This series investigates the persistent problem of racial residential segregation in the San Francisco Bay Area by applying novel research methods and fresh analytical tools to better understand the extent and nature of the problem. The first research brief in this series revealed the true extent of racial residential segregation in the Bay Area, illustrated with the first-ever segregation maps of the region, counties, and major metropolitan areas using the Divergence Index. That brief summarized the extent and patterns of segregation within all nine counties.
The second brief in this series examined the demographic patterns that lay behind the reality of segregation. Zooming out, this brief vividly depicted changes in the absolute and relative numbers of different racial groups over time, from 1850 to the present, in a series of original graphics. In doing so, that brief contextualized patterns of residential settlement by race through various waves of immigration and other historical events, including changes to federal immigration laws, and the effects those patterns had on the demographics of the Bay Area. In addition to examining changes in racial subpopulations in the Bay Area as a whole, we also examined concentrations within all nine counties. That brief also illustrated changes to racial demographics in recent years, including the stark effects of gentrification, by presenting “change maps” indicating communities that experienced dramatic changes in racial representation.
The third brief in this series compared different measures of segregation over time to better understand different facets of the problem. Each measure of segregation conveys different, but complementary information about the nation of racial concentration and isolation. Comparing measures also helped us better understand critical trends in a more nuanced way. We examined, for example, the rising levels of Latinx and Asian segregation, and the persistently high levels of Black segregation, even as the overall level of Black segregation has declined over time. By juxtaposing different measures of segregation, we were able to draw out distinctive patterns of racial isolation and segregation for different groups over time, which may be masked by over-reliance on a single measure.
To accompany our third brief, we launched a new, interactive online tool that allows users to switch between various measures of segregation and observe changes in the level of segregation with each measure over time. Using a slider, users can also compare the level of segregation for each of the six different measures of segregation during any interval between 1970 and 2010, and observe changes over that time. Users can thus see which communities have become more segregated, and which have become less. We believe this tool is one of the most sophisticated but easy-to-use segregation mapping tools ever created.
Our main purpose in this series has been to illustrate the degree of segregation that persists in the Bay Area in the hope of raising public awareness about the lingering extent of this problem. Despite the enduring significance of race and salience of racial inequality in the Bay Area, too often racial segregation itself is not a part of the discussion. We have lost sight of the centrality of racial segregation to the production of racial inequality. To drive home this point, in this brief we turn to the question of the effects of segregation. Specifically, we will illustrate the specific correlations with segregation and a variety of life outcomes in the San Francisco Bay Area.
We find, for example, that in the SF Bay Area:
- For highly segregated Black and/or Latinx neighborhoods, household income is $27,000 less per year, on average, than in low-segregation neighborhoods, and home values are $131,000 less.
- People residing in highly segregated Black/Latinx neighborhoods participate in the labor force at about the same level as residents of highly segregated white neighborhoods, but earn only 39 percent as much income.1
- In fact, segregated white neighborhoods have more than double the household incomes ($123,701 v. $48,843) and home values ($899,765 v. $440,620) of highly segregated Black and/or Latinx neighborhoods.
- Adults in highly segregated Black/Latinx neighborhoods are only 25 percent as likely to have bachelors' degrees as adults in segregated white neighborhoods. Similarly, only a third of fourth-graders tested as proficient in math and reading in highly segregated Black/Latinx neighborhoods, compared to 70 percent in white neighborhoods.
- Life expectancy is more than five years greater in white neighborhoods (84 years) than highly segregated Black/Latinx neighborhoods (79 years).
Overall, we find that highly segregated Black/Latinx neighborhoods correlate with negative life outcomes for all people in those communities, including rates of poverty, income, educational attainment, home values, and health outcomes.
Before describing our findings in more detail, without being exhaustive, we briefly summarize the literature on harmful effects of racial residential segregation. There is a large body of social science literature documenting the harmful effects of racial segregation going back more than half a century. This literature documents the effects of racial segregation on political polarization, educational attainment, health outcomes, public provision, and much more.
Prior Research on the Harmful Effects of Racial Segregation in the United States
There is wide-ranging and robust literature on the harmful effects of segregation in education. One longitudinal study of children assigned to desegregated schools found that for Black boys, spending time in desegregated schools as a child reduced by 14.7 percent the probability of spending time in jail by age 30. Each additional year of exposure to desegregated schools increased Black men’s annual earnings by roughly 5 percent, increased their wages by 2.9 percent, and led to an annual work effort that was 39 hours higher. At the same time, for these Black male adults the probability of poverty decreased by between 1.6 and 1.9 percentage points. Overall, five years spent in desegregated schools yielded an estimated 25 percent increase in annual earnings and increased annual work effort of 195 hours.2
In another very recent national study, racial school segregation is strongly associated with the magnitude of achievement gaps in third grade, and with the rate at which gaps grow from third to eighth grade. The study found that racial segregation appears to be harmful because it concentrates minority students in high-poverty schools, which are, on average, less effective than lower-poverty schools.3
Racial segregation has long been known to be associated with harmful health effects. Concentrated housing inequity also disproportionately exposes Black communities to environmental pollutants and isolates Black populations from essential health resources such as improved recreational spaces; quality pharmacies, clinics, and hospitals; and healthy food options.4
Communities of color often have less access to grocery stores, child care facilities, and other important neighborhood resources. They are also more likely to have hazardous waste facilities in close proximity.5
Black youth who were exposed to desegregated schools as young children experienced a 23 percent reduction in deviant behavior, for example. And they were 15 percent less likely to end up behind bars by the age of 30.6
Black children are also more likely to live in neighborhoods that have low opportunity specifically for Black children, while white children tend to live in neighborhoods that show higher upward mobility for white children. This indicates that Black-white disparities are increased by the differences in childhood neighborhoods.7
Employment & Income
Black men (ages 25-34) have lower unemployment rates and higher income rates (by $4,000) in moderately segregated neighborhoods than highly segregated neighborhoods.8
White households typically had higher incomes and access to a range of federal home loan programs. Single-family zoning produced racially segregated neighborhoods without explicit race-based ordinances. Greater tax base and support from federal programs meant that these areas could afford public goods that others could not and, as a result, experienced greater real estate appreciation. Simultaneously, city planners zoned areas adjacent to neighborhoods with apartment buildings and multifamily units (which were predominantly low-income and Black) for industrial and commercial use, concentrating poverty and exposing these communities to dangerous environmental hazards. Chronic devaluation of Black-owned property contributes to differences in home values and appreciation.9
In low-segregation counties, demographic change is predictive of a decrease in opposition to permissive immigration policy. In counties with medium levels of segregation, the effects of demographic change largely disappear, while growth in counties that are highly segregated is predictive of an increase in opposition to permissive immigration policies.10
This summary is only a partial accounting of the literature on the harmful effects of segregation at a national scale. Our purposes here are somewhat narrower. In this research brief, we investigate the specific effects of racial residential segregation on communities in the San Francisco Bay Area, not as a matter of historical interest, but of present-day, ongoing concern. We examine and present statistically significant correlations with levels of segregation and particular life and community outcomes along a range of measures. Although we are unable to present these relationships as causal, we present our findings to suggest a relationship between segregation and well-being. Whether or not segregation causes these differences, it does characterize the living conditions and outcomes of the people in these neighborhoods.
Despite a large amount of interest and scholarship done on the existence of segregation in the Bay Area and its history, there is a lack of output on the impacts of segregation in the Bay Area, as opposed to its general dynamics across the country.11 Alternatively, research purporting to examine the effects of segregation often look at racial demographics instead. Therefore, we believe our findings are largely, if not entirely, novel for understanding the impacts of segregation in the Bay Area.12
Our main focus is to isolate the effects of, or at least the associations with, racial residential segregation in the Bay Area. To do this, we examine the correlation between segregation and current-day living standards. As discussed in Part 3 of this series, we use the Divergence Index as our primary measure of tract segregation. The level of segregation described herein refers to the divergence score, and whether it is high, moderate, or low.13
Because the Divergence Index measures the extent to which a given tract’s racial dynamics differ from its surrounding area,14 it can capture two very different types of segregation: segregation in which whites are isolated from other groups of people, and segregation in which one or more non-white groups are segregated from the general population.
To better understand the effects of these two types of segregation, we split tracts into two groups: tracts where the majority of the population is white and tracts that are predominantly Black and/or Latinx. We call these tracts: highly segregated white neighborhoods, and highly segregated Black/Latinx neighborhoods, respectively.15
We perform two types of analysis to understand the effects of segregation:
First, we run correlations between the overall level of segregation and outcomes for children who lived in those tracts in 1990, as well as other conditions found in those tracts from the current Census.16, 17
Second, we examine actual average outcomes associated with levels and specific types of segregation. This is different from the first analysis in that it indicates absolute values (such as dollar amount or educational attainment) instead of measuring the strength of the relationship between the outcome and segregation.
We find consistent differences in outcomes associated with segregated conditions in each of our analyses, suggesting that these differences may be robust for an even greater range of outcomes than those studied here. In all of these experiments, the summary values are weighted by tract population, to control for the varying size of these neighborhoods.
For some of these analyses, we present interactive graphics which will allow you to switch between different outcomes to directly observe the effects or associations with the level of segregation.18 In displaying differences and averages, we ignore moderate-divergence tracts in order to focus on the extremes of high and low levels of segregation.
In each experiment, we find troubling, but predictable, outcomes.
Isolating the Impact of Black & Latinx Segregation
To isolate the impact of Black/Latinx segregation, we separate Black/Latinx tracts from the rest of the Bay Area and analyze the difference between the most highly segregated Black/Latinx tracts and the least segregated Bay Area tracts.19 We find that highly segregated, predominantly Black/Latinx neighborhoods correlate with negative life outcomes for all people in those communities, including rates of poverty, income, educational attainment, and health outcomes.
For all the indicators we were able to measure, low-segregation neighborhoods show better outcomes, and in most cases, the differences are quite large. These differences accumulated in income and wealth, with median home values in highly segregated neighborhoods being valued at $131,000 less, and incomes lagging behind low segregated neighborhoods by over $27,000 per year. The table below illustrates the difference between highly segregated Black and Latinx neighborhoods and non-segregated neighborhoods for 17 different indicators, including the percent of residents earning below the poverty line and student test scores. The magnitude of the difference is indicated in the last column.
|The Characteristics of Black/Latinx Segregation|
|Measure20||Neighborhoods with Low Segregation||Neighborhoods with High Black/Latinx Segregation||Difference|
|% Below Poverty Line||9%||19%||-9.8|
|% Employed (20-60)||76%||69%||7.3|
|% Employed (Men 20-60)||81%||76%||5.3|
|% Employed (Women 20-60)||71%||62%||9.7|
|% in Labor Force - Female (20-60)||77%||69%||7.7|
|% in Labor Force - Male (20-60)||88%||85%||2.5|
|% in Labor Force (20-60)||82%||77%||4.9|
|% With Bachelor's Degree||38%||16%||22.1|
|Free/Reduced School Lunch||47%||76%||-28.9|
|High School Graduation Rate||89%||85%||3.7|
|Life Expectancy (Years)||81.3||79.0||2.3|
|Math Proficiency (4th Grade)||46%||30%||16.2|
|Median Home Values||$572,168||$440,620||$131,548|
|Median Household Income||$75,960||$48,843||$27,117|
|People Above 200% Poverty21||77%||55%||22.2|
|Reading Proficiency (4th Grade)||51%||33%||17.6|
Neighborhoods where Blacks and Latinxs are segregated from the rest of the population show strong and troubling patterns of poor neighborhood characteristics. The strongest correlates with high levels of segregation for non-white neighborhoods are income and poverty. Compared to low-divergence neighborhoods, high-divergence, predominantly Black/Latinx neighborhoods have higher neighborhood poverty rates, lower household incomes, and fewer people with an income of at least double the poverty threshold. This is predicted by the segregation literature, which suggests that the purpose of segregation is to segregate people from opportunity. Families and individuals in segregated communities experience multiple, compounding forms of disadvantage.
It is important to note that these effects are structural, meaning that they are predictive for members of all racial groups residing in those neighborhoods, and not simply members of a particular racial group.22
White vs. Black/Latinx Segregation
In the section immediately above, we compared highly segregated Black/Latinx neighborhoods with outcomes in neighborhoods that have low levels of segregation. This, however, understates the disparity that segregation creates in the Bay Area. To bring this disparity into full view, we also need to compare the neighborhoods just analyzed with highly segregated white neighborhoods.
Highly segregated white neighborhoods show an enormous hoarding of resources. Home values and incomes are more than double those in highly segregated Black/Latinx neighborhoods. Highly segregated Black/Latinx neighborhoods have a labor force participation rate that is 95 percent of highly segregated white neighborhoods, but earn only 39 percent of the income of those nighborhoods.
The differences between highly segregated white and highly segregated Black/Latinx neighborhoods are illustrated in the table below, using the same variables we examined earlier. The differences, found in the final column, are much greater.
|White Segregation vs Black/Latinx Segregation|
|% Below Poverty Line||5%||19%||-14.6|
|% Employed (20-60)||77%||69%||7.9|
|% Employed (Men 20-60)||85%||76%||9.7|
|% Employed (Women 20-60)||69%||62%||7.1|
|% in Labor Force - Female (20-60)||73%||69%||3.8|
|% in Labor Force - Male (20-60)||90%||85%||4.5|
|% in Labor Force (20-60)||81%||77%||3.6|
|% With Bachelor's Degree||65%||16%||48.8|
|Free/Reduced School Lunch||19%||76%||-57.6|
|High School Graduation Rate||93%||85%||7.8|
|Life Expectancy (Years)||84.0||79.0||5.0|
|Math Proficiency (4th Grade)||68%||30%||38.7|
|Median Home Values||$899,765||$440,620||$459,145|
|Median Household Income||$123,701||$48,843||$74,859|
|People Above 200% Poverty||90%||55%||35.0|
|Reading Proficiency (4th Grade)||71%||33%||37.7|
The differences in high-segregation white and Black/Latinx neighborhoods are by no means limited to income and wealth, however. Educational attainment lags greatly in predominantly Black/Latinx and segregated neighborhoods. This is seen in both adults and children. Adults in highly segregated Black/Latinx neighborhoods are only 25 percent as likely to have bachelors' degrees. Children in those neighborhoods also suffer from subpar schools, with a lower graduation rate. Only a third of fourth-graders test as proficient in math and reading in highly segregated Black/Latinx neighborhoods, which is less than half the rate (70 percent) of fourth-graders who test as proficient in highly segregated white neighborhoods.
The health effects are also pronounced. Life expectancy is more than five years greater in white neighborhoods (84 years) than highly segregated Black/Latinx neighborhoods (79 years). 23 Internal testing on the effects of highly segregated white and Black/Latinx neighborhoods indicates that these differences are largely consistent for all racial groups in these neighborhoods, not simply caused by the racial distribution within them.
The following tool allows you to visualize these differences by switching between indicators. Click the drop-down menu to select the indicator you wish to view.
Highly segregated, predominantly white neighborhoods are associated with extremely positive life outcomes in the Bay Area, including lower rates of poverty, higher home values (and greater wealth accumulation), greater educational attainment, and test scores. Again, this is irrespective of the race of the resident.
White segregation, on the other hand, is associated with nearly the opposite outcomes of Black and Latinx segregation. In highly segregated tracts where whites are the majority, poverty is lower and household incomes are higher per year. The greatest correlation with high white segregation is in home values.
With increased wealth comes greater educational success in highly segregated white neighborhoods. These neighborhoods have more adults with bachelors' degrees, higher fourth-grade reading and math scores, and much lower rates of free and reduced-price lunch acceptance. Other measures indicate that this wealth becomes entrenched, with more homeowners living in these neighborhoods and a higher life expectancy.
The charts above show both actual outcomes associated with specific levels of segregation and the differences between white and Black/Latinx segregation. Below, we illustrate the same findings, but in term of correlations rather than actual outcomes.
Correlation with Segregation and Upward Mobility
Using the Opportunity Atlas dataset provided publicly by Opportunity Insights, we conduct a novel analysis correlating levels of segregation in 1990 with outcomes for the children who grew up in those tracts.24 The largest correlations for Black/Latinx divergence were in the decreased likelihood of a child living in a low-poverty neighborhood as an adult.25This holds true across the parental income distribution.
Children growing up in predominantly non-white, segregated neighborhoods in the 1980s did much worse as adults.
|Long-Term Outcomes in Black/Latinx Segregated Neighborhoods|
|% in Top 20% Family Income||(All)||45%||35%||10.6|
|% in Top 20% Family Income||Bottom 25%||19%||12%||6.8|
|% in Top 20% Family Income||Bottom 50%||23%||16%||7.4|
|% Living in Low-Poverty Neighborhood||(All)||63%||46%||16.9|
|% Living in Low-Poverty Neighborhood||Bottom 25%||53%||36%||16.9|
|% Living in Low-Poverty Neighborhood||Bottom 50%||56%||39%||16.8|
|Adult Family Income Percentile||(All)||66%||59%||7.1|
|Adult Family Income Percentile||Bottom 25%||47%||42%||5.4|
|Adult Family Income Percentile||Bottom 50%||52%||47%||5.8|
|Percent in Jail||(All)||0.4%||0.7%||-0.3|
|Percent in Jail||Bottom 25%||1.2%||1.8%||-0.6|
|Percent in Jail||Bottom 50%||0.7%||1.2%||-0.5|
|Stayed in Childhood Home||(All)||17%||32%||-14.2|
|Stayed in Childhood Home||Bottom 25%||25%||30%||-4.2|
|Stayed in Childhood Home||Bottom 50%||23%||30%||-7.4|
|Stayed in Childhood Tract||(All)||20%||27%||-7.5|
|Stayed in Childhood Tract||Bottom 25%||21%||26%||-4.6|
|Stayed in Childhood Tract||Bottom 50%||21%||26%||-4.7|
People who grew up in low-income households were especially sensitive to the impacts of highly segregated neighborhoods. The interaction of poverty and segregation is pronounced. This is primarily seen not only in the children’s own incomes, but also in a decreased likelihood of moving to a low-poverty neighborhood in adulthood. Children born into highly segregated neighborhoods are more likely to live in high-poverty neighborhoods as adults, compared to children with parents at the same income level, but who grow up in less segregated neighborhoods (a 17 percent difference).
The chart below illustrates the relationship between neighborhood segregation by race, and adult outcomes using the Opportunity Atlas findings.
The divergent effects of white and Black/Latinx segregation are also clear in the correlation measurements of childhood outcomes. The strongest correlates with segregation here are in adult incomes and likelihood of moving into a low-poverty neighborhood. This is perhaps not surprising, as poverty itself highly correlates with these measures, as shown above. The intersection of segregation and poverty is a complicated one, which warrants further study.
Racial inequality is a well-known fact of life in America, and the Bay Area is no exception. What is often overlooked, however, is the role of racial residential segregation in shaping these outcomes. This brief explored this question by examining the associations of highly segregated neighborhoods with a range of outcomes.
While the presence of poor conditions cannot prove causality, it is clear from the data that segregation both in the United States as a whole and in the Bay Area specifically is accompanied by a host of predictable and disturbing impacts. This ranges from health and education to income and employment outcomes.
All the data points in one direction: racial segregation in the Bay Area, like the rest of the country, accompanies a hoarding of resources by some communities at the expense of others, thereby resulting in consistently negative outcomes for people of color. The perpetuation of this unequal access to opportunity has real effects on people’s current lives, and the ability for their families to succeed in the future. At the same time, racial segregation contributes to the concentration of poverty for marginalized groups, which also produces deleterious effects above and beyond the absence of life-enhancing resources.
The next and final report in this series will describe what we can do about racial residential segregation. Without focusing on particular legislative initiatives or bills, we will suggest broad policy areas that can facilitate greater integration and reduce segregation.
- 1. Labor force participation is defined as either being currently employed or seeking employment.
- 2. Long-run Impacts of School Desegregation & School Quality on Adult Attainments (2011), NBER Working Paper No. 16664. https://www.nber.org/papers/w16664.
- 3. Reardon, S.F., Weathers, E.S., Fahle, E.M., Jang, H., & Kalogrides, D. (2019). Is Separate Still Unequal? New Evidence on School Segregation and Racial Academic Achievement Gaps. https://cepa.stanford.edu/content/separate-still-unequal-new-evidence-sc....
- 4. Time for justice: Tackling race inequalities in health and housing (2016), Matthew et al., Brookings Institute. https://www.brookings.edu/research/time-for-justice-tackling-race-inequa....
- 5. Systemic Inequality: Displacement, Exclusion, and Segregation (2019), Solomon et al, Center for American Progress https://www.americanprogress.org/issues/race/reports/2019/08/07/472617/s...
- 6. "Long-Run Impacts of School Desegregation and School Quality on Adult Attatinments,” Johnson. https://psidonline.isr.umich.edu/publications/Workshops/SES_HAG/desegreg....
- 7. Raj Chetty et al. (2018). “The opportunity atlas: mapping the childhood rates of social mobility.” Retrieved from https://opportunityinsights.org/wp-content/uploads/2018/10/atlas_paper.pdf.
- 8. Systemic Inequality: Displacement, Exclusion, and Segregation (2019), Solomon et al, Center for American Progress. https://tcf.org/content/report/attacking-black-white-opportunity-gap-com....
- 9. Systemic Inequality: Displacement, Exclusion, and Segregation (2019), Solomon et al, Center for American Progress. https://tcf.org/content/report/attacking-black-white-opportunity-gap-com...
- 10. “Policy makers should focus on reducing segregation to alleviate hostility towards immigration.” Arora, Maneesh. https://blogs.lse.ac.uk/usappblog/2019/05/02/policy-makers-should-focus-....
- 11. See for example, “Marin County Human Development Report,” Sarah Burd-Sharps, Kristen Lewis: http://www.measureofamerica.org/docs/APOM_Final-SinglePages_12.14.11.pdf, or A Way Forward: Addressing Mobile Segregation in the Bay Area, Frankfurth: https://ncg.org/news/way-forward-addressing-mobile-segregation-bay-area.
- 12. We thank Rachel Heydemann for her assistance with the background research for this report.
- 13. See part three for details on how these categories are distinguished from each other, here: https://haasinstitute.berkeley.edu/racial-segregation-san-francisco-bay-.....
- 14. As in part three of this report, the surrounding area for a tract is its core-based statistical area (CBSA), a combination of micro- and metropolitan statistical areas. Each CBSA is consistent with county and tract boundaries. In this report, divergence is calculated using five racial groups, white, Latinx, Black, Asian, and all other groups. This is a minor change from part three, which used only the first four groups in the divergence calculation. For information on Core-Based Statistical Areas visit https://www.census.gov/topics/housing/housing-patterns/about/core-based-....
- 15. Predominantly Black/Latinx tracts are defined as tracts where the sum of Blacks and Latinx composes the plurality of residents. Predominantly white tracts are those in which whites compose the majority of the population. There are 124 (of 1,588) highly segregated tracts that fit neither definition, which we do not analyze in this report. This is to due to small sample size and differences neighborhood dynamics, which made it difficult to analyze other (primarily Asian) majority-minority tracts as a unified group. We hope to explore such neighborhoods in future literature on the topic.
- 16. Correlations are calculated between a specific measure (e.g. median home value) and divergence across Census tracts within the bay area. Those tracts are split into predominantly white or Black/Latinx tracts, as defined above. Correlations are weighted by the population in each tract, and de-meaned by the average values of its Core-Based Statistical Area (CBSA) to control for geographic differences.
- 17. Data for 1990 tract data normalized to 2010 census tracts provided by Geolytics, Inc. "Neighborhood Change Database (1970-2010)", GeoLytics, Inc., East Brunswick, NJ, 2004.
- 18. All graphs created using Plotly. Plotly Technologies Inc. Collaborative data science. Montréal, QC, 2015. https://plot.ly.
- 19. Low-segregation tracts are drawn from all Bay Area tracts with a low divergence score, while highly segregated Black/Latinx are as defined in endnote 16.
- 20. Measures are taken from the following sources: 2010 American Community Survey - bachelor’s degrees, home values, median household income, poverty, employment, labor force, and owner-occupied housing; California Department of Education - 4th-grade test scores, graduation rates, and free/reduced-price lunch (2018); National Center of Health Statistics - life expectancy (2010-15).
- 21. In other worlds, the percentage of people earning twice the 2010 U.S. poverty income threshold.
- 22. To confirm this, internal testing was done by weighting each outcome by the number of people of each race in each tract. Values were consistent between races.
- 23. National Center for Health Statistics. U.S. Small-Area Life Expectancy Estimates Project (USALEEP): Life Expectancy Estimates File for California, 2010-2015]. National Center for Health Statistics. 2018. Available from: https://www.cdc.gov/nchs/nvss/usaleep/usaleep.html.
- 24. All data for 1990 reflects: 1) 1990 segregation, and 2) 2015 outcomes of children born between 1978 and 1983 sampling 96.2% of that cohort. 1990 segregation is used because it represents the first census containing that entire cohort. Data provided publicly Opportunity Insights at https://opportunityinsights.org/data/.
- 25. Correlations are performed on each variable against divergence, weighting by the total number of children in each tract and de-meaning by CBSA.