 The measures in Table 2 are compared to metropolitan averages to control for very wide variations at the metropolitan level among the 50 largest metros. For instance, the non-white share of population in the late 2000s varies from just 12 percent in the Pittsburgh metropolitan area to 67 percent in the Los Angeles metropolitan area. Tax base, poverty, income and home value data also vary dramatically from metro to metro. 13 Tax capacity measures a community’s ability to raise revenues from its tax base with typical tax rates (by each region’s standards). Tax capacity data were available for 43 of the 50 largest metropolitan areas. The excluded areas are Baltimore MD, Birmingham AL, Kansas City MO, New Orleans LA, Providence RI, St. Louis MO, and Washington D.C. The tax capacity measure included local property, sales and income taxes. Where more than one tax is used, the measure was calculated as the revenue forthcoming from each tax if the average regional tax rate was applied to the actual local tax base.

The most troubling signs for diverse communities are the clear indications that many are in the midst of racial transition.14 Integrated suburbs show the most rapid racial change (relative to their individual metros) of all of the community types. The non-white share of population in a typical diverse suburb increased from 65 percent of the regional average in 2000 to 78 percent in 2010.15

The diverse suburbs are evenly split between Democrats and Republicans. They are more likely than other types of suburbs to switch parties from one election to another and, as a result, can often decide the balance of state legislatures and the Congress, or determine the outcome of gubernatorial and presidential elections.16 If the diverse suburbs banded together to form a political faction, it would be hard to deny them.

2. Predominantly non-white suburbs

Twelve percent of the large metros’ population, 20 million people, lived in 478 different predominantly non-white suburban areas (municipalities where more than 60 percent of the population was non-white). This group showed the greatest percentage increases of any of the community types. In just 10 years, the number of municipalities in the group increased by 53 percent and the number of residents rose by 72 percent. The increases reflect the fact that many of these municipalities were racially integrated suburbs in past decades, completing the transition to predominantly non-white during the 2000s. In 2010, 77 percent of the population in these communities were non-white (representing 16 million non-whites) compared to 61 percent in central cities (representing 30 million non-whites).

A variety of indicators show that predominantly non-white suburbs suffer many of the ills often attributed solely to central cities, and more. The tax-capacity comparisons in Table 2 show that predominantly non-white suburbs suffer the most from tax-base woes associated with recent and ongoing social change. They have by far the lowest tax bases, at just 66 percent of regional averages. Although central cities and predominantly non-white suburbs have very similar median incomes and poverty rates, central cities typically show dramatically stronger tax bases—in fact, more than 20 percentage points better. Home values, income, and poverty also worsened (compared to regional averages) in predominantly non-white suburbs while they were stable or improving in central cities.

14 Table 2 shows racial shares compared to metropolitan averages to control for the very wide variation in racial mixes at the metropolitan level among the 50 largest metros. The non-white share of population in 2005-09 varies from just 12 percent in the Pittsburgh metropolitan area to 67 percent in the Los Angeles metropolitan area. 15 The relative non-white shares were calculated in both years based on how communities were classified in 2010. The cited change therefore represents growing non-white shares during the decade in communities classified as diverse at the end of the period. 16 Myron Orfield, American Metropolitics: The New Suburban Reality (Washington D.C.: Brookings Institution, 2002), 155-72; Myron Orfield and Thomas Luce, Region: Planning the Future of the Twin Cities (Minnesota: University of Minnesota Press, 2010), 273-92; John B. Judis and Ruy Teixeira, The Emerging Democratic Majority (New York: Scribner, 2002) 69-117; David Brooks, On Paradise Drive (New York: Simon and Schuster, 2004), 1- 15.

Predominantly non-white suburbs have displaced central cities as the most Democratic communities in metropolitan America. Nearly 70 percent of votes cast in all elections in these areas in 2008 went to Democratic candidates, slightly higher than the typical share in a central city.17

3. Predominantly white suburbs

Only 18 percent of large metro residents live in predominantly white suburbs (areas that are both more than 80 percent white and at least 10 percent urbanized). This is the only community type which shrunk in size between 2000 and 2010, falling to 30 million people in 2,478 municipalities in 2010 from 39 million in 2,984 places in 2000. Predominantly white suburbs are still largely residential and show little fiscal or social stress. They have significantly fewer jobs per resident than central cities and diverse suburbs, and job growth has been slow. But household incomes and home values are high enough to offset the lack of non-residential property—typical local tax bases per capita are the highest of all the community types. Social stress, as indicated by poverty rates, is also low in these areas, implying that they can provide high levels of local public services at relatively low tax rates. Predominantly white suburbs are the slowest growing suburban type on average, but they are not completely isolated from racial change—they show an increase in non-white shares second only to diverse suburbs.

Not surprisingly, a typical community in this category is majority Republican. However, the balance in 2008 was closer than one might expect—54 percent Republican and 46 percent Democratic.

4. The Exurbs

The smallest suburban community type is the exurbs—places where less than 10 percent of land was urban in 2000. Although these largely outlying areas are the fastest-growing type, they still represented only 10 percent of large metro populations in 2010, up slightly from 9 percent in 2000. These areas show some signs of fiscal stress, although not nearly to the same degree as predominantly non-white suburbs. Their tax bases are significantly lower than diverse or predominantly white suburbs, making it difficult for them to finance all the costs of growth. They also show signs of rural poverty—their poverty rates are similar to diverse suburbs, but their income and home values are lower. In addition, they have by far the lowest number of jobs per capita of the community types (although jobs are growing relatively quickly), and are the areas hurt most by high gas prices of recent years. Finally, exurban areas are the most Republican of the community types, with 61 percent of votes in 2008 going to Republican candidates.

17 The political data include 43 of the 50 largest metropolitan areas where local area election data were available.

T able 1: G

eneral C haracteristics of the C

om m

unity T ypes in the 50 L

argest M etropolitan A

reas, 2000 - 2010

P opulation

M edian

P opulation**

Jobs per 100 Job**

% 2010

Share Share

% of L

and G

row th (%

) R

esidents G

row th (%

) D

em ocratic

C om

m unity T

ype (in 2010) N

um ber

P opulation

(M etro)

(Suburban) U

rban 2000-2010

2008 2003 - 2008

2008

C entral C

ities 68

49,199,197 29

-- 89

4 59

7 67

D iverse Suburbs

1,376 52,748,396

31 44

98 15

40 9

50 P

redom inantly N

on-w hite Suburbs

478 20,122,337

12 17

100 11

29 6

68 P

redom inantly W

hite Suburbs 2,478

30,180,578 18

25 88

12 30

3 46

E xurbs

2,176 16,983,337

10 14

0 17

13 14

D efinitions:

N on-w

hite Segregated: M unicipalities w

ith m ore than 60 percent of the population non-w

hite in 2005-09 and m ore than 10 percent of land urban.

Integrated: M unicipalities w

ith non-w hite shares betw

een 20 and 60 percent in 2005-09 and m ore than 10 percent of land urban.

P redom

inantly w hite: M

unicipalities w ith w

hite shares greater than 80 percent in 2005-09 and m ore than 10 percent of land urban.

E xurbs: M

unicipalities w ith less than 10 percent of total land area urban (by C

ensus definition of urban) in 2000.

**: P opulation grow

th and job grow th are changes based on 2010 com

m unity classifications.

Sources: B

ureau of the C ensus, 2000 C

ensus of P opulation and the A

m erican C

om m

unity Survey, 2009 (population, race, poverty, land area, urban land). B

ureau of the C ensus, L

ocal E m

ploym ent D

ynanics (jobs). V

arious state and local agencies (election results for 43 of the 50 m etros).

5. Central Cities

Although this is a paper about suburbs, the central cities bear mentioning. With one-third of metropolitan populations, they have tremendous power as potential allies to some types of suburbs. They share many of the same vulnerabilities to racial discrimination with the older suburbs, and together with the diverse and predominantly non-white suburbs would, constitute two-thirds of regional populations and political strength. They are a heterogeneous group, ranging from relatively wealthy, whiter cities like Boston, Seattle and San Francisco, to deeply segregated, nearly bankrupt cities like Detroit, Milwaukee, and Cleveland. As a group they have tax capacities that are close to the integrated and predominantly white suburbs and far above the non-white suburbs and exurbs. But they also have the highest poverty rates and lowest average incomes.

Central cities have greater potential for renewal than many suburbs. Their historic role in metropolitan job and housing markets means they have high-density central business districts, high-end housing neighborhoods, parks, cultural attractions, amenities, and public infrastructure that offset some of the disadvantages associated with the concentrations of poverty that emerged in the post-World War II period. This means that they are better positioned than many suburbs to deal with the effects of socioeconomic transition. Whereas many fully developed suburbs that originally developed as bedroom communities are at a disadvantage because they tend to have little commercial-industrial tax base to offset the declining home values that often accompany socioeconomic transition.

C. Geographic Distribution of the Community Types In most regions in 2000, inner suburbs surrounding central cities were a mix of integrated

and non-white segregated suburbs, interspersed with a few predominantly white areas. During the next 10 years, this halo of racially integrated and non-white segregated areas moved outward into contiguous middle suburbs. At the same time, many inner-suburban communities that were integrated in 2000 became non-white segregated. Maps 3 – 8 show how this story played out, with variations on the overall theme, in three of the nation’s largest metropolitan areas: Chicago, New York, and Dallas.

In Chicago, by 2010, (Maps 3 and 4), a cluster of municipalities south of the central city that were predominantly non-white in 2000 expanded to include a significant number of nearby municipalities that were diverse in 2000. A similar pattern developed directly west of the city of Chicago, where a few integrated areas re-segregated and many other predominantly white areas became diverse. Overall, the number of predominantly non-white communities increased by more than 70 percent in less than a decade, from 28 to 48, while the number of racially diverse areas increased by more than half, from 81 to 123.

Despite the fact that the metro-wide, non-white share of the population increased by only four points,18 many Chicago metropolitan municipalities experienced rapid racial change during the decade. The community of South Holland had clearly crossed a threshold of change by

18 The non-white share of the population increased from 41 percent in 2000 to 45 percent in 2010

2000—the non-white share of South Holland’s population increased by 26 percentage points in just 10 years, from 56 percent in 2000 to 82 percent in 2010. Two other nearby communities that were actually still predominantly white in 2000 moved very rapidly into the diverse category. Thornton went from six percent non-white in 2000 to 21 percent in 2010, while Lansing moved almost all the way through the “diverse” range in just 10 years, changing from 18 percent non- white in 2000 to 48 percent in 2010. Similar changes occurred in the inner western suburbs where, for instance, the non-white share went from 48 to 68 percent in Berkeley and from 57 percent to 75 percent in Hillside. Markham and Harvey, two communities that were already predominantly non-white in 2000, each continued racial transitions—from 84 percent to 90 percent non-white in Markham and from 94 percent to 96 percent non-white for Harvey— suggesting that the process may not stop until communities are virtually all non-white.

Although the changes were not as dramatic in the central part of the New York metropolitan area (Maps 5 and 6), a band of suburban racial change nearly surrounds the city of New York. The most pronounced changes were in New Jersey (west and southwest of New York and surrounding Newark) where several integrated areas transitioned to non-white segregated and a substantial number of predominantly white municipalities became integrated. For instance, the non-white share of the population in Harrison town (bordering Newark) went from 53 percent in 2000 to 65 percent in 2010. And in neighboring Kearney, the non-white share increased from 40 percent to 51 percent (in contrast to a regional non-white share increase of only five points, from 46 to 51 percent). Parts of Long Island (east of the city) also showed clear signs of racial change, as Oyster Bay, Huntington, and several other places made the transition from predominantly white to diverse.

The inner suburbs directly north of the city in New York show many newly integrated areas in 2010. Changes in this area were slower—Tuckahoe went from 30 to 33 percent non- white while Scarsdale went from 18 to 20 percent—suggesting some potential for stably integrated outcomes in the future.

Dallas shows the most dramatic pattern of racial change in its inner suburbs (Maps 7 and 8). This partially reflects that fact that metropolitan-level racial change was rapid in Dallas, where the non-white share of the population rose nine points from 41 percent to 50 percent. However, the region-wide change does not explain all of the local area trends. For instance, virtually the entire ring of inner suburbs along the southern and western borders of the city of Dallas made the transition from integrated to predominantly non-white. A string of municipalities in this part of the region went through dramatic change—non-white shares went from 55 to 83 percent in DeSoto, from 49 to 74 percent in Cedar Hill, from 53 to 71 percent in Grand Prairie, and from 52 to 69 percent in Irving.

At the same time, a whole new band of suburbs north and northwest of the city went from predominantly white to diverse. North Richland Hills went from 17 percent non-white to 25 percent, Grapevine from 18 percent to 28 percent, and Flower Mound from 13 percent to 22 percent.

Chicago

Gary

Aurora

Joliet

Elgin

Hobart

Merrill- ville

Hammond

Waukegan Gurnee

Barrington Hills

Bartlett

Bolingbrook

Orland Park

Zion

Romeo- ville

Palatine

Schaum- burg

Glenview

McHenry

Skokie

Lake Forest Crystal Lake

Tinley Park

Lisle

Plainfield

Wheaton

Dyer

Addison

Alsip

Batavia

Niles

Frank- fort

Des Plaines

Geneva

Cary

Northbrook

Elm- hurst

St. Charles

Scherer- ville

Lom- bard

Long Grove

Darien

Matteson

Crete

Algonquin

West Chicago

Griffith

Grays- lake

Downers Grove

Arlington Heights

Wheeling

Mun- ster

Cicero

Wayne

Hoffman Estates

Highland Park

Lansing

Lemont

Harvey

Mund- elein

Oswego Oak

Lawn

Mokena

East Chicago

New Lenox

Evanston

Liberty -ville

Chann- ahon

Oak Brook

Itasca

Crest Hill

Wads- worth

Roselle

High- land

Lock- port

Old Mill

Creek

Mett- awa

Dolton

Carol Stream

Mount Prospect

Elk Grove Village

Lake Villa

Inverness

Glen Ellyn

Vernon Hills

Burr Ridge

Buffalo Grove

Park Ridge

Lake Station

Volo

Chicago Heights

Lake in the Hills

Deer- field

Montgomery

Lyn- wood

Homer Glen

Wilmette

Stream- wood

Lake Zurich

Calumet City

Markham

North Chicago

Steger

Hins- dale

South Elgin

Oak Forest

Carpentersville

Ben- sen-

ville

Ber- wyn

South Holland

Oak Park

Han- over Park

Villa Park

Johnsburg

Bloomingdale

Warren- ville

Burbank

Spring Grove

Kildeer

Home- wood

Glencoe

Bedford Park

Lakemoor

Barring- ton

Wauconda

Park Forest

Winnetka

North Aurora

South Barrington

Worth

Lake Bluff

Deer Park

River- dale

Palos Hills

Just- ice

Prairie Grove

Lyons

Morton Grove

Blue Island

Lincoln- shire

Shore- wood

River- woods

Winfield

Melrose Park

Mc- Cook

Summit

North- field

Hazel Crest

Crest- wood

May- wood

Hill- side

Palos Hts.

RLB

Island Lake

Willow Springs

Midlothian

Thorn- ton

Bell- wood

CCH

Ringwood

East Dundee

Whiting

Stickney

Burn- ham

Lincoln- wood

Norridge

Evergreen Park

Bannock- burn

CRD

Rose- mont

Elmwood Park

Fox Lake Hills

Forest View

Park City

RLP

Golf

Hebron

Tower Lakes

Calumet Park

Rockdale

TLV

Palos Park

Highwood

Harwood Heights

Kenilworth

Home- town

RLH

Merrionette Park

Wood- ridge

Naperville

Beach Park

WILL

COOK

LAKE

KANE

LAKE MCHENRY

GRUNDY

DUPAGE

KENDALL ¡¢80

¡¢90

¡¢55

¡¢90

¡¢94

¡¢294

¡¢355

¡¢294

¡¢94

¡¢80

¡¢55

£¤14

¡¢57

¡¢94

¡¢290

¡¢90

¡¢80

¡¢290

Map 3: CHICAGO REGION (CENTRAL AREA) Community Type by Municipality and County Unincorporated Area, 2000

Data Source: U.S. Census Bureau.

Lake Michigan

- Lindenhurst - La Grange Park - North Barrington - Northlake - North Riverside - Orland Hills - Olympia Fields - Oakwood Hills - Port Barrington - Prospect Heights - Phoenix - Robbins - Rockdale - River Forest - River Grove - Round Lake - Round Lake Beach - Round Lake Heights - Round Lake Park - Rolling Meadows - Richton Park - Riverside - South Chicago Heights - Sleepy Hollow - Schiller Park - Sauk Village - Third Lake Village - Trout Valley - Westchester - West Dundee - Willowbrook - Westmont - Wood Dale - Western Springs

LI LP NB NL NR O OF OH PB PR PX RB RD RF RG RL RLB RLH RLP RM RP RS SC SH SP SV TLV TV W WD WL WM WO WS

- Berkeley - Brookfield - Bridgeview - Broadview - Clarendon Hills - Country Club Hills - Countryside - Chicago Ridge - Dixmoor - Forest Park - Ford Heights - Fox Lake - Flossmoor - Franklin Park - Fox River Grove - Forest View - Glendale Heights - Green Oaks - Glenwood - Hodgkins - Hainesville - Holiday Hills - Hickory Hills - Hawthorne Woods - Lake Barrington - La Grange

B BF BV BW C CCH CO CRD D F FH FL FM FP FR FV GH GO GW H HA HH HI HW LB LG

Legend

Definitions: Predominately non-white: Municipalities with more than 60% of the population non-white in 2000 and more than 10% of land urban. Diverse: Municipalities with non-white shares between 20% and 60% in 2000 and more than 10% of land urban. Predominantly white: Municipalities with white shares greater than 80% in 2000 and more than 10% of land urban. Exurbs: Municipalities with less than 10% of total land area urban (by Census definition of urban) in 2000.

Central Cities Predominately non-white Diverse Predominately white

(1) (28) (81)

(205) Exurb (88)

$ Miles

0 5 10

myott001

Text Box

Chicago

Gary

Aurora

Joliet

Elgin

Hobart

Merrill- ville

Hammond

Waukegan Gurnee

Barrington Hills

Bartlett

Bolingbrook

Orland Park

Zion

Romeo- ville

Palatine

Schaum- burg

Glenview

McHenry

Skokie

Lake Forest Crystal Lake

Tinley Park

Lisle

Plainfield

Wheaton

Dyer

Addison

Alsip

Batavia

Niles

Frank- fort

Des Plaines

Geneva

Cary

Northbrook

Elm- hurst

St. Charles

Scherer- ville

Lom- bard

Long Grove

Darien

Matteson

Crete

Algonquin

West Chicago

Griffith

Grays- lake

Downers Grove

Arlington Heights

Wheeling

Mun- ster

Cicero

Wayne

Hoffman Estates

Highland Park

Lansing

Lemont

Harvey

Mund- elein

Oswego Oak

Lawn

Mokena

East Chicago

New Lenox

Evanston

Liberty -ville

Chann- ahon

Oak Brook

Itasca

Crest Hill

Wads- worth

Roselle

High- land

Lock- port

Old Mill

Creek

Mett- awa

Dolton

Carol Stream

Mount Prospect

Elk Grove Village

Lake Villa

Inverness

Glen Ellyn

Vernon Hills

Burr Ridge

Buffalo Grove

Park Ridge

Lake Station

Volo

Chicago Heights

Lake in the Hills

Deer- field

Montgomery

Lyn- wood

Homer Glen

Wilmette

Stream- wood

Lake Zurich

Calumet City

Markham

North Chicago

Steger

Hins- dale

South Elgin

Oak Forest

Carpentersville

Ben- sen-

ville

Ber- wyn

South Holland

Oak Park

Han- over Park

Villa Park

Johnsburg

Bloomingdale

Warren- ville

Burbank

Spring Grove

Kildeer

Home- wood

Glencoe

Bedford Park

Lakemoor

Barring- ton

Wauconda

Park Forest

Winnetka

North Aurora

South Barrington

Worth

Lake Bluff

Deer Park

River- dale

Palos Hills

Just- ice

Prairie Grove

Lyons

Morton Grove

Blue Island

Lincoln- shire

Shore- wood

River- woods

Winfield

Melrose Park

Mc- Cook

Summit

North- field

Hazel Crest

Crest- wood

May- wood

Hill- side

Palos Hts.

RLB

Island Lake

Willow Springs

Midlothian

Thorn- ton

Bell- wood

CCH

Ringwood

East Dundee

Whiting

Stickney

Burn- ham

Lincoln- wood

Norridge

Evergreen Park

Bannock- burn

CRD

Rose- mont

Elmwood Park

Fox Lake Hills

Forest View

Park City

RLP

Golf

Hebron

Tower Lakes

Calumet Park

Rockdale

TLV

Palos Park

Highwood

Harwood Heights

Kenilworth

Home- town

RLH

Merrionette Park

Wood- ridge

Naperville

Beach Park

WILL

COOK

LAKE

KANE

LAKE MCHENRY

GRUNDY

DUPAGE

KENDALL ¡¢80

¡¢90

¡¢55

¡¢90

¡¢94

¡¢294

¡¢355

¡¢294

¡¢94

¡¢80

¡¢55

£¤14

¡¢57

¡¢94

¡¢290

¡¢90

¡¢80

¡¢290

Map 4: CHICAGO REGION (CENTRAL AREA) Community Type by Municipality and County Unincorporated Area, 2010

Data Source: U.S. Census Bureau.

Lake Michigan

LI LP NB NL NR O OF OH PB PR PX RB RD RF RG RL RLB RLH RLP RM RP RS SC SH SP SV TLV TV W WD WL WM WO WS

B BF BV BW C CCH CO CRD D F FH FL FM FP FR FV GH GO GW H HA HH HI HW LB LG

Legend

Definitions: Predominately non-white: Municipalities with more than 60% of the population non-white in 2010 and more than 10% of land urban. Diverse: Municipalities with non-white shares between 20% and 60% in 2010 and more than 10% of land urban. Predominantly white: Municipalities with white shares greater than 80% in 2010 and more than 10% of land urban. Exurbs: Municipalities with less than 10% of total land area urban (by Census definition of urban) in 2000.

Central Cities Predominately non-white Diverse Predominately white

(1) (48)

(123) (144)

Exurb (92)

$ Miles

0 5 10

myott001

Text Box

New York City

Vernon

Kent

Hunting- ton

West Milford

Hempstead Town

Carmel

Franklin

Bedford

Long Hill

Edison

Oyster Bay

Baby- lon

Rockaway Twp

Somers

Yorktown

Philipstown

Cortlandt

Wayne

Southeast

Clarkstown

Patterson

Mahwah

Newark

Putnam Valley

Morris

Lewisboro

Ringwood

Ramapo

Harding

Stony Point

Yonkers

Kinnelon

North Castle

New Castle

Montville

Harrison

Woodbridge

Pound Ridge

North Salem

Orangetown

Clifton

Piscataway

Linden

Green- burgh

Union

Denville

Mount Pleasant

Rye

Haverstraw Twp

Jersey City

Livingston

Elizabeth

Kearny

Hanover

Fairfield

Millburn

Paramus

Parsippany- Troy Hills

Oakland

Chatham Twp

Boonton Twp

Alpine

Wanaque

Paterson

West Orange

Clark

Wyckoff

White Plains

Summit

New Rochelle

Teaneck

Lloyd Harbor

West- field

Ramsey

Scars- dale

Scotch Plains

Mont- clair

Franklin Lakes

Blooming- dale

Glen Cove

Bayonne

Plainfield

Secaucus

Old Westbury

Tenafly

East Hanover

Watchung

Pequannock

Airmont

Lincoln Park

Florham Park

Cran- ford

Totowa

Lodi

Free- port

South Plainfield

Carteret

Fair Lawn

Ridge- wood

Nutley

Madison

Rahway

Mutton- townBloom-

field

Peekskill

Spring- field

Closter

Lynd- hurst

Garden City

Engle- wood

Carlstadt

Montvale

Passaic

River Vale

North Hempstead

Brook- ville

Saddle River

Montebello

Roseland

Verona

North Bergen

Briarcliff Manor

Lawrence

Ossining

Perth Amboy

Berkeley Heights

Belle- ville

Green Brook

Roselle

Hill- side

Old Tappan

Sands Point

Ossining Twp

West Caldwell

Oradell

Cedar Grove

Middle- sex

Hackensack

Hillsdale Butler

Rye Brook

Allen- dale

Maple- wood

Hempstead

Chestnut Ridge

East Orange

Latting- town

Mount Vernon

Irvington

Fort Lee

Hawthorne

Norwood

Mountain- side

Pomona

Irving- ton

Tarry- town

Boonton

Kings Point

East- chester

Suffern

Old Brookville

Metuchen

Hillburn

Emerson

Wesley Hills

Upper Saddle River

Glen Rock

Haworth

Little Falls

Chatham

Garfield

Mamaroneck Village

Morristown

Ridgefield

North Hills

Upper Brookville

Mount Kisco

Oyster Bay

Cove

Croton- on-Hudson

Dumont

Bergen- field

Westbury

Valley Stream

Cresskill

Wash- ington

East Hills

Mamaroneck

Park Ridge

Woodcliff Lake

North Haledon

Mineola

Sloatsburg

Matine- cockLeonia

New Providence

Riverdale Westwood

Waldwick

Demarest

Lyn- brook

Ruther- ford

Laurel Hollow

West Paterson

Amity- ville

Rockville Centre

Saddle Brook

Pompton Lakes

North Caldwell

Morris Plains

Dobbs Ferry

Kenilworth

Port Chester

New Milford

Long Beach

Elmwood Park

Mountain Lakes

Bayville

Haverstraw

North Plainfield

River Edge

New Hempstead

Ardsley

North Arlington

Ho-Ho-Kus

Spring Valley

Moonachie

City of Orange

Flower Hill

Fanwood

Buchanan

Hoboken Lake Success

Pleasantville

Harrison

Haledon

Little Ferry

North Tarrytown

Maywood

South Orange Village

Northvale

Sea Cliff

Floral Park

Highland Park

Essex Fells

Nyack

Englewood Cliffs

Cove Neck

Massapequa Park

Union City

Great Neck

Midland Park

Dunellen

Malverne

Elmsford

Ridgefield Park Teterboro

Pelham

Upper Nyack

Fair- view

Rockleigh

Roselle Park

East Rutherford Roslyn

Hastings- on-Hudson

Nelsonville

Bronxville

Pelham Manor

Larchmont

Wallington

Farmingdale

West Haverstraw

Centre Island

Edgewater

Piermont

Garwood

Cliffside Park

Weehawken

East Rockaway

Caldwell

West New York

Tuckahoe

Cedarhurst

New Hyde Park

Cold Spring

Brewster

South Nyack

Williston ParkGreat Neck Estates

Manorhaven

Prospect Park

Atlantic Beach Island Park

Woodsburgh

Winfield

Stewart Manor

Grand View- on-Hudson

Harrington Park

Bogota

Wood-Ridge

Roslyn Harbor Palisades Park

Hasbrouck Heights

Rochelle Park

Glen Ridge

Borough

Plandome

East Williston

Munsey Park

Plandome Manor

Roslyn Estates

Kaser New

Square

Mill Neck

Kensington

Port Washington North

Saddle Rock

Great Neck Plaza

Guttenberg

Hewlett Neck

Bellerose

Baxter Estates

Russell Gardens

Plandome Heights

East Newark

South Floral Park

South Hackensack

Hewlett Harbor

Hewlett Bay Park

UV15

¡¢80

¡¢495

UV27

¡¢78

¡¢287

RICHMOND

ESSEX

UNION

PASSAIC BERGEN

BRONX

NEW YORK

QUEENS

KINGS

NASSAU

PUTNAM

ROCKLAND

WESTCHESTER

FAIRFIELD

ORANGE

¡¢95

¡¢87

¡¢84

Map 5: NEW YORK REGION (CENTRAL REGION): Community Type by Municipality, 2000

NJ NY

NY CT

Atlantic Ocean

$ Miles

0 10

Data Source: U.S. Census Bureau.

Legend Central Cities Predominately non-white Diverse Predominately white

(2) (28)

(141) (339) (53)Exurb

Definitions: Predominantly non-white: Municipalities with more than 60% of the population non-white in 2000 and more than 10% of land urban. Diverse: Municipalities with non-white shares between 20% and 60% in 2000 and more than 10% of land urban. Predominantly white: Municipalities with white shares greater than 80% in 2000 and more than 10% of land urban. Exurbs: Municipalities with less than 10% of total land area urban (by Census definition of urban) in 2000.

myott001

Text Box

New York City

Vernon

Kent

Hunting- ton

West Milford

Hempstead Town

Carmel

Franklin

Bedford

Long Hill

Edison

Oyster Bay

Baby- lon

Rockaway Twp

Somers

Yorktown

Philipstown

Cortlandt

Wayne

Southeast

Clarkstown

Patterson

Mahwah

Newark

Putnam Valley

Morris

Lewisboro

Ringwood

Ramapo

Harding

Stony Point

Yonkers

Kinnelon

North Castle

New Castle

Montville

Harrison

Woodbridge

Pound Ridge

North Salem

Orangetown

Clifton

Piscataway

Linden

Green- burgh

Union

Denville

Mount Pleasant

Rye

Haverstraw Twp

Jersey City

Livingston

Elizabeth

Kearny

Hanover

Fairfield

Millburn

Paramus

Parsippany- Troy Hills

Oakland

Chatham Twp

Boonton Twp

Alpine

Wanaque

Paterson

West Orange

Clark

Wyckoff

White Plains

Summit

New Rochelle

Teaneck

Lloyd Harbor

West- field

Ramsey

Scars- dale

Scotch Plains

Mont- clair

Franklin Lakes

Blooming- dale

Glen Cove

Bayonne

Plainfield

Secaucus

Old Westbury

Tenafly

East Hanover

Watchung

Pequannock

Airmont

Lincoln Park

Florham Park

Cran- ford

Totowa

Lodi

Free- port

South Plainfield

Carteret

Fair Lawn

Ridge- wood

Nutley

Madison

Rahway

Mutton- townBloom-

field

Peekskill

Spring- field

Closter

Lynd- hurst

Garden City

Engle- wood

Carlstadt

Montvale

Passaic

River Vale

North Hempstead

Brook- ville

Saddle River

Montebello

Roseland

Verona

North Bergen

Briarcliff Manor

Lawrence

Ossining

Perth Amboy

Berkeley Heights

Belle- ville

Green Brook

Roselle

Hill- side

Old Tappan

Sands Point

Ossining Twp

West Caldwell

Oradell

Cedar Grove

Middle- sex

Hackensack

Hillsdale Butler

Rye Brook

Allen- dale

Maple- wood

Hempstead

Chestnut Ridge

East Orange

Latting- town

Mount Vernon

Irvington

Fort Lee

Hawthorne

Norwood

Mountain- side

Pomona

Irving- ton

Tarry- town

Boonton

Kings Point

East- chester

Suffern

Old Brookville

Metuchen

Hillburn

Emerson

Wesley Hills

Upper Saddle River

Glen Rock

Haworth

Little Falls

Chatham

Garfield

Mamaroneck Village

Morristown

Ridgefield

North Hills

Upper Brookville

Mount Kisco

Oyster Bay

Cove

Croton- on-Hudson

Dumont

Bergen- field

Westbury

Valley Stream

Cresskill

Wash- ington

East Hills

Mamaroneck

Park Ridge

Woodcliff Lake

North Haledon

Mineola

Sloatsburg

Matine- cockLeonia

New Providence

Riverdale Westwood

Waldwick

Demarest

Lyn- brook

Ruther- ford

Laurel Hollow

West Paterson

Amity- ville

Rockville Centre

Saddle Brook

Pompton Lakes

North Caldwell

Morris Plains

Dobbs Ferry

Kenilworth

Port Chester

New Milford

Long Beach

Elmwood Park

Mountain Lakes

Bayville

Haverstraw

North Plainfield

River Edge

New Hempstead

Ardsley

North Arlington

Ho-Ho-Kus

Spring Valley

Moonachie

City of Orange

Flower Hill

Fanwood

Buchanan

Hoboken Lake Success

Pleasantville

Harrison

Haledon

Little Ferry

North Tarrytown

Maywood

South Orange Village

Northvale

Sea Cliff

Floral Park

Highland Park

Essex Fells

Nyack

Englewood Cliffs

Cove Neck

Massapequa Park

Union City

Great Neck

Midland Park

Dunellen

Malverne

Elmsford

Ridgefield Park Teterboro

Pelham

Upper Nyack

Fair- view

Rockleigh

Roselle Park

East Rutherford Roslyn

Hastings- on-Hudson

Nelsonville

Bronxville

Pelham Manor

Larchmont

Wallington

Farmingdale

West Haverstraw

Centre Island

Edgewater

Piermont

Garwood

Cliffside Park

Weehawken

East Rockaway

Caldwell

West New York

Tuckahoe

Cedarhurst

New Hyde Park

Cold Spring

Brewster

South Nyack

Williston ParkGreat Neck Estates

Manorhaven

Prospect Park

Atlantic Beach Island Park

Woodsburgh

Winfield

Stewart Manor

Grand View- on-Hudson

Harrington Park

Bogota

Wood-Ridge

Roslyn Harbor Palisades Park

Hasbrouck Heights

Rochelle Park

Glen Ridge

Borough

Plandome

East Williston

Munsey Park

Plandome Manor

Roslyn Estates

Kaser New

Square

Mill Neck

Kensington

Port Washington North

Saddle Rock

Great Neck Plaza

Guttenberg

Hewlett Neck

Bellerose

Baxter Estates

Russell Gardens

Plandome Heights

East Newark

South Floral Park

South Hackensack

Hewlett Harbor

Hewlett Bay Park

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RICHMOND

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Map 6: NEW YORK REGION (CENTRAL REGION): Community Type by Municipality, 2010

NJ NY

NY CT

Atlantic Ocean

$ Miles

0 10

Data Source: U.S. Census Bureau.

Legend Central Cities Predominately non-white Diverse Predominately white

(2) (50)

(210) (250) (53)Exurb

Definitions: Predominantly non-white: Municipalities with more than 60% of the population non-white in 2010 and more than 10% of land urban. Diverse: Municipalities with non-white shares between 20% and 60% in 2010 and more than 10% of land urban. Predominantly white: Municipalities with white shares greater than 80% in 2010 and more than 10% of land urban. Exurbs: Municipalities with less than 10% of total land area urban (by Census definition of urban) in 2000.

myott001

Text Box

Scurry

Cooper Pecan Gap

Aurora Rhome

New- ark

New Fairview

Boyd

Decatur

Alvord

Chico

Bridgeport

Paradise

Runaway Bay

Lake Bridge- port

Mabank

Cresson

Dallas Fort Worth

Plano

Frisco

Irving

Arlington

Denton

Garland

McKinney

Grand Prairie

Wylie

Allen

Mes- quite

Lewis- ville

Ennis

Mansfield

Midlo- thian

Carroll- ton

Cedar Hill

Grape- vine

Greenville

Cleburne Waxahachie

Keller

Flower Mound

DeSoto Lan-caster

Terrell

Rowlett Rich-

ardson

Eu- less

RWReno

Burleson

South- lake

DISH

Coppell

Azle

Lucas

Weatherford Hurst

AR North-

lake

Mineral Wells

The Colony

Sachse

Haslet

Heath Forney

Joshua

Fate

Fairview

Ovilla

Hutchins

Alma

Red Oak

Duncanville

Royse City

Combine Wilmer

Crowley

Kaufman

Farmers Branch

M R

Celina Melissa

Commerce

Balch Springs Talty

Kennedale

Cross Roads

Princeton

Add- ison

Ferris

Willow Park

Little Elm

McLendon-Chisholm

Ponder

Sanger

Keene

Prosper Campbell

Alvarado

Palmer

Justin HV

Italy

Hebron

Cool

Venus

Krum

Kemp Rosser

Aledo Crandall

Anna

Aubrey

Annetta

Pilot Point

Milford

Godley

University Park

Springtown

Annetta North

Caddo Mills

Everman Oak Ridge

Lavon

Oak Grove

Pecan Hill

Millsap

Josephine

Grandview

Highland Park

Nevada

Quinlan

Wolfe City

New Hope

West Tawakoni

Annetta South

Cottonwood

Weston

Pantego

Celeste

Cross Timber

Post Oak Bend City

Briaroaks

Grays Prairie

Lone Oak

Rio Vista

EV DG

Krugerville BlueRidge

Hackberry

Maypearl

Cockrell Hill

Bardwell

Garrett

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Neylandville Farmersville

CC LD

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Hawk Cove

Union Valley

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£¤380

£¤377

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NAVARRO

ANDERSON

COOKE GRAYSON FANNIN

HILL

DENTON COLLIN

HUNT

KAUFMAN

HENDERSON

ELLIS

DALLAS

ROCKWALL

TARRANT

PARKER

JOHNSON

HOPKINS

RAINS

Map 7: DALLAS - FORT WORTH REGION: Community Type by Municipality and County Unincorporated Area, 2000

JACK

MONTAGUE

- Argyle - Benbrook - Bartonville - Bedford - Blue Mound - Corral City - Copper Canyon - Corinth - Colleyville - Dalworthington Gardens - Double Oak - Edgecliff Village - Forest Hill - Glenn Heights - Haltom City - Hickory Creek - Highland Village - Lakeside - Lowry Crossing - Lake Dallas - Lincoln Park - Lakewood Village - Lake Worth

AR B BA BF BM C CC CO CV DG

DO EV FH GH HC HI HV LA LC LD LP LV LW

- Murphy - North Richland Hills - Oak Leaf - Oak Point - Parker - Pelican Bay - Roanoke - Richland Hills - River Oaks - Rockwall - Saginaw - Seagoville - Sansom Park - St. Paul - Shady Shores - Sunnyvale - Trophy Club - Westlake - Watauga - Westover Hills - White Settlement - Westworth Village

M NR

OL OP P PB R RH RO RW SA SG SN SP SS SV TC W WA WH WS WV

$ Miles

0 20

Legend Central Cities Predominately non-white Diverse Predominately white

(3) (5)

(48) (67) (87)Exurb

myott001

Text Box

Scurry

Cooper Pecan Gap

Aurora Rhome

New- ark

New Fairview

Boyd

Decatur

Alvord

Chico

Bridgeport

Paradise

Runaway Bay

Lake Bridge- port

Mabank

Cresson

Dallas Fort Worth

Plano

Frisco

Irving

Arlington

Denton

Garland

McKinney

Grand Prairie

Wylie

Allen

Mes- quite

Lewis- ville

Ennis

Mansfield

Midlo- thian

Carroll- ton

Cedar Hill

Grape- vine

Greenville

Cleburne Waxahachie

Keller

Flower Mound

DeSoto Lan-caster

Terrell

Rowlett Rich-

ardson

Eu- less

RWReno

Burleson

South- lake

DISH

Coppell

Azle

Lucas

Weatherford Hurst

AR North-

lake

Mineral Wells

The Colony

Sachse

Haslet

Heath Forney

Joshua

Fate

Fairview

Ovilla

Hutchins

Alma

Red Oak

Duncanville

Royse City

Combine Wilmer

Crowley

Kaufman

Farmers Branch

M R

Celina Melissa

Commerce

Balch Springs Talty

Kennedale

Cross Roads

Princeton

Add- ison

Ferris

Willow Park

Little Elm

McLendon-Chisholm

Ponder

Sanger

Keene

Prosper Campbell

Alvarado

Palmer

Justin HV

Italy

Hebron

Cool

Venus

Krum

Kemp Rosser

Aledo Crandall

Anna

Aubrey

Annetta

Pilot Point

Milford

Godley

University Park

Springtown

Annetta North

Caddo Mills

Everman Oak Ridge

Lavon

Oak Grove

Pecan Hill

Millsap

Josephine

Grandview

Highland Park

Nevada

Quinlan

Wolfe City

New Hope

West Tawakoni

Annetta South

Cottonwood

Weston

Pantego

Celeste

Cross Timber

Post Oak Bend City

Briaroaks

Grays Prairie

Lone Oak

Rio Vista

EV DG

Krugerville BlueRidge

Hackberry

Maypearl

Cockrell Hill

Bardwell

Garrett

Sanctuary

Neylandville Farmersville

CC LD

Hudson Oaks

Hawk Cove

Union Valley

Ennis £¤281

¡¢30

¡¢45

£¤75

£¤380

£¤377

¡¢35W

¡¢20

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¡¢20

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UV199

Data Source: U.S. Census Bureau.

WISE

DELTA

NAVARRO

ANDERSON

COOKE GRAYSON FANNIN

HILL

DENTON COLLIN

HUNT

KAUFMAN

HENDERSON

ELLIS

DALLAS

ROCKWALL

TARRANT

PARKER

JOHNSON

HOPKINS

RAINS

Map 8: DALLAS - FORT WORTH REGION: Community Type by Municipality and County Unincorporated Area, 2010

JACK

MONTAGUE

AR B BA BF BM C CC CO CV DG

DO EV FH GH HC HI HV LA LC LD LP LV LW

M NR

OL OP P PB R RH RO RW SA SG SN SP SS SV TC W WA WH WS WV

$ Miles

0 20

Legend Central Cities Predominately non-white Diverse Predominately white

(3) (15) (68) (37) (91)Exurb

myott001

Text Box

IV. Opportunities and Challenges

A. Opportunities

In the new multi-racial America, diverse suburbs now represent the best hope for realizing the dream of equal opportunity. The population of racially diverse suburbs in the 50 largest metropolitan areas is now greater than the combined population of the central cities in those metros. These integrated communities and neighborhoods offer the best chances to eliminate the racial disparities in economic opportunity that have persisted for decades. They offer the most equal access to good schools and a clear path to living-wage employment for all their residents. They are the places where whites and non-whites have the best relations and the most positive perceptions of one another. They offer the best chances for people of color to participate and succeed in the educational and economic mainstream.

Scholarly evidence on the benefits of school integration highlights the importance of integrated communities. Extensive research literature documents that racial and economic segregation hurts children and that the potential positive effects of creating more integrated schools are broad and long-lasting. The research shows that integrated schools boost academic achievement (defined as test scores, attainment (years in school and number of degrees) and expectations), improve opportunities for students of color, and generate valuable social and economic benefits including better jobs with better benefits and greater ease living and working in diverse environments in the future. Integrated schools also enhance the cultural competence of white students and prepare them for a more diverse workplace and society.

Attending racially integrated schools and classrooms improves the academic achievement of minority students (measured by test scores).19 Since the research also shows that integrated schools do not lower test scores for white students, they are one of the very few strategies demonstrated to ease one of the most difficult public policy problems of our time—the racial achievement gap. Other academic benefits for minority students include completing more years of education and higher college attendance rates. Long-term economic benefits include a tendency to choose more lucrative occupations in which minorities are historically underrepresented.20

19 Russell W. Rumberger and Gregory J. Palardy, “Does Segregation Still Matter? The Impact of Student Composition on Academic Achievement in High School,” Teachers College Record, 107, no. 9 (2005): 1999-2045; Roslyn Arlin Mickelson, “Segregation and the SAT,” Ohio State Law Journal, 67 (2006): 157-99; Roslyn Arlin Mickelson, “The Academic Consequences of Desegregation and Segregation: Evidence from the Charlotte- Mecklenburg Schools,” North Carolina Law Review, 81 (2003): 1513-62; Kathryn Borman et al., “Accountability in a Postdesegregation Era: The Continuing Significance of Racial Segregation in Florida’s Schools,” American Educational Research Journal, 41, no. 3 (2004): 605-31; Geoffrey D. Borman and N. Maritza Dowling, “Schools and Inequality: A Multilevel Analysis of Coleman’s Equality of Educational Opportunity Data” (paper presented at the annual meeting of the American Educational Research Association, San Francisco, CA, 2006). 20 R. L. Crain and J. Strauss, “School Desegregation and Black Occupational Attainments: Results from a Long- Term Experiment” (Center for Social Organization of Schools, 1985); Goodwin Liu and William Taylor, “School Choice to Achieve Desegregation,” Fordham Law Review, 74 (2005): 791; Jomills H. Braddock and James M. McPartland, “How Minorities Continue to be Excluded from Equal Employment Opportunities: Research on Labor Market and Institutional Barriers,” Journal of Social Issues, 43, no. 1 (1987): 5-39; Janet Ward Schofield,

Integrated schools also generate long-term social benefits for students. Students who experience interracial contact in integrated school settings are more likely to live, work, and attend college in more integrated settings.21 Integrated classrooms improve the stability of interracial friendships and increase the likelihood of interracial friendships as adults.22 Both white and non-white students tend to have higher educational aspirations if they have cross-race friendships.23 Interracial contact in desegregated settings decreases racial prejudice among students and facilitates more positive interracial relations.24 Students who attend integrated schools report an increased sense of civic engagement compared to their segregated peers.25

Diverse suburbs recommend themselves in many other ways as well. In general, they show many fewer signs of social or economic stress than central cities and non-white segregated suburbs—the other community types with significant numbers of minority households. They offer higher incomes, lower poverty, better home values, and stronger local tax bases (Table 2). They also show many characteristics associated with economic and environmental sustainability—they are denser, more likely to be fully developed (and therefore more walkable) and to be located in central areas (offering better access to transit), and are home to more jobs per capita than predominantly white suburbs or exurbs (Table 1 and Maps 3 – 8). Additionally, revitalizing and redeveloping these communities through increased density, walkability and transit is more environmentally sustainable than the all-too-common practice of abandoning these areas in favor of new, low-density, automobile dependent communities built on greenfield land. Finally, diverse suburbs are politically mixed, providing the potential for meaningful political participation and limiting the risks associated with dominance by a single party.

“Maximizing the Benefits of Student Diversity: Lessons from School Desegregation Research,” in Diversity Challenged: Evidence on the Impact of Affirmative Action (Cambridge, MA: Harvard Education Press, 2001): 99; Orley Ashenfelter, William J. Collins, and Albert Yoon, “Evaluating the Role of Brown vs. Board of Education in School Equalization, Desegregation, and the Income of African Americans,” American Law and Economics Review, 8, no. 2 (2006): 213-248; Michael A. Boozer et al., “Race and School Quality Since Brown v. Board of Education,” Brookings Papers on Economic Activity (Microeconomics) (1992): 269-338. 21 Jomills H. Braddock, Robert L. Crain, and James M. McPartland, “A Long-Term View of School Desegregation: Some Recent Studies of Graduates as Adults,” Phi Delta Kappan, 66, no. 4 (1984): 259-64. 22 Maureen Hallinan and Richard Williams, “The Stability of Students’ Interracial Friendships,” American Sociological Review, 52 (1987): 653-64; Richard D. Kahlenberg, All Together Now (Washington, D.C.: Brookings Institution Press, 2001), 31. 23 Maureen Hallinan and Richard Williams, “Students’ Characteristics and the Peer Influence Process,” Sociology of Education, 63 (1990): 122-32. 24 Thomas Pettigrew and Linda Tropp, “A Meta-Analytic Test of Intergroup Contact Theory,” Journal of Personality and Social Psychology, 90 (2006): 751-83; Melanie Killen and Clark McKown, “How Integrative Approaches to Intergroup Attitudes Advance the Field,” Journal of Applied Developmental Psychology, 26 (2005): 612-22; Jennifer Jellison Holme, Amy Stuart Wells and Anita Tijerina Revilla, “Learning through Experience: What Graduates Gained by Attending Desegregated High Schools,” Equity and Excellence in Education, 38, no. 1 (2005): 14-24. 25 Michal Kurlaender, John T. Yun, “Fifty Years After Brown: New Evidence of the Impact of School Racial Composition on Student Outcomes,” International Journal of Educational Policy, Research and Practice, 6, no. 1 (2005): 51-78.

B. The Challenge of Resegregation and Economic Decline

Resegregation is the primary challenge facing many diverse communities and neighborhoods. Many currently integrated areas are actually in the midst of social and economic change—change that is often very rapid. Integrated communities in the United States have a hard time staying integrated for more than ten or twenty years, and many communities that were once integrated have now resegregated and are largely non-white. The process is driven by a wide variety of factors, including housing discrimination, inequitable school attendance policies, and racial preferences shaped by past and present discrimination.

Data for municipalities and census tracts clearly show the vulnerability of integrated neighborhoods to racial transition. Table 3 summarizes racial transition in municipalities in the 50 largest metropolitan areas between 2000 and 2010. In just 10 years, 160 of the 1,107 communities (16 percent) classified as diverse in 2000 made the transition to predominantly non- white. A similar percentage of predominantly white municipalities made the transition to diverse.26

26 Table 1 does not show exurbs. Since exurbs are defined by urbanization rate in 2000 in both years—2010 urbanization data are not yet available—none made the transition to another classification during the period.

Neighborhood (census tract) data for a longer period provide better indicators of how vulnerable integrated areas are to racial transition. Table 4 summarizes the data for racial transition in census tracts in the 50 largest metropolitan areas for the period between 1980 and 2005-09.27 It shows how neighborhoods of all types changed during the 1980s, 1990s, and 2000s. Neighborhoods that were integrated in 1980 were much less stable than predominantly white or predominantly non-white neighborhoods. More than a fifth (21 percent) of the census tracts that were integrated in 1980 had crossed the 60 percent threshold into the predominantly non-white category during the 1980s. Another 28 percent of them had made the transition by 2000 (more than doubling the total to 49 percent). By 2005-09, only 56 percent of the neighborhoods that had been integrated in 1980 had become predominantly non-white. Another four percent became predominantly white during the period, leaving only 40 percent of the 1980 integrated neighborhoods in the 2010 integrated category.

The analysis also shows that once a neighborhood makes the transition to predominantly non-white it is very likely to stay that way. Predominantly non-white neighborhoods were, by far, the most stable group—93 percent of neighborhoods that were in this group in 1980 were still predominantly non-white 25 years later.28 This highlights how rare another often-cited risk to traditional minority neighborhoods—gentrification—actually is. Contrary to widespread fears of gentrification, the data clearly show that once a neighborhood becomes predominantly non- white it virtually never reverts to predominantly white. Just two census tracts out of the nearly 1,500 that were predominantly non-white in 1980 became predominantly white in the next three decades, and only seven percent of them became diverse. Similarly, only four percent of diverse neighborhoods became predominantly white during the period. If gentrification involves bringing more middle-income family households into previously segregated neighborhoods then metropolitan America actually needs much more gentrification, not less. Indeed, in most cases, it could just as aptly be called “urban racial reintegration” rather than “gentrification”.29

27 The most recent data with census tracts boundaries consistent with earlier years are from the Census American Community Survey, which reports averages for the period from 2005 to 2009 for census tracts. Census tracts in the more recent 2006-2010 data are not contiguous with earlier years and cannot be used for this comparison. 28 The percentage was even higher in central cities—94 percent of 3,647 census tracts that were predominantly non- white in central cities in 1980 were still non-white in 2005-09. By 2005-09, central cities had 5,876 census tracts qualifying as predominantly non-white, compared to 4,697 in suburbs. At the same time, they had only 3,426 diverse tracts compared to 8,196 in suburbs. 29 At the same time, there are a few significant cases (at least in large cities like New York, San Francisco, Washington D.C., Chicago) where the racial composition of traditionally black neighborhoods have become whiter and if not predominantly white, then different enough to create real animosities. See generally, Bruce Norris, Clybourne Park: a Play (New York: Faber and Faber, 2011); Nathan McCall, Them (New York: Washington Square Press, 2004).

Table 5 shows how transition rates for diverse neighborhoods varied decade by decade. It shows the transition rates for suburban neighborhoods that were integrated at the beginning of each decade (rather than only those which were integrated in 1980). The results show that transition rates were high and relatively stable during the period—20 to 30 percent of integrated neighborhoods resegregated every 10 years.

Chart 2 shows how vulnerable integrated neighborhoods are to racial transition in another way. The chart shows how likely it was that a neighborhood that was integrated in 1980 would remain integrated, become predominantly non-white, or become predominantly white during the next 25-29 years as a function of its racial composition in 1980.30 Integrated neighborhoods with very modest non-white shares at the beginning of the period were highly vulnerable to change. In fact, any neighborhood with a non-white share greater than just 23 percent was more likely to become resegregated during the next 25 years than to remain integrated. The likelihood that a neighborhood would resegregate rises steadily with the 1980 non-white share until roughly 85

30 The lines are smoothed by taking 5 percentage point moving averages. Orfield and Luce (2010) report similar findings for a variety of integrated neighborhood types using a more complex neighborhood classification scheme for the period from 1980 to 2000. Much like Chart 1, the analysis revealed turnover points for each of the integrated types at very modest non-white shares. (Turnover points are the minority share in a neighborhood at which it becomes more likely than not that the neighborhood will re-segregate.) The analysis shows turnover points between 24 to 38 percent non-white, depending on the type of neighborhood. Neighborhoods that were white-Hispanic integrated in 1980 were more likely to re-segregate by 2000 than to remain integrated if their Hispanic share exceeded 24 percent. The corresponding percentages for white-black or multi-ethnic integrated neighborhoods were 30 and 38 percent.

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Racial steering occurs when “housing providers direct prospective homebuyers interested in equivalent property to different areas according to their race.”34 Through steering, real estate agents limit the housing choices of non-white buyers disproportionately to unstably integrated or predominantly non-white neighborhoods and limit the choice of whites disproportionately to predominately white neighborhoods. 35

Recent studies document that significant levels of steering still occur in metropolitan housing markets and are likely increasing. 36 The most extensive recent study (by the Urban Institute) included 4,600 paired tests in 23 metropolitan areas. The study found statistically significant rates of steering in the homes that prospective buyers of different races were shown, in the frequency of home inspections for buyers of different races, and in the editorial comments regarding schools and other neighborhood characteristics made by realtors.

Audits conducted by the National Fair Housing Alliance in twelve metropolitan areas also show clear patterns of steering of middle- and upper-income non-white and white homebuyers. In these studies black and Latino middle- and upper-income families were steered toward racially diverse suburban school attendance areas and told local schools were excellent. White families with similar incomes, credit histories, and backgrounds were told the same schools were not good and were steered toward much whiter school attendance areas that were not shown to the non-white homebuyers.37

Research shows that private lenders continue to deny mortgages to potential minority homebuyers at disproportionate rates.38 Nationally, middle- and upper-income blacks are 33 John Yinger, Closed Doors: Opportunities Lost (New York: Russell Sage Foundation, 1995). 34 Gladstone Realtors v. Village of Bellwood, 441 U.S. 91, 94 (1979). 35 24 CFR Part 14, § 100.70 (a); see also George Galster, “By Words and Deeds: Racial Steering by Real Estate Agents in the U.S. in 2000,” J. American Planning Association 71 (2005): 253. 36 Margery Austin Turner et al., “Discrimination in Metropolitan Housing Markets: National Results from Phase I of HDS 2000” (working paper, University of Connecticut, 2003); National Fair Housing Alliance, “Unequal Opportunity – Perpetuating Housing Discrimination in America: 2006 Fair Trends Housing Report” (2006); George Galster, “Racial Steering by Real Estate Agents: A Review of the Audit Evidence,” Review of Black Political Economy 18 (1990): 105-129; Galster “Racial Steering by Real Estate Agents: Mechanisms and Motivations,” Review of Black Political Economy 19 (1990): 39-63. 37“The Crisis of Segregation, 2007 Fair Housing Trends Report,” National Fair Housing Alliance (2007). 38 Stephen Ross and John Yinger, The Color of Credit: Mortgage Discrimination, Research Methodology and Fair Lending Enforcement (Cambridge, MA: MIT Press, 2003); William Apgar and Allegra Calder, “The Dual Mortgage Market: The Persistence of Discrimination in Mortgage Lending,” in Xavier de Souza Briggs (ed.) The Geography of Opportunity: Race and Housing Choice in Metropolitan America. (Washington, D.C.: Brookings Institution Press), p. 102. Melvin L. Oliver and Thomas M. Shapiro suggest that racial discrepancies in mortgage rejection rates emanate from the fact that “loan officers were far more likely to overlook flaws in the credit scores of white applicants or to arrange creative financing for them than they were in the case of black applicants.” Melvin L. Oliver and Thomas M. Shapiro, Black Wealth/White Wealth: A New Perspective on Racial Inequality (New York: Routledge, 1995): 139.

approximately 1.5 to 2.5 times more likely to be denied a mortgage than middle- and upper- income whites. In fact, upper-income blacks (with 120% or more of metropolitan median income) are as likely to be denied a mortgage as lower-income whites (with less than 80% of metropolitan median income).39 Data for the Twin Cities show that loan-denial rates for the lowest-income whites (households with incomes less than $39,000 per year) were much lower than those for black applicants in any income category, including the highest (households with incomes greater than $157,000).40

Subprime loans typically create greater risks and costs for borrowers due to higher interest rates and other disadvantageous (for borrowers) loan terms. The concentration of subprime lending activity is due to the targeted marketing of mortgages and the lack of traditional prime bank branch locations in predominately non-white and racially transitioning neighborhoods.41 National studies also find racial disparities in subprime lending rates even when controlling for neighborhood and borrower characteristics, including individual credit factors.42 Similarly, subprime lending rates in the Twin Cities were greater for blacks at all income levels than for the lowest-income whites. New research suggests that segregation, not credit rating or other financial factors, was the largest determinant of variations in subprime lending rates across the nation’s metropolitan areas when subprime lending was at its peak.43 Segregation was also the primary predictor of foreclosures.44 As a result, U.S. black and Hispanic borrowers are more than twice as likely as whites to have mortgages that are seriously

39 In 2000 the home-purchase-denial rate for upper-income blacks was 21.6% compared to 9.9% for upper-income whites and 21.5% for low- to lower-middle-income whites. For refinances, 30.1% of upper-income blacks were denied compared to 15.2% of upper-income whites and 25.9% of low- and lower-middle-income whites. Calculations drawn from 2005 and 2010 Home Mortgage Disclosure Act National Aggregate Reports, Tables 5-2 and 5-3, http://www.ffiec.gov/hmdaadwebreport/NatAggWelcome.aspx. 40 “Communities in Crisis: Race and Mortgage Lending in the Twin Cities,” Institute on Race and Poverty, (2009): 15 (chart 3). 41 William C. Apgar and Allegra Calder, “The Dual Mortgage Market: The Persistence of Discrimination in Mortgage Lending,” in The Geography of Opportunity: Race and Housing Choice in Metropolitan America, ed. Xavier de Souza Briggs (Washington DC: Brookings Institution Press, 2005), 101; Kellie K. Kim-Sung and Sharon Hermanson, “Experience of Older Refinance Mortgage Loan Borrowers: Broker- and Lender-Originated Loans,” AARP Public Policy Institute Data Digest 83 (2003), http://assets.aarp.org/rgcenter/econ/dd65_workers.pdf; John T. Metzger, “Clustered Spaces: Racial Profiling in Real Estate Investment” (paper presented at Lincoln Institute of Land Policy Conference, July 26-28, 2001). 42 Debbie Bocian, Keith Ernst and Wei Li, “Unfair Lending: The Effect of Race and Ethnicity on the Price of Subprime Mortgages.” Center for Responsible Lending (2006), http://www.responsiblelending.org/mortgage- lending/research-analysis/rr011-Unfair_Lending-0506.pdf; Thomas P. Boehm, Paul D. Thistle and Alan Schlottman, “Rates and Race: An Analysis of Racial Disparities in Mortgage Rates,” Housing Policy Debate 17, no. 1 (2006); Paul Calem, Kevin Gillen and Susan Wachter, “The Neighborhood Distribution of Subprime Mortgage Lending,” Journal of Real Estate Finance and Economics 29, no. 4 (2006). Marsha J. Courchane, Brian J. Surette and Peter M. Zorn, “Subprime Borrowers: Mortgage Transitions and Outcomes,” Journal of Real Estate Finance and Economics 29, no. 4 (2004): 365-92. 43See Greg Squires and Derek Hydra, “Metropolitan Segregation and the Subprime Lending,” Housing Policy Debate (forthcoming). 44Jacob Rugh and Douglas Massey, “Racial Segregation and the American Foreclosure Crisis,” American Sociological Review 75, no. 5 (2010): 629.

delinquent or have completed foreclosure, according to recent research by The Center for Responsible Lending.45

Neighborhoods also experience racial discrimination. Non-white and integrated neighborhoods do not receive the level of prime credit commensurate with the income and credit histories of their residents. The red-lining (or “pink-lining,” a less-severe form of redlining) of these neighborhoods intensifies segregation and resegregation.46 Data for the Twin Cities, for example, show that in predominately white neighborhoods (neighborhoods that are less than 30 percent people of color), 72 percent of all loan applications are made to prime home-purchase lenders, compared to only 52 percent of applications in integrated neighborhoods (with 30 to 49 percent people of color) and 34 percent of applications in segregated non-white neighborhoods (with 50 percent or more people of color). The differences are also evident for refinance applications.47

2. Exclusionary zoning Exclusionary zoning occurs when communities—through their zoning codes,

development agreements, or development practices—do not allow for their fair share of the region’s affordable housing to be built. While exclusionary zoning has been declared unconstitutional in several states and is prohibited in others by legislation, it remains very common in predominantly white suburbs and it intensifies both racial and social segregation.48 Recent research shows that local exclusionary land-use regulation practices—anti-density regulations in particular—played a large part in determining segregation levels and changes in the 50 largest metropolitan areas in the 1990s, accounting for as much as 35 percent of the difference between the most and least segregated metropolitan areas.49

3. Discriminatory Local School Attendance Policies Racial diversity in neighborhood schools almost always precedes racial diversity in

neighborhoods. As schools become increasingly diverse in either racially diverse or predominantly white neighborhoods, white parents frequently seek attendance-boundary alterations, transfer policies, or new school buildings or additions allowing them to attend whiter

45 Debbie Gruenstein Bocian et al., “Lost Ground, 2011: Disparities in Mortgage Lending and Foreclosures,” Center for Responsible Lending (2011): 4, http://www.responsiblelending.org/mortgage-lending/research-analysis/Lost- Ground-2011.pdf. 46 Gregory D. Squires and William Vélez, “Neighborhood Racial Composition and Mortgage Lending: City and Suburban Differences,” Journal of Urban Affairs 9, no. 3 (1987): 217-32. 47 “Communities in Crisis: Race and Mortgage Lending in the Twin Cities,” Institute on Race and Poverty (2009): 25 table 4, http://www.irpumn.org/uls/resources/projects/IRP_mortgage_study_Feb._11th.pdf. 48 Jonathan Rothwell and Douglas Massey, “The Effect of Density Zoning on Racial Segregation in US Urban Areas,” Urban Affairs Review 6 (2009): 779; Jonathan Rothwell and Douglas Massey, “The Effect of Density Zoning on Class Segregation in US Metropolitan Areas," Social Science Quarterly 91 (December 2010): 1123. 49 Jonathan Rothwell, “Racial Enclaves and Density Zoning: The Institutionalized Segregation of Racial Minorities in the United States,” American Law and Economics Review 13, no. 1 (2011): 290-358.

schools. While these practices and policies can violate federal law, they are common and there is little oversight.50

These discriminatory educational practices result in predominantly non-white or unstably integrated schools in racially integrated or even predominantly white neighborhoods. Such schools intensify steering and mortgage lending discrimination in relation to the school attendance area, accelerating resegregation. Recent national research found that local school boundaries created schools that were considerably more segregated than their neighborhoods. Had their boundaries more clearly reflected school capacity and neighborhood proximity, American schools would be 14 to 15 percent less segregated.51

4. Discriminatory Placement of Low-Income Housing

The Fair Housing Act forbids building a disproportionate share of low-income housing in poor-and-segregated or integrated-but-resegregating neighborhoods when it is possible to build that same housing in low-poverty, high-opportunity white or stably integrated neighborhoods. Yet numerous recent studies demonstrate that federal, state, and local governments continue to build a disproportionate share of subsidized low-income housing in poor and predominantly minority neighborhoods. Where data are available on suburban placement, they show that a disproportionate share of low-income, subsidized housing is located in racially diverse suburbs, increasing the speed of resegregation.52

5. White Prejudice and Preferences

Conservative advocates argue that resegregation is the result of the preference of whites and non-whites to live near people like themselves. However, surveys show that both whites and non-whites desire to live in integrated neighborhoods, but an ideal integrated neighborhood for whites has a larger percentage of whites than the ideal neighborhoods of non-whites.53 As a 50 See Myron Orfield and Thomas Luce, Region: Planning the Future of the Twin Cities (Minneapolis: University of Minnesota Press, 2009): 133-4, for a description of the use of a discontinuous boundary in a Twin Cities school district that served this purpose. 51 See Meredith Paige Richards, “The Gerrymandering of Educational Boundaries and the Segregation of American Schools” (draft dissertation, University of Texas at Austin, 2012). 52 Casey J. Dawkins, “Exploring the Spatial Distribution of Low Income Housing Tax Credits,” Assisted Housing Research Cadre Report for the U.S. Department of Housing and Urban Development (2011), http://www.huduser.org/publications/pdf/Dawkins_ExploringLIHT_AssistedHousingRCR04.pdf; see also Myron Orfield and Thomas Luce, Region: Planning the Future of the Twin Cities (Minneapolis: University of Minnesota Press, 2009). 53 See, for instance, Camille Zubrinsky Charles, “The Dynamics of Racial Residential Segregation,” Annual Review of Sociology, vol. 29 (2003): 167-207; Camille Zubrinsky Charles, “Can We Live Together? Racial Preferences and Neighborhood Outcomes,” in The Geography of Opportunity: Race and Housing Choice in Metropolitan America, ed. Xavier de Souza Briggs (Washington DC: Brookings Institution Press, 2005), 45; Mary Pattillo, “Black Middle Class Neighborhoods,” Annual Review of Sociology 31 (2005): 321; Lincoln Quillian, “Why is Black-White Residential Segregation So Persistent?: Evidence on Three Theories from Migration Data,” Social Science Research 31 (2002): 197-229; and Keith R. Ihlanfeldt and Benjamin Scafidi, “Whites’ Neighborhood Racial Preferences and Neighborhood Racial Composition in the United States: Evidence from the Multi-City Study of Inequality,” Housing Studies 19, no. 3 (2004): 325-59.

neighborhood becomes more racially diverse, the share of non-white residents reaches the point where whites no longer prefer the neighborhood well before it reaches the ideal point for potential non-white residents. Thus, at relatively low levels of diversity, even if whites and non- whites are leaving the neighborhood at proportional rates as a result of normal housing turnover, replacement rates are likely to be heavily skewed toward non-white entrants to the neighborhood.

It is critical to understand that these preferences, for both white and non-whites, have been shaped by existing and past discrimination. It is likely that whites and non-whites may feel uncomfortable living with each other because our segregated society has given us little experience of doing so.

Moreover, non-whites may not prefer to live in very white neighborhoods because they anticipate that discrimination of some kind by whites will lower their quality of life.54 Because of both steering and segregated living patterns, there is asymmetry between whites and non-whites in terms of information about neighborhoods. Non-whites might seek whiter neighborhoods if they had better information on school poverty (quality) and the stability and equity growth of residential property values in whiter neighborhoods.

Whites may have low tolerance for diversity simply because of invidious racial bias that should not be sanctioned. Alternatively, practices like steering, mortgage-lending discrimination, unfair subsidized-housing placement, and discriminatory school-boundary drawing often place diverse neighborhoods in the midst of racial transition and disinvestment, which may also reduce whites’ tolerance for racial diversity.

In the end, if discrimination was eliminated—or reduced—and integration could become both more common and more stable, it is likely that preferences would change and become more compatible.

54 See generally Joe Feagin, Living with Racism: The Black Middle-Class Experience (Boston: Beacon Press, 1995).

V. Strategies to Achieve Stably Integrated Suburbs Racial instability and resegregation are the dominant U.S. pattern. However, stable racial

integration has been achieved by neighborhoods, cities, large urban counties, and even at metropolitan scales. Stable integration does not happen by accident, but is almost always the product of clear race-conscious strategies, hard work, and political collaboration among local governments. Stable integration measures work best when local, state, and federal governments and the private sector are cooperating with strong multi-racial citizen involvement.

The following are concrete strategies that can foster residential stability in diverse communities:

A. Local Stable-Integration Plans Housing markets are regional, and housing discrimination operates on a regional scale.

Hence regional remedies to address housing discrimination are the most effective. Nevertheless, dozens of communities have created effective local stable-integration plans. Case studies illustrate the potential value of proactive, multifaceted strategies.55 Such strategies can include:

• local fair-housing ordinances; • public and private funding of pro-integrative home-loan and insurance-purchase

programs; • cooperative efforts with local school districts to ensure high-quality, stably

integrated schools; • community-safety programs in diverse neighborhoods; • marketing efforts to encourage local chambers of commerce, rental property

owners, and realtors to view diverse communities as potentially strong markets; • public-relations campaigns to encourage positive media stories of community

successes; • financial support of pro-integrative community-based organizations; and • support of public forums to defuse racial misunderstanding and promote the value

of integrated communities.56

Experience shows the success of such initiatives. For example, Hanover Park, a western suburb of Chicago, went through rapid racial change in the early 2000s, going from 47 percent non-white in 2000 to 62 percent in 2010. In contrast, Oak Park, a community about 15 miles away that has a well-known stable-integration program,57 showed much greater stability, with a non-white share that grew from 34 percent to 36 percent during the same period.

55 Philip Nyden et al., “The Emergence of Stable Racially and Ethnically Diverse Communities: A Case Study of Nine U.S. Cities,” Housing Policy Debate 8 (1997): 491, 512. 56 Ibid., 523-25. 57 See generally Oak Park Regional Housing Center, http://www.oprhc.org.

Similar contrasts can be seen in the Cleveland area. Two suburban areas without stable- integration programs—Euclid and Maple Heights—each showed dramatic racial change between 2000 and 2010. The non-white share of the population increased by 23 points in Euclid (from 34 percent to 57 percent) and by 23 points in Maple Heights (from 49 percent to 72 percent). During the same period, two nearby communities with nationally recognized pro-integrative housing programs were much more stable. Shaker Heights went from 41 percent non-white to 46 percent while Cleveland Heights went from 48 percent to 51 percent.

B. Civil Rights Enforcement The most obvious way to promote integrated communities is through enforcement of the

national Fair Housing Act, which prohibits racial steering, mortgage-lending discrimination, and disproportionate building of subsidized housing in integrated communities.

Neighborhoods were once zoned by race, racially restrictive covenants kept neighborhoods rigidly segregated, real estate agent rules required racial steering in some areas, and the federal government endorsed the redlining of non-white and integrated neighborhoods. This sort of clear and overt discrimination is gone, and as a result residential integration has improved slowly—however— less obvious and often-covert racial discrimination in the housing market remains common.

One of the best ways to document modern housing discrimination is through paired testing. To do this, researchers assemble a large group of paired white and non-white testers of the housing market. Each pair of white and non-white testers has similar incomes, credit histories, education, and personal backgrounds. The testers are trained to approach and interact with real estate agents and banks in the exactly the same manner. For example, both the white and non-white tester might ask a real estate agent to show them the best house, in the best neighborhood, with the best schools that they can afford. Illegal discrimination occurs when these paired testers are shown neighborhoods with different racial characteristics, receive different credit treatment, or are treated differently by sellers or rental agents. Without such paired testing, it is hard to detect, much less prove, such discrimination.

HUD, the federal agency charged with enforcing the Fair Housing Act is now conducting metropolitan-level, paired-testing steering studies to make sure that all parts of suburbia are open to non-white buyers, to ensure that non-white buyers are not disproportionately steered toward racially diverse neighborhoods and school-attendance areas and to confirm that white buyers are not steered away from these same areas to white neighborhoods. If and when evidence is found of steering or other housing discrimination, HUD and appropriate local authorities should take enforcement actions to ensure that such discrimination stops.

The government has been collecting mortgage data through the Home Mortgage and Disclosure Act for forty years. It has revealed profound disparities in the treatment of white and non-white individuals and among predominantly white, predominantly non-white, and integrated

neighborhoods.58 The data suggest discrimination under the Fair Housing Act, and federal, state, and local authorities have an obligation to take action.

HUD and state and local governments should also abide by the housing siting rules to ensure that new low-income housing is not sited disproportionately in racially integrated or transitioning areas. Whiter and more affluent developing suburbs should be prioritized for funding and incentives should be created to encourage fairness and stable metropolitan-level integration.

Finally, local, state, and federal education authorities have an obligation to ensure that local school-district-boundary decisions, school-transfer policies, and capital decisions are fair under the Titles II and VI of the 1964 Civil Rights Act and under state and federal constitutions.

C. State and Metropolitan Actions against Exclusionary Zoning Some states, either by legislative or judicial action, require all communities to provide for

their fair share of affordable housing. Oregon and its largest metropolitan area, Portland, provide excellent examples of state- and metropolitan-level actions that promote and maintain integrated communities. At the state level, Oregon’s Land Use and Development Commission Goal 10, promulgated in 1973, requires that regional and local comprehensive plans “encourage the availability of adequate numbers of needed housing units at price ranges and rent levels which are commensurate with the financial capabilities of Oregon households and allow for flexibility of housing location, type and density.”59

At the regional level, the Portland metropolitan area’s regional planning policies have helped to reduce segregation by encouraging all developing communities to provide for their fair share of affordable housing. The area has a strong regional planning agency (Portland Metro) that enforces a regional growth boundary designed to focus new development in core areas. Research for the 1990s shows that the most common measure of black-white segregation—the dissimilarity index—declined more rapidly in regions with growth-containment policies. Black- white racial segregation has in fact decreased in the Portland region—it is now one of the nation’s least class-segregated metropolitan areas.60

Similarly, Montgomery County, Maryland, provides the best example of pro-integrative policies at the county scale. Thirty years ago, the county—a wealthy suburban area directly

58 See “Communities in Crisis: Race and Mortgage Lending in the Twin Cities,” Institute on Race and Poverty (2009): 25 table 4, http://www.irpumn.org/uls/resources/projects/IRP_mortgage_study_Feb._11th.pdf, for data on the Twin Cities. See “Segregated Communities: Segregated Finance,” Institute on Race and Poverty (2009): charts 11 and 12, http://www.irpumn.org/uls/resources/projects/Summary_FINAL.pdf, for Seattle and Portland data. 59 Oregon Admin. R. 660-015-0000(10). 60 Todd Swanstrom et al., “Pulling Apart: Economic Segregation among Suburbs and Central Cities in Major Metropolitan Areas,” Brookings Institution (2004): 8-9, http://www.brookings.edu/~/media/research/files/reports/2004/10/metropolitanpolicy%20swanstrom/20041018_eco nsegregation.pdf; Arthur Nelson and Susan Wachter, “Growth Management and Affordable Housing Policy,” Journal of Housing and Community Development Law 12 (2003).

northwest of Washington, D.C.—adopted its Moderately Priced Dwelling Unit (MPDU) program.61 The MPDU requires that any new housing development of fifty or more units set aside 12.5 to 15 percent of the units for households earning 65 percent or less of the regional median income.

Non-whites have been the primary beneficiaries of the Montgomery County program. As of the late 1990s, people of color occupied 80 percent of the new public-housing rental units, and from 1991 to 1998, people of color accounted for approximately 55 percent of the purchasers of moderately priced dwelling units.62 At the same time, and at least partly as a result of these proactive housing policies, Montgomery County schools have made enormous strides in reducing the educational achievement gap between poor non-whites and affluent whites.63

In New Jersey, where the state supreme court declared in the Mount Laurel cases that every city in a metropolitan region has an obligation to provide for its fair share of affordable housing, research has found gains in educational achievement, health, and many other benefits for low-income non-white families moving to affordable housing in white affluent suburbia. 64

D. Metropolitan School Integration Strategies The Supreme Court’s 1974 decision in Milliken v. Bradley65 stopped most school

integration plans at the borders of a local school district. After Milliken, most school desegregation efforts were only temporarily successful—if not counterproductive—because they tended to encourage white flight to adjacent, whiter school districts.

Despite Milliken, metropolitan-level integration plans were in implemented in fifteen metropolitan areas in the 1970s and 1980s.66 The majority of these plans occurred in the Border States and the southeast. While hardly a liberal bastion historically, this part of the country proved to be more racially progressive than either the Deep South or the highly fragmented and segregated north. Practical political, business, and religious leaders in several Border States sought to avoid the trauma of a drawn-out racial struggle by designing sustainable integration 61 See Karen Destoral Brown, “Expanding Affordable Housing Through Exclusionary Zoning: Lessons from the Washington Metropolitan Area,” Brookings Insititution (2001): 5, http://www.brookings.edu/es/urban/publications/inclusionary.pdf; David Rusk, “Inside Game/Outside Game: The Emerging Anti-Sprawl Coalition,” New Century Housing (2000): 13. 62 Florence W. Roisman, “Opening the Suburbs to Racial Integration,” Western New England Law Review 23 (2001): 78-9. 63 Heather Schwartz, “Housing Policy Is School Policy: Economically Integrative Housing Promotes Academic Success in Montgomery County, Maryland,” The Century Foundation (2010), http://tcf.org/publications/pdfs/housing-policy-is-school-policy-pdf/Schwartz.pdf. 64 Douglas S. Massey et al., Climbing Mount Laurel: Affordable Housing and Social Mobility in an American Suburb (Princeton, NJ: Princeton University Press, forthcoming). 65 Milliken v. Bradley, 418 U.S. 717 (1974). 66 The included metropolitan areas were Charlotte NC, Daytona Beach FL, Greensboro NC, Indianapolis IN, Lakeland FL, Las Vegas NV, Louisville KY, Nashville TN, Orlando FL, Pensacola FL, Wilmington DE, Raleigh- Durham NC, Sarasota FL, Tampa-St. Petersburg FL, and West Palm Beach FL.

plans. Many also had the advantage of a less-fragmented structure of local government. In most cases, it was possible to use relatively large county-wide school districts encompassing all or most of the relevant housing market as the vehicle for integration efforts.

The availability of a metropolitan option in these areas increased the chances that the resulting integration would be long-lasting. The inclusion of most of white suburbia in the plans meant that all schools in a large area, though integrated, would be majority white and middle- class and academically strong. Moreover, by including all metropolitan schools, whites had nowhere to flee except private schools—a prohibitively expensive option for most middle-class households. In the metro integration areas, private-school attendance was no greater than the national average.

Forty years of history and data demonstrate that integrated neighborhoods in regions with large-scale, metro-wide school-integration plans were much more stable than in metropolitan areas without such plans.67 Chart 2 (Section III.B) shows that in areas without metro school integration, census tracts that were more than 23 percent non-white in 1980 were more likely to become majority non-white than remain integrated. In these areas, neighborhoods that were between 30 and 60 percent non-white had very little chance of remaining integrated. For example, neighborhoods that were 50 percent non-white had an 85 percent chance of becoming 60 percent non-white by 2009.

Chart 3 shows the same relationship—the likelihood that a neighborhood would remain integrated between 1980 and 2005-09 or resegregate as a function of its racial composition in 1980—for the 15 metropolitan areas that had large-scale school integration plans. In contrast with the results for metros with no such plans, integrated neighborhoods in regions with metro (or nearly metro-scale) school-integration plans were much more stable. Neighborhoods between 20 and 33 percent non-white were much more likely (between 55 and 65 percent likely) to remain integrated than to resegregate. And neighborhoods between 33 and 50 percent non-white had a roughly 50 percent chance of remaining stably integrated over twenty-five years.68

67 Myron Orfield and Thomas Luce, “Minority Suburbanization and Racial Change” (paper presented at Race and Regionalism Conference, May 6-7, 2005), http://www.irpumn.org/uls/resources/projects/Minority Subn_050605wMAPS.pdf. 68 When limited to 369 integrated census tracts in the suburbs of metro-plan metros or to the period from 1980 to 2000, the results are similar. The findings are also not the result of demographic differences between the two groups. The metro-plan regions were a bit more white in 1980, with a white share roughly six percentage points greater than in the other metros (82 percent versus 76 percent), but demographic changes were virtually identical over the period (the white share declined by 16 points in the metro-plan metros and by 18 points in the others). The results are also very similar for the time period between 1980 and 2000, the period when the most plans were in effect. Finally, statistical analysis shows that the relationship between metro-scale integration plans and greater stability in integrated neighborhoods remains when controlling for factors other than the initial racial mix. Location in a metropolitan area with a large-scale plan remains positively associated (at the 99 percent confidence level) with the likelihood that a tract would remain integrated between 1980 and 2005-09 in a logit regression, which controls for beginning-of-period racial mix in census tracts, beginning-of-period racial mix in metropolitan areas, racial change at the metropolitan level, total metropolitan population, and population growth at the tract level. This is the result whether the regression includes only the metro-plan metropolitan areas included in the 50 largest metropolitan areas or all 15 metro-plan metros.

The most commonly cited example of a large-scale desegregation plan still in existence illustrates these results very well. Raleigh (Wake County), North Carolina, which implemented metropolitan level desegregation of its schools in the 1970s, not only has schools that rank among the nation’s most integrated, but its neighborhoods are also among the least segregated. It has also been one of the fastest-growing metros in the country.

Schools move through racial transition earlier, faster, and often more completely than neighborhoods. Unless there is a very powerful mobilization on the housing front, racially diverse suburbs need regional plans for schools if they are to avoid resegregation because most white and middle-class families are not going to buy homes in areas with largely nonwhite, unstable, and increasingly poor schools.

Chart 3

VI. Conclusion

More than half of suburban residents in America’s largest metropolitan areas live in places that are threatened economically because of un-redressed housing discrimination and the resulting resegregation. In these communities homeowners and business owners alike lose equity every year because these laws are not enforced. These communities that were built at great public expense will unnecessarily become blighted and abandoned, and citizens will be taxed to create new communities of escape. Rather than becoming America’s most expensive disposable product, these communities should be recycled, renewed, and redeveloped. As the largest suburban block of voters—and the most politically volatile—diverse suburbs should be able to command the attention of political leaders and policy makers. These communities, in combination with central cities and predominantly non-white suburbs (which have many common interests), have the metro majority of local officials, legislators and members of Congress, and therefore should be able to ensure the enforcement of existing laws and the creation of new laws necessary to stabilize neighborhoods and schools in metro America. All of these types of communities are hurt by current patterns of housing discrimination and resegregation. Together they could form a majority political coalition to advance these reforms.

The largest barrier to this change is lack of understanding. The general public, particularly the politically pivotal diverse suburbs and their elected officials, simply do not understand the dire consequences of resegregation or the clear benefits that strong fair-housing policies provide to their communities. Thus it is important to begin large public-education efforts to help the integrated suburbs understand what is happening to them and how many communities are in a similar position. These efforts would explain that stronger fair-housing policies would strengthen their residential market, increase prime low-cost credit, stabilize their schools, and allow strong potential for redevelopment. At present, many in these areas think just the opposite; they incorrectly believe that fair housing will increase the speed and severity of the already occurring resegregation and decline.

A key to stability—or transition—is what residents and potential residents think the future of a community will be. Many whites are perfectly willing to live in a diverse community but do not want to be in a predominately black or Latino community, or a community that shows clear signs of economic and social decline. Similarly, they are very willing to have their kids go to a diverse school, but not to one that has resegregated or is in the process of rapid transition.

Most currently diverse communities are in the process of resegregation, but have no real plans to do anything about it. Diverse suburban communities need technical support (since they have very limited staff and knowledge) to help them deal with their housing and school issues and, if possible, financial support to implement their plans. The truth is that most diverse suburbs have no idea of how to address resegregation, and they have no external framework of advice and support. A federal or state initiative of school and housing agencies to support stable and successful diversity in suburban communities would be a very well received. If this initiative was managed as a purely voluntary process, then it would be a political advantage rather than a cost.

Because the diverse suburbs do not realize how many communities are in a similar situation, they are more likely to avoid discussing the issue of resegregation for fear that calling attention to the problems may make them worse. But if public-education efforts made diverse

suburbs aware that resegregation is common, they could then cooperate with the large number of similarly affected communities and develop political and reform efforts.

Existing membership organizations for municipalities, such as the League of Cities, involve all types of cities and suburbs, rich and poor, white and non-white. As voluntary membership organizations, they risk losing members who disagree with their actions. Thus they are consensus- and status-quo-oriented and may be unlikely to take any strong position on the issues necessary for suburban stability. Given this reality, the diverse suburbs must form their own organizations, support them with dues, and seek government and private grants to fund their reform efforts. Once created, these organizations should use their political power, in every way they can, at the state and federal levels, to ensure that current laws are enforced or changed to support their stability and redevelopment. Some relatively new organizations of older suburbs exist—in Cleveland, Michigan, and New Jersey, for instance—but this process needs to accelerate.

Metropolitan America is at a crossroads. The places in the country that have worked to create stable integration have been rewarded for their efforts. Louisville, Raleigh, Portland, and Montgomery County are not only some of the most desirable places to live for people of all races in the United States, but have strong, resilient economies. If racially diverse suburbs can become politically organized and exercise the power of their numbers—in their own self-interest—they can help to ensure both the stability of their communities and the future opportunity and prosperity of a multi-racial metropolitan America.

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