Metropolitan Futures Initiative Quarterly Report

Metropolitan Futures Initiative (MFI) Quarterly Report: Rising inequality and neighborhood mixing: Comparing across metropolitan areas February 1, 2020

Presented by the Metropolitan Futures Initiative (MFI) School of Social Ecology University of California, Irvine Š January 2020

Metropolitan Futures Initiative (MFI) Quarterly Report: Rising inequality and neighborhood mixing: Comparing across metropolitan areas • January 1, 2020

Rising inequality and neighborhood mixing: Comparing across metropolitan areas February 1, 2020

Report Authors:

John R. Hipp

Kevin Kane

Graduate Student Researcher: Benjamin Forthun

Cite this Report: Cite this report: Kane, Kevin, John R. Hipp, and Benjamin Forthun (2020) “Rising inequality and neighborhood mixing: Comparing across metropolitan areas” MFI Quarterly Report: 2020_1. Irvine, CA: Metropolitan Futures Initiative (MFI), University of California Irvine. February 1, 2020.

Also see this accompanying peer reviewed paper: Kane, Kevin and John R. Hipp. 2019. "Rising Inequality and Neighborhood Mixing in U.S. Metro Areas." Regional Studies 53:1680-1695.

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Metropolitan Futures Initiative (MFI) Quarterly Report: Rising inequality and neighborhood mixing: Comparing across metropolitan areas • January 1, 2020

About the Metropolitan Futures Initiative (MFI) The Metropolitan Futures Initiative (MFI) (http://socialecology.uci.edu/mfi) in the School of Social Ecology at the University of California, Irvine aims to develop an improved understanding of communities and their potential for integrative and collaborative planning and action to ensure a bright future for the region. It approaches these goals by bringing together an interdisciplinary research team along with the insights and techniques of "big data" research.

By combining various large longitudinal and spatial data sources, and then employing cutting edge statistical analyses, the goal is to come to a better understanding of how the various dimensions of the social ecology of a region move together to produce the outcomes observed within our neighborhoods.

With initial focus on Orange County and its location within the larger Southern California area, The Metropolitan Futures Initiative is a commitment to build communities that are economically vibrant, environmentally sustainable, and socially just by partnering the School of Social Ecology’s world class, boundary-crossing scholarship with expertise throughout Southern California.

The MFI Quarterly Report series presents cutting edge research focusing on different dimensions of the Southern California region, and the consequences for neighborhoods in the region.

Reports released each quarter focus on issues of interest to the public as well as policymakers in the region. In addition, the MFI webpage (mfi.soceco.uci.edu) provides interactive mapping applications that allow policymakers and the public to explore more deeply the data from each Quarterly Report.

The MFI gratefully acknowledges the Heritage Fields El Toro, LLC for their funding support.

This research does not reflect the views of the Southern California Association of Governments (SCAG).

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Metropolitan Futures Initiative (MFI) Quarterly Report: Rising inequality and neighborhood mixing: Comparing across metropolitan areas • January 1, 2020

The MFI Research Team: John R. Hipp is the Director of the Metropolitan Futures Initiative (MFI). He is a professor in the Department of Criminology, Law and Society, the Department of Policy, Planning, and Design, and the Department of Sociology, at the University of California Irvine. He is also co-director of the Irvine Lab for the Study of Space and Crime (ILSSC). His research interests focus on how neighborhoods change over time, how that change both affects and is affected by neighborhood crime, and the role networks and institutions play in that change. He approaches these questions using quantitative methods as well as social network analysis.

Jae Hong Kim is a member of the MFI Executive Committee and a faculty member in the Department of Urban Planning and Public Policy at the University of California, Irvine. His research focuses on urban economic development, land use change, and the nexus between these two critical processes.

His academic interests also lie in institutional environments — how institutional environments shape urban development processes — and urban system modeling. His scholarship attempts to advance our knowledge about the complex mechanisms of contemporary urban development and to develop innovative urban planning strategies/tools for both academics and practitioners.

Kevin Kane is an affiliated researcher with the MFI and a demographer at the Southern California Association of Governments. He is an economic geographer interested in the quantitative spatial analysis of urban land-use change and urban development patterns, municipal governance, institutions, and economic development. His research uses land change as an outcome measure – in the form of changes to the built environment, shifting patterns of employment, or the socioeconomic composition of places – and links these to drivers of change including policy, structural economic shifts, or preferences for how we use and travel across urban space.

Benjamin Forthun is a Ph.D. student in the department of Criminology, Law and Society, at the University of California, Irvine. His research interests are focused on geographic information system (GIS) analysis, aggregate spatial research, and neighborhood definitions and boundaries.

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Metropolitan Futures Initiative (MFI) Quarterly Report: Rising inequality and neighborhood mixing: Comparing across metropolitan areas • January 1, 2020

Table of Contents

Page

Results in Brief

7

Predicting neighborhood mixing

8

Describing metropolitan areas based on neighborhood mixing

13

Top 20 and Bottom 20 for neighborhood mixing

16

Focusing on metropolitan areas within each of four regions in the U.S.

30

Northeast region

31

Midwest region

34

South region

37

West region

40

Conclusions 43

Appendix

Page

Technical Appendix: Data Sources Used

44

List of Tables, Figures and Maps

Page

Figure 1. Example egohoods in Chicago, IL.

8

Income Mixing (Inequality) By Neighborhood (2010)

10

Education Level Mixing (Inequality) By Neighborhood (2010)

11

Occupation Type Mixing (Inequality) By Neighborhood (2010)

12

Table E D1: Rank-based analysis of example cities

14

Table 1. Top 20 MSAs based on neighborhood income mixing

18

Table 2. Bottom 20 MSAs based on neighborhood income mixing

19

Neighborhood Income Mixing in U.S. MSAs, 2010 West

20

Neighborhood Income Mixing in U.S. MSAs, 2010 East

21

Table 3. Top 20 MSAs based on neighborhood education mixing

22

Table 4. Bottom 20 MSAs based on neighborhood education mixing

23

Neighborhood Education Mixing in U.S. MSAs, 2010 West

24

Neighborhood Education Mixing in U.S. MSAs, 2010 East

25

Table 5. Top 20 MSAs based on neighborhood occupation mixing

26

Table 6. Bottom 20 MSAs based on neighborhood occupation mixing

27

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Metropolitan Futures Initiative (MFI) Quarterly Report: Rising inequality and neighborhood mixing: Comparing across metropolitan areas • January 1, 2020

List of Tables, Figures and Maps (cont.)

Page

Neighborhood Occupation Mixing in U.S. MSAs, 2010 East

29

Table NE1. Top 10 MSAs based on neighborhood income mixing in Northeast

31

Table NE2. Bottom 10 MSAs based on neighborhood income mixing in Northeast

31

Table NE3. Top 10 MSAs based on neighborhood education mixing in Northeast

32

Table NE4. Bottom 10 MSAs based on neighborhood education mixing in Northeast

32

Table NE5. Top 10 MSAs based on neighborhood occupation mixing in Northeast

33

Table NE6. Bottom 10 MSAs based on neighborhood occupation mixing in Northeast

33

Table MW1. Top 10 MSAs based on neighborhood income mixing in Midwest

34

Table MW2. Bottom 10 MSAs based on neighborhood income mixing in Midwest

34

Table MW3. Top 10 MSAs based on neighborhood education mixing in Midwest

35

Table MW4. Bottom 10 MSAs based on neighborhood education mixing in Midwest

35

Table MW5. Top 10 MSAs based on neighborhood occupation mixing in Midwest

36

Table MW6. Bottom 10 MSAs based on neighborhood occupation mixing in Midwest

36

Table S1. Top 10 MSAs based on neighborhood income mixing in South

37

Table S2. Bottom 10 MSAs based on neighborhood income mixing in South

37

Table S3. Top 10 MSAs based on neighborhood education mixing in South

38

Table S4. Bottom 10 MSAs based on neighborhood education mixing in South

38

Table S5. Top 10 MSAs based on neighborhood occupation mixing in South

39

Table S6. Bottom 10 MSAs based on neighborhood occupation mixing in South

39

Table W1. Top 10 MSAs based on neighborhood income mixing in West

40

Table W2. Bottom 10 MSAs based on neighborhood income mixing in West

40

Table W3. Top 10 MSAs based on neighborhood education mixing in West

41

Table W4. Bottom 10 MSAs based on neighborhood education mixing in West

41

Table W5. Top 10 MSAs based on neighborhood occupation mixing in West

42

Table W6. Bottom 10 MSAs based on neighborhood occupation mixing in West

42

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Metropolitan Futures Initiative (MFI) Quarterly Report: Rising inequality and neighborhood mixing: Comparing across metropolitan areas • January 1, 2020

Results in Brief • Greenville, NC has the highest level of neighborhood income mixing. It also has relatively high neighborhood education mixing, but relatively low neighborhood occupation mixing • Ogden-Clearfield, UT has the lowest level of neighborhood income mixing, but very high neighborhood occupation mixing.

They have many creative class workers, but few retirees.

• Two MSAs near Washington D.C. (California-Lexington Park, MD and Washington-Arlington-Alexandria) have low neighborhood income mixing, along with many creative class workers but few retirees.

• The college town of Columbia, MO has the highest neighborhood education mixing.

• New York, NY has the second highest neighborhood education mixing • Santa Fe, NM has high neighborhood education mixing and many creative class workers.

• The 5 MSAs with the lowest education mixing have few patents, fewer creative class workers and lower GDP per capita.

These are Madera, CA, Altoona, PA, Weirton-Steubenville, WV-OH, Dalton, GA, and Lima, OH. • The top four MSAs in neighborhood occupation mixing also have low levels of neighborhood income mixing: Wausau, WI, Sheboygan, WI, Columbus, IN, and Appleton, WI. Three of these MSAs are in Wisconsin and they all have relatively few college students.

• Carbondale-Marion, IL has the lowest level of neighborhood occupation mixing. They also have high levels of neighborhood income mixing and many college students.

• Neighborhood occupation mixing is generally quite high in the Midwest, as nine of the top ten MSAs overall are in the Midwest.

• Neighborhood income mixing is generally quite high in the South as they constitute 10 of the top 15 MSAs in the U.S. • Neighborhood education mixing is generally lower in the South.

• Neighborhood occupation mixing is generally lower in the South.

• Neighborhood income mixing is generally lower in the West.

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Metropolitan Futures Initiative (MFI) Quarterly Report: Rising inequality and neighborhood mixing: Comparing across metropolitan areas • January 1, 2020

Predicting neighborhood mixing Introduction In a nation in which overall measures of inequality are steadily rising and political polarization seems to be the rule rather than the exception, this research asks how much – if any – of this can be traced to the way that we use urban space to segregate ourselves?

Disciplines such as urban planning, sociology, geography, and economics have long weighed in on determinants of and impacts of neighborhood mixing—or lack thereof. Land use patterns across urban areas are fabulously complex and idiosyncratic, especially when comparing urban regions against each other. This makes it challenging to evaluate the extent to which like and non-like people live in close proximity in cities, and even more challenging to link to overall increases in inequality and polarization.

Figure 1. Example egohoods in Chicago, IL.

Richard Florida has, in numerous studies including his 2017 book The New Urban Crisis, drawn this connection in the context of the distinctive location patterns and also the economic productivity of the “creative class,” arguing, broadly, that fast-growing, “superstar” regions characterized by innovation, venture capital, and price appreciation tend to be the most unequal overall. The rationale is that, since a region’s land use patterns are by and large determined by the location decisions of the wealthy, a superstar city might have more tendency to segregate.

This research attempts to provide new insights into some of the spatial relationships involved in both neighborhood mixing and regional inequality through an investigation of 381 metropolitan areas in the U.S. using advanced measurement strategies and analysis methods, as explained below.

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Metropolitan Futures Initiative (MFI) Quarterly Report: Rising inequality and neighborhood mixing: Comparing across metropolitan areas • January 1, 2020

First, we address the issue of what is proximity in a region? The most typical way, empirically and conceptually, is to use a standard geographic unit like a census tract or ZIP code, comparing, for example, how many rich and poor live in the same neighborhood.

However, most people don’t know what census tract they live in, and ZIP code geographies have been rightly criticized for often having odd boundaries – they exist for mail delivery, not because they’re in any way reflective of the people in them. We propose an egocentric measure of neighborhood – what’s called an egohood -- which has your census block group in the center plus all other block groups within 1.5 miles. This is intended to capture a person’s activity space where they may have meaningful proximity to others at places like nearby schools, stores, or parks.

In this way, we can measure the people that surround the fairly small geography of a census block group to see how mixed a neighborhood is – and subsequently, how mixed are the neighborhoods across an entire region, the logic being that some regions have inherently more segregated neighborhoods whereas people mix more in other regions. In order to go beyond just the spatial separation of rich and poor, we also analyze mixing across educational attainment levels and occupational categories (service, working, and creative class) to capture other elements of wealth and class.

Secondly, explanations for inequality in a region are not simply linear. While a connection can be drawn between a region’s level of inequality and factors such as recent growth, new housing construction, education levels, crime rates, and voting, these characteristics are not independent of each other and affect different regions differently. To estimate models which explain neighborhood mixing, we use and adapt a new machine learning technique called Kernel Regularized Least Squares (KRLS) which allows not only an evaluation of the magnitude of an effect but how it affects the impact of other factors on mixing.

We call these “ingredients” and investigate, across all 381 regions in the US, which of them are most strongly associated with how mixed a region’s neighborhoods are.

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Metropolitan Futures Initiative (MFI) Quarterly Report: Rising inequality and neighborhood mixing: Comparing across metropolitan areas • January 1, 2020

INCOME MIXING (INEQUALITY)

BY NEIGHBORHOOD (2010) Measured by Gini Coefficient; high values are most mixed/most "unequal" within a neighborhood Rank

Metropolitan Statistical Area (MSA)

Gini Coefficient

1

Greenville, NC

0.461

2

Brownsville-Harlingen, TX

0.455

3

Morgantown, WV

0.451

4

McAllen-Edinburg-Mission, TX

0.450

5

Athens-Clarke County, GA

0.448

6

College Station-Bryan, TX

0.446

7

Corvallis, OR

0.445

8

Bloomington, IN

0.440

9

New Orleans-Metairie, LA

0.438

10

El Centro, CA

0.438

Mean Value across all MSAs

0.399

372

Cheyenne, WY

0.3643

373

Anchorage, AK

0.3639

374

Sheboygan, WI

0.3631

375

Norwich-New London, CT

0.3618

376

Provo-Orem, UT

0.3588

377

Columbus, IN

0.3580

378

Fairbanks, AK

0.3564

379

Washington-Arlington-Alexandria, DC-VA-MD-WV

0.3550

380

California-Lexington Park, MD

0.3506

381

Ogden-Clearfield, UT

0.3484

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Metropolitan Futures Initiative (MFI) Quarterly Report: Rising inequality and neighborhood mixing: Comparing across metropolitan areas • January 1, 2020

EDUCATION LEVEL MIXING

BY NEIGHBORHOOD (2010) Measured by Entropy; high values are most mixed Rank

Metropolitan Statistical Area (MSA)

Entropy

1

Columbia, MO

0.929

2

New York-Newark-Jersey City, NY-NJ-PA

0.925

3

Napa, CA

0.924

4

Santa Fe, NM

0.922

5

Miami-Fort Lauderdale-West Palm Beach, FL

0.921

6

Athens-Clarke County, GA

0.921

7

San Jose-Sunnyvale-Santa Clara, CA

0.920

8

New Haven-Milford, CT

0.920

9

Charlottesville, VA

0.919

10

Missoula, MT

0.918

Mean Value across all MSAs

0.871

372

Hinesville, GA

0.814

373

Lake Havasu City-Kingman, AZ

0.814

374

Mansfield, OH

0.812

375

Jacksonville, NC

0.812

376

Visalia-Porterville, CA

0.810

377

Lima, OH

0.809

378

Dalton, GA

0.808

379

Weirton-Steubenville, WV-OH

0.807

380

Altoona, PA

0.800

381

Madera, CA

0.798

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Metropolitan Futures Initiative (MFI) Quarterly Report: Rising inequality and neighborhood mixing: Comparing across metropolitan areas • January 1, 2020

OCCUPATION TYPE MIXING

BY NEIGHBORHOOD (2010) Measured by Entropy; high values are most mixed Rank

Metropolitan Statistical Area (MSA)

Entropy

1

Wausau, WI

0.942

2

Sheboygan, WI

0.935

3

Columbus, IN

0.927

4

Appleton, WI

0.927

5

Fargo, ND-MN

0.924

6

Racine, WI

0.924

7

Bismarck, ND

0.923

8

Chambersburg-Waynesboro, PA

0.922

9

Cedar Rapids, IA

0.921

10

Fond du Lac, WI

0.921

Mean Value across all MSAs

0.869

372

Lake Havasu City-Kingman, AZ

0.801

373

Brownsville-Harlingen, TX

0.798

374

Brunswick, GA

0.798

375

Laredo, TX

0.797

376

Jacksonville, NC

0.791

377

McAllen-Edinburg-Mission, TX

0.791

378

Sierra Vista-Douglas, AZ

0.789

379

Sebring, FL

0.787

380

Atlantic City-Hammonton, NJ

0.785

381

Carbondale-Marion, IL

0.784

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Metropolitan Futures Initiative (MFI) Quarterly Report: Rising inequality and neighborhood mixing: Comparing across metropolitan areas • January 1, 2020

Mixing in Regions The previous table shows what mixing looks like at the scale of a region. While Richard Florida suggests that inequality is a trait of superstar regions, neighborhood mixing – a measure of spatial equality in a region – has a different look. The list of regions which are in fact the least spatially segregated by income are mid-sized, poorer regions in the South and Texas (such as Greenville, Brownsville, McAllen, and New Orleans) while those which are most spatially segregated by income are in Alaska, Utah, and Washington, D.C. By and large superstars are neither the most – nor the least mixed.

Meanwhile, regions which are the most mixed by educational attainment are some of the largest metros as well as college towns: New York City, Miami, San Jose, Charlottesville, New Haven, and Athens, Georgia while the most spatially segregated by education level are found in Appalachia, the South, and California's central valley. Occupational mixing follows a crisper pattern nationwide – the Midwest has more occupational mixing while the South and Southwest are more spatially segregated by job type.

Since this research’s objective is to let patterns emerge from the data, we consider four broad categories of “ingredients” which have been discussed in the context of “superstar” regions: • Economic well-being: Metro-level GDP/capita, income growth, and unemployment • Business and production: Fortune 1000 headquarters, venture capital investment, patenting, and unionization rates • Housing: home value, share of the housing stock which is recently built (last 10 years), and the share of long-tenured householders • Demographic/cultural/political: Senior citizen (over 65) share of the population, a measure of racial/ethnic mixing, violent crime rate, 2012 presidential election results, and a measure of religious adherence

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Metropolitan Futures Initiative (MFI) Quarterly Report: Rising inequality and neighborhood mixing: Comparing across metropolitan areas • January 1, 2020

TableE D1: Rank-based analysis of example cities*

Lake Havasu City- Kingman, AZ Mansfield, OH

McAllen- Edinburg -Mission, TX

San Francisco- Oakland -Hayward, CA Sheboygan, WI

Total Population

212

318

70

11

331

College educated share

381

366

351

12

229

Percent creative class

364

330

378

4

236

Unionization rate

167

116

358

83

257

Median home value

286

360

346

19

262

Patents

290

340

356

3

166

Median household income

371

333

380

5

113

Income growth ('00-'10)

89

30

333

196

35

Obama vote (2012)

356

265

11

3

198

Pct new housing

12

344

9

349

284

Residential stability

319

93

188

140

31

Lake Havasu City- Kingman, AZ Mansfield, OH

McAllen- Edinburg -Mission, TX

San Francisco- Oakland -Hayward, CA Sheboygan, WI

Income Mixing

188

366

4

227

374

Educational Mixing

373

374

368

12

271

Occupational Mixing

372

74

377

262

2

*All variables correspond to those described in the paper and reflect 2010 conditions. Ranks are out of 381 US MSAs.

Our model results show that overall, many of these measures are tightly correlated—the way they impact a region’s level of neighborhood mixing might be called “nonlinear and combinatorial” in that they affect different regions differently. Some examples can be instructive: Despite being a part of the Sunbelt housing boom (#12 in new housing), Lake Havasu City scores quite low on a number of typical socioeconomic indicators including college education, creative class share, income, patenting, and home values, and had amongst the lowest Democratic voting in 2012. Lake Havasu City is middle-of-the-pack in income mixing, but amongst the most segregated by education and occupation. Meanwhile, Mansfield, Ohio, a small Midwestern metro, shares several of these measures with Lake Havasu City: lower education, income, creative class share, and fairly low Democratic voting. However it differs in having few new homes and far higher residential stability, characteristic of its Midwest, "rustbelt" location. Mansfield sorts by education and income, but is more occupationally mixed.

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Metropolitan Futures Initiative (MFI) Quarterly Report: Rising inequality and neighborhood mixing: Comparing across metropolitan areas • January 1, 2020 San Francisco is a unique case but prominent in innovation and urban literature. It ranks very high in invention, home price, income, education, creative class share, and liberal voting. In San Francisco's case, this is a recipe for very high neighborhood educational mixing, but low-to-mid income and occupational mixing. Glaeser et al. (2009) found Sheboygan to be one of the most equal metro areas by income overall. Sheboygan is older, more stable, less expensive to live in, and moderate to below-moderate in several typical socioeconomic measures. Sheboygan’s residents tend to sort very strongly by income, but tend to mix by occupational category. In contrast, the residents of McAllen, Texas sort by income - but not by education or occupation. McAllen votes liberal and has more new housing, but scores very low in nearly all typical socioeconomic well-being measures.

Modeling why some metropolitan areas have more mixing Using the KRLS machine learning technique allows us to parse through some of these observations more systematically. Overall, we find that when a metro area’s occupational blend (its mix of creative class, working class, and service class jobs) is more polarized, its neighborhoods tend to be more segregated by income. Residents sort themselves in regions by myriad factors, but broad distinctions between occupational groups show strong effects.

Income growth, on the whole, is related to greater neighborhood income mixing. However, this effect diminishes if a region is too highly concentrated in “creative class” jobs. This is consistent with the concern over inequality in superstar regions—it may also manifest itself in residents segregating by neighborhood too. Regions whose residents spatially segregate themselves by education tend to have fewer senior citizens, people who move a lot (residential instability), and high college education rates. This too appears consistent with the notion of a highly educated, younger, mobile, tech-oriented creative class workforce—and our results hint that this could lead to greater spatial segregation by education level.

Finally, land use patterns matter. A region’s share of newly constructed housing (i.e. within the last ten years) shows a number of mitigating effects. Overall, more new housing means less mixing—consistent with the idea of homogenous city areas that have similarly aged and similarly priced housing stock. This makes sense—when large (usually greenfield) developments are built, they form new neighborhoods which attract residents of a similar demographic and income level, whereas regions whose land use patterns are more mixed are more socioeconomically mixed as well. Our results show that this relationship is strengthened when the region also has lower incomes, less inventive activity, fewer creative class workers, and less Democratic voting. One possible explanation may be found in the relative age of the data we needed to use in order to assemble such a wide range of characteristics (approximately 2012)— this may suggest a component of neighborhood segregation in fast-growing cities hit hard by the housing & financial crisis around that time. Paradoxically, though, areas where people tend to stay put (long housing tenure) is related to more income segregation too; however, this only existed in the presence of lower education levels or fewer creative class workers.

We intentionally excluded neighborhood racial/ethnic mixing as an outcome measure in our models as many social scientists lament that race is correlated with so many different social outcomes that it requires a more thorough, independent analysis. This is especially true as a region’s history of redlining or race-based spatial separation is a key element of its historical development trajectory—this is something we did not include explicitly in this research. That notwithstanding, regions with higher Black or Hispanic population shares led to more neighborhood income mixing, but less neighborhood mixing by occupation.

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Metropolitan Futures Initiative (MFI) Quarterly Report: Rising inequality and neighborhood mixing: Comparing across metropolitan areas • January 1, 2020

Describing metropolitan areas based on neighborhood mixing Top 20 and Bottom 20 for neighborhood mixing In this section of the Report, we describe the level of mixing along the three dimensions, and highlight the metropolitan areas with the highest, or lowest, levels of mixing. Rather than providing the score for the MSA on each measure, we provide its rank (from 1 to 381 for all MSAs). We also list the ranking of the MSAs for a few other key measures: 1) Gross Domestic Product per capita (GDP), which captures the overall level of income in the MSA; 2) percent college students in the MSA (to capture college towns); 3) percent aged 65 and above in the MSA, which captures retirement meccas; 4) the percent of workers classified as part of the creative class, which is a measure of the type of workers in the MSA; and 5) change in household income over the prior decade, which captures the direction of economic change in the MSA. In Table 1 we list the top 20 MSAs based on level of neighborhood income mixing.

• These MSAs have the relatively highest level of income mixing in their neighborhoods among all MSAs. • The highest level of neighborhood income mixing occurs in Greenville, NC, which also has a somewhat high level of neighborhood education mixing (ranked 71 of 381 MSAs). However, there are relatively low levels of neighborhood occupation mixing in Greenville, NC, whereas it has high levels of college students but relatively fewer retirees.

• Brownsville-Harlingen, TX and McAllen-Edinburg-Mission, TX have high levels of neighborhood income mixing, but low levels of neighborhood education and occupation mixing, as well as low levels of GDP per capita, college students, retired, and creative class workers.

• Morgantown, WV has the third highest level of income mixing, as well as many college students and increasing household income.

• The college town of Athens-Clarke County, GA has high levels of neighborhood income and education mixing.

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Metropolitan Futures Initiative (MFI) Quarterly Report: Rising inequality and neighborhood mixing: Comparing across metropolitan areas • January 1, 2020

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Metropolitan Futures Initiative (MFI) Quarterly Report: Rising inequality and neighborhood mixing: Comparing across metropolitan areas • January 1, 2020

Table 1. Top 20 MSAs based on neighborhood income mixing

Name

Greenville, NC

Change Income Education Occupation GDP per College

Creative in household mixing mixing mixing capita students Retired class income

1

71

333

217

19

349

112

296

Brownsville-Harlingen, TX 2 348 373 377 314 307 368 195 Morgantown, WV

3 95 309 76 18 278 150 6

McAllen-Edinburg-Mission, TX 4 368 377 378 281 356 330 49 Athens-Clarke County, GA

5

6

342

255

13

343

72

183

College Station-Bryan, TX

6

53

327

300

6

369

52

72

Corvallis, OR

7

14

190

14

8

254

7

318

Bloomington, IN

8

151

355

194

4

324

81

316

New Orleans-Metairie, LA

9

36

143

22

212

247

208

53

El Centro, CA

10

353

343

353

149

333

269

157

Carbondale-Marion, IL

11

90

381

272

24

152

270

159

Ithaca, NY

12

203

346

155

2

320

42

41

Johnson City, TN

13

51

211

337

108

62

178

134

Gainesville, FL

14

52

367

178

10

301

32

33

Hammond, LA

15 237 187 323 136 283 212 16

Monroe, LA

16 241 353 218 226 216 342 177

Mobile, AL

17

197

272

182

239

211

335

225

Florence-Muscle Shoals, AL

18

109

140

356

230

40

285

324

Lafayette, LA

19

333

198

58

261

287

317

9

Lake Charles, LA

20

217

322

19

306

229

324

94

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Metropolitan Futures Initiative (MFI) Quarterly Report: Rising inequality and neighborhood mixing: Comparing across metropolitan areas • January 1, 2020

Table 2. Bottom 20 MSAs based on neighborhood income mixing

Name

Ogden-Clearfield, UT

Change Income Education Occupation GDP per College

Creative in household mixing mixing mixing capita students Retired class income

381

236

11

268

213

357

17

221

California-Lexington Park, MD 380

127

55

62

125

340

1

3

Washington-Arlington Alexandria, DC-VA-MD-WV

379

24

308

5

122

350

4

10

Fairbanks, AK

378

309

47

53

62

381

63

17

Columbus, IN

377

206

3

27

351

159

223

208

Provo-Orem, UT

376

223

130

335

33

379

31

129

Norwich-New London, CT

375

41

239

31

235

134

76

52

Sheboygan, WI

374

271

2

96

307

121

312

347

Anchorage, AK

373

123

45

10

145

378

154

31

Cheyenne, WY

372

280

31

60

187

234

87

12

Fort Wayne, IN

371

275

67

137

218

243

294

356

Fond du Lac, WI

370

323

10

216

251

98

309

257

Vallejo-Fairfield, CA

369

84

59

245

168

286

22

147

Salt Lake City, UT

368

138

88

32

132

371

222

253

Reno, NV

367 113 271 119 115 248 204 336

Mansfield, OH

366

374

74

322

342

49

361

352

Ventura, CA

365

50

110

81

154

264

15

139

Appleton, WI

364

254

4

115

304

267

253

294

Wausau, WI

363

255

1

133

310

141

243

244

Manchester-Nashua, NH

362

16

17

43

262

260

59

86

Oxnard-Thousand Oaks-

Table 2 shows the bottom 20 MSAs based on level of neighborhood income mixing. These are metro areas with the most segregation across neighborhoods based on income.

• Ogden-Clearfield, UT has the least neighborhood income mixing, but very high neighborhood occupation mixing. They have many creative class workers, but few retirees.

• Two MSAs near D.C. (California-Lexington Park, MD and Washington-Arlington-Alexandria) have low neighborhood income mixing, along with many creative class workers, increasing household income, but few retirees.

• Columbus, IN has low neighborhood income mixing but high occupation mixing, along with few college students.

• Sheboygan, WI has low neighborhood income mixing but high occupation mixing. They also have fewer creative class workers and have experienced quite low increases in average household income.

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Metropolitan Futures Initiative (MFI) Quarterly Report: Rising inequality and neighborhood mixing: Comparing across metropolitan areas • January 1, 2020

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Metropolitan Futures Initiative (MFI) Quarterly Report: Rising inequality and neighborhood mixing: Comparing across metropolitan areas • January 1, 2020

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Metropolitan Futures Initiative (MFI) Quarterly Report: Rising inequality and neighborhood mixing: Comparing across metropolitan areas • January 1, 2020

Table 3. Top 20 MSAs based on neighborhood education mixing

Name

Columbia, MO

Change Income Education Occupation GDP per College

Creative in household mixing mixing mixing capita students Retired class income

68

1

359

161

14

358

53

79

Jersey City, NY-NJ-PA

137

2

293

12

169

206

75

75

Napa, CA

183

3

13

59

258

90

138

78

Santa Fe, NM

69

4

282

93

225

84

6

133

West Palm Beach, FL

36

5

305

114

167

67

121

281

Athens-Clarke County, GA

5

6

342

255

13

343

72

183

Santa Clara, CA

314

7

203

2

97

312

25

295

New Haven-Milford, CT

176

8

171

91

116

128

67

123

Charlottesville, VA

94

9

222

105

40

168

34

18

Missoula, MT

33

10

258

174

38

288

171

48

New York-Newark-

Miami-Fort Lauderdale-

San Jose-Sunnyvale-

Providence-Warwick, RI-MA 101 11 150 150 103 129 148 63 San Francisco-OaklandHayward, CA

227

12

262

6

91

228

19

186

194

13

249

8

80

204

55

66

7

14

190

14

8

254

7

318

Fort Collins, CO

120

15

64

192

35

261

12

265

Manchester-Nashua, NH

362

16

17

43

262

260

59

86

293

17

185

13

105

130

96

103

351

18

274

3

250

182

47

197

Boston-CambridgeNewton, MA-NH Corvallis, OR

Hartford-West HartfordEast Hartford, CT Bridgeport-StamfordNorwalk, CT

Lexington-Fayette, KY 85 19 128 72 59 305 136 222 Santa Rosa, CA

266

20

132

153

133

153

35

269

Table 3 shows the top 20 MSAs based on level of neighborhood education mixing.

• The college town of Columbia, MO has the highest neighborhood education mixing.

• New York, NY has the second highest neighborhood education mixing and very high GDP per capita.

• Napa, CA has high neighborhood education and occupation mixing.

• Santa Fe, NM has high neighborhood education mixing and many creative class workers.

• There are several creative class MSAs on this list: San Jose-Sunnyvale-Santa Clara, CA, San Francisco-Oakland-Hayward, CA, Boston-Cambridge-Newton, MA, Corvallis, OR and Fort Collins, CO. Most of these MSAs also have very high GDP per capita, and very high numbers of patents, indicating much innovation.

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Metropolitan Futures Initiative (MFI) Quarterly Report: Rising inequality and neighborhood mixing: Comparing across metropolitan areas • January 1, 2020

Table 4. Bottom 20 MSAs based on neighborhood education mixing

Name

Change Income Education Occupation GDP per College

Creative in household mixing mixing mixing capita students Retired class income

Madera, CA

236 381 284 368 323 279 133 182

Altoona, PA

149

380

166

250

341

33

353

37

Weirton-Steubenville, WV-OH

66

379

160

349

280

23

271

114

Dalton, GA

196

378

204

258

328

311

381

379

Lima, OH

278

377

286

110

111

115

379

302

Visalia-Porterville, CA

116 376 319 365 286 354 234 180

Jacksonville, NC

353 375 376 144 161 377 174 45

Mansfield, OH

366

374

74

322

342

49

361

352

Lake Havasu City-Kingman, AZ 188

373

372

381

369

12

232

293

Hinesville, GA

319

372

347

142

96

380

27

65

Johnstown, PA

191

371

213

333

231

20

334

82

The Villages, FL

241

370

370

379

381

1

287

23

Danville, IL

259

369

139

305

375

65

344

271

McAllen-Edinburg-Mission, TX 4 368 377 378 281 356 330 49 Bakersfield, CA

197

367

337

226

271

365

167

58

Hanford-Corcoran, CA 275 366 334 352 245 375 100 162 Houma-Thibodaux, LA

54

365

85

56

356

262

356

8

Cumberland, MD-WV

79

364

194

355

124

30

298

98

Beaumont-Port Arthur, TX

62

363

212

80

316

199

321

88

Lebanon, PA

335

362

108

313

321

39

185

101

Table 4 shows the bottom 20 MSAs based on level of neighborhood education mixing. These are metro areas with the most segregation across neighborhoods based on education.

• The bottom 5 MSAs based on education mixing also have few patents, indicating lower innovation. These are Madera, CA, Altoona, PA, Weirton-Steubenville, WV-OH, Dalton, GA, and Lima, OH. These five MSAs also tend to have fewer creative class workers and lower GDP per capita.

• Two MSAs have very low levels of neighborhood mixing based on education, occupation, and income: Jacksonville, NC and Hinesville, GA. They also have fewer retirees.

• Mansfield, OH has very low neighborhood education mixing and income mixing, as well as few college students and creative class workers, and lower income and change in household income over the prior decade.

• Three MSAs have low neighborhood education and occupation mixing: Lake Havasu City-Kingman, AZ, The Villages, FL, and McAllen-Edinburg-Mission, TX. They also have low GDP per capita. But whereas McAllen has few retirees, the other two MSAs are retiree meccas with few college students.

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Metropolitan Futures Initiative (MFI) Quarterly Report: Rising inequality and neighborhood mixing: Comparing across metropolitan areas • January 1, 2020

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Metropolitan Futures Initiative (MFI) Quarterly Report: Rising inequality and neighborhood mixing: Comparing across metropolitan areas • January 1, 2020

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Metropolitan Futures Initiative (MFI) Quarterly Report: Rising inequality and neighborhood mixing: Comparing across metropolitan areas • January 1, 2020

Table 5. Top 20 MSAs based on neighborhood occupation mixing

Name

Change Income Education Occupation GDP per College

Creative in household mixing mixing mixing capita students Retired class income

Wausau, WI

363

255

1

133

310

141

243

244

Sheboygan, WI

374

271

2

96

307

121

312

347

Columbus, IN

377

206

3

27

351

159

223

208

Appleton, WI

364

254

4

115

304

267

253

294

Fargo, ND-MN

98

75

5

44

32

336

194

34

Racine, WI

231

82

6

225

300

202

141

330

Bismarck, ND

288

120

7

113

179

181

120

5

Waynesboro, PA

338

295

8

342

360

46

128

140

Cedar Rapids, IA

345

290

9

23

189

177

179

166

Fond du Lac, WI

370

323

10

216

251

98

309

257

Ogden-Clearfield, UT

381

236

11

268

213

357

17

221

Lancaster, PA

312 169 12 179 287 97 267 239

Napa, CA

183

3

13

59

258

90

138

78

York-Hanover, PA

350

258

14

212

326

149

134

99

Grand Island, NE

359

356

15

158

374

125

337

36

Casper, WY

213

304

16

7

242

236

302

4

Manchester-Nashua, NH

362

16

17

43

262

260

59

86

Janesville-Beloit, WI

326

265

18

314

313

180

196

370

Grand Junction, CO

237

73

19

283

232

100

182

13

Mount Vernon-Anacortes, WA

347

164

20

139

371

61

227

282

Chambersburg-

Table 5 shows the top 20 MSAs based on level of neighborhood occupation mixing.

• The top four MSAs in neighborhood occupation mixing also have low levels of neighborhood income mixing: Wausau, WI, Sheboygan, WI, Columbus, IN, and Appleton, WI. Besides the fact that three of these MSAs are in Wisconsin, they also all have relatively few college students.

• Three MSAs have experienced a strong increase in average household income: Bismarck, ND, Casper, WY, and Grand Junction, CO. Casper also has very high GDP per capita.

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Metropolitan Futures Initiative (MFI) Quarterly Report: Rising inequality and neighborhood mixing: Comparing across metropolitan areas • January 1, 2020

Table 6. Bottom 20 MSAs based on neighborhood occupation mixing

Name

Change Income Education Occupation GDP per College

Creative in household mixing mixing mixing capita students Retired class income

Carbondale-Marion, IL

11

90

381

272

24

152

270

159

Atlantic City-Hammonton, NJ

110

65

380

88

214

136

363

153

Sebring, FL

284

347

379

380

380

3

293

355

Sierra Vista-Douglas, AZ

209

293

378

301

185

35

57

15

McAllen-Edinburg-Mission, TX 4 368 377 378 281 356 330 49 Jacksonville, NC

353 375 376 144 161 377 174 45

Laredo, TX

31 330 375 369 209 376 352 169

Brunswick, GA

60

332

374

331

320

95

265

178

Brownsville-Harlingen, TX 2 348 373 377 314 307 368 195 Lake Havasu City-Kingman, AZ 188

373

372

381

369

12

232

293

Homosassa Springs, FL

226

343

371

371

379

4

172

234

The Villages, FL

241

370

370

379

381

1

287

23

Las Vegas-HendersonParadise, NV

325 191 369 128 292 289 318 291

Tallahassee, FL

92

61

368

228

20

338

9

219

Gainesville, FL

14

52

367

178

10

301

32

33

Kahului-Wailuku-Lahaina, HI 331 200 366 131 347 220 263 145 Sebastian-Vero Beach, FL

73

92

365

324

362

6

220

353

Valdosta, GA

84

225

364

309

50

332

329

274

Punta Gorda, FL

260

161

363

376

377

2

246

323

147

214

362

234

330

25

255

289

Myrtle Beach-ConwayNorth Myrtle Beach, SC-NC

Table 6 shows the bottom 20 MSAs based on level of neighborhood occupation mixing. These MSAs have higher levels of segregation across neighborhoods by occupation.

• Carbondale-Marion, IL has the lowest level of neighborhood occupation mixing. They also have high levels of neighborhood income mixing and a high presence of college students.

• Atlantic City-Hammonton, NJ has the second lowest level of neighborhood occupation mixing as well as very few creative class workers.

• The retirement community of Sebring, FL has the third lowest level of neighborhood occupation mixing, few college students, very low GDP per capita, and has experienced very weak increases in household income over the prior decade.

• Three MSAs have low levels of neighborhood occupation mixing, low GDP per capita, few creative class workers, and few retirees: Laredo, TX, Brownsville-Harlingen, TX, and Valdosta, GA. • Two MSAs with similar low levels of neighborhood occupation mixing are different on other characteristics: whereas Las Vegas Henderson-Paradise, NV has few creative class workers, Tallahassee, FL has many creative class workers and many college students.

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Metropolitan Futures Initiative (MFI) Quarterly Report: Rising inequality and neighborhood mixing: Comparing across metropolitan areas • January 1, 2020

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Metropolitan Futures Initiative (MFI) Quarterly Report: Rising inequality and neighborhood mixing: Comparing across metropolitan areas • January 1, 2020

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Metropolitan Futures Initiative (MFI) Quarterly Report: Rising inequality and neighborhood mixing: Comparing across metropolitan areas • January 1, 2020

Focusing on metropolitan areas within each of four regions in the U.S. In this section we zero in on the top and bottom metropolitan areas within each of the four major regions in the U.S.: the Northeast, the Midwest, the South, and the West. The rankings that are listed are the national ranking. Thus, for example, in Table NE1 that lists the top 10 MSAs based on neighborhood income mixing in the Northeast, the highest ranking MSA is Ithaca, NY, which had only the 12th highest level of neighborhood income mixing in the U.S. State College, PA was only ranked 34th for the whole U.S., but has the second highest overall level of neighborhood income mixing in the Northeast.

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Metropolitan Futures Initiative (MFI) Quarterly Report: Rising inequality and neighborhood mixing: Comparing across metropolitan areas • January 1, 2020 Northeast region

Table NE1. Top 10 MSAs based on neighborhood income mixing in Northeast

Rank Name

Income mixing

Education mixing

Occupation mixing

1

Ithaca, NY

12

203

346

2

State College, PA

34

288

330

3

Springfield, MA

61

45

248

4

Pittsburgh, PA

80

178

153

5

Buffalo-Cheektowaga-Niagara Falls, NY

93

42

273

6

Vineland-Bridgeton, NJ

96

355

354

7

Providence-Warwick, RI-MA

101

11

150

8

Atlantic City-Hammonton, NJ

110

65

380

9

Bangor, ME

112

235

315

10

Scranton--Wilkes-Barre--Hazleton, PA

114

205

97

• Ithaca, NY has the highest level of neighborhood income mixing in the Northeast, but low levels of neighborhood occupation mixing.

• State College, Pa has the second highest level of neighborhood income mixing in the Northeast, and also has low levels of neighborhood occupation mixing.

Table NE2. Bottom 10 MSAs based on neighborhood income mixing in Northeast

Rank Name

Income mixing

Education mixing

Occupation mixing

1

Norwich-New London, CT

375

41

239

2

Manchester-Nashua, NH

362

16

17

3

Bridgeport-Stamford-Norwalk, CT

351

18

274

4

York-Hanover, PA

350

258

14

5

Gettysburg, PA

348

243

34

6

Reading, PA

346

204

76

7

East Stroudsburg, PA

344

146

102

8

Harrisburg-Carlisle, PA

343

126

87

9

Chambersburg-Waynesboro, PA

338

295

8

1 0

Burlington-South Burlington, VT

337

34

173

• •

•

Norwich-New London, CT has the lowest level of neighborhood income mixing in the Northeast.

Manchester-Nashua, NH and Bridgeport-Stamford-Norwalk, CT have the second and third lowest levels of neighborhood income mixing in the Northeast along with high levels of education mixing. Manchester-Nashua also has high levels of neighborhood occupation mixing.

York-Hanover, PA and Chambersburg-Wayneboro, PA have low levels of neighborhood income mixing along with high levels of neighborhood occupation mixing.

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Metropolitan Futures Initiative (MFI) Quarterly Report: Rising inequality and neighborhood mixing: Comparing across metropolitan areas • January 1, 2020

Table NE3. Top 10 MSAs based on neighborhood education mixing in Northeast

Rank Name

Income mixing

Education mixing

Occupation mixing

1

New York-Newark-Jersey City, NY-NJ-PA

137

2

293

2

New Haven-Milford, CT

176

8

171

3

Providence-Warwick, RI-MA

101

11

150

4

Boston-Cambridge-Newton, MA-NH

194

13

249

5

Manchester-Nashua, NH

362

16

17

6

Hartford-West Hartford-East Hartford, CT

293

17

185

7

Bridgeport-Stamford-Norwalk, CT

351

18

274

8

Albany-Schenectady-Troy, NY

235

22

301

9

Worcester, MA-CT

273

23

123

10

Kingston, NY

267

30

241

• New York, NY has the highest level of neighborhood education mixing in the Northeast.

Table NE4. Bottom 10 MSAs based on neighborhood education mixing in Northeast

Rank Name

Income mixing

Education mixing

Occupation mixing

1

Altoona, PA

149

380

166

2

Johnstown, PA

191

371

213

3

Lebanon, PA

335

362

108

4

Vineland-Bridgeton, NJ

96

355

354

5

Utica-Rome, NY

192

335

288

6

Elmira, NY

148

318

280

7

Syracuse, NY

166

316

242

8

Bloomsburg-Berwick, PA

122

307

98

9

Chambersburg-Waynesboro, PA

338

295

8

10

Watertown-Fort Drum, NY

272

289

357

• Altoona, PA has the lowest level of neighborhood education mixing in the Northeast.

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Metropolitan Futures Initiative (MFI) Quarterly Report: Rising inequality and neighborhood mixing: Comparing across metropolitan areas • January 1, 2020

Table NE5. Top 10 MSAs based on neighborhood occupation mixing in Northeast

Rank Name

Income mixing

Education mixing

Occupation mixing

1

Chambersburg-Waynesboro, PA

338

295

8

2

Lancaster, PA

312

169

12

3

York-Hanover, PA

350

258

14

4

Manchester-Nashua, NH

362

16

17

5

Gettysburg, PA

348

243

34

6

Allentown-Bethlehem-Easton, PA-NJ

307

80

52

7

Reading, PA

346

204

76

8

Harrisburg-Carlisle, PA

343

126

87

9

Portland-South Portland, ME

290

87

89

10

Scranton--Wilkes-Barre--Hazleton, PA

114

205

97

• Chambersburg-Waynesboro, PA has the highest levels of neighborhood occupation mixing in the Northeast, along with low levels of neighborhood income mixing.

Table NE6. Bottom 10 MSAs based on neighborhood occupation mixing in Northeast

Rank Name

Income mixing

Education mixing

Occupation mixing

1

Atlantic City-Hammonton, NJ

110

65

380

2

Watertown-Fort Drum, NY

272

289

357

3

Vineland-Bridgeton, NJ

96

355

354

4

Ithaca, NY

12

203

346

5

Trenton, NJ

308

89

339

6

State College, PA

34

288

330

7

Ocean City, NJ

204

112

317

8

Bangor, ME

112

235

315

9

Albany-Schenectady-Troy, NY

235

22

301

1 0

Pittsfield, MA

141

67

295

• Atlantic City-Hammonton, NJ has the lowest levels of neighborhood occupation mixing in the Northeast.

33

Metropolitan Futures Initiative (MFI) Quarterly Report: Rising inequality and neighborhood mixing: Comparing across metropolitan areas • January 1, 2020 Midwest region

Table MW1. Top 10 MSAs based on neighborhood income mixing in Midwest

Rank Name

Income mixing

Education mixing

Occupation mixing

1

Bloomington, IN

8

151

355

2

Carbondale-Marion, IL

11

90

381

3

Manhattan, KS

21

74

314

4

Lawrence, KS

28

27

307

5

Huntington-Ashland, WV-KY-OH

35

310

277

6

Kalamazoo-Portage, MI

53

108

93

7

Niles-Benton Harbor, MI

64

266

238

8

Weirton-Steubenville, WV-OH

66

379

160

9

Columbia, MO

68

1

359

10

Champaign-Urbana, IL

71

47

279

• Bloomington, IN has the highest level of neighborhood income mixing in the Midwest. It also has very low levels of neighborhood occupation mixing.

Table MW2. Bottom 10 MSAs based on neighborhood income mixing in Midwest

Rank Name

Income mixing

Education mixing

Occupation mixing

1

Columbus, IN

377

206

3

2

Sheboygan, WI

374

271

2

3

Fort Wayne, IN

371

275

67

4

Fond du Lac, WI

370

323

10

5

Mansfield, OH

366

374

74

6

Appleton, WI

364

254

4

7

Wausau, WI

363

255

1

8

Sioux Falls, SD

361

149

24

9

Grand Island, NE

359

356

15

10

Monroe, MI

356

324

28

• •

•

Columbus, IN has the lowest level of neighborhood income mixing in the Midwest.

Several of these MSAs have very low levels of neighborhood income mixing along with very high levels of neighborhood occupation mixing: Columbus, IN, Sheboygan, WI, Fond du Lac, WI, Appleton, WI, Wausau, WI, and Grand Island, NE. Several of the MSAs on this list have high concentrations of college students.

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Metropolitan Futures Initiative (MFI) Quarterly Report: Rising inequality and neighborhood mixing: Comparing across metropolitan areas • January 1, 2020

Table MW3. Top 10 MSAs based on neighborhood education mixing in Midwest

Rank Name

Income mixing

Education mixing

Occupation mixing

1

Columbia, MO

68

1

359

2

Lawrence, KS

28

27

307

3

Lincoln, NE

265

46

53

4

Champaign-Urbana, IL

71

47

279

5

Chicago-Naperville-Elgin, IL-IN-WI

208

58

206

6

Cincinnati, OH-KY-IN

178

60

95

7

Grand Forks, ND-MN

124

62

82

8

Mankato-North Mankato, MN

298

68

68

9

Minneapolis-St. Paul-Bloomington, MN-WI

320

72

60

10

Manhattan, KS

21

74

314

• Columbia, MO has the highest level of neighborhood education mixing in the Midwest.

• Several of the MSAs on this list have high concentrations of college students.

Table MW4. Bottom 10 MSAs based on neighborhood education mixing in Midwest

Rank Name

Income mixing

Education mixing

Occupation mixing

1

Weirton-Steubenville, WV-OH

66

379

160

2

Lima, OH

278

377

286

3

Mansfield, OH

366

374

74

4

Danville, IL

259

369

139

5

Jackson, MI

269

361

115

6

Muskegon, MI

232

359

283

7

Grand Island, NE

359

356

15

8

Wheeling, WV-OH

103

351

207

9

Youngstown-Warren-Boardman, OH-PA

171

350

205

10

Canton-Massillon, OH

252

345

165

• Weirton-Steubenville, WV-OH has the lowest level of neighborhood education mixing in the Midwest.

35

Metropolitan Futures Initiative (MFI) Quarterly Report: Rising inequality and neighborhood mixing: Comparing across metropolitan areas • January 1, 2020

Table MW5. Top 10 MSAs based on neighborhood occupation mixing in Midwest

Rank Name

Income mixing

Education mixing

Occupation mixing

1

Wausau, WI

363

255

1

2

Sheboygan, WI

374

271

2

3

Columbus, IN

377

206

3

4

Appleton, WI

364

254

4

5

Fargo, ND-MN

98

75

5

6

Racine, WI

231

82

6

7

Bismarck, ND

288

120

7

8

Cedar Rapids, IA

345

290

9

9

Fond du Lac, WI

370

323

10

10

Grand Island, NE

359

356

15

• Wausau, WI has the highest level of neighborhood occupation mixing in the Midwest.

• Nine of these ten MSAs are also in the top 10 for the highest levels of neighborhood occupation mixing in the entire U.S., indicating that neighborhood occupation mixing is quite high, in general, in the Midwest.

Table MW6. Bottom 10 MSAs based on neighborhood occupation mixing in Midwest

Rank Name

Income mixing

Education mixing

Occupation mixing

1

Carbondale-Marion, IL

11

90

381

2

Columbia, MO

68

1

359

3

Ann Arbor, MI

75

218

358

4

Bloomington, IN

8

151

355

5

Muncie, IN

155

210

340

6

Saginaw, MI

126

339

335

7

Flint, MI

115

340

323

8

Manhattan, KS

21

74

314

9

Springfield, IL

295

131

310

10

Lawrence, KS

28

27

307

• Carbondale-Marion, IL has the lowest level of neighborhood occupation mixing in the Midwest.

36

Metropolitan Futures Initiative (MFI) Quarterly Report: Rising inequality and neighborhood mixing: Comparing across metropolitan areas • January 1, 2020 South region

Table S1. Top 10 MSAs based on neighborhood income mixing in South

Rank Name

Income mixing

Education mixing

Occupation mixing

1

Greenville, NC

1

71

333

2

Brownsville-Harlingen, TX

2

348

373

3

Morgantown, WV

3

95

309

4

McAllen-Edinburg-Mission, TX

4

368

377

5

Athens-Clarke County, GA

5

6

342

6

College Station-Bryan, TX

6

53

327

7

New Orleans-Metairie, LA

9

36

143

8

Johnson City, TN

13

51

211

9

Gainesville, FL

14

52

367

10

Hammond, LA

15

237

187

• •

•

•

Greenville, NC has the highest level of neighborhood income mixing in the South.

These 10 MSAs all rank in the top 15 of MSAs in the U.S. for neighborhood income mixing, indicating that neighborhood income mixing is quite high in the South, in general.

Two of these MSAs (Brownsville-Harlingen, TX and McAllen-Edinbur-Mission, TX have low levels of neighborhood education and occupation mixing.

Several others have low levels of neighborhood occupation mixing

Table S2. Bottom 10 MSAs based on neighborhood income mixing in South

Rank Name

Income mixing

Education mixing

Occupation mixing

1

California-Lexington Park, MD

380

127

55

2

Washington-Arlington-Alexandria, DC-VA-MD-WV

379

24

308

3

Jacksonville, NC

353

375

376

4

Virginia Beach-Norfolk-Newport News, VA-NC

352

77

116

5

Raleigh, NC

332

115

250

6

Richmond, VA

327

81

221

7

Elizabethtown-Fort Knox, KY

324

357

236

8

Dover, DE

322

171

72

9

Hinesville, GA

319

372

347

10

Killeen-Temple, TX

313

322

338

• The bottom two MSAs for neighborhood income mixing in the South are the D.C. MSAs of California-Lexington Park, MD and Washington-Arlington-Alexandria.

• Three MSAs (Jacksonville, NC, Hinesville, GA, and Killeen-Temple, TX) have very low levels of neighborhood mixing based on income, education, and occupation.

37

Metropolitan Futures Initiative (MFI) Quarterly Report: Rising inequality and neighborhood mixing: Comparing across metropolitan areas • January 1, 2020

Table S3. Top 10 MSAs based on neighborhood education mixing in South

Rank Name

Income mixing

Education mixing

Occupation mixing

1

Miami-Fort Lauderdale-West Palm Beach, FL

36

5

305

2

Athens-Clarke County, GA

5

6

342

3

Charlottesville, VA

94

9

222

4

Lexington-Fayette, KY

85

19

128

5

Baltimore-Columbia-Towson, MD

289

21

219

6

Washington-Arlington-Alexandria, DC-VA-MD-WV

379

24

308

7

Auburn-Opelika, AL

22

26

299

8

Asheville, NC

136

29

157

9

Harrisonburg, VA

108

35

61

1 0

New Orleans-Metairie, LA

9

36

143

• Miami-Fort Lauderdale-West Palm Beach, FL has the highest level of neighborhood education mixing in the South.

• Athens-Clarke County, GA has the second highest level of neighborhood education mixing in the South, along with very high neighborhood income mixing but has very low neighborhood occupation mixing.

Table S4. Bottom 10 MSAs based on neighborhood education mixing in South

Rank Name

Income mixing

Education mixing

Occupation mixing

1

Dalton, GA

196

378

204

2

Jacksonville, NC

353

375

376

3

Hinesville, GA

319

372

347

4

The Villages, FL

241

370

370

5

McAllen-Edinburg-Mission, TX

4

368

377

6

Houma-Thibodaux, LA

54

365

85

7

Cumberland, MD-WV

79

364

194

8

Beaumont-Port Arthur, TX

62

363

212

9

Beckley, WV

76

358

292

10

Elizabethtown-Fort Knox, KY

324

357

236

• •

•

Dalton, GA has the lowest level of neighborhood education mixing in the South.

These MSAs have some of the lowest levels of neighborhood education mixing in the entire U.S., indicating that neighborhood education mixing is generally relatively lower in the South.

Four of these MSAs also have low levels of neighborhood occupation mixing (Jacksonville, NC, Hinesville, GA, The Villages, FL, and McAllen-Edinburg-Mission, TX).

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Metropolitan Futures Initiative (MFI) Quarterly Report: Rising inequality and neighborhood mixing: Comparing across metropolitan areas • January 1, 2020

Table S5. Top 10 MSAs based on neighborhood occupation mixing in South

Rank Name

Income mixing

Education mixing

Occupation mixing

1

Winchester, VA-WV

287

107

23

2

Rome, GA

44

142

32

3

Owensboro, KY

161

298

33

4

Midland, TX

81

229

37

5

Decatur, AL

153

251

38

6

Warner Robins, GA

251

242

42

7

California-Lexington Park, MD

380

127

55

8

Harrisonburg, VA

108

35

61

9

Hagerstown-Martinsburg, MD-WV

311

187

65

10

Dover, DE

322

171

72

• Winchester, VA-WV has the highest level of neighborhood occupation mixing in the South.

• Neighborhood occupation mixing generally is lower in the South compared to the U.S., based on the ranking values for these top 10 MSAs.

Table S6. Bottom 10 MSAs based on neighborhood occupation mixing in South

Rank Name

1

Sebring, FL

2

McAllen-Edinburg-Mission, TX

3

Income mixing

Education mixing

Occupation mixing

284

347

379

4

368

377

Jacksonville, NC

353

375

376

4

Laredo, TX

31

330

375

5

Brunswick, GA

60

332

374

6

Brownsville-Harlingen, TX

2

348

373

7

Homosassa Springs, FL

226

343

371

8

The Villages, FL

241

370

370

9

Tallahassee, FL

92

61

368

10

Gainesville, FL

14

52

367

• Sebring, FL has the lowest level of neighborhood occupation mixing in the South.

• Neighborhood occupation mixing generally is lower in the South compared to the U.S., based on the ranking values for these bottom 10 MSAs.

39

Metropolitan Futures Initiative (MFI) Quarterly Report: Rising inequality and neighborhood mixing: Comparing across metropolitan areas • January 1, 2020 West region

Table W1. Top 10 MSAs based on neighborhood income mixing in West

Rank Name

Income mixing

Education mixing

Occupation mixing

1

Corvallis, OR

7

14

190

2

El Centro, CA

10

353

343

3

Grants Pass, OR

26

279

230

4

Las Cruces, NM

32

56

361

5

Missoula, MT

33

10

258

6

Pueblo, CO

43

129

313

7

Eugene, OR

49

160

151

8

Chico, CA

63

179

289

9

Santa Fe, NM

69

4

282

10

Boulder, CO

72

141

251

• Corvallis, OR has the highest level of neighborhood income mixing in the West, as well as high levels of neighborhood education mixing.

• Two other MSAs have high levels of neighborhood income and education mixing in the West: Missoula, MT and Santa Fe, NM.

Table W2. Bottom 10 MSAs based on neighborhood income mixing in West

Rank Name

Income mixing

Education mixing

Occupation mixing

1

Ogden-Clearfield, UT

381

236

11

2

Fairbanks, AK

378

309

47

3

Provo-Orem, UT

376

223

130

4

Anchorage, AK

373

123

45

5

Cheyenne, WY

372

280

31

6

Vallejo-Fairfield, CA

369

84

59

7

Salt Lake City, UT

368

138

88

8

Reno, NV

367

113

271

9

Oxnard-Thousand Oaks-Ventura, CA

365

50

110

10

Logan, UT-ID

360

96

40

• Ogden-Clearfield, UT has the lowest level of neighborhood income mixing in the West, along with high levels of neighborhood occupation mixing.

• Based on these rankings, these MSAs rank among the lower levels of neighborhood income mixing across all MSAs in the U.S.

40

Metropolitan Futures Initiative (MFI) Quarterly Report: Rising inequality and neighborhood mixing: Comparing across metropolitan areas • January 1, 2020

Table W3. Top 10 MSAs based on neighborhood education mixing in West

Rank Name

Income mixing

Education mixing

Occupation mixing

1

Napa, CA

183

3

13

2

Santa Fe, NM

69

4

282

3

San Jose-Sunnyvale-Santa Clara, CA

314

7

203

4

Missoula, MT

33

10

258

5

San Francisco-Oakland-Hayward, CA

227

12

262

6

Corvallis, OR

7

14

190

7

Fort Collins, CO

120

15

64

8

Santa Rosa, CA

266

20

132

9

Santa Maria-Santa Barbara, CA

182

25

228

10

Urban Honolulu, HI

357

28

234

• Napa, CA has the highest levels of neighborhood education mixing in the West, along with high levels of neighborhood occupation mixing.

Table W4. Bottom 10 MSAs based on neighborhood education mixing in West

Rank Name

Income mixing

Education mixing

Occupation mixing

1

Madera, CA

236

381

284

2

Visalia-Porterville, CA

116

376

319

3

Lake Havasu City-Kingman, AZ

188

373

372

4

Bakersfield, CA

197

367

337

5

Hanford-Corcoran, CA

275

366

334

6

Yuma, AZ

258

360

325

7

El Centro, CA

10

353

343

8

Farmington, NM

130

352

92

9

Longview, WA

230

344

49

10

Merced, CA

97

342

267

• •

Madera, CA has the lowest level of neighborhood education mixing in the West.

The 2nd through 7th ranked MSAs on this list not only have low levels of neighborhood education mixing but also low levels of neighborhood occupation mixing: Visalia-Porterville, CA, Lake Havasu City-Kingman, AZ, Bakersfield, CA, Hanford-Corcoran, CA, Yuma, AZ, and El Centro, CA.

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Metropolitan Futures Initiative (MFI) Quarterly Report: Rising inequality and neighborhood mixing: Comparing across metropolitan areas • January 1, 2020

Table W5. Top 10 MSAs based on neighborhood occupation mixing in West

Rank Name

Income mixing

Education mixing

Occupation mixing

1

Ogden-Clearfield, UT

381

236

11

2

Napa, CA

183

3

13

3

Casper, WY

213

304

16

4

Grand Junction, CO

237

73

19

5

Mount Vernon-Anacortes, WA

347

164

20

6

Wenatchee, WA

291

101

27

7

Cheyenne, WY

372

280

31

8

Billings, MT

292

122

35

9

Great Falls, MT

262

216

36

10

Coeur d'Alene, ID

239

292

39

• Ogden-Clearfield, UT has the highest level of neighborhood occupation mixing in the West, but the lowest level of neighborhood income mixing.

Table W6. Bottom 10 MSAs based on neighborhood occupation mixing in West

Rank Name

Income mixing

Education mixing

Occupation mixing

1

Sierra Vista-Douglas, AZ

209

293

378

2

Lake Havasu City-Kingman, AZ

188

373

372

3

Las Vegas-Henderson-Paradise, NV

325

191

369

4

Kahului-Wailuku-Lahaina, HI

331

200

366

5

Las Cruces, NM

32

56

361

6

Prescott, AZ

139

233

351

7

Flagstaff, AZ

217

177

348

8

El Centro, CA

10

353

343

9

Bakersfield, CA

197

367

337

10

Hanford-Corcoran, CA

275

366

334

• •

• • •

Sierra Vista-Douglas, AZ has the lowest level of neighborhood occupation mixing in the West.

Two MSAs have low levels of neighborhood occupation and income mixing: Las Vegas-Henderson-Paradise, NV and KaahuluiWailuku-Lahaina, HI. Lake Havasu City-Kingman, AZ has very low levels of neighborhood occupation and education mixing.

Las Cruces, NM has low levels of neighborhood occupation mixing but high levels of neighborhood income and education mixing.

El Centro, CA has low levels of neighborhood occupation and education mixing, but high levels of neighborhood income mixing.

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Metropolitan Futures Initiative (MFI) Quarterly Report: Rising inequality and neighborhood mixing: Comparing across metropolitan areas • January 1, 2020

Conclusions Myriad idiosyncratic factors have led to the way in which the privileged and less privileged have sorted themselves across urban neighborhoods, including development timing, construction trends, central city vibrancy, and the prevalence of stable, wealthy enclaves. We intuitively know many of these things, but drawing robust empirical conclusions linking them— especially outside the confines of a single region—is another matter. The research described in this Report is exploratory rather than confirmatory; however, it makes some key methodological and illustrative advancements about the relationship between inequality and neighborhood mixing—all too important in today’s polarized political environment. We show that implicating superstar cities alone is an insufficient explanation, and that there are some strong connections between factors that make them superstars and the level of exposure that people have to others who are not like them.

43

Metropolitan Futures Initiative (MFI) Quarterly Report: Rising inequality and neighborhood mixing: Comparing across metropolitan areas • January 1, 2020

Technical Appendix: Data Sources Used Dependent Variables We used pySAL software to construct block group egohood measures, in effect replacing each block group characteristic with the average (or total) value of all block groups within 1.5 miles. Although the size of the radius of these buffers—what Hipp and Boessen (2013) refer to as egohoods—is to some extent arbitrary, we chose a size that was between smaller egohoods used in studies predicting crime rates (typically ¼ or ½ mile radius) and a prior Report we did that used 2.5 and 5 mile egohoods to measure jobs-housing balance, which is a broader process more closely related to commuting. Our measure is meant to capture the activity space of a neighborhood whereby a resident has some meaningful contact with others at schools, parks, stores, and local institutions. Whereas the median block group population in US metros is 1,268, the median block group egohood is comprised of five block groups and contains 10,093 residents.

We computed the degree of income mixing in each egohood based on the eleven discrete household income categories reported by the American Community Survey (ACS) 5-year estimates from 2008-12. We computed the Gini coefficient of income in each egohood, and then used the mean of the block group egohoods’ Gini coefficients because it is a single, ordered, continuous measure of mixing and considers mixing overall, not relative to the region. Whereas at the regional or national levels a higher Gini value denotes inequality, at the neighborhood scale it reflects income mixing, or the extent to which a neighborhood area contains households with a wide range of incomes. We take the mean value across all block group egohoods in an MSA to capture the typical neighborhood experience.

Our second outcome measure captures the level of education mixing in neighborhoods using the entropy across five educational categories from the ACS: no high school diploma, high school diploma, some college, bachelor’s degree, and graduate degree.

The third outcome measure is the level of occupational mixing in neighborhoods which is an entropy measure based on Florida’s (2017) distinction between creative, service, and working class occupations.

Other measures We measure regional economic well-being based on 1) average household income from the ACS and 2) MSA GDP per capita from the US Bureau of Economic Analysis. We also constructed a measure of growth in household income over the previous decade (2000-2010).

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Metropolitan Futures Initiative (MFI) Quarterly Report: Rising inequality and neighborhood mixing: Comparing across metropolitan areas • January 1, 2020

We measure the business environment and economic productivity of regions with measures of: 1) the number of Fortune 1000 companies with headquarters locations in a region, which is provided by ProximityOne (2017); 2) the total dollars (logged) of venture capital invested in that MSA from 2010-2015; 3) the number of patents (logged) issued to inventors in each MSA from the US Patent Office over 2010-2015; 4) percent of a region’s employees working in creative industries; 5) percent of employees in an MSA who belong to a union, which is compiled by the Census’ Current Population Survey.

We constructed several housing-related variables from the ACS: 1) average home value is included; 2) share of a region’s homes built in the last ten years; 3) percentage of households who have been in the same home one year to capture residential instability; 4) housing occupancy rate to capture a measure of housing market vibrancy.

We included five measures capturing the demographic composition of the region from the ACS: 1) percent retirement-age individuals (65 and above); 2) percent youth (aged 0 to 19); 3) percent Black; 4) percent Latino; 5) racial/ethnic heterogeneity in the region based on a Herfindahl index of five groups (Asian, Black, Latino, White, and other race). We also created measures of: 6) violent crime rate (per 1,000 persons) in an MSA by computing the mean of the Uniform Crime Report data for police agencies in the region; 7) percentage who voted for President Obama in 2012; 8) percent religious adherents from the American Religious Data Archive (ARDA).

References Hipp, John R. and Adam Boessen. (2013). “Egohoods as waves washing across the city: A new measure of “neighborhoods”.

Criminology.51(2): 287-327.

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