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Presented by the Metropolitan Futures Initiative (MFI) School of Social Ecology

University of California, Irvine © August 2020

Metropolitan Futures Initiative (MFI) Quarterly Report:

Inequality and segregation in Southern California

August 1, 2020

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

John R. Hipp

Inequality and segregation in Southern California August 1, 2020

MFI Research Team

Cite this Report: Cite this report: Hipp, John R. and Benjamin Forthun (2020) “Inequality and segregation in Southern California” MFI Quarterly Report: 2020_2. Irvine, CA: Metropolitan Futures Initiative (MFI), University of California Irvine. August 1, 2020.

Graduate Student Researcher: Benjamin Forthun

Jae Hong Kim

Metropolitan Futures Initiative (MFI) Quarterly Report: Rising inequality and neighborhood mixing: Comparing across metropolitan areas • August 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 web page (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.

Metropolitan Futures Initiative (MFI) Quarterly Report: Rising inequality and neighborhood mixing: Comparing across metropolitan areas • August 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 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.

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 GIS analysis, aggregate spatial

research, and neighborhood definitions and boundaries.

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

Page Results in Brief ......................................................................................................................................................................................................................7

Background ............................................................................................................................................................................................................................8

The Orange Crush: Studying inequality over time ........................................................................................................................................8

Goals of this report ..................................................................................................................................................................................................8

Section 2: Population and racial/ethnic change in the six Counties over time .................................................................................................9

Population growth ...................................................................................................................................................................................................9

Racial/ethnic transformation ............................................................................................................................................................................. 12

Section 3: The economy .................................................................................................................................................................................................... 18

Income and inequality .......................................................................................................................................................................................... 19

Labor market characteristics .............................................................................................................................................................................. 23

Section 4: Housing and the cost of living ....................................................................................................................................................................30

Section 5: Local Focus: How has Orange County changed since 2000? ............................................................................................................36

Section 6: Conclusions and what have we learned? ................................................................................................................................................ 37

Appendix Page Technical Appendix A: Data Sources Used.................................................................................................................................................................38

List of Figures Page Figure 2.1 County population ................................................................................................................................................................................ 10

Figure 2.2 County population density .................................................................................................................................................................11

Figure 2.3 County percent White ..........................................................................................................................................................................13

Figure 2.4 County percent Latino ........................................................................................................................................................................ 14

Figure 2.5 County racial/ethnic heterogeneity ..................................................................................................................................................15

Figure 2.6 County racial segregation (Theil) ..................................................................................................................................................... 16

Figure 3.1 County percent in poverty .................................................................................................................................................................. 19

Figure 3.2 County real median income ($100s 1983) .......................................................................................................................................20

Figure 3.3 County income inequality ....................................................................................................................................................................21

Figure 3.4 County income segregation (Theil) ................................................................................................................................................. 22

Figure 3.5 County unemployment rate ............................................................................................................................................................... 23

Figure 3.6 Los Angeles County: Education ........................................................................................................................................................24

Table of Contents

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

List of Figures (cont.) Page Figure 3.7 Orange County: Education ................................................................................................................................................................. 25

Figure 3.8 Riverside County: Education .............................................................................................................................................................26

Figure 3.6 San Bernardino County: Education .................................................................................................................................................. 27

Figure 3.7 San Diego County: Education ............................................................................................................................................................28

Figure 3.8 Ventura County: Education ................................................................................................................................................................29

Figure 4.1 County real median rent ($100s 1983) ..............................................................................................................................................31

Figure 4.2 County real median home value ($100s 1983) .............................................................................................................................. 32

Figure 4.3 County ratio of median income to average U.S. large county ....................................................................................................33

Figure 4.4 County ratio of median home value to average U.S. large county ........................................................................................... 34

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

• This Report studies the demographic and economic change that has occurred in six counties in Southern California since 1970.

• All Southern California counties have more racial/ethnic mixing at the county level than average U.S. large counties

• San Bernardino County has experienced the biggest increase in Latinos since 1990 and now has the highest percentage of Latinos

• Orange County has gone from the least racial/ethnic mixing in 1970 to the most by 2018.

• Despite the high levels of racial/ethnic mixing, the degree to which different groups live in the same neighborhoods varies across

counties. Los Angeles County has the highest level of racial segregation across neighborhoods, whereas the Inland Empire

counties have the least.

• Los Angeles County has the highest level of income inequality.

• Ventura County has the lowest level of income inequality.

• After rising in earlier years, income inequality in Orange County has held relatively steady since 2000.

• Income segregation is higher in Southern California counties compared to average U.S. large counties.

• Income segregation rose sharply in the 2000s, though it has fallen a bit since 2010.

• Incomes are rising fastest in San Diego County since 2000. Whereas the median income in San Diego County was 10% higher than

the average large county in the U.S. in 2000, it was 30% higher by 2018.

• Incomes in Los Angeles County went from equal to the average large county in the U.S. in 2000 to 20% higher by 2018.

• Median rents and home values are rising faster in Southern California since 2000.

• Orange County has the highest median rents and home values in 2018.

• In 2018, whereas the median income in Orange County is 50% higher than the average large county in the U.S., median rents are

90% higher, and median home values are 210% higher.

Results in Brief

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

In 2009, members of our research team released a previous report (The Orange Crush: The Squeezing of Orange County's Middle

Class) that focused on Orange County. This prior Report studied data over the thirty year period from 1970 to 2000 to explore the

demographic and economic changes in Orange County. This prior Report highlighted that whereas Orange County has seen a booming

population since the post-World War II days of 1940 and many economic successes, there was also evidence of growing inequality in

Orange County. The study found evidence of less housing affordability for low- and middle-income families, along with a dearth of

mid-level jobs.

Background The Orange Crush: Studying inequality over time

In the present Report, we have two broad goals. First, we wished to expand our lens and explore not just the transitions that

have occurred in Orange County, but also those that have occurred in the other counties in the Southern California Region.

Second, we updated our data to include the intervening years since 2000 and to assess what has occurred more recently across

the counties of the region.

In this Report, we mimic much of the information presented in that earlier report, presenting similar graphs and figures. We also

present similar maps for each of the counties in county-specific appendices that are available on our website.

We focus on the following counties in the region: Los Angeles, Orange, Riverside, San Bernardino, San Diego, and Ventura. In

addition, in all of the graphs we also plot the average value of the measure for all other large counties in the U.S. (at least 60,000

population). This gives a reasonable comparison to our Southern California counties to get a sense of how what is occurring in our

counties compares with what is generally occurring in other large counties in the U.S. The sections of this Report detail: 1) the growth

in population and the racial/ethnic change in the counties; 2) the change and the state of the economy; 3) housing and the cost of

living. There are additional maps for each county drilling down to the city or tract-level, showing the levels and changes in some of the

measures discussed in this Report. These maps can be found on our website.¹

Goals of this report

1 These are the locations of the maps by county: Los Angeles: https://mfi.soceco.uci.edu/files/2020/08/appendix_LA.pdf Orange: https://mfi.soceco.uci.edu/files/2020/08/appendix_OC.pdf Riverside: https://mfi.soceco.uci.edu/files/2020/08/appendix_RIV.pdf San Bernardino: https://mfi.soceco.uci.edu/files/2020/08/appendix_SB.pdf San Diego: https://mfi.soceco.uci.edu/files/2020/08/appendix_SD.pdf Ventura: https://mfi.soceco.uci.edu/files/2020/08/appendix_VEN.pdf

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

Population growth We begin by studying how the population has changed

in each of the counties in the region from 1930 to the most

recent data available (2018). The first graph shows the

change in population in each of the counties over the last

90 years (with Los Angeles County values plotted on the

right-hand axis given that it is so much larger). The second

graph plots the change in population density in each of the

counties since 1940. Population density is computed by

dividing the total population by the total land area. Note that

this leads to challenges when comparing across counties,

given that certain counties can have large undeveloped

areas that will mathematically reduce the population density

calculation. However, we simply note this challenge, and

highlight that comparing across time within a county is

nonetheless reasonable.

Section 2 Population and racial/ethnic change in the six Counties over time

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

Figure 2.1

• Los Angeles County has exhibited steady growth in population

• Although San Diego was the second largest county in the region in 1950, Orange County had caught up to them by 1970

• The Inland Empire Counties (Riverside and San Bernardino) showed a steep growth in population after 1980, and Riverside has passed San Bernardino in population since 2000.

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

Figure 2.2

• Los Angeles had the highest population density prior to 1970

• Orange County has experienced a sharp increase in population density since 1950, and passed Los Angeles County after 1970

• San Diego County has the third highest density in the region

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

Racial/ethnic transformation In this next section, we focus on how the racial/ethnic

composition of these counties has changed over time. In the

first graph, we show how the percent white in each county has

changed since 1940. Measuring the composition of non-white

residents based on the Census is more difficult in earlier years,

and therefore in the second graph we show the change in percent

Latino since 1970. The third graph constructs a measure of racial/

ethnic mixing in each county (the Herfindahl Index, a commonly

used measure of racial/ethnic heterogeneity that measures the

relative composition of the major racial/ethnic groups: whites,

African-Americans, Latinos, Asians, and other races). This graph

shows these changes since 1970: higher values indicate counties

that have a greater mix of different groups, whereas a value close

to zero would indicate a homogeneous county with all residents

being just a single racial/ethnic group.

While we can consider the composition of different groups in a county, a separate question is whether they actually live in the

same neighborhoods. This gets at the question of segregation, and the fourth graph constructs a measure of how much racial/ethnic

segregation exists in each county in each decade since 1970. Higher values indicate counties in which racial/ethnic groups tend to live

in segregated neighborhoods from one another, whereas lower values indicate counties in which neighborhoods tend to have a greater

mix of racial/ethnic groups. This graph uses the Theil Index, which is a measure of segregation based on the five racial/ethnic groups

used in this Report.

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

Figure 2.3

• All of these southern California counties have experienced a sharp drop in percent white since 1960

• Los Angeles County was the early decliner, and went below 50 percent white by 1980

• The second sharpest decline in recent years is San Bernardino County which has fallen in percent White composition since 1980, and became majority minority in the 1990s

• The third lowest percent White now is seen in Riverside County, which became majority minority around 2000, as did Orange County.

• All counties in Southern California have seen a sharper decrease in percent White compared to other large counties in the U.S.

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

Figure 2.4

• After 1970, Los Angeles County had the highest percent Latino for several decades, and hit 1/3 Latino by 1980

• San Bernardino County has shown the biggest increase since 1990, hit 50 percent Latino around 2010, and now has the highest percent Latino in the region

• Riverside County has experienced a large increase since 1990 and has now caught Los Angeles County in Latino representation.

• Ventura County now has the fourth highest concentration of Latinos in the region.

• San Diego and Orange Counties have the smallest concentrations of Latinos in the region, and are around 1/3 Latino.

• All counties in Southern California have considerably higher percentage of Latinos compared to other large counties in the U.S.

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

• From 1970 to 2000 Los Angeles County had the highest level of racial/ethnic mixing in the region

• However, these counties now have similar levels of racial/ethnic mixing

• Orange County had the lowest racial/ethnic mixing in 1970, but now is right at the top.

• The increase in racial/ethnic mixing in counties outside of Southern County has only begun in earnest since 1990

Figure 2.5

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

Figure 2.6

• Los Angeles County has consistently had the highest level of racial/ethnic segregation among the counties in the region; however, it experienced a drop in segregation levels between 1970 and 1990.

• The Inland Empire Counties have the lowest levels of racial/ethnic segregation. Their levels of segregation are now similar to a typical large county in the rest of the U.S.

• Orange County has experienced an increase in segregation levels, particularly during the 1980s and 1990s

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

To see how this racial/ethnic composition is located across the cities within each of the counties, you can look at the appendix

specifically associated with each county.² For example, you will find there that we plot a map of the percent white in each of the cities in the county in 2018. After that, we present a map that drills down to neighborhoods and shows the percent white in each census tract

in the county in 2018. The third map in each of the appendices shows the level of racial/ethnic mixing in each of the cities in a county.

Note that even in a county with much mixing overall, it is possible that there can be less mixing in each of the cities (to the extent that

there is segregation across cities). Likewise, there can also be segregation across neighborhoods, and therefore the fourth map in each

appendix shows the level of racial/ethnic mixing in each census tract in the county.

2 These are the locations of the maps by county: Los Angeles: https://mfi.soceco.uci.edu/files/2020/08/appendix_LA.pdf Orange: https://mfi.soceco.uci.edu/files/2020/08/appendix_OC.pdf Riverside: https://mfi.soceco.uci.edu/files/2020/08/appendix_RIV.pdf San Bernardino: https://mfi.soceco.uci.edu/files/2020/08/appendix_SB.pdf San Diego: https://mfi.soceco.uci.edu/files/2020/08/appendix_SD.pdf Ventura: https://mfi.soceco.uci.edu/files/2020/08/appendix_VEN.pdf

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

In this section, we focus on the economy in each of the

counties. One way to measure the economy is to focus on those

who are at the bottom of the income scale. To assess this, the

first graph here shows the change in the percent of residents

in poverty since 1970. Another way to consider the economic

situation in a county is to focus on the income of the typical

resident in a county. To do this, we constructed a measure of the

median household income of residents in a county in a particular

decade (the median is the income in which half the households

have less income, and half have more income). To take into

account the effects of inflation over this time period, we adjust

these values to a constant based on 1983 dollars, and therefore

present the real median income (adjusted for inflation) since 1950

in the second graph shown here.3

The third graph focused on economic inequality. This graph constructs a measure of the Gini coefficient for each county at each

decade since 1970. The Gini is a commonly used measure of the degree of inequality in an income distribution. Higher values of the

Gini indicate counties with higher levels of inequality, and lower values capture counties with less inequality. Whereas inequality

captures differences among all residents, a separate question is the degree to which households with different levels of income live

in separate neighborhoods. We address this in the fourth graph which measures the level of economic segregation in each county

since 1960.

Section 3 The economy

3 We used the consumer price index for these calculations.

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

• Los Angeles County had the highest poverty levels from 1980 to 2000, but now has been caught by San Bernardino County

• Riverside County had the highest poverty rate in 1970, but has remained at a relatively steady level since then and now is in the middle of the pack.

• San Bernardino County was in the middle of the pack in 1980, but then experienced a consistent rise up through 2010 and now has the highest poverty rate in the region.

Figure 3.1

Income and inequality

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

Figure 3.2

• Orange and Ventura Counties have consistently had the highest median income levels (adjusted for inflation) in the region since 1970.

• San Diego County has experienced a consistent increase in median income since 2000 and has nearly caught Ventura County for #2 by 2018.

• The Inland Empire Counties have had the lowest median income levels since 2000.

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

• Los Angeles County has consistently had the highest income inequality since 1980.

• San Diego County had the highest level of income inequality in 1970, and has generally been the second highest since 2000.

• Riverside has seen a rise in income inequality recently and had the second highest levels in 2018.

• Whereas Orange County was tied with Ventura County for the lowest level of income inequality in 1970, inequality has risen since 1980 and Orange County has been near the middle of the pack in recent years.

• Ventura County has had the lowest level of inequality since 1980.

Figure 3.3

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

Figure 3.4

• Income segregation is generally higher in Southern California counties compared to large counties in the rest of the U.S., especially since 1990.

• Income segregation in southern California is rising since 2000.

• Los Angeles County had the highest income segregation from 1970 to 2000.

• San Diego County had the highest income segregation in 1960, but has remained near the middle of the pack since 1970.

In the appendix for each county, we present maps showing how these economic measures play out spatially in each county. We

present a map showing the percent in poverty in each city in the county, to show the extent to which poverty levels vary across cities

within the same county. We then present a map showing the average household income in each city in the county in 2018. We then

present a dynamic picture of the cities in each county by presenting a map that shows the percentage change in income in each city

of the county from 1980 to 2018. The next two maps show the level of inequality in each city in the county in 2018, and the change in

inequality in each of the cities from 1970 to 2018

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

• Orange County generally has the lowest unemployment rate since 1980.

• The Inland Empire Counties were particularly hard hit in the economic crash of the late 2000s, and have had the highest unemployment rates since 2000.

Figure 3.5

Labor market characteristics In this section, we consider characteristics of the labor market. The first graph shows how the level of unemployment has changed

since 1950 across these counties. The subsequent bar graphs show the composition of the county population based on educational

attainment.

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

Figure 3.6

• In each of the counties, there has been a general decrease in the percentage of population without a high school degree, and a general increase in the percentage of the population with a Bachelor’s degree from 1970 to 2018. The percentage with only a High School degree has generally held steady over time.

• In Los Angeles County, the percentage without a High School degree has been almost cut in half (from almost 40% to 20%), whereas the percentage with at least a Bachelor’s degree has risen from 12% to 32%.

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

Figure 3.7

• In Orange County the percentage with at least a Bachelor’s degree has risen from 15% to over 40%.

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

Figure 3.8

• The Inland Empire counties (Riverside and San Bernardino) have experienced a more modest increase in residents with at least a Bachelor’s degree, but a larger increase in those with just a High School degree.

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

Figure 3.9

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

Figure 3.10

• San Diego County has also experienced a large increase in the percentage with at least a Bachelor’s degree from 15% to almost 40%.

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

Figure 3.11

• In Ventura County the percentage with at least a Bachelor’s degree has risen similarly to LA county.

In each Appendix, we drill down in spatial scale to view the labor market characteristics in each of the counties. We present a map that shows the unemployment rate for each of the cities in 2018. We then present a map that shows the percentage of the population in each city with at least a Bachelor’s degree in 2018.

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

In this section we focus on housing, and the cost of living for

residents in these Southern California counties. For all of these

graphs, we adjust the values for inflation. The first graph shows

the change in real median rent (adjusted for inflation) across

these counties since 1960. The second graph shows the change

in real home values (adjusted for inflation) since 1960.

To directly compare income levels in these Southern Counties

with the rest of the U.S., the third graph shows the ratio of the

median income in each of these counties to the average median

income in the rest of the large counties in the U.S. In this graph,

values of 1 indicate that the median income in the county is

the same as the other large counties in the U.S.; values greater

than 1 indicate that the median income is greater in the county

compared to the rest of the U.S.

In the fourth graph we compare the average rents in Southern California counties to large counties in the rest of the U.S. by plotting

the ratio of median rent in each of these counties to the average median rent of the remaining large counties in the U.S. The final graph

plots the ratio of the median home value of these counties to the average median home value in the remaining large counties in the U.S.

Section 4 Housing and the cost of living

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

• Median rents are rising faster in southern California compared to the U.S. in general

• Orange County has consistently had the highest median rents since 1960.

• Ventura County has the second highest median rents, especially since 1970.

• The Inland Empire counties consistently have the lowest median rents.

• Nonetheless, the average median rent is lower in large counties outside of Southern California.

Figure 4.1

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

• The pattern for median home values is generally the same as for median rents.

• The one exception is that Ventura County does not have higher median home values than Los Angeles and San Diego Counties.

Figure 4.2

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

• In this graph, we compare the median income of southern California counties to the average median income of large counties in the U.S.

• In this graph, values of 1 indicate that the median income in the county is the same as the rest of the U.S.; values greater than 1 indicate that the median income is greater in the county compared to the rest of the U.S., and values less than 1 indicate that the median income is less in the county compared to the rest of the U.S.

• Orange County has consistently had a median income that is about 40 percent higher than the median income in a typical large U.S. county, and this is about 50 percent higher in 2018.

• Ventura County has the second highest median income, though it has fallen a bit in recent years.

• San Diego County has shown a steep increase in median income since 2000, going from about 10 percent higher to 30 percent higher than a typical large U.S. County.

• San Bernardino County has a similar median income to the average large county in the U.S., but is the lowest in Southern California.

Figure 4.3

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

• There was a large run-up in median rents in southern California relative to large U.S. Counties during the 1980s, especially in Orange and Ventura Counties.

• During the 1980s Orange and Ventura Counties went from median rents 30-50 percent higher than large U.S. counties to more than twice as much

• There has been a similar run-up, but smaller, in median rents in the region since 2000 compared to large U.S. Counties.

• San Diego County has experienced the sharpest increase in median rents since 2000.

Figure 4.4

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

• The pattern for median home values is the same as for median rents, just at a more extreme scale relative to large counties in the U.S.

• Whereas median rents in Orange County are almost twice as high as in typical large U.S. Counties, median home values are more than three times as high.

• Median home values in Southern California counties have risen faster than typical large U.S. counties since 2000.

Figure 4.5

In each appendix, we drill down to neighborhoods to show the distribution of home values in each of the counties. We first present a map that displays the average home value in each census tract in the county in 2018. We then present a map that shows the change in real average home values from 1980 to 2018 in each neighborhood in the county.

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

Up to this point we have described the changes experienced across the six counties of the region. Given that our Report in 2009

focused exclusively on Orange County, and described various changes that the county had experienced up to that point, in this section

we highlight some further changes that Orange County has experienced since 2000.

Up to 2000 Orange County had been experiencing a large increase in population. Although that increase has continued in recent

years, it nonetheless has slowed down. The county has continued the process of a shrinking proportion of white residents, and has

fallen below 50% down to about 40% now. However, the increase in percent Latinos has slowed considerably in the last 20 years.

Nonetheless, the relative presence of other groups have continued to rise in the county, and thus the level of racial/ethnic heterogeneity

has continued to rise over the last 20 years and now it has the most racial heterogeneity of any county in the region. At the same time,

racial segregation has held steady, and even decreased slightly, halting the rise in segregation that had occurred from 1980 to 2000.

Whereas Orange County had experienced a steady increase in the percent in poverty from 1970 to 2000, and this steady increase

maintained during the 2000s, since 2010 the percent in poverty has fallen back to 2000 levels. Orange County has generally

experienced a steady increase in real median incomes over time (with setback exceptions in the 1970s and 1990s), and since 2000 these

incomes have steadily increased. Another notable shift is that the sharp increase in income inequality in Orange County that occurred

from 1980 to 2000 has at least leveled off since then. It now is at a level that is the same as the average large county in the U.S. Income

segregation did rise during the 2000s, although this was the case for all counties in the region (and the average large U.S. county). It

is worth mentioning that the workforce continues to increase in level of education: the percent with at least a Bachelor’s degree

continues its steady increase over the last 50 years, and increased from 30% of the county in 2000 to 40% in 2018. Also, the percentage

without a high school degree has fallen since 2000.

Most troublesome for the county is the cost of living which may be associated with the aforementioned socio-economic trends.

On the one hand, the median household income in the county increased from 40% higher than the average large U.S. county in

2000 to 50% higher in 2018. On the other hand, rents increased from 64% higher in 2000 to 91% higher in 2018. Worse yet, home

values increased from 125% higher than the average large U.S. county in 2000 to 210% higher in 2018. Affording housing in these

circumstances is understandably difficult for households.

Section 5 Local Focus: How has Orange County changed since 2000?

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

This Report has documented the dramatic demographic and economic changes across the counties in the Southern California region.

By using Census data over a long period of time—since 1970 for some measures, and even further back for other measures—we have

been able to describe these changes. There are also maps of these changes for cities and neighborhoods within each of the region’s

counties available on our website as separate Appendices.

The counties in the region have all continued to experience steady population increases over the last 90 years. They have also

experienced notable demographic changes since 1960, with a steady decrease in the percent White along with a steady increase in

the Latino composition. As a consequence, the level of racial/ethnic heterogeneity has been rising over time, and is much higher than

typical large counties in the rest of the U.S. A hopeful sign is that the level of racial segregation has tended to modestly drift downwards

since 1960 for these counties, indicating more racial/ethnic mixing within neighborhoods.

We also explored the economic circumstances of these counties. Whereas the percent in poverty has tended to show a steady

increase in these counties going back to the 1980s, there is at least some evidence that it has fallen in the last 10 years. And real median

income has shown a steady upward trajectory since 1950 (with the exception of setbacks in the 1970s and 1990s). However, income

inequality has generally shown an upward trajectory over time in these counties. The level of income segregation has not made large

changes, with the exception of the 2000s in which it jumped up in all of these counties, especially San Bernardino.

A challenge is the cost of living in the region, particularly in the coastal counties. On the one hand, the ratio of the median household

income in these counties to the typical large county in the U.S. has remained relatively steady since 1960, and is higher than the

typical U.S. county for all but San Bernardino. However, the ratio of median rents in the region’s counties to the typical U.S. county is

considerably higher, and has risen further since 2000. This is evidence of the lack of affordability of housing. The numbers are even

worse for home values, as the ratio of median home values for the counties of the region to the typical U.S. county are greater yet,

and have risen quite sharply since 2000. Overall, whereas median income is about 23% higher in the region compared to the typical

large U.S. county in recent years, median rents are 50-60% higher, and median home values are 110-140% higher. In the Inland Empire

counties, these median home values are about 30-60% higher than the typical large U.S. county, whereas in the coastal counties they

are 150-180% higher. Such housing affordability issues will remain a key important factor to address into the foreseeable future.

Section 6 Conclusions and what have we learned?

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

Technical Appendix A: Data Sources Used The data used in this Report come from the U.S. Census Bureau. The data from the earlier years up through the year 2000

come from the U.S. Census. The data in the years since then (2010 and 2018) come from the US Census Bureau’s 2010 and

2018 American Community Surveys.

We used data from the U.S. decennial censuses to construct our measures. We computed the percent of various racial/ethnic

groups: white, African-American, Latino, Asian, and other races. We constructed a measure of the racial/ethnic heterogeneity

in the county (or city, or neighborhood) by using a Herfindahl index (Gibbs and Martin 1962: 670) of these same five racial/

ethnic groupings, which takes the following form:

(1)

Where G represents the proportion of the population of ethnic group j out of J ethnic groups. Subtracting from 1 makes this a measure of heterogeneity.

We computed economic resources as the median income in the county. We measured overall income inequality by utilizing

the Gini coefficient, which is defined as:

(2)

Where xi is the household’s income for 1999 as reported in the 2000 census, µ is the mean income value, the households are arranged in ascending values indexed by i, up to n households in the sample. Because the data are binned (as income is coded into various ranges of values), we will take this into account by utilizing the Pareto-linear procedure (Aigner and Goldberger

1970; Kakwani and Podder 1976), which Nielsen and Alderson (1997) adapted from the U.S. Census Bureau strategy.4

To adjust income, home values, and rents for inflation, we divided the value in a particular year by the value of the Consumer

Price Index (CPI). The CPI is based on 1983 dollar values, and therefore 1983 has a value of 100. Thus, the graphs can be

interpreted as the value of interest based on 100s of 1983 dollars.

4 We used the prln04.exe program previously provided by Francois Nielsen at the following website: http://www.unc.edu/~nielsen/data/data.htm.

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