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. 22
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.
27
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.
30
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. 31
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.
32
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.
34
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).
38
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.
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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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