How the g-word poisons public discourse on making cities better

We’re pleased to publish this guest post from Akron’s Jason Segedy.  It originally appeared on his blog Notes from the Underground. Drawing on his practical experience in a rust-belt city, he offers a compelling new insight on the casual way that “gentrification” is invoked in serious discussions about the future of our cities.

By Jason Segedy

Gentrification (noun) – the process by which people of (often modest) means who were once castigated for abandoning the city are now castigated for returning to the city

Gentrification. It is a word that we hear with increasing frequency in contemporary discussions about American cities. But what does that word really mean? And, even more importantly, what does it mean in the context of the region that I live in and love – the Rust Belt?

Does gentrification mean the displacement of the poor – pushed aside to make way for the affluent? Or does it mean reinvestment in economically distressed neighborhoods that haven’t seen any significant investment in decades?

It is important to be clear about the meaning of this increasingly ambiguous term, because what needs to happen in the vast majority of urban neighborhoods in the legacy cities of the Rust Belt is far less ambiguous.

Despite over 50 years of well-intended social programs, concentrated generational poverty, entrenched socioeconomic segregation, and the resulting lack of social and economic opportunity for urban residents, still remain the biggest challenges for the older industrial cities of this region. 

As Joe Cortright says, in his brilliant piece, Cursing the Candle, “Detroit’s problem is not inequality, it’s poverty…The city has a relatively high degree of equality at a very low level of income.”

And, as the Brookings Institution’s Alan Berube says, “It’s hard to imagine that the city will do better over time without more high-income individuals.”

High poverty rates in cities like Akron, Buffalo, Cleveland, and Detroit, are partially due to regional economic conditions and structural economic challenges related to deindustrialization.

But, overwhelmingly, concentrated poverty in these cities is due to private disinvestment in the urban core, made manifest by upper and middle-class flight to the suburbs, socioeconomic and racial segregation, and the loss of neighborhood retail and basic services. Today, the geographic disparities in household income between the central city and the surrounding suburbs remain profound.

In Akron, Buffalo, Cleveland, and Detroit, respectively, 24%, 31%, 35%, and 36% of the population lives in poverty, as compared to 14%, 14%, 15%, and 15% in these cities’ respective metropolitan areas. Keep in mind that these metropolitan area figures include the core city – meaning that poverty rates in the remainder of the metro area are even lower.

Gentrification is a hot topic of conversation in coastal cities, like New York, Washington, and San Francisco, with expensive living costs that are also home to influential journalists.

Writing about gentrification is becoming a cottage industry for many pundits and urban policy wonks. Many of the pieces that have been penned on the topic are important, thought-provoking, and well-reasoned.

But as more and more people in the Rust Belt read these accounts, and take them out of their geographic context, alarm over gentrification (particularly on the left) is steadily growing in metropolitan areas and housing markets where it should be the least of our urban policy concerns.

In the eastern Great Lakes region, with its low-cost of living, depressed housing markets, and surfeit of vacant and abandoned properties, most of the changes that are being held out as disturbing examples of gentrification, and are provoking hand-wringing in places like Buffalo, Cleveland, and Detroit, simply amount to the return of the middle class (with a sprinkling of the truly affluent) to several small pockets of the city.

The degree to which these fledgling positive examples of private reinvestment in long-neglected neighborhoods have truly taken root and have begun to influence regional housing markets is still uncertain. As for documented cases of low-income residents being uprooted and displaced by spiraling housing costs – these have proven even more elusive.

While it can be unclear whether the return of middle class and affluent residents to a neighborhood will really do anything to improve economic conditions for the poor, it is an ironclad certainty that a continued lack of socioeconomic diversity, and its concomitant concentrated poverty, will improve nothing and help no one in these cities – the poor most of all.

For 50 years now, people, jobs, and economic opportunities have steadily left our cities for the suburbs. The status-quo in our region is, indisputably, one of widespread, entrenched urban poverty, geographically separated from (predominately suburban) economic opportunity.

Yet, even the earliest signs of neighborhood revitalization, and nascent attempts at building new housing and opening small businesses in these cities are frequently opposed by people who are convinced that they are acting in the name of social justice.

Sincere as these anti-gentrification sentiments might be, I believe that they are harmful, and, if allowed to derail incipient efforts to reinvest in urban neighborhoods, simply serve to ensure that the existing dynamic of socioeconomic segregation will remain unchanged.

In many cases, the very people who claim to be fighting the current unjust system are inadvertently perpetuating it. Gentrification alarmists have yet to come to grips with the fact that their position usually serves to reinforce the existing, highly inequitable, situation.

Many critics of Rust Belt gentrification are holding cities to an unreasonable standard, and placing them in an impossible situation.

If much of the city remains poor and run-down, this is proof that the city does not care, and is not trying hard enough.

If, on the other hand, parts of the city begin to attract new residents and investment, this is proof that the city does not care, and is not trying hard enough.

Heads I win. Tails you lose.

Sometimes, it seems that the only thing that people dislike more than the status-quo, is doing anything substantive to change it.

In Akron, 81% of the people who work in the city, and earn over $40,000 per year (hardly a king’s ransom), live outside of the city. It is unclear how Akronites living in poverty will be better off if these people remain in the suburbs.

Let’s get concrete. If you are a well-educated, middle, or upper income person (and if you’re reading this, you probably are), and you live in an economically diverse urban neighborhood, is your presence a bad thing for your community?

Should you move, instead, to a suburban community that is likely to be highly-segregated and economically homogeneous?

If you are an entrepreneur starting-up in the urban core, should you decide to open your business somewhere else? And how, precisely, will doing that help the community that you are leaving behind?

When middle class people return to urban neighborhoods, they have some disposable income, which helps create markets for retail and small business, that, in turn, provide basic services and job opportunities for the urban poor.

This means that urban residents who are struggling to get by may no longer need to over-extend themselves to purchase a car, or endure long and inconvenient bus rides to access entry-level jobs and basic services in far-flung suburbs, but instead may be able to save time and money by walking to businesses in their own neighborhoods.

With the return of middle and upper income residents, business districts and housing markets, long dormant, may begin to approach at least minimum levels of functionality and attractiveness to prospective entrepreneurs, investors, and residents.

For existing urban homeowners, the gradual rise in property values, in areas with extremely depressed and artificially low home prices, often means the difference between a house ultimately being rehabilitated, or it beginning a tortuous cycle of neglect and decline, culminating in demolition.

This is especially important in the legacy cities of the eastern Great Lakes, where low property values and a glut of vacant and abandoned properties, rather than financially crippling housing costs, are the largest real estate challenge. And, unlike superstar cities on the coasts, cities in this region still have large percentages of households that are comprised of working-class homeowners living in single-family homes.

Take it from someone like me, who lives in a city with 96,000 housing units, where only 16 single-family homes were built last year, while nearly 500 were torn down, and where the median value of an owner-occupied house is $78,000.

To be sure, the return of new housing, small businesses, and more affluent residents is not a panacea, and there may be legitimate concerns, at some point, about how people moving back to the city might result in rising rents and higher property taxes for existing residents.

But in the end, I have yet to see a proven model for improving economic conditions in an urban neighborhood that is predicated on ensuring that concentrated poverty remains. Maintaining the status-quo in urban neighborhoods, in the name of opposing gentrification, will do nothing to help the poorest and most vulnerable residents.

Cities typically begin to rebound with small successes in individual neighborhoods, attracting new housing and jobs, and eventually building upward and outward from there – setting the stage for further incremental investment by the private sector.

If we urbanists truly believe that socioeconomically and ethnically diverse neighborhoods are as important as is often claimed, we cannot panic every time a new house is built, a new person moves in, or a new business opens. These are overwhelmingly good things for neighborhoods and cities that have seen precious little investment for decades.

Should we remain vigilant, and work together, in a cross-sector manner, to help ensure that the rising tide is actually lifting all the boats?

Absolutely.

Should we double-down on the status-quo in our region – one of entrenched poverty and racial segregation, because we are afraid of what any type of socioeconomic change could mean for a neighborhood?

Absolutely not.

Squelching private investment in the urban core is the wrong solution to the wrong problem. It will only serve to ensure that lower income, middle income, and upper income people continue to live apart in separate and unequal enclaves, and it will make social and economic conditions in our urban neighborhoods worse, rather than better.

If we are really serious about breaking down barriers in our neighborhoods, and celebrating socioeconomic diversity, then we have to come to grips with what that means and what that looks like.

Yes, it is complicated, and messy, but it is simply not good enough anymore to say that the status-quo is unacceptable.

We need more than words. We need to act. We need to fight the correct enemy. We need to do more than curse the darkness. We need to light a candle.

We don’t need more top-down economic silver bullets. We need collaborative, incremental change – person-by-person, neighborhood-by-neighborhood, informed by humility, prudence, sensitivity, wisdom, and love for our neighbors.

Working together, we can become a much better-connected, more cohesive, coherent, and equitable place. The only people who can stop us from becoming that place are we ourselves.

It’s not enough anymore to be against something. It’s time to be for something.

Jason Segedy is the Director of Planning and Urban Development for the City of Akron, Ohio. Segedy has worked in the urban planning field for the past 22 years, and is an avid writer on urban planning and development issues, blogging at Notes from the Underground. A lifelong resident of Akron’s west side, Jason is committed to the city, its people, and its neighborhoods. His passion is creating great places and spaces where Akronites can live, work, and play. 

Cultural appropriation: Theft or Smorgasbord?

If it weren’t for cultural appropriation, would America have any culture at all?

In Portland, two women opened a food cart business–Kook’s Burritos–selling burritos based on ones that they’d seen and tasted during a trip to Puerto Novo, Mexico. They were frank, telling reporters that they’d hung out watching local vendors prepare tortillas, to see if they could glean the tricks of the trade. Returning stateside, after some trial and error, they came up with a version that they thought matched the original, and opened their business. What quickly ensued was a web-based war of words that lambasted the two Portland women for cultural appropriation–essentially profiting by stealing the knowledge of Mexican chefs. The storm of controversy, and death threats, prompted the women to close their business.

This isn’t an isolated issue: There’s even a controversy over the cultural ownership of Poutine. Quebecois are furious that it’s being rebranded as a “Canadian” dish, because it hails strictly from Quebec. “Poutine is a Québécois creation, not a Canadian one,  and suggesting otherwise ignores that poutine ‘has been used as a form of stigma against a minority group that is still at risk of cultural absorption.'”

The transnational appropriation of food has a long history. Marco Polo is generally credited with stealing the idea of noodles during his visit to China. (And apparently Japanese ramen is another cross cultural noodle appropriation). Thanks to cultural appropriation, some foods have gone from local oddities to global standards in just a generation or two. Prior to World War II, pizza was essentially unknown outside of Naples. Returning GI’s brought it back to the US; and it spread globally (as did America’s faux German “hamburger.”) Why, if we’re concerned about cultural appropriation, isn’t someone insisting that Domino’s and Pizza Hut pay royalties to the Neapolitans? Maybe it has something to do with the fact that most pizza itself is  an amalgam of New World tomatoes and old world ingredients. So apparently, we simultaneously have cultural appropriation and cultural imperialism: stealing pizza from the Neapolitans and foisting it off on the rest of the world.

Other key examples of cultural appropriation and adaptation abound in the food business:

  • Starbucks traces its inspiration to Howard Schultz’s trip to Italy in the early 1980s. He cribbed the cafe’ formula and even the job title “Barista” from Italy’s coffee shops.
  • Oregon’s microbrew industry was led by pioneering firms like Widmer Brewing. Kurt Widmer studied the brewing arts in Germany and based the company’s signature Hefeweizen based on what he learned there. (And now Widmer sells its beers in Europe.)

Imitation is the sincerest form of plagiarism

The website Uproxx has a summary of the web furor about the purloined burritos and an interesting roundtable conversation by four food journalists. In some respects, the criticisms of cultural appropriation from the foodie press is a bit rich: it’s an industry that consists in no small part of celebrating novelty and fashion, and elevating food heroes (who all borrow heavily from established chefs and cuisines). There’s a lot of back and forth here; to give you a flavor of the conversation, here’s food writer Zach Johnston:

Saying that people can’t cook another culture’s food that they adore and bring that food home to open it up to a wider audience is the same as saying Joe Rogan or Vince Mancini are culturally appropriating Brazilian culture because they practice Jiu Jitsu. Martial Arts — like cooking and eating — is a unifier, not a divider. And we can’t dismiss logistical reality. 70 percent of Americans are white. Cooking is a trainable and malleable endeavor. White people are going to make samosas, tacos, and bratwurst in America. And American food culture is better for it.

Portland’s alt-weekly, Willamette Week has its own forum with local chefs representing a range of ethnic cuisines and backgrounds.  Chef Ahn Luu, who runs a Vietnamese-Cajun restaurant in Portland argues:

If you’re cooking Thai food outside of Thailand—even in Myanmar or China—it’s not gonna be authentic. All food travels around the world, and every culture has their own version. It’s all getting blown way out of proportion, and people are taking it too seriously. It’s food. If it’s good, eat it.

In that vein, Bloomberg View columnist Noah Smith is an unabashed advocate of cultural appropriation. He argues that it benefits both the appropriators, and those from whom it is appropriated. The appropriators get access to a wider variety of goods and services, they get beneficial mutations to their own products, and cultural appropriation often triggers technological change. Those from whom the technology was appropriated benefit from broader demand for their products, more jobs for immigrants, and greater cultural empathy.

And without cultural appropriation, it’s possible to get stuck in a very bad equilibrium. According to Paul Krugman, the reason that English food was so bad for so long (and has gotten better in recent decades) was because the country suffered from too little cultural appropriation.  But in recent years, the flood (at least pre-Brexit) of immigrants to the United Kingdom, and the holiday travels of the English exposed them (and their taste buds) to a wider range of choices, and as a result, English food has improved dramatically.

Plus, two women operating a food cart part-time is (forgive us) really small potatoes when it comes to cultural appropriation. Its hard to see how one can get terribly upset with a couple of women imitating the food they saw and tasted on a trip (and tried to faithfully copy) and a giant corporation bastardizing an entire nation’s cuisine (we’re looking at you Taco Bell and Olive Garden).  If this is a problem at all, isn’t the real issue the huge imbalance of power between corporations and solo entrepreneurs? Here’s another example. The Dominguez family based in Hood River, Oregon manufactures an extremely popular brand of tortilla chips called “Juanita’s” (trust us:  they’re excellent), which is distributed in the Pacific Northwest.  A couple of years ago, a entirely new brand started showing up on store shelves in Oregon & Washington:  Josefina’s–with a similar red and green bag. Though it didn’t say so on the packaging, the Josefina’s chips were manufactured by the nation’s largest snack chip company (Lays).

Cities and the Cultural Re-Mix

Arguably, cultural appropriation and remixing is at the heart of what cities do. As Jane Jacobs wrote, cities bring together people with different backgrounds and ideas, and mix them serendipitously in ways that create the “new work” that drives progress. The sheer variety of different and interesting things that are available in cities is one of the chief attractions of urban living. The ever-changing smorgasbord of consumption choices–which borrow ideas from all over the world–are what make cities interesting and dynamic places to live. For those with a taste for variety, it turns out that the cost of living in big cities is actually lower than in other places.

Ultimately, a lot of the argument is over the ownership of ideas. We’re strong believers in a knowledge economy, and the continuous development of new and better ideas (from microchips to drugs to better ways to sew a shirt to better ways to make a cup of coffee) are all things that make us better off and which propel economic growth. That said, who owns and who profits from any particular piece of knowledge is an unsettled and contentious area. For some things we grant very strong legal rights (patents, copyrights) and have private businesses that aggressively exploit their value (drug guy). In other areas, and food is one, its almost impossible to control intellectual property like recipes, and imitation and learning produce widespread spillovers.

One of the good things about knowledge as a factor of production is that it is, as Paul Romer has observed, non-rival. You and I can both make use of the same idea without diminishing its utility to either of us. And, for what its worth, we practice what we preach at City Observatory:  All of our work is published under a Creative Commons Attribution (CC-A) license, so anyone is free to copy, republish and reuse our content, subject only to the proviso that they acknowledge our original work.  So please, appropriate away.

 

Integration and social interaction: Evidence from Intermarriage

Reducing segregation does seem to result in much more social interaction, as intermarriage patterns demonstrate

Change doesn’t happen fast, but it happens more frequently and more quickly when we have integrated communities

One of the regular critiques of urban integration is that while we might get people from different backgrounds to live in the same neighborhood, that doesn’t necessarily mean that they interact socially on a regular basis.  Earlier, for example, we took a close and critical look at Derek Hyra’s claim that mixed-income, mixed-race communities fell short of improving the lot of the disadvantaged because of the persistence of what he called “micro-segregation.”  Even though they might live in the same neighborhood, people from these different groups still associated primarily with other people with similar backgrounds. We thought there were a lot of problems with this argument (most notably, that the data show a strong positive impact of mixed income neighborhoods for the lifetime prospects of poor kids, notwithstanding micro-segregation).

Ruth Negga and Joel Edgerton star in “Loving,” the story of a couple that challenged the constitutionality of a Virginia ban on interracial marriage.

While we have some useful measures of residential segregation (compiled from Census data), its harder to come by data that illustrate the extent to which people from different racial and ethnic groups spend time associating with each other. A new report from the Pew Charitable Trusts sheds an interesting light on the most personal inter-group interaction: racial/ethnic intermarriage.  It has been half a century since the Supreme Court struck down state bans on interracial marriage in Loving v. Virginia. The data show that intermarriage has increased five-fold from 3 percent of newlyweds in the 1960s to about 17 percent today. The trend has been propelled in part by the nation’s growing diversity, and also due to changing attitudes about intermarriage.  Pew used data from the most recent American Community Survey to calculate the rate of intermarriage between people from different racial and ethnic groups in each of the nation’s metropolitan areas. Pew’s ranking shows that intermarriage is much more common in some metros than in others. In the West, intermarriage rates tend to be much higher, for example, than they are in the South.  (Urban areas have higher intermarriage rates than rural ones, as well).

In part, these differences reflect the regional variation in attitudes toward intermarriage. But the opportunities for intermarriage also hinge directly on the racial and ethnic composition of a metropolitan area.  More diverse areas tend to have greater opportunity for intermarriage than more homogenous ones. The University of North Carolina’s Philip Cohen took the Pew data and compared it to the racial and ethnic diversity of each metropolitan area, and computed an adjusted intermarriage score for each metro area.  Given an area’s racial and ethnic composition, how much intermarriage did it exhibit. This ranking gives us a much clearer idea of where intermarriage is common and apparently socially acceptable, and where different racial and ethnic groups are, in practice, mixing. (See Cohen’s blog for full details).

We thought we’d use these data to look at the correlation between metropolitan segregation and intermarriage. Given an area’s racial and ethnic diversity, are people from different groups more (or less) likely to intermarry depending on the segregation of the metro area? The following chart shows the white-non-white segregation index for each metro area (on the horizontal axis) compared to the demographically adjusted intermarriage rate (from Philip Cohen).  Higher values on the white-non-white segregation index correspond to higher levels of segregation; the index shows the percent of persons in a region who would have to move to a different census tract so that each tract would have the same white/non-white balance as the metropolitan area of which it was a part. (We extracted the segregation index data from an excellent commentary on housing diversity by Trulia’s Cheryl Young.)

These data show a strong negative correlation between segregation and intermarriage. People who live in highly segregated metropolitan areas are much less likely to marry someone from a different racial and ethnic group than those who live in the least segregated areas. Compare, for example, Philadelphia and Austin.  Philadelphia is one of the most segregated large cities (dissimilarity index .65); Austin one of the least segregated (.38). Philadelphia’s intermarriage rate is about half that of Austin’s (.16 vs. .32).

Its possible to imagine that the correlation between segregation and intermarriage reflects both personal opportunities and social values. In less segregated communities, people from different racial and ethnic groups are–by definition–more likely to come into close proximity to one another. But segregation may also reflect (or influence) broader social attitudes about whether interracial relationships are tolerated. These data are very consistent with the notion that greater physical integration of people from different racial and ethnic groups is associated with greater inter-personal interaction.

Of course, the usual caveats about correlation not proving causation apply to this analysis. But it is nonetheless striking that after controlling for the diversity of metropolitan population, intermarriage is much more common in places with low levels of segregation than in places that are more highly segregated. This evidence is highly consistent with the thesis that social interaction among people from different racial and ethnic groups is enhanced by greater integration.

Integration and social interaction: Evidence from Intermarriage

Reducing segregation does seem to result in much more social interaction, as intermarriage patterns demonstrate

Yesterday, we took a close and critical look at Derek Hyra’s claim that mixed-income, mixed-race communities fell short of improving the lot of the disadvantaged because of the persistence of what he called “micro-segregation.”  Even though they might live in the same neighborhood, people from these different groups still associated primarily with other people with similar backgrounds. We thought there were a lot of problems with this argument (most notably, that the data show a strong positive impact of mixed income neighborhoods for the lifetime prospects of poor kids, notwithstanding micro-segregation).

Ruth Negga and Joel Edgerton star in “Loving,” the story of a couple that challenged the constitutionality of a Virginia ban on interracial marriage.

While we have some useful measures of residential segregation (compiled from Census data), its harder to come by data that illustrate the extent to which people from different racial and ethnic groups spend time associating with each other. A new report from the Pew Charitable Trusts sheds an interesting light on the most personal inter-group interaction: racial/ethnic intermarriage.  Its been 50 years since the Supreme Court struck down state bans on interracial marriage in Loving v. Virginia. The data show that intermarriage has increased five-fold from 3 percent of newlyweds in the 1960s to about 17 percent today. The trend has been propelled in part by the nation’s growing diversity, and also due to changing attitudes about intermarriage.  Pew used data from the most recent American Community Survey to calculate the rate of intermarriage between people from different racial and ethnic groups in each of the nation’s metropolitan areas. Pew’s ranking shows that intermarriage is much more common in some metros than in others. In the West, intermarriage rates tend to be much higher, for example, than they are in the South.  (Urban areas have higher intermarriage rates than rural ones, as well).

In part, these differences reflect the regional variation in attitudes toward intermarriage. But the opportunities for intermarriage also hinge directly on the racial and ethnic composition of a metropolitan area.  More diverse areas tend to have greater opportunity for intermarriage than more homogenous ones. The University of North Carolina’s Philip Cohen took the Pew data and compared it to the racial and ethnic diversity of each metropolitan area, and computed an adjusted intermarriage score for each metro area.  Given an area’s racial and ethnic composition, how much intermarriage did it exhibit. This ranking gives us a much clearer idea of where intermarriage is common and apparently socially acceptable, and where different racial and ethnic groups are, in practice, mixing. (See Cohen’s blog for full details).

We thought we’d use these data to look at the correlation between metropolitan segregation and intermarriage. Given an area’s racial and ethnic diversity, are people from different groups more (or less) likely to intermarry depending on the segregation of the metro area? The following chart shows the white-non-white segregation index for each metro area (on the horizontal axis) compared to the demographically adjusted intermarriage rate (from Philip Cohen).  Higher values on the white-non-white segregation index correspond to higher levels of segregation; the index shows the percent of persons in a region who would have to move to a different census tract so that each tract would have the same white/non-white balance as the metropolitan area of which it was a part. (We extracted the segregation index data from an excellent commentary on housing diversity by Trulia’s Cheryl Young.)

These data show a strong negative correlation between segregation and intermarriage. People who live in highly segregated metropolitan areas are much less likely to marry someone from a different racial and ethnic group than those who live in the least segregated areas. Compare, for example, Philadelphia and Austin.  Philadelphia is one of the most segregated large cities (dissimilarity index .65); Austin one of the least segregated (.38). Philadelphia’s intermarriage rate is about half that of Austin’s (.16 vs. .32).

Its possible to imagine that the correlation between segregation and intermarriage reflects both personal opportunities and social values. In less segregated communities, people from different racial and ethnic groups are–by definition–more likely to come into close proximity to one another. But segregation may also reflect (or influence) broader social attitudes about whether interracial relationships are tolerated. These data are very consistent with the notion that greater physical integration of people from different racial and ethnic groups is associated with greater inter-personal interaction.

Of course, the usual caveats about correlation not proving causation apply to this analysis. But it is nonetheless striking that after controlling for the diversity of metropolitan population, intermarriage is much more common in places with low levels of segregation than in places that are more highly segregated. This evidence is highly consistent with the thesis that social interaction among people from different racial and ethnic groups is enhanced by greater integration.

Socioeconomic mixing is essential to closing the Kumbaya gap

Integrated neighborhoods produce more mixing, but don’t automatically generate universal social interaction. What should we make of that?

Our recent report, America’s Most Diverse, Mixed Income Neighborhoods identifies those places where people from different racial and ethnic backgrounds and from different income strata all live in close proximity to one another. We’ve counted more than 1,300 neighborhoods with nearly 7 million residents that have high levels of racial/ethnic and income integration. In these places, at least, people from different backgrounds share a common neighborhood.  But is that enough? Some critics complain that while people may live close to one another in such places, there may be little meaningful interaction. Today we consider this issue.

In one idealized view of the world, economically integrated neighborhoods would have widespread and deep social interactions among people from different backgrounds. We’d tend to be color-blind and class-blind, and no more (or less) likely to interact with people from different groups than with people similar to ourselves. In practice, even in neighborhoods with a high degree of racial or income diversity, it still tends to be the case that people primarily associate with people like themselves. Even in the most integrated neighborhoods, there’s a “kumbaya” gap. Should we we regard that as a sign of failure?

That’s the argument that Derek Hyra makes about gentrifying neighborhoods, like U Street, in Washington DC. Blacks and whites, rich and poor live in close proximity to one another, but primarily associate only with people like themselves in daily live. Last week’s CityLab article interviewing Hyra is entitled: “Gentrification doesn’t mean diversity.” The article’s URL is “gentrifying neighborhoods aren’t really diverse.”

The point Hyra actually makes isn’t that the neighborhoods aren’t diverse, per se, but that within the neighborhoods, people still associate primarily with people with similar demographic characteristics. We may have alleviated segregation at one level, but in personal interactions, there’s still “micro-segregation.”

CityLab’s Tanvi Misri interviews Hyra about his new book, —Race, Class and Politics in the Cappuccino City.  Hyra observes that Washington, DC’s U Street neighborhood is now more racially and economically diverse, but notes that its still the case that people mostly associate with others of similar backgrounds in places like churches, stores and coffee shops. His argument seems to be, sure, its great that so-called gentrifying neighborhoods are more integrated, but since people of different races/classes, aren’t socializing directly, its basically a failure.  From the interview:

Elaborate on what’s positive and what’s problematic about this change, and with this perception of the neighborhood.

We have been so segregated in the United States and that now that whites are attracted and willing to move into what was formerly a low-income African-American neighborhood does symbolize some progress, in terms of race relations in the United States. That we have mixed-income, mixed-race neighborhoods, I think, is a very positive thing.

But that diversity not necessarily benefiting the former residents. Most of the mechanisms by which low-income people would benefit from this change are related to social interaction—that low-, middle-, and upper-income people would start to talk to one another. They would problem solve with one another. They would all get involved civically together to bolster their political power. But what we’re really seeing is a micro-level segregation. You see diversity along race, class, sexual orientation overall, but when you get into the civic institutions—the churches, the recreation centers, the restaurants, the clubs, the coffee shops—most of them are segregated. So you’re not getting a meaningful interaction across race, class, and difference. If we think that mixed-income, mixed-race communities are the panacea for poverty, they’re not.

Is the failure to reach maximum kumbaya really an indication that more socioeconomic mixing isn’t a good thing?  We don’t think so, for several reasons  First, unless you first get mixed income, mixed race neighborhoods, you have almost no chance having the opportunity for regular  social interactions. When we live in neighborhoods widely segregated by race and/or income its even more difficult to establish these boundary-crossing personal relationships. Socioeconomic mixing is necessary, even if it alone isn’t sufficient–especially immediately–to produce deeper interactions.

Second, “kumbaya” integration is probably an unrealistic goal: even within our neighborhoods (and socioeconomic groups) we do spend our personal time disproportionately with people who share our own peculiar interests. That’s true even within economically homogenous neighborhoods: people tend to spend much more time and develop stronger relationships with people most like them.

Third:  The evidence of overwhelming that mixed income neighborhoods (kumbaya or not) have big benefits, especially for lower income kids.  They get more resources, can access stronger networks, find better partners and career paths, etc.  The evidence from the Equality of Opportunity project, led by Raj Chetty, the research of Patrick Sharkey, and Eric Chyn’s study of Chicago Housing Authority residents all confirm that simply moving to a more mixed income neighborhood materially improves the life outcomes of poor kids. In addition, an important aspect of the socioeconomic mixing in the civic commons is promoting the kind of interactions that help us develop an awareness–imperfect and incomplete as it may be–that there are real people who have very different lives and expectations than we do.

Fourth, we know what happens when people don’t have this kind of first hand familiarity with a more diverse population. It shows up plainly in the results of the last election.  People who lived in communities with limited exposure to immigrants, or in neighborhoods that were predominantly white, segregated enclaves were much more likely to vote for Donald Trump than Hillary Clinton, even after controlling for other characteristics (party affiliation, age, and income) than others.  After sifting through national polling and demographic data Gallup’s  Jonathan Rothwell concludes:

“The analysis provides clear evidence that those who view Trump favorably are disproportionately living in racially and culturally isolated zip codes and commuting zones. Holding other factors, constant support for Trump is highly elevated in areas with few college graduates and in neighborhoods that standout within the larger commuting zone for being white, segregated enclaves, with little exposure to blacks, Asians, and Hispanics.”

The more separated we are from one another, the more likely we are to not support broad-based policies that promote equality and opportunity.  In the absence of more U Streets, we get policies that produce more and more segregated suburbs and neighborhoods of concentrated poverty.  We shouldn’t fixate on the failure of U Street to achieve some imaginary ideal; instead we should recognize that its essential to do many more “U Streets”  just to offset the scale of the segregation everywhere else. Fighting segregation comes first; Kumbaya will come, if it comes at all, later.

If we set impossibly high expectations about the nature of integration, and when we’re provided with anecdotes that recent and long-time residents in a community don’t associate much with one another, it’s tempting–but wrong–to conclude the whole thing was an epic fail.  As with so much in this field, that makes the perfect the enemy of the good, or at least the somewhat better.

 

Integration and the Kumbaya gap

Gentrifying neighborhoods produce more mixing, but don’t automatically generate universal social interaction. What should we make of that?

In one idealized view of the world, economically integrated neighborhoods would have widespread and deep social interactions among people from different backgrounds. We’d tend to be color-blind and class-blind, and no more (or less) likely to interact with people from different groups than with people similar to ourselves. In practice, even in neighborhoods with a high degree of racial or income diversity, it still tends to be the case that people primarily associate with people like themselves. Even in the most integrated neighborhoods, there’s a “kumbaya” gap. Should we we regard that as a sign of failure?

That’s the argument that Derek Hyra makes about gentrifying neighborhoods, like U Street, in Washington DC. Blacks and whites, rich and poor live in close proximity to one another, but primarily associate only with people like themselves in daily live. Last week’s CityLab article interviewing Hyra is entitled: “Gentrification doesn’t mean diversity.” The article’s URL is “gentrifying neighborhoods aren’t really diverse.”

The point Hyra actually makes isn’t that the neighborhoods aren’t diverse, per se, but that within the neighborhoods, people still associate primarily with people with similar demographic characteristics. We may have alleviated segregation at one level, but in personal interactions, there’s still “micro-segregation.”

CityLab’s Tanvi Misri interviews Hyra about his new book, —Race, Class and Politics in the Cappuccino City.  Hyra observes that Washington, DC’s U Street neighborhood is now more racially and economically diverse, but notes that its still the case that people mostly associate with others of similar backgrounds in places like churches, stores and coffee shops. His argument seems to be, sure, its great that so-called gentrifying neighborhoods are more integrated, but since people of different races/classes, aren’t socializing directly, its basically a failure.  From the interview:

Elaborate on what’s positive and what’s problematic about this change, and with this perception of the neighborhood.

We have been so segregated in the United States and that now that whites are attracted and willing to move into what was formerly a low-income African-American neighborhood does symbolize some progress, in terms of race relations in the United States. That we have mixed-income, mixed-race neighborhoods, I think, is a very positive thing.

But that diversity not necessarily benefiting the former residents. Most of the mechanisms by which low-income people would benefit from this change are related to social interaction—that low-, middle-, and upper-income people would start to talk to one another. They would problem solve with one another. They would all get involved civically together to bolster their political power. But what we’re really seeing is a micro-level segregation. You see diversity along race, class, sexual orientation overall, but when you get into the civic institutions—the churches, the recreation centers, the restaurants, the clubs, the coffee shops—most of them are segregated. So you’re not getting a meaningful interaction across race, class, and difference. If we think that mixed-income, mixed-race communities are the panacea for poverty, they’re not.

Is the failure to reach maximum kumbaya really an indication that more socioeconomic mixing isn’t a good thing?  We don’t think so, for several reasons  First, unless you first get mixed income, mixed race neighborhoods, you have almost no chance having the opportunity for regular  social interactions. When we live in neighborhoods widely segregated by race and/or income its even more difficult to establish these boundary-crossing personal relationships. Socioeconomic mixing is necessary, even if it alone isn’t sufficient–especially immediately–to produce deeper interactions.

Second, “kumbaya” integration is probably an unrealistic goal: even within our neighborhoods (and socioeconomic groups) we do spend our personal time disproportionately with people who share our own peculiar interests. That’s true even within economically homogenous neighborhoods: people tend to spend much more time and develop stronger relationships with people most like them.

Third:  The evidence of overwhelming that mixed income neighborhoods (kumbaya or not) have big benefits, especially for lower income kids.  They get more resources, can access stronger networks, find better partners and career paths, etc.  The evidence from the Equality of Opportunity project, led by Raj Chetty, the research of Patrick Sharkey, and Eric Chyn’s study of Chicago Housing Authority residents all confirm that simply moving to a more mixed income neighborhood materially improves the life outcomes of poor kids. In addition, an important aspect of the socioeconomic mixing in the civic commons is promoting the kind of interactions that help us develop an awareness–imperfect and incomplete as it may be–that there are real people who have very different lives and expectations than we do.

Fourth, we know what happens when people don’t have this kind of first hand familiarity with a more diverse population. It shows up plainly in the results of the last election.  People who lived in communities with limited exposure to immigrants, or in neighborhoods that were predominantly white, segregated enclaves were much more likely to vote for Donald Trump than Hillary Clinton, even after controlling for other characteristics (party affiliation, age, and income) than others.  After sifting through national polling and demographic data Gallup’s  Jonathan Rothwell concludes:

“The analysis provides clear evidence that those who view Trump favorably are disproportionately living in racially and culturally isolated zip codes and commuting zones. Holding other factors, constant support for Trump is highly elevated in areas with few college graduates and in neighborhoods that standout within the larger commuting zone for being white, segregated enclaves, with little exposure to blacks, Asians, and Hispanics.”

The more separated we are from one another, the more likely we are to not support broad-based policies that promote equality and opportunity.  In the absence of more U Streets, we get policies that produce more and more segregated suburbs and neighborhoods of concentrated poverty.  We shouldn’t fixate on the failure of U Street to achieve some imaginary ideal; instead we should recognize that its essential to do many more “U Streets”  just to offset the scale of the segregation everywhere else. Fighting segregation comes first; Kumbaya will come, if it comes at all, later.

If we set impossibly high expectations about the nature of integration, and when we’re provided with anecdotes that recent and long-time residents in a community don’t associate much with one another, it’s tempting–but wrong–to conclude the whole thing was an epic fail.  As with so much in this field, that makes the perfect the enemy of the good, or at least the somewhat better.

 

Volunteering as a measure of social capital

Volunteering is one of the hallmarks of community; here are the cities with the highest rates of volunteerism

The decline of the civic commons, the extent to which American’s engage with one another in the public realm, especially across class lines, has been much remarked upon. Our report, Less in Common, explores the many dimensions along with the fabric of our connections to one another has become increasingly strained over several decades: we are less likely to socialize with neighbors, we travel in isolation, increasingly we recreate in private, rather than public space, and as a result, the strength of a shared public realm has deteriorated.

In his book Bowling Alone, Robert Putnam popularized the term “social capital.” Putnam also developed a clever series of statistics for measuring social capital. He looked at survey data about interpersonal trust (can most people be trusted?) as well as behavioral data (do people regularly visit neighbors, attend public meetings, belong to civic organizations?). Putnam’s measures try to capture the extent to which social interaction is underpinned by widely shared norms of openness and reciprocity.

As economist Brad DeLong explains,

. . . at some deep level human sociability is built on gift-exchange—I give you this, you give me that, and rough balance is achieved, but in some sense we both still owe each other and still are under some kind of mutual obligation to do things to further repay each other.

This sense of mutual obligation is important both to society, and the the effective function of markets. When we live in communities, places where most people have a strong sense of mutual obligation to look out for and take care of one another, social problems are lessened and economies run more smoothly.

It’s difficult to come up with a single, clear-cut indicator of social capital, so we and other researchers have ended up relying on a patchwork of different measures to judge the degree to which different cities exhibit high or low levels of civic interaction.

One of the most fundamental of these measures is volunteering. It’s long been a staple of American lore–since DeToqueville–that we regularly engage non-remunerated community activities.

Our data come from the Corporation for National and Community Service. It works with the Census Bureau to conduct a nationally representative survey exploring the degree to which Americans engage in a range of volunteer activities.

Across the nation’s largest metropolitan areas, about 27 percent of adults reported having volunteered in their local community the past year. The volunteering ethic is strongest in Salt Lake City and the Twin Cities of Minneapolis and St. Paul, where more than a third of adults volunteer.  Conversely, volunteering is much lower than the national average in cities such as Miami, New Orleans, New York and Las Vegas.

This measure stands in stark contrast to our measure of “anti-social capital” the number of security guards per capita in each metropolitan area, which we wrote about earlier this year. Not surprisingly, cities that rank high in our measure of anti-social capital (Miami, New Orleans, and Las Vegas) are all in the “top five” for security guards per capita and in the bottom five for volunteering.  Conversely, the cities with the fewest security guards per capita (Minneapolis, Portland, Grand Rapids and Rochester) are all in the top ten for volunteering.

While any ranking always implies that there are winners and losers, we interpret the variation we see here a bit more optimistically. These data imply that what happens in a metropolitan are can affect its degree of social capital. Fixing this problem from the top down may seem daunting, but improving social capital from the bottom up is something than can be done at the community level. No matter where you live, we’re sure there are opportunities for you to volunteer to help make your city a better place.

Migration is making counties more diverse

Migration, especially by young adults, is increasing racial and ethnic diversity in US counties

As we related last week, a new report from the Urban Institute quantifies the stark economic costs of racial and income segregation in the United States. Places with higher levels of segregation have lower incomes for African-Americans, lower rates of educational attainment, and higher rates of serious crimes. Reducing segregation by race and class is an important and unfinished agenda for achieving greater social justice, and improving our economy.

But how can we reduce segregation? As we all know, its difficult and expensive to build new housing in established neighborhoods. There’s often opposition to new development, whether its infill housing in cities, or affordable housing in suburbs. But while the housing stock can change only slowly, the occupants of housing units are changing all the time–the average renter has lived in her apartment less than two years, for example. The critical question is whether the regular and on-going movement of people in and out of different housing and different neighborhoods is reinforcing existing patterns of segregation, or whether its creating greater diversity.

A new report–Moving to Diversity–from the Richelle Winkler a sociologist at Michigan Technological University, and Kenneth Johnson, a demographer at University of New Hampshire, looks at the way in which population movements are changing the face of America’s counties.  Counties turn out to be a convenient unit for analysis, because its possible to accurately separate out the effects of births, deaths and net migration by race and ethnicity. The report looks at population change between 1990 and 2010, and focus on four broad racial-ethnic categories: non-hispanic whites, non-hispanic blacks, hispanic persons, and all other racial-ethnic combinations. To compute the effects of migration, the authors calculate what each county’s demographics would look like based on its racial and ethnic composition in the base year (for example 1990) forecast forward simply to reflect the effects of births and deaths of the base year population. The difference between that estimate and the actual observed value in the end year (for example 2000), is the net effect of migration on county demographics.

The report offers several key insights into the ways in which migration is influencing the racial and ethnic composition of different counties. First, its the movement of younger people, especially young adults which is contributing to the big increases in county-level diversity.  The movement of those 20-39 accounted for the biggest changes in both black-white and hispanic white diversity. Moves by older adults actually tended to decrease racial and ethnic diversity (think white people moving to even “whiter” counties, and so on).  But overall, the trend toward greater diversity is driven by the young, who are both more likely to move, and when they move, tend to move to more diverse locations.

 

There are important city-to-suburb and suburb-to-city components to migration. Young white people contribute to greater diversity by moving from whiter counties (disproportionately in suburbs) to urban counties that tend to have more persons of color. Conversely, Black and Hispanic migrants exhibit net migration in the other direction, from less white urban counties to whiter suburban ones. The effect of both kinds of migration is to increase diversity in both counties.  As Winkler and Johnson explain:

Blacks and Hispanics of all ages migrated to areas that were “whiter,” thereby increasing diversity. The movements of the white population have been more complex, however, with impacts that vary considerably by age. White young adults (age 20–39) moved from predominantly white counties to counties with larger black and Hispanic population shares, often in large urban centers. The net flow of white young adults into central-city counties increased the white young adult population there by approximately 20 percent in the 1990s and again in the 2000s. The outflow of these same young white adults from suburban and rural counties to big urban cores also contributed to more diversity in these origin areas by diminishing the number of whites there.

While the overall effect of migration was to increase integration by race and ethnicity, this didn’t occur everywhere. Winkler and Johnson estimate that migration significantly increased diversity in  about 10 percent of counties, a modestly increased diversity in about 56 percent of counties, had little effect one way or another in about 32 percent of counties, and resulted in noticeably less diversity in only about 2 percent of counties.

The distinct age structure of these migration trends suggests that future migration will also tend to increase diversity. Young people are much more likely to migrate than older ones. The persistence of the migration of white young adults to cities, coupled with the migration of persons of color to suburbs makes both areas more diverse than they would otherwise have been.

In the face of a growing body of evidence on the negative effects of segregation, its good to know that the individual migration decisions of people in the up and coming generations are contributing to growing diversity at the county level in the US.

Getting to critical mass in Detroit

Last month, we took exception to critics of Detroit’s economic rebound who argued that it was a failure because the job and population growth that the city has enjoyed has only reached a few neighborhoods, chiefly those in and around the downtown. A key part of our position was that successful development needs to achieve critical mass in a few locations because there are positive spillover effects at the neighborhood level. One additional house in each of 50 scattered neighborhoods will not have the mutually reinforcing effect of building 50 houses in one neighborhood. Similarly, building new housing, a grocery store, and offices in a single neighborhood makes them all more successful than they would be if they were spread out among different neighborhoods. What appears to some as “unequal” development is actually the only way that revitalization is likely to take hold in a disinvested city like Detroit.  That’s why we wrote:

. . . development and city economies are highly dependent on spatial spillovers. Neighborhoods rebound by reaching a critical mass of residents, stores, job opportunities and amenities.  The synergy of these actions in specific places is mutually self-reinforcing and leads to further growth. If growth were evenly spread over the entire city, no neighborhood would benefit from these spillovers. And make no mistake, this kind of spillover or interaction is fundamental to urban economics; it is what unifies the stories of city success from Jane Jacobs to Ed Glaser.  Without a minimum amount of density in specific places, the urban economy can’t flourish.  Detroit’s rebound will happen by recording some small successes in some places and then building outward and upward from these, not gradually raising the level of every part of the city.

While this idea of agglomeration economies is implicit in much of urban economics, and while the principle is well-understood, its sometimes difficult to see how it plays out in particular places. A new research paper prepared by economists Raymond Owens and Pierre-Daniel Sarte of the Federal Reserve Bank of Richmond and Esteban Rossi-Hansberg of Princeton University  tries to explore exactly this issue in the city of Detroit. If you don’t want to read the entire paper, CityLab’s Tanvi Misra has a nice non-technical synopsis of the article here.

The important economic insight here is the issue of externalities: In this case, the success of any persons investment in a new house or business depends not just on what they do, but whether other households and businesses invest in the same area. If a critical mass of people all build or fix up new houses in a particular neighborhood (and/or start businesses) they’ll benefit from the spillover effects of their neighbors. If they invest–and others don’t–they won’t get the benefit of these spillovers.

Analytically this produces some important indeterminacy in possible outcomes. Multiple different equilibria are possible depending on whether enough people, businesses, developers and investment all “leap” into a neighborhood at a particular time. So whether and how fast redevelopment occurs is likely to be a coordination problem.

Without coordination among developers and residents Owens, Rossi-Hansberg and Sarte argue, some neighborhoods that arguably have the necessary fundamentals to rebound won’t take off. Immediately adjacent to downtown Detroit, for example, there are hundreds of acres of vacant land that offer greater proximity to downtown jobs and amenities than other places. Why, the authors ask, “do residential developers not move into these areas and develop residential communities where downtown workers can live?”

To answer that question, the NBER paper builds a very complex economic model that represents these spillover effects, and estimates the potential for each neighborhood to add value if it can move from its current underdevelopment equilibrium. In this map illustrating their findings, the neighborhoods with the darkest colors have the highest potential value if development takes place.

The authors measure the potential for future growth by estimating the total increase in rents associated with additional housing development and population growth in each neighborhood. Some neighborhoods are well-positioned for development to take-off, and would show the biggest gains in activity, if the coordination problem could be overcome. That coordination problem is apparent in neighborhoods near downtown Detroit: even though it would make sense to invest, no one wants to be the first investor, for fear that other’s won’t invest.  So Owens, Rossi-Hansberg and Sarte suggest this obstacle might be overcome if we could create a kind of  “investment insurance”–if you invest in this neighborhood, then we’ guarantee a return on your home or business.

As a thought experiment, the authors estimate the amount of a development guarantee that would be needed to trigger a minimum level of investment needed to get a neighborhood moving toward its rebuilding. In theory, offering developers a financial guarantee that their development would be successful could get them to invest in places they wouldn’t choose to invest today. That investment, in turn, would trigger a kind of positive feedback effect that would generate additional development, and the neighborhood would break out of its low-development equilibrium. If the author’s estimates are correct, its unlikely that the guarantees would actually need to be paid.

While this concept appears sound in theory, much depends on getting the estimates right, and also on figuring out how to construct a system of guarantees that doesn’t create its own incentive problems. In effect, however, this paper should lend some support to those in Detroit who are attempting to make intensive, coordinated investments in a few neighborhoods.

More broadly, this paper reminds us of the salience of stigma to neighborhood development. Once a neighborhood acquires a reputation in the collective local consciousness for being a place that is risky, declining, crime-ridden or unattractive, it may be difficult or impossible to get a first-mover to take the necessary investment that could turn things around. The collective action problem is that no one individual will move ahead with investment because they fear (rationally) that others won’t, based on an area’s reputation.  A big part of overcoming this is some action that changes a neighborhood’s reputation and people’s expectations, so that they’re willing to undertake investment, which then becomes a self-fulfilling prophecy.  While economists tend to think that the only important guarantees are financial , there are other ways that city leaders could actively work to change a neighborhood’s reputation and outlook and give potential residents and  investors some assurance that they won’t be alone if they are among the first to move.   New investments, for example, like the city’s light rail system, may represent a signal that risks are now lower in the area’s it serves than they have been.

Cursing the candle

How should we view the early signs of a turnaround in Detroit?

Better to light a single candle than simply curse the darkness. The past decades have been full of dark days for Detroit, but there are finally signs of a turnaround, a first few glimmers that the city is stemming the downward spiral of economic and social decline. But for at least a few critics that’s not good enough: not content with cursing the darkness, they’re also cursing the first few candles that have been lit, for the sin of failing to resolve the city’s entire crushing legacy of decline everywhere, for everyone, and all at once.

Flickr: Uetchy

Michigan State political scientist Laura Reese and Wayne State urban affairs expert Gary Sands have written an essay “Detroit’s recovery: The glass is half-full at best,” for Conversation which was reprinted at CityLab as “Is Detroit really making a comeback?” The article is based on a longer academic treatment of this subject by Reese, Sanders and co-authors, entitled “It’s safe to come, we’ve got lattes,” in the journal Cities.  (This is one of those rare cases where the mass media version of an article is more measured and less snarky than the title of the companion academic piece, but I digress.)

Reese and Sands set about the apparently obligatory task of offering a contrarian view to stories in the popular press suggesting that Detroit has somehow turned the corner on its economic troubles and is starting to come back. We, too, are wary of glib claims that everything is fine in Detroit.  It isn’t. The city still bears the deep scars of decades of industrial decline coupled with dramatic failure of urban governance. The nascent rebound is evident only in a few places.

There’s a kind of straw man argument here.  Is Detroit “back?” As best I can tell, no one’s making that argument. The likelihood that the city will restore the industrial heyday of the U.S. auto industry, replete with a profitable oligopoly and powerful unions that negotiate high wages for modestly skilled work, just isn’t in the cards.  As Ed Glaeser has pointed out, it’s rare that cities reinvent their economies.  But when they do–as in the cases of Boston and New York–it’s because they’ve managed to do an extraordinary job of educating their local populations, and that base of talent has served as the critical resource for generating new economic activity. Detroit’s still far from that point.

And there’s no one who should think a renaissance will happen quickly if it happens at all. History is littered with examples of once flourishing cities that failed for centuries to find a second act: Athens was long deserted, Venice had its empire and economy collapse, Bruges had its harbor silt-up. In each case, these city’s early economies lived hard, died young and left a beautiful (architectural) corpse.  It’s really only been in the 20th century that each of these cities revived to any degree after their historical decline.

That said, there’s clear evidence that Detroit has stanched the economic hemorrhage. After a decade of year over year of job losses, Wayne County has chalked up five consecutive years of year-over-year job growth. True, the county is still down more than 150,000 jobs from its peak but has gained back 50,000 jobs in the past five years.

While this article presents a number of useful facts that remind us how far Detroit has to go, there are a lot of unresolved contradictions here.  In successive paragraphs, the authors decry the lackluster performance of Detroit home prices (they’re still way below housing bubble levels and haven’t rebounded nearly as well as in other cities), but then go on to decry the unfolding gentrification of the city.  You can’t have it both ways.  Either housing is cheap and devalued, or the city is becoming more expensive.

Why neighborhood level equality is a misleading metric for urban well being

Reese and Sands seem to be upset that Detroit’s nascent recovery is somehow unequal; that some parts of the city are rebounding while others still decline.

“. . . within the city recovery has been highly uneven, resulting in greater inequality.”

Detroit’s problem is not inequality, it’s poverty.  As the Brookings Institution’s Alan Berube put it:

“Detroit does not have an income inequality problem—it has a poverty problem. It’s hard to imagine that the city will do better over time without more high-income individuals.”

To be sure, more higher income residents and new restaurants, condos and office buildings may bring poverty into sharper contrast, but had those same higher income jobs and households located in the suburbs (or some other city), its far from obvious that poor Detroiters would somehow be better off.

As a result, the only way that Detroit is likely to improve its economy is to become at least somewhat less equal.  The city has a relatively high degree of equality at a very low level of income. The reason this has occurred, in large part, is those upper and middle income households—those with the means to do so—have exited the city in large numbers, leaving poor people behind.

We’ve long called out the misleading nature of inequality statistics when applied to small geographies. What’s called “inequality” at the neighborhood level is actually a sign of economic mixing, or economic integration—a neighborhood where high, middle and low income families live in close proximity and where there are housing opportunities at a range of price points.

Incomes in central cities are almost always more unequally distributed than in the metropolitan areas in which they are located, but this is because cities are more diverse and inclusive. At small geographies, this statistic says more about integration than it does about inequality.  

At a highly local level, “equality” is generally achieved in one of two ways:  by having a community that is so undesirable that no one with the means to live elsewhere chooses to stay, leaving an “equal” but very poor neighborhood. Alternatively, high levels of neighborhood equality can be achieved through the application of exclusionary zoning laws that make it illegal and impossible for low (and in some cases even middle income) families to live in an area. As in some exclusive suburbs, such as Flower Mound, Texas and Bethesda, Maryland — two of the highest scoring cities on equality — the equality is only for high income families.

Indeed, the big problem in American cities, as we’ve documented in our report “Lost in Place” is that in poor neighborhoods income is actually too equal. Neighborhoods of concentrated poverty, where more than 30 percent of the population lives below the poverty line, have tripled in the past 40 years. As the work of Raj Chetty and others has shown, neighborhoods of concentrated poverty permanently lower the lifetime earnings prospects of poor kids. Otherwise similar children who grow up in more mixed income (meaning unequal) neighborhoods have higher lifetime earnings.

As a practical matter, the only way forward for the Detroit economy is if more middle income and even upper income families choose to move to the city (or stay there as their fortunes improve). That will nominally make some of the income numbers look less “equal” but will play a critical role in creating the tax base and the local consumption spending that will —gradually— lead to further improvements in Detroit’s nascent economic rebound.

Where do we start? Achieving critical mass

The second fundamental critique in the City Lab piece is an argument that the city’s redevelopment efforts are failures because they aren’t producing improvements for everyone, everywhere in the city all at once. To date, the city’s successes have been recorded in downtown, Midtown and a few nearby neighborhoods, but because other parts of the city have continued to deteriorate and depopulate, the assumption is Detroit must be failing.

This critique ignores the fundamental fact that development and city economies are highly dependent on spatial spillovers. Neighborhoods rebound by reaching a critical mass of residents, stores, job opportunities and amenities.  The synergy of these actions in specific places is mutually self-reinforcing and leads to further growth. If growth were evenly spread over the entire city, no neighborhood would benefit from these spillovers. And make no mistake, this kind of spillover or interaction is fundamental to urban economics; it is what unifies the stories of city success from Jane Jacobs to Ed Glaser.  Without a minimum amount of density in specific places, the urban economy can’t flourish.  Detroit’s rebound will happen by recording some small successes in some places and then building outward and upward from these, not gradually raising the level of every part of the city.

Scale: Making the perfect the enemy of the good

Anyone familiar with Detroit knows that the city’s most overwhelming problem is one of operating and paying for a city built for two million people with a population (and consequently a tax base) less than half that size. The city is still wrestling with the difficult challenge of triage—reducing its footprint and shrinking its service obligations to match its resources.

And that’s the final point that’s so disturbing about the CityLab critique. Reese and Sands  argue that Detroit needs more more jobs  and resources for, among other things, educating its kids. No one doubts this. But where will that money come from? Certainly not from federal or state governments. It will have to come in large part from growing a local tax base, which is contingent on creating viable job centers and attracting and retaining more residents, including more middle and upper income residents.

Make no mistake: the scale here is daunting. The authors offer the helpful observation that if Detroit just somehow had another 100,000 jobs paying $10 per hour, it would pump more than $2 billion a year into the city’s economy. (Keep in mind that from 2001 through 2010, the Wayne County lost about twice that many jobs). While their math is impeccable, their economics are mystical.  This is the academic equivalent of the old Steve Martin joke about how to get a million dollars tax free:  “Okay, first, get a million dollars.”

It would be great if we could craft a sudden solution that would immediately create hundreds of thousands of jobs and drop billions of dollars in wages and money for schools and public services into Detroit. But that’s simply not going to happen. Instead, progress on a smaller scale has to start somewhere, has to involve new jobs, new residents and new investment in a few neighborhoods and then build from there. Businesses will start-up or move in, a few at a time, more in some neighborhoods than others, and then over time grow, providing more jobs and paying more taxes.

It’s going to be a long, hard road ahead for Detroit. And that road will lead to a different and smaller Detroit than existed in, say, the 1950s.  That road is made even harder by critics that damn the first few candles for shedding too little light.

 

What makes America great, as always: Immigrants

Happy Independence Day, America!

All Americans are immigrants (Even the Native American tribes trace their origins to Asians who migrated over the Siberian-Alaskan land bridge during the last ice age). And this nation of immigrants has always grown stronger by embracing newcomers who want to share in, and help build the American dream. So here, on Independence Day, is a short reminder of why immigration matters to our economic success, viewed as we usually do, through the lens of our nation’s cities.

Lighting the way to a stronger US economy since 1886.

 

America is a nation of immigrants, and its economy is propelled and activated by its openness to immigration and the new ideas and entrepreneurial energy that immigrants provide. Its commonplace to remind ourselves that many of the nation’s greatest thinkers and entrepreneurs, Andrew Carnegie, Albert Einstein, Andy Grove and hundreds of others, were immigrants, if not refugees. All six of America’s 2016 Nobel Laureates were immigrants. The fact that America stood as a beacon of freedom, and a haven from hate and oppression, has continually renewed and added to the nation’s talent and ideas. Immigration has also played a critical role in helping revitalize many previously depressed urban areas and neighborhoods. As Joel Mokyr explains in his terrific new book “A Culture of Growth,” the key factor triggering the Enlightenment and the Industrial Revolution was the ease with which heterodox and creative thinkers could find sanctuary in other countries and spread their thinking across borders. The US was founded on the kind of openness and tolerance than underpinned this process, and flourished accordingly.

The critical role of immigration is abundantly clear when we look at the health and productivity of the nation’s urban economies. The metro areas with the highest fractions of foreign-born well-educated workers are among the nation’s most productive.

Metros with the most foreign born talent

Our benchmark for measuring foreign-born talent is to look at the proportion of a region’s college-educated population born outside the United States. We tap data from the Census Bureau’s American Community Survey, which tells us what share of those aged 25 and older who have at least a four-year college degree were born outside the United States. (This tabulation doesn’t distinguish between those who came to the US as children and were educated here, and those who may have immigrated to the US later in life as adults, but shows the gross effect of all immigration). In the typical large metropolitan area in the United States, about one in seven college educated adults was born outside the nation. And in some of our largest and most economically important metropolitan areas, the share is much higher: a majority of those with four-year or higher degrees in Silicon Valley are from elsewhere, as are a third of the best educated in New York, Los Angeles, and Miami.

 

Foreign born talent and productivity

We’ve plotted the relationship between the share of a metropolitan area’s college-educated population born outside the United States and its productivity, as measured by gross metropolitan product per capita.  Gross metropolitan product is the regional analog of gross domestic product, the total value of goods and services produced, and is calculated by the Bureau of Economic Analysis.  The sizes of the circles shown in this chart are proportional to the population of each of these metropolitan areas.

These data show a clear positive relationship between the presence of foreign-born talent and productivity.  Several of the nation’s most productive metropolitan areas–San Jose, San Francisco, New York and Seattle–all have above average levels of foreign-born persons among their best educated.

Of course, these data represent only a correlation, and there are good reasons to believe that the arrows of causality run in both directions: more well-educated immigrants make an area more productive and more productive areas tend to attract (and retain) more talented immigrants. But it’s striking that some of the nation’s most vibrant economies, places that are at the forefront of generating the new ideas and technology that sustain US global economic leadership, are places that are open and welcoming to the best and brightest from around the world.

There are a lot of reasons to oppose President Trump’s ban on immigration from these Islamic countries. The most important reasons are moral, ethical and legal. But on top of them, there’s a strongly pragmatic, economic rationale as well: the health and dynamism of the US economy, and of the metropolitan areas that power the knowledge-driven sectors of that economy, depend critically on the openness to smart people from around the world.

 

 

Openness to immigration drives economic success

Last Friday, President Trump signed an Executive Order effectively blocking entry to the US for nationals of seven countries—Iraq, Iran, Libya, Somalia, Sudan, Syria, and Yemen. We’ll leave aside the fearful, xenophobic and anti-American aspects of this policy: others have addressed them far more eloquently than we can at City Observatory.  And while there’s no question that the moral, ethical and constitutional problems with this order are more that sufficient to invalidate it, to these we’ll add an economic angle, which though secondary, is hardly minor.

Lighting the way to a stronger US economy since 1886.

 

America is a nation of immigrants, and its economy is propelled and activated by its openness to immigration and the new ideas and entrepreneurial energy that immigrants provide. Its commonplace to remind ourselves that many of the nation’s greatest thinkers and entrepreneurs, Andrew Carnegie, Albert Einstein, Andy Grove and hundreds of others, were immigrants, if not refugees. All six of America’s 2016 Nobel Laureates were immigrants. The fact that America stood as a beacon of freedom, and a haven from hate and oppression, has continually renewed and added to the nation’s talent and ideas. Immigration has also played a critical role in helping revitalize many previously depressed urban areas and neighborhoods. As Joel Mokyr explains in his terrific new book “A Culture of Growth,” the key factor triggering the Enlightenment and the Industrial Revolution was the ease with which heterodox and creative thinkers could find sanctuary in other countries and spread their thinking across borders. The US was founded on the kind of openness and tolerance than underpinned this process, and flourished accordingly.

The critical role of immigration is abundantly clear when we look at the health and productivity of the nation’s urban economies. The metro areas with the highest fractions of foreign-born well-educated workers are among the nation’s most productive.

Metros with the most foreign born talent

Our benchmark for measuring foreign-born talent is to look at the proportion of a region’s college-educated population born outside the United States. We tap data from the Census Bureau’s American Community Survey, which tells us what share of those aged 25 and older who have at least a four-year college degree were born outside the United States. (This tabulation doesn’t distinguish between those who came to the US as children and were educated here, and those who may have immigrated to the US later in life as adults, but shows the gross effect of all immigration). In the typical large metropolitan area in the United States, about one in seven college educated adults was born outside the nation. And in some of our largest and most economically important metropolitan areas, the share is much higher: a majority of those with four-year or higher degrees in Silicon Valley are from elsewhere, as are a third of the best educated in New York, Los Angeles, and Miami.

 

Foreign born talent and productivity

We’ve plotted the relationship between the share of a metropolitan area’s college-educated population born outside the United States and its productivity, as measured by gross metropolitan product per capita.  Gross metropolitan product is the regional analog of gross domestic product, the total value of goods and services produced, and is calculated by the Bureau of Economic Analysis.  The sizes of the circles shown in this chart are proportional to the population of each of these metropolitan areas.

These data show a clear positive relationship between the presence of foreign-born talent and productivity.  Several of the nation’s most productive metropolitan areas–San Jose, San Francisco, New York and Seattle–all have above average levels of foreign-born persons among their best educated.

Of course, these data represent only a correlation, and there are good reasons to believe that the arrows of causality run in both directions: more well-educated immigrants make an area more productive and more productive areas tend to attract (and retain) more talented immigrants. But it’s striking that some of the nation’s most vibrant economies, places that are at the forefront of generating the new ideas and technology that sustain US global economic leadership, are places that are open and welcoming to the best and brightest from around the world.

There are a lot of reasons to oppose President Trump’s ban on immigration from these Islamic countries. The most important reasons are moral, ethical and legal. But on top of them, there’s a strongly pragmatic, economic rationale as well: the health and dynamism of the US economy, and of the metropolitan areas that power the knowledge-driven sectors of that economy, depend critically on the openness to smart people from around the world.

 

 

The constancy of change in neighborhood populations

Neighborhoods are always changing; half of all renters move every two years.

There’s a subtle perceptual bias that underlies many of the stories about gentrification and neighborhood change. The canonical journalistic account of gentrification focuses on the observable fact that different people now live in a neighborhood than used to live there at some previous time. We seem to assume that most neighborhoods are stable and unchanging, and that absent some dramatic change, like gentrification, the people who lived in that neighborhood are the same ones who lived their a decade ago, and without such change, would be likely to live there a decade hence.  But constant population change or turnover is a regular feature of most neighborhoods, a fact confirmed by a recent study. To summarize the key takeaways:

  • The population of urban neighborhoods is always changing because moving is so common, especially for renters.
  • There’s little evidence that gentrification causes overall rates of moving to increase, either for homeowners or renters.
  • Homeowners don’t seem to be affected at all, and there’s no evidence that higher property taxes (or property tax breaks) influence moving decisions.
  • While involuntary moves for renters increase slightly in gentrified neighborhoods, there’s no significant change in total moves

In an article published in Urban Affairs Review, “Gentrification, Property Tax Limitation and Displacement,” Isaac William Martin and Kevin Beck present their analysis of longitudinal data from the Panel Survey of Income Dynamics that track family moves over more than a decade.  An un-gated version of the paper is available here. One of the challenges of studying gentrification and neighborhood change is that most data simply provides snapshots of a neighborhood’s population at a given point in time, and provides little information about the comings and goings of different households. The PSID sample is unusual, in that in tracks households and individuals over a period of decades–this study uses data on the movement of household heads from 1987 through 2009. Martin and Beck were able to access confidential data that reports neighborhood location and enables them to identify the movement of households to different neighborhoods.

Richard Florida reviewed the Martin and Beck paper at City Lab and highlighted two of the study’s key findings:  that homeowners don’t seem to be displaced by gentrification and a subsidiary finding that property taxes (and tax breaks for homeowners) don’t seem to affect displacement.  These are both significant findings, but we want to step back and look at the broader picture this study paints of how neighborhoods change, because this study provides a useful context for understanding the complex dynamics of migration that are often left out of discussions of gentrification.

Change is a constant–Most renters have moved on after two years

One of the most striking findings from this study is how frequently renters move. These data show than in any given two-year period a majority (54 percent) of renter households had moved to a different neighborhood. The average tenure (length of time they’ve lived in their current residence) is on average 1.7 years. (Table 1).  Moving rates are lower (16 percent over two years) for homeowners, and average tenures are considerably longer (4.9 years, on average).  But the important thing to keep in mind is just how much volatility and turnover there is in neighborhood populations. Statistically, if about half of all renters move out of a neighborhood every two years, the probability than any current renter will live in that neighborhood ten years hence is about 3 percent (0.5 raised to the fifth power).

Many of the public discussions of gentrification assume that somehow, in the absence of gentrification, neighborhoods would somehow remain just the same, and that few or no residents would move away. This study shows reminds us that this isn’t true. In addition, we know that for poor neighborhoods that don’t see reductions in poverty rates, that population steadily declines. Lost in Place, our own study of poor neighborhoods shows that over 4 decades, the three-quarters of poor neighborhoods that didn’t rebound lost 40 percent of their population.

Most moves are voluntary

Unlike many other studies, the Martin and Beck paper is able to use survey data to try and discern the motivations for household moves. Broadly speaking they divide moves into “voluntary” and “involuntary” moves.  The PSID asks movers why they moved, and those that respond to this open-ended question with answers coded as “moved in response to outside events including being evicted, health reasons, divorce, joining the armed services, or other involuntary reasons” are treated as involuntary moves.As they note, the distinction isn’t always as sharp as one would like, and it may be that some respondents rationalize some involuntary moves as voluntary ones, but the self-reported data are clear:  among renters, voluntary moves dramatically outnumber involuntary ones.  About 54 percent of all renters moved in the last two years; about 13 percent of all renters reported an involuntary move.  That means that about 75 percent of all renter moves were voluntary and about 25 percent of renter moves were involuntary.  As Margery Turner and her colleagues at the Urban Institute have shown, moving to another neighborhood is often the way poor families get better access to jobs, better quality schools, safer neighborhoods and better housing.

Gentrification has no impact on overall renter moves, but is associated with a small increase in involuntary moves

One of the most important studies of gentrification is Lance Freeman’s 2005 paper “Displacement or Succession?: Residential Mobility in Gentrifying Neighborhoods” which found that gentrification had essentially no effect on the rate at which households moved out of gentrifying neighborhoods.  Martin and Beck replicate this finding for all moves by renter households, they write:

Consistent with Freeman’s findings, Model 2 indicates that we cannot be confident that the average effect of gentrification on the probability of moving out is different from zero.

Graphically, Martin and Beck’s findings are can be depicted as follows.  About 54 percent of all renters move within two years. According to Martin and Beck’s modeling, the probability that a person in a gentrifying neighborhood moves in two years is about 1.7 percentage points  greater than for the typical person (after controlling for individual household characteristics). That suggests that for a typical resident, their probability of moving in a gentrifying neighborhood is about 55.7 percent, but that estimate in not statistically significant.

When they look just at “involuntary” moves, however, they find that there is a statistically significant effect of gentrification on the probability of moving.  Specifically, they find that rental households in in gentrifying neighborhoods are about 2.6 percent points more likely to report an in “involuntary move” in the past two years than those who don’t live in gentrifying neighborhoods.  Its important to put that in context.  According to the paper, about 54% of all renters moved in the last two years, and about 13 percent of them experienced an “involuntary move.”  The estimate in the paper is that the effect of living in a gentrifying neighborhood is about a 2.6 percentage point increase in the likelihood of an “involuntary” move.  That means if the average renter has a 13 percent chance of an involuntary move, a renter in a gentrifying neighborhood has a 15.6 percent chance of such a move.  These results are shown below:

Here, the estimate that a renter makes an involuntary move from a gentrifying neighborhood  (+2.6 percentage points) is greater than the 95 percent confidence interval, which suggest that there is a statistically significant difference between the share of the population experiencing involuntary moves in gentrifying neighborhoods as compared to all neighborhoods.


What would that look like in a typical neighborhood?  If you have a neighborhood with 2,000 households (about 5,000 people, with about 2.5 persons per household), and about half are renters and half are homeowners, you would expect of the 1,000 renting households that about 130 households would experience an involuntary move over a two year period.  If that tract gentrified, you would expect an additional 26 households to experience an “involuntary move.” But you would also expect 530 total households to have moved out of the neighborhood in that time, for all reasons, voluntary and involuntary.  These data put the scale of the gentrification effect in perspective. Whether or not they gentrify, there’s going to be enormous change in the renter population of any given urban neighborhood.

Gentrification has no impact on homeowner moves

Martin and Beck find no evidence that homeowners in gentrifying neighobrhoods are more likely to move, either in the aggregate, or involuntarily.  They test a number of different models of the connection between gentrification and moving: none produce statistically significant correlations between gentrification and moving; in some cases (though statistically insignificant) the correlation is negative: gentrification is associated with fewer homeowners moving from a neighborhood.  Their conclusion: for homeowners, their study “produces no evidence of displacement from gentrifying neighborhoods.”

Property taxes (and tax breaks) seem to have no connection with homeowner movement from gentrifying neighborhoods

One popular argument is that gentrification pushes up property values and results in higher property taxes for homeowners, and that especially for households with a fixed income, the burden of higher property taxes is likely to force them to move. Martin and Beck look closely at this question, and examine how changes in property assessments and property taxes correlate with the probability of moving. They find that there’s no statistically significant link between property taxes and moving in gentrifying neighborhoods.  Several states and localities have enacted property tax or assessment limitations, in part with the objective of lessening the financial exposure of fixed income households to the burden of higher property taxes. Martin and Beck look at the relationship between such limits and the probability of moving, and find that such limits don’t seem to have any effect on whether homeowners move out of gentrifying neighborhoods or not.

While homeowners in gentrifying neighborhoods have to shoulder the burden of paying higher property taxes, its typically only because their homes have appreciated more in value. In most cities, property taxes are levied at a rate equal to about 1 to 2 percent of a property’s market value, so the wealth effect of property appreciation dwarfs the negative income effect of having to pay higher property taxes.

Urban renters are a highly mobile group. Most renting households are likely to have changed neighborhoods in the past two years. We observe the same overall level of movement out of neighborhoods whether they gentrify or not.  This study suggests that somewhat more of those moves would be involuntary rather than voluntary.

 

 

The constancy of change in neighborhood populations

Neighborhoods are always changing; half of all renters move every two years.

There’s a subtle perceptual bias that underlies many of the stories about gentrification and neighborhood change. The canonical journalistic account of gentrification focuses on the observable fact that different people now live in a neighborhood than used to live there at some previous time. We seem to assume that most neighborhoods are stable and un-changing, and that absent some dramatic change, like gentrification, the people who lived in that neighborhood are the same ones who lived their a decade ago, and without such change, would be likely to live there a decade hence.  But constant population change or turnover is a regular feature of most neighborhoods, a fact confirmed by a recent study. To summarize the key takeaways:

  • The population of urban neighborhoods is always changing because moving is so common, especially for renters.
  • There’s little evidence that gentrification causes overall rates of moving to increase, either for homeowners or renters.
  • Homeowners don’t seem to be affected at all, and there’s no evidence that higher property taxes (or property tax breaks) influence moving decisions.
  • While involuntary moves for renters increase slightly in gentrified neighborhoods, there’s no significant change in total moves

In an article published in Urban Affairs Review, “Gentrification, Property Tax Limitation and Displacement,” Isaac William Martin and Keven Beck present their analysis of longitudinal data from the Panel Survey of Income Dynamics that track family moves over more than a decade.  An un-gated version of the paper is available here. One of the challenges of studying gentrification and neighborhood change is that most data simply provides snapshots of a neighborhood’s population at a given point in time, and provides little information about the comings and goings of different households. The PSID sample is unusual, in that in tracks households and individuals over a period of decades–this study uses data on the movement of household heads from 1987 through 2009. Martin and Beck were able to access confidential data that reports neighborhood location and enables them to identify the movement of households to different neighborhoods.

Richard Florida reviewed the Martin and Beck paper at City Lab and highlighted two of the study’s key findings:  that homeowners don’t seem to be displaced by gentrification and a subsidiary finding that property taxes (and tax breaks for homeowners) don’t seem to affect displacement.  These are both significant findings, but we want to step back and look at the broader picture this study paints of how neighborhoods change, because this study provides a useful context for understanding the complex dynamics of migration that are often left out of discussions of gentrification.

Change is a constant–Most renters have moved on after two years

One of the most striking findings from this study is how frequently renters move. These data show than in any given two-year period a majority (54 percent) of renter households had moved to a different neighborhood. The average tenure (length of time they’ve lived in their current residence) is on average 1.7 years. (Table 1).  Moving rates are lower (16 percent over two years) for homeowners, and average tenures are considerably longer (4.9 years, on average).  But the important thing to keep in mind is just how much volatility and turnover there is in neighborhood populations. Statistically, if about half of all renters move out of a neighborhood every two years, the probability than any current renter will live in that neighborhood ten years hence is about 3 percent (0.5 raised to the fifth power).

Many of the public discussions of gentrification assume that somehow, in the absence of gentrification, neighborhoods would somehow remain just the same, and that few or no residents would move away. This study shows reminds us that this isn’t true. In addition, we know that for poor neighborhoods that don’t see reductions in poverty rates, that population steadily declines. Our own study of poor neighborhoods shows that over 4 decades, the three-quarters of poor neighborhoods that didn’t rebound lost 40 percent of their population.

Most moves are voluntary

Unlike many other studies, the Martin and Beck paper is able to use survey data to try and discern the motivations for household moves. Broadly speaking they divide moves into “voluntary” and “involuntary” moves.  The PSID asks movers why they moved, and those that respond to this open-ended question with answers coded as “moved in response to outside events including being evicted, health reasons, divorce, joining the armed services, or other involuntary reasons” are treated as involuntary moves.As they note, the distinction isn’t always as sharp as one would like, and it may be that some respondents rationalize some involuntary moves as voluntary ones, but the self-reported data are clear:  among renters, voluntary moves dramatically outnumber involuntary ones.  About 54 percent of all renters moved in the last two years; about 13 percent of all renters reported an involuntary move.  That means that about 75 percent of all renter moves were voluntary and about 25 percent of renter moves were involuntary.  As Margery Turner and her colleagues at the Urban Institute have shown, moving to another neighborhood is often the way poor families get better access to jobs, better quality schools, safer neighborhoods and better housing.

Gentrification has no impact on overall renter moves, but is associated with a small increase in involuntary moves

One of the most important studies of gentrification is Lance Freeman’s 2005 paper “Displacement or Succession?: Residential Mobility in Gentrifying Neighborhoods” which found that gentrification had essentially no effect on the rate at which households moved out of gentrifying neighborhoods.  Martin and Beck replicate this finding for all moves by renter households, they write:

Consistent with Freeman’s findings, Model 2 indicates that we cannot be confident that the average effect of gentrification on the probability of moving out is different from zero.

Graphically, Martin and Beck’s findings are can be depicted as follows.  About 54 percent of all renters move within two years. According to Martin and Beck’s modeling, the probability that a person in a gentrifying neighborhood moves in two years is about 1.7 percentage points  greater than for the typical person (after controlling for individual household characteristics). That suggests that for a typical resident, their probability of moving in a gentrifying neighborhood is about 55.7 percent, but that estimate in not statistically significant.

When they look just at “involuntary” moves, however, they find that there is a statistically significant effect of gentrification on the probability of moving.  Specifically, they find that rental households in in gentrifying neighborhoods are about 2.6 percent points more likely to report an in “involuntary move” in the past two years than those who don’t live in gentrifying neighborhoods.  Its important to put that in context.  According to the paper, about 54% of all renters moved in the last two years, and about 13 percent of them experienced an “involuntary move.”  The estimate in the paper is that the effect of living in a gentrifying neighborhood is about a 2.6 percentage point increase in the likelihood of an “involuntary” move.  That means if the average renter has a 13 percent chance of an involuntary move, a renter in a gentrifying neighborhood has a 15.6 percent chance of such a move.  These results are shown below:

Here, the estimate that a renter makes an involuntary move from a gentrifying neighborhood  (+2.6 percentage points) is greater than the 95 percent confidence interval, which suggest that there is a statistically significant difference between the share of the population experiencing involuntary moves in gentrifying neighborhoods as compared to all neighborhoods.


What would that look like in a typical neighborhood?  If you have a neighborhood with 2,000 households (about 5,000 people, with about 2.5 persons per household), and about half are renters and half are homeowners, you would expect of the 1,000 renting households that about 130 households would experience an involuntary move over a two year period.  If that tract gentrified, you would expect an additional 26 households to experience an “involuntary move.” But you would also expect 530 total households to have moved out of the neighborhood in that time, for all reasons, voluntary and involuntary.  These data put the scale of the gentrification effect in perspective. Whether or not they gentrify, there’s going to be enormous change in the renter population of any given urban neighborhood.

Gentrification has no impact on homeowner moves

Martin and Beck find no evidence that homeowners in gentrifying neighobrhoods are more likely to move, either in the aggregate, or involuntarily.  They test a number of different models of the connection between gentrification and moving: none produce statistically significant correlations between gentrification and moving; in some cases (though statistically insignificant) the correlation is negative: gentrification is associated with fewer homeowners moving from a neighborhood.  Their conclusion: for homeowners, their study “produces no evidence of displacement from gentrifying neighborhoods.”

Property taxes (and tax breaks) seem to have no connection with homeowner movement from gentrifying neighborhoods

One popular argument is that gentrification pushes up property values and results in higher property taxes for homeowners, and that especially for households with a fixed income, the burden of higher property taxes is likely to force them to move. Martin and Beck look closely at this question, and examine how changes in property assessments and property taxes correlate with the probability of moving. They find that there’s no statistically significant link between property taxes and moving in gentrifying neighborhoods.  Several states and localities have enacted property tax or assessment limitations, in part with the objective of lessening the financial exposure of fixed income households to the burden of higher property taxes. Martin and Beck look at the relationship between such limits and the probability of moving, and find that such limits don’t seem to have any effect on whether homeowners move out of gentrifying neighborhoods or not.

While homeowners in gentrifying neighborhoods have to shoulder the burden of paying higher property taxes, its typically only because their homes have appreciated more in value. In most cities, property taxes are levied at a rate equal to about 1 to 2 percent of a property’s market value, so the wealth effect of property appreciation dwarfs the negative income effect of having to pay higher property taxes.

Urban renters are a highly mobile group. Most renting households are likely to have changed neighborhoods in the past two years. We observe the same overall level of movement out of neighborhoods whether they gentrify or not.  This study suggests that somewhat more of those moves would be involuntary rather than voluntary.

 

 

Constant change and gentrification

A new study of gentrification sheds light on how neighborhoods change.  Here are the takeaways:

  • The population of urban neighborhoods is always changing because moving is so common, especially for renters.
  • There’s little evidence that gentrification causes overall rates of moving to increase, either for homeowners or renters.
  • Homeowners don’t seem to be affected at all, and there’s no evidence that higher property taxes (or property tax breaks) influence moving decisions.
  • While involuntary moves for renters increase slightly in gentrified neighborhoods, there’s no significant change in total moves

We’ve been closely reading a new study on gentrification and neighborhood change. In an article published in Urban Affairs Review, “Gentrification, Property Tax Limitation and Displacement,” Isaac William Martin and Keven Beck present their analysis of longitudinal data from the Panel Survey of Income Dynamics that track family moves over more than a decade.  An un-gated version of the paper is available here. One of the challenges of studying gentrification and neighborhood change is that most data simply provides snapshots of a neighborhood’s population at a given point in time, and provides little information about the comings and goings of different households. The PSID sample is unusual, in that in tracks households and individuals over a period of decades–this study uses data on the movement of household heads from 1987 through 2009. Martin and Beck were able to access confidential data that reports neighborhood location and enables them to identify the movement of households to different neighborhoods.

Richard Florida reviewed the Martin and Beck paper at City Lab and highlighted two of the study’s key findings:  that homeowners don’t seem to be displaced by gentrification and a subsidiary finding that property taxes (and tax breaks for homeowners) don’t seem to affect displacement.  These are both significant findings, but we want to step back and look at the broader picture this study paints of how neighborhoods change, because this study provides a useful context for understanding the complex dynamics of migration that are often left out of discussions of gentrification.

Change is a constant–Most renters have moved on after two years

One of the most striking findings from this study is how frequently renters move. These data show than in any given two-year period a majority (54 percent) of renter households had moved to a different neighborhood. The average tenure (length of time they’ve lived in their current residence) is on average 1.7 years. (Table 1).  Moving rates are lower (16 percent over two years) for homeowners, and average tenures are considerably longer (4.9 years, on average).  But the important thing to keep in mind is just how much volatility and turnover there is in neighborhood populations. Statistically, if about half of all renters move out of a neighborhood every two years, the probability than any current renter will live in that neighborhood ten years hence is about 3 percent (0.5 raised to the fifth power).

Many of the public discussions of gentrification assume that somehow, in the absence of gentrification, neighborhoods would somehow remain just the same, and that few or no residents would move away. This study shows reminds us that this isn’t true. In addition, we know that for poor neighborhoods that don’t see reductions in poverty rates, that population steadily declines. Our own study of poor neighborhoods shows that over 4 decades, the three-quarters of poor neighborhoods that didn’t rebound lost 40 percent of their population.

Most moves are voluntary

Unlike many other studies, the Martin and Beck paper is able to use survey data to try and discern the motivations for household moves. Broadly speaking they divide moves into “voluntary” and “involuntary” moves.  The PSID asks movers why they moved, and those that respond to this open-ended question with answers coded as “moved in response to outside events including being evicted, health reasons, divorce, joining the armed services, or other involuntary reasons” are treated as involuntary moves.As they note, the distinction isn’t always as sharp as one would like, and it may be that some respondents rationalize some involuntary moves as voluntary ones, but the self-reported data are clear:  among renters, voluntary moves dramatically outnumber involuntary ones.  About 54 percent of all renters moved in the last two years; about 13 percent of all renters reported an involuntary move.  That means that about 75 percent of all renter moves were voluntary and about 25 percent of renter moves were involuntary.  As Margery Turner and her colleagues at the Urban Institute have shown, moving to another neighborhood is often the way poor families get better access to jobs, better quality schools, safer neighborhoods and better housing.

Gentrification has no impact on overall renter moves, but is associated with a small increase in involuntary moves

One of the most important studies of gentrification is Lance Freeman’s 2005 paper “Displacement or Succession?: Residential Mobility in Gentrifying Neighborhoods” which found that gentrification had essentially no effect on the rate at which households moved out of gentrifying neighborhoods.  Martin and Beck replicate this finding for all moves by renter households, they write:

Consistent with Freeman’s findings, Model 2 indicates that we cannot be confident that the average effect of gentrification on the probability of moving out is different from zero.

Graphically, Martin and Beck’s findings are can be depicted as follows.  About 54 percent of all renters move within two years. According to Martin and Beck’s modeling, the probability that a person in a gentrifying neighborhood moves in two years is about 1.7 percentage points  greater than for the typical person (after controlling for individual household characteristics). That suggests that for a typical resident, their probability of moving in a gentrifying neighborhood is about 55.7 percent, but that estimate in not statistically significant.

When they look just at “involuntary” moves, however, they find that there is a statistically significant effect of gentrification on the probability of moving.  Specifically, they find that rental households in in gentrifying neighborhoods are about 2.6 percent points more likely to report an in “involuntary move” in the past two years than those who don’t live in gentrifying neighborhoods.  Its important to put that in context.  According to the paper, about 54% of all renters moved in the last two years, and about 13 percent of them experienced an “involuntary move.”  The estimate in the paper is that the effect of living in a gentrifying neighborhood is about a 2.6 percentage point increase in the likelihood of an “involuntary” move.  That means if the average renter has a 13 percent chance of an involuntary move, a renter in a gentrifying neighborhood has a 15.6 percent chance of such a move.  These results are shown below:

Here, the estimate that a renter makes an involuntary move from a gentrifying neighborhood  (+2.6 percentage points) is greater than the 95 percent confidence interval, which suggest that there is a statistically significant difference between the share of the population experiencing involuntary moves in gentrifying neighborhoods as compared to all neighborhoods.


What would that look like in a typical neighborhood?  If you have a neighborhood with 2,000 households (about 5,000 people, with about 2.5 persons per household), and about half are renters and half are homeowners, you would expect of the 1,000 renting households that about 130 households would experience an involuntary move over a two year period.  If that tract gentrified, you would expect an additional 26 households to experience an “involuntary move.” But you would also expect 530 total households to have moved out of the neighborhood in that time, for all reasons, voluntary and involuntary.  These data put the scale of the gentrification effect in perspective. Whether or not they gentrify, there’s going to be enormous change in the renter population of any given urban neighborhood.

Gentrification has no impact on homeowner moves

Martin and Beck find no evidence that homeowners in gentrifying neighobrhoods are more likely to move, either in the aggregate, or involuntarily.  They test a number of different models of the connection between gentrification and moving: none produce statistically significant correlations between gentrification and moving; in some cases (though statistically insignificant) the correlation is negative: gentrification is associated with fewer homeowners moving from a neighborhood.  Their conclusion: for homeowners, their study “produces no evidence of displacement from gentrifying neighborhoods.”

Property taxes (and tax breaks) seem to have no connection with homeowner movement from gentrifying neighborhoods

One popular argument is that gentrification pushes up property values and results in higher property taxes for homeowners, and that especially for households with a fixed income, the burden of higher property taxes is likely to force them to move. Martin and Beck look closely at this question, and examine how changes in property assessments and property taxes correlate with the probability of moving. They find that there’s no statistically significant link between property taxes and moving in gentrifying neighborhoods.  Several states and localities have enacted property tax or assessment limitations, in part with the objective of lessening the financial exposure of fixed income households to the burden of higher property taxes. Martin and Beck look at the relationship between such limits and the probability of moving, and find that such limits don’t seem to have any effect on whether homeowners move out of gentrifying neighborhoods or not.

While homeowners in gentrifying neighborhoods have to shoulder the burden of paying higher property taxes, its typically only because their homes have appreciated more in value. In most cities, property taxes are levied at a rate equal to about 1 to 2 percent of a property’s market value, so the wealth effect of property appreciation dwarfs the negative income effect of having to pay higher property taxes.

Urban renters are a highly mobile group. Most renting households are likely to have changed neighborhoods in the past two years. We observe the same overall level of movement out of neighborhoods whether they gentrify or not.  This study suggests that somewhat more of those moves would be involuntary rather than voluntary.

 

This post has been revised to correct typographical errors, and to replace an earlier data table with charts illustrating the same information.

How urban geometry creates neighborhood identity

Does geometry bias our view of how neighborhoods work?

Imagine a neighborhood that looks like this:

On any given block, there might be a handful of small apartment buildings—three-flats—which are usually clustered near intersections and on major streets. Everything else is modest single-family homes, built on lots the same size as the three-flats.

What kind of community is this? Well, if you were to walk, bike, or drive around it, you would spend most of your time in front of these bungalows, which make up, on the block pictured above, fully 75 percent of the buildings. Visually, they define the landscape; the three-flats are accents, notable but clearly in the minority.

If you lived in this community—particularly if you lived in one of the bungalows—this visual character might be something you’re attached to, and identify with. You might begin to define your neighborhood by these bungalows, and expect the neighborhood’s future changes to conform with this identity.

And yet there’s something curious here: equal numbers of families live in bungalows and three-flats in the neighborhood pictured above. There are nine bungalows, each with one family; and three three-flats, each with three. (And if any of those three-flats have converted garden apartments, there are more people in the three-flats!)

But basic rules of geometry mean that if there are equal numbers of people in higher-density and lower-density housing types in the same neighborhood, the people in the lower-density housing will take up much more space—and, maybe, have an advantage in defining the identity of their neighborhood. (You’ve certainly noticed a similar dynamic with maps of the presidential race by county: a sea of low-density counties in red visually swamps the fewer, but much higher-density, counties in blue.)

Does this matter? I think yes, because the power to define a neighborhood’s publicly accepted identity also brings with it a great amount of power in shaping its future development. That’s especially the case in cities like Chicago, where local aldermen representing relatively small areas have near-veto power over new housing, businesses, and many transportation decisions within their wards. A group of people who manage to convince their alderman that a particular development, or streetscape, is “out of character” with the neighborhood’s identity is often able to defeat it.

This is especially relevant because the low-density/high-density housing usually corresponds to other axes of unbalanced power: within any given neighborhood, people in higher-density housing usually have lower average incomes, and are more likely to be people of color. What’s more, they’re also likely to be younger and renters rather than owners—and so statistically less likely to sit on a neighborhood board, or attend public meetings. A dynamic that privileges the ability of people in low-density housing to define and shape their neighborhood, then, is likely to reinforce some of the most basic inequalities of American society.

Nor is this only a theoretical issue. I thought of it after reading articles like this one, about Jefferson Park on the far northwest side of Chicago. “Should Jefferson Park Keep Suburban Vibe?” the headline asks, referring to some locals’ opposition to any new multifamily housing. Much of Jefferson Park looks a good deal like my imaginary neighborhood above; it’s generally identified with the city’s much-loved “bungalow belt” of early twentieth century single-family homes. Thus its identity as “suburban,” relative to the denser neighborhoods to the east.

But this widely accepted identity—one taken for granted in the headline of a story about whether the neighborhood ought to accept new high-density residents—is an artifact of urban geometry. According to the Chicago area’s metropolitan planning organization, 72 percent of the residential land in Jefferson Park is taken up with single-family homes. But most people who live in Jefferson Park—52 percent—actually live in an apartment or condo.

There’s obviously no smoking gun here about the power to define the future of the neighborhood. Can a neighborhood where most people live in multifamily housing be said to have a “suburban vibe” in this sense? If not, does that mean any of the people who strongly oppose new multifamily housing, and the people who would live in it, would change their minds? Or would their rhetoric be less powerful to those (probably the vast majority) who don’t have a strong opinion? To the alderman?

It’s hard to know. But it seems unlikely—especially if you believe any of the arguments made by people like Sonia Hirt about the cultural power of the idea of the single-family home—that these sorts of constructed identities don’t have some kind of effect on the paths that neighborhoods take.

Of course, there is a flip side to the way that urban geometry distorts people’s perceptions of how most of their neighbors live. And that’s that it’s possible to add much more housing without changing the visual character of the neighborhood in the same proportion. The question is whether it’s possible to add that housing—contributing, on average, to more diverse, affordable, and sustainable cities—when people believe (rightly or wrongly) that the character of their neighborhood must change to accommodate it.

 

More evidence for peer effects: Help with homework edition

There’s a large a growing body of research that shows the importance of peer effects on lifetime economic success of kids.  For example, while the education level your parents is a strong determinant of your level of education, it turns out that the education level of your neighbors is nearly half as strong.  Much of this effect has to do with the level of resources and performance level of local schools:  people who live in neighborhoods with lots of well-educated people have schools with more resources and stronger parental support.  And there’s also a fair argument that a better educated peer group provides access to social networks and role models that shape aspirations and opportunities.

Parental investment. (“Homework helping hand.” Flickr: Pete)

A new University of Chicago working paper from Josh Kinsler and Ronni Pavan underscores another, more subtle way that peer effects operate in schools.  It’s titled:  “Parental Beliefs and Investment in Children:  The Distortionary Impact of Schools.”  We know that one critical factor in explaining student achievement is what education scholars call “parental investment.”  By this they mean the amount of time (rather than money) that parents dedicate to helping advance their child’s learning by, for example, helping with homework, or participating in school activities, or arranging tutoring or extra-curricular learning opportunities.

The study uses data from a national longitudinal survey covering Kindergarten, First- and Third-Grade students, and looks at the connection between parental beliefs about student performance generally, and in math and reading, and how this the amount of time parents spend helping children do homework and similar activities.

Kinsler and Pavan find that there’s a strong correlation between parental beliefs about their child’s relative performance and their investment in these kinds of time intensive learning activities.  Parents who think their children are at or above the average, tend to invest less time in doing things like helping with homework.  And there’s the critical part of their finding:  parents tend to base their assessment of their child’s performance relative to other students in his or her class or school, rather than other schools, or the state or nation as a whole.  This is mostly unsurprising: parents are going to get most of their information about academic performance by comparing their child to his or her classmates.

But the effect of this “local bias” in comparisons is that parents of students attending low performing schools will tend to have an inflated assessment of how well their child is doing–relative to all other students.  This over-optimism will lead them to under invest in helping with homework, and doing other things to enrich their child’s educational opportunities.  Its well-understood that low-income and single-parent households already start off with more limited time and resources to help support their children’s education.  What this suggests is that given all the competing demands for their time and attention, they may be lulled into a false sense that their children are doing “well-enough” in school.  As Kinsler and Pavan conclude:

Parents of low skill children who attend schools where average skill is also low will perform fewer remedial type investments than parents of similarly able children who attend schools where average skill is higher. Because of the tendency for students and families to sort into schools and neighborhoods, low skill children are more likely to attend schools where average skill is also low. As a result, the distortion in parental beliefs generated by local skill comparisons leads to underinvestment for low skill children.

As a result, one of the subtle and pernicious ways that economic segregation and the concentration poverty influence children’s lifetime incomes is by giving parents (and probably children) too limited a basis for measuring their performance and lead them to under-invest in educational skills.

How diverse are the neighborhoods white people live in?

Overall, America is becoming more diverse, but in many places the neighborhoods we live in remain quite segregated. The population of the typical US metropolitan area has a much more ethnically and racially mixed composition than it did just a few decades ago. Overall, measured levels of segregation between racial and ethnic groups are declining. But change at the neighborhood level, particularly in the neighborhoods that are home to the “typical” white family, have changed more in some places than others.

Our interest in this subject was kindled last month with an analysis of the latest American Community Survey data, released last month, prepared by the Brookings Institution’s venerable demographer, Bill Frey. In a post entitled, “White neighborhoods get modestly more diverse, new census data show,” Frey looked at the racial and ethnic composition of the nation’s largest metropolitan areas, and gave us his first blush analysis of the unfolding trends of growing diversity and gradually receding racial and ethnic segregation.

The big picture is that American metro areas are becoming more diverse. In the 100 largest metro areas, the share of the population that is white and non-Hispanic has declined from 64 percent in 2000 to 56 percent in 2011-15.  And conversely the share that is Latino, Black, Asian or some other racial-ethnic category has increased from 36 percent to 44 percent. But while metro areas are becoming more diverse, the neighborhood in which the typical white resident lives is much less diverse than the overall metro area. In 2011-15, the typical white resident in a metro area lived in a neighborhood than was 72 percent white, down slightly from a level of 79 percent in 2000.  In 2000, the average white resident lived in a neighborhood that has 15 percentage points (79% – 64%) more “white” than the metro area, in 2011-15, the typical white resident lived in a neighborhood than was 16 percentage points (72% – 56%) more white than the overall metro area.

 

Frey’s key point is that while America’s metro population is becoming increasingly diverse, especially with the growth of the Latino and Asian populations, most white Americans still live in neighborhoods that are disproportionately white, especially when compared to the overall racial and ethnic composition of the metropolitan area in which they are located. The best way to neatly summarize the complex relationship between neighborhood and metropolitan racial ethnic composition for this purpose is to look at the share of the population categorized as white at the metropolitan level, and then compare it the the share of the population that is white in the neighborhood in which the typical white resident in a particular metropolitan area lives.  Statistically, by “typical” we mean median.  Frey computes the share of the white population in the census tract in each metropolitan area which includes the population-weighted median white resident, with tracts sorted by white share of the population.  Essentially, this means that half of the metro area’s white population lives in a tract with a white share of population higher than this “typical” number and half lives in a tract that has a white share of population lower than this number.

Of course, at City Observatory, we wanted to dig deeper.  And Brookings and Frey have publicly posted their tabulation of the ACS data  (you can download the spreadsheets here).

Metro versus neighborhood

A major factor influencing the demographic composition of the typical white neighborhood is the metropolitan area in which it is located. In more diverse metro areas, the typical white resident tends to live in a neighborhood with a smaller share of white population.  We’ve plotted the relationship between the white share of the metropolitan area population (shown on the horizontal axis of the chart) against the share of the white population in which the typical white resident lives.  The upward sloping line and strong correlation confirms that metro diversity influences neighborhood diversity.

You can think of the line as showing the typical relationship between the share of the population in a metropolitan area that is white and the average share of the population in a typical white neighborhood that is white.  Metropolitan areas above that line have a higher fraction of whites in the typical white neighborhood than you would expect given their demographics, while metropolitan areas below the line are ones where the typical white resident lives in a neighborhood with a smaller share of white residents than you would expect, given the national pattern.  So, for example, consider the difference between Portland and St. Louis.  The two metro areas have a nearly identical share of white population (74 percent in St. Louis, 75 percent in Portland). But the average white St. Louis resident lives in a neighborhood that is 85 percent white, while the average Portland resident lives in a neighborhood that is 77 percent white.  A second example: Memphis and Las Vegas have a similar share of white population (45 percent and 46 percent respectively). Yet the average white Memphis resident lives in a neighborhood that is 66 percent white, while the average Las Vegas white resident lives in a neighborhood that is 54 percent white.

Measuring the neighborhood effect

The difference between the overall share of the metro area population that is white and the white population’s share of the typical white neighborhood is a good indicator of the neighborhood effect:  the extent to which the white population is segregated into neighborhoods that are whiter than the metro area itself.  In effect, the difference between these two neighborhoods is an indicator of how segregated whites are from non-whites in the metropolitan area.  If every census tract had the same white/non-white shares as the metropolitan area as a whole, there would be zero white/non-white segregation.  (Notice that mathematically, the median share white in white census tracts cannot exceed the share white in the entire metropolitan area).   In the following table, we rank metropolitan areas according to the difference in share white in the typical white neighborhood minus the metro area share that is white.

In the median large metropolitan area (for example Hartford, St. Louis or Charlotte), the typical white resident lives in a neighborhood that is about ten percentage points whiter than the metropolitan area as a whole.  The places with the largest difference between the white share of the metro population and the white share of the typical white neighborhood include Miami, New York and Los Angeles, where white residents live in neighborhoods that are about 22 percent more white than the metropolitan area.  The places with the smallest difference between the typical white neighborhood and the metro area average include Portland (2 percentage points), Pittsburgh and Salt Lake City (both about 4 percentage points).

The upshot is that when it comes to the lived experience of diversity, some factors are global, but important ones are still local. Its possible to live in a very diverse metropolitan area, with a high fraction of non-Hispanic white residents, and still have a high level of segregation, so that white residents live in places where they have very high levels of white neighbors. And the converse is also true, in some

Note

Throughout this commentary, we follow Bill Frey’s condensed version of Census Bureau racial and ethnic categories.  By white, we mean persons who report to Census that they have a single race (white) and who report they are not of Hispanic origin.  Frey’s report includes data for African-Americans and Asian-Americans who report a single race, persons of Hispanic origin, regardless of race, and a final category including all other persons.  As we do regularly here at City Observatory, Frey reports data for metropolitan areas with a population of 1 million or more.

Cities and Elections

It’s election day, 2016. Here’s some of what we know about cities and voting.

Well, at last. Today is election day. While we’re all eagerly awaiting the results of the vote, we thought we’d highlight a few things we know about voting, especially as they relate to cities. Its food for thought as we get ready to digest and understand the results today’s elections.

It's time. (Flickr: Amanda Wood).
It’s time. (Flickr: Amanda Wood).

Democrats and density

In the past few elections, there’s been an increasingly strong relationship between population density and the share of the vote going to the democratic candidate. Dave Troy has plotted county level election returns from 2012 against population density. Low density counties voted overwhelmingly for Mitt Romney; higher density ones voted for Barack Obama. That same pattern is likely to be in evidence today.

Density and Voting

As a result, as Emily Badger wrote in The New York Times last week, the Republican party has essentially abandoned cities in presidential elections.

Homeowners are voters

Regular readers of City Observatory are very familiar with the homevoter hypothesis propounded by William Fischel, which observes that homeowners participate actively in the formation of local policies, as a way of protecting and enhancing the value of their homes. The practical implication is that homeowners support density restrictions and other policies that tend to raise home values and rents. In contrast, renters are generally under-represented in the electorate, especially in purely local elections. More data on that point was presented recently by the website ApartmentList.com. According to tabulations of self-reported data from the Census, for voting in the 2012 general election, about 77% of homeowners vote, compared to only 58% of renters – in other words, homeowners are 25% more likely to make their voice heard in an election. Part of the difference is explained by length of tenure—homeowners have generally lived in their houses longer than renters, but homeowners are more likely to vote than renters for any given length of tenure. Homeowners who have lived in their homes for 1-2 years are more likely to vote than renters who have lived in their homes for more than five years.

The gerontocracy of local elections

A new study from Portland State University takes a close look at the demographics of voter turnout in local elections. (Full disclosure: it’s lead authors include our friends and colleagues Phil Keisling and Jason Jurjevich). Their final report, “Who votes for Mayor?” provides a detailed look at turnout patterns in 50 of the nation’s largest cities. A particular virtue of this study is that it uses data from election records—more than 22 million in all–rather than after-the-fact surveys, which can be subject to mis-reporting (respondents may be reluctant to tell pollsters than they didn’t vote). Among their key findings: older people are much more likely to vote, especially in purely local elections than are younger ones. In cities, the variation in turnout by age heavily skews who chooses mayors and other local leaders. In the typical local election, the median voter is a full generation older than the overall electorate. As a result, at the municipal level, we have a gerontocracy, rather than a fully functioning democracy.

Moving and voting

Recent survey data for the current election, collected in September, zeroed in on an interesting aspect of voter preference: whether someone lived in or near the place where they were born. Summarizing the results of the Atlantic/PRRI survey, Daniel Cox and Robert Jones examined the presidential preference of white voters based on how close they lived to where they were born. Whites who reported living in their childhood hometown favored Trump 57 to 31 percent; those who lived outside their home town, but within two hours favored Trump 50 to 41 percent, and those who lived more than two hours away favored Clinton 46 to 40 percent. Of course, migration is a non-random and self-selected behavior, and is strongly correlated with education. But a key point here is that those whites who’ve chosen to move and live in different places are statistically more likely to favor Hillary Clinton than Donald Trump.

screenshot-2016-11-02-10-11-19

In this election—as in every election—many key urban issues, including age, education, migration, density and homeownership—play important roles in shaping electoral outcomes. In the next few days we’ll be examining the results of the 2016 election to see what role each of these factors has played.

 

 

 

Market timing and racial wealth disparities

One of the enduring features of American inequality is the wide disparity in homeownership rates between white Americans and Latinos and African-Americans. And because homeownership has — or at least was, historically — a principal means by which families built wealth, this disparity in homeownership translated into or amplified racial and ethnic wealth disparities.  There are, of course, many reasons for the disparities in homeownership rates: discrimination in home sales, employment, and education figure as prominent explanations, as does red-lining and exploitative lending practices.

This observation seemingly leads to a straight-forward policy response:  if we want to redress our wealth disparity, we ought to be promoting wider homeownership, especially for racial and ethnic groups, like Latinos and African-Americans. And because access to housing finance is central to ownership, that leads to proposals to liberalize or loosen up lending standards.  That’s exactly the case that was made recently by the Urban Institute, which said:

For a full mortgage market recovery, we need to expand the credit box again. A number of reforms can be undertaken to encourage lending to creditworthy borrowers who would have qualified before the housing boom. A return to 2005 and 2006 lending practices would be ill-fated, but the pendulum has unquestionably swung too far. Today’s tight standards have locked out many prospective borrowers from homeownership, disproportionately preventing African American and Hispanic families from building wealth and benefiting from the recovery.

The Urban Institute provides copious details on the patterns of mortgage lending, by race and ethnicity over the past decade and a half (with very cool mapping, both nationally and by metropolitan areas). They show that since 2006, mortgage lending to African-Americans and Latinos, as a share of all mortgage originations has fallen sharply.

But would loosening mortgage restrictions and opening up housing finance really result in economic gains to those now shut out of the housing market? Perhaps the most fundamental advice in investing (and the hardest to follow in practice) is “Buy low, sell high.” In a technical sense, this is referred to as “market timing.”

The big lesson of the housing bubble and subsequent bust is that market timing matters a lot to investment results.  If you bought your home in 2000 (or better yet, sometime in the previous decade), you not only saw big gains in the bubble, but you probably came out the other side with your head (and your mortgage) above water. But if you bought at the peak of the bubble, in 2005 or 2006, and especially if you purchased your home with a highly leveraged 90 or 95 percent mortgage (as many did) you saw your investment wiped out–and more.

And as the Urban Institute data make clear, the groups most likely to end up in this wealth destroying “buy high, sell low” situation are Latinos and African Americans:  “African American and Hispanic borrowers took out a greater share of mortgages as housing prices neared their peak, arguably the worst time to take out a loan.”  In 2001, when we were in a recession and house prices (by standards of that decade) were low, these groups made up just 13 percent of all new home mortgages. When lending standards loosened up, the share of minority borrowers surged, and in 2006, African-Americans and Latinos made up almost twice as big a share (23 percent) of new mortgages.  And then, as the bubble collapsed, and lending standards tightened, they were again squeezed out of the mortgage market.

ui_mortgage_race

As we’ve noted before at City Observatory, rotten market timing is just one of the problems confronting minority borrowers. In addition, they tend to be charged higher interest rates (typically because they have lower credit scores), they tend to buy in neighborhoods with greater volatility and downside risk, and were–as several billion dollar plus settlements attest–victimized by exploitative lending practices.

From the standpoint of policy, and trying to tackle this persistent wealth gap, the question going forward is whether housing investment in the future will out-perform other investments. Plus, as we’ve pointed out, there’s an inherent tension between treating housing as a wealth-building policy and achieving housing affordability. While relaxing lending standards further (and allowing borrowers to take on greater leverage) might help more Latino and African-American families buy homes, its far from clear than its a strategy that will enable them to build wealth.  It would be great if housing purchases were a risk-free investment that would guarantee a reasonable rate of return, and if all borrowers had the same opportunity to buy at the same terms and at the same times. But that’s not the way the housing market works, even–or especially–when lending standards are relaxed. If we want to redress the big gaps in wealth among racial and ethnic groups in the US, we’ll probably need to consider other policies to do so.

Are integrated neighborhoods stable?

More American neighborhoods are becoming integrated–and are staying that way

It’s rare that some obscure terminology from sociology becomes a part of our everyday vernacular, but “tipping point” is one of those terms. Famously, Thomas Schelling used the tipping point metaphor to explain the dynamics of residential segregation in the United States.  His thesis was that white residents were willing to live in a mixed race neighborhood, but only when whites were still a comfortable majority of its population. Above some level–the tipping point–whites would continue to live in a mixed race neighborhood only when whites remained a comfortable majority of its residents.  The notion of a tipping point has a dour implication for neighborhood change, it implies that mixed race neighborhoods, when they occur, are unstable and temporary transitional states between longer and more durable periods of segregation.

Many public discussions of neighborhood change implicitly assume that once put in motion, these tipping point dynamics ultimately cause a neighborhood to switch from one segregated category to another. For example, when she coined the term “gentrification” British sociologist Ruth Glass described the phenomenon as a complete transformation: “Once this process of ‘gentrification’ starts in a district it goes on rapidly until all or most of the original working class occupiers are displaced and the whole social character of the district is changed”  In this formulation, as in many public debates, there’s no expectation that a neighborhood will achieve and maintain an integrated state.

A paper published  by Kwan Ok Lee in the Journal of Urban AffairsTemporal Dynamics of Racial Segregation in the United States: An Analysis of Household Residential Mobility–looks at the processes of neighborhood change by race in the United States over the past four decades to see whether the instability of integrated neighborhoods implied by the “tipping point” theory is actually borne out in practice.  The results are surprising.

Lee’s paper looks at data on the racial and ethnic composition of census tracts in the United States.  Tracts are neighborhood-sized units developed by the Census Bureau that have an average population of about 4,000 persons.  Lee classified each of these census tracts according to the race and ethnicity of its population into one of six groups (predominantly white, predominantly black, predominantly other, black-white, white-other, black-other and multiethnic).  The exact definitions are complicated, but in general tracts with more than 80 percent of the population in one group were classified as predominantly in that group; multi-ethnic neighborhoods were those where no one group was a majority of the tract’s population (more details below). Lee’s paper traces neighborhood change in each of these tracts over two 20-year periods, 1970 to 1990 and 1990 to 2010. There’s a lot in this paper, but we think there are three particularly interesting findings.

First, the data show the growing diversity and modestly declining segregation of US neighborhoods.  The share of all neighborhoods that were predominantly white in the US declined from 67 percent in the 1970-1990 period to 57 percent in the 1990-2010 period.  Over this time period, the pace of transition to more racially mixed neighborhoods accelerated.  One in four predominantly white neighborhoods in 1970 became racially mixed over the next two decades; in 1990 one in three of predominantly white neighborhoods became racially mixed.   Similarly, the rate of transition in predominantly black neighborhoods also accelerated; about 19 percent of predominantly black neighborhoods in 1970 became racially mixed over the next 20 years; that fraction increased to about 24 percent between 1990 and 2010, as illustrated on the following chart.

lee_transition

Second, black-white neighborhoods became much more stable.  Black-white neighborhoods were those between 10% and 50% non-Hispanic black, and less than 10% Hispanic or non-Hispanic Asian. Of black-white neighborhoods in 1970, forty percent transitioned away from being racially mixed in the 20 years between 1970 and 1990.  Of the black-white neighborhoods in 1990, only 20 percent transitioned away from being racially mixed between 1990 and 2010; in effect, the rate of “tipping out” of integration declined by half.

Third, the number of truly multi-ethnic neighborhoods nearly doubled, from about 1.6 percent of all neighborhoods in 1970-1990 to about 3 percent of all neighborhoods in 1990-2010.  The definition of multiethnic is tracts that were at least 10% non-Hispanic black, at least 10 percent Hispanic or non-Hispanic Asian, and at least 40 percent non-Hispanic white. Once they became multi-ethnic, from 1990 to 2010, about 90 percent of them remained multi-ethnic for the next twenty years.

In all, its now the case that predominantly white neighborhoods are more likely to become racially mixed (one in three) than racially-mixed neighborhoods are likely to become dominated by a single racial/ethnic group (one in five).  And though they constitute a small share of the total, multi-ethnic neighborhoods are growing, and, once-established, persistent.

Lee also used data from the Panel Survey of Income Dynamics to follow the actual moves of thousands of families over several decades.  She found that once families moved into racially mixed neighborhoods, they tended to stay in those neighborhoods, or when they moved, they moved to other racially mixed neighborhoods.  She found that about 68 percent to 86 percent of black and white movers residing in racially mixed neighborhoods moved within their current neighborhoods or moved to other mixed neighborhoods during 1991–2009.

While much of our nation remains substantially segregated by race, Lee’s analysis points to at least a couple of hopeful signs.  The pace of desegregation, as measured by the transition of neighborhoods from predominantly black or predominantly white to a more multi-racial mix has accelerated. And once established, it appears that multi-racial neighborhoods tend to stay that way, and that few households in such neighborhoods make subsequent moves that lead to re-segregation.

 

Are integrated neighborhoods stable?

Its rare that some obscure terminology from sociology becomes a part of our everyday vernacular, but “tipping point” is one of those terms. Famously, Thomas Schelling used the tipping point metaphor to explain the dynamics of residential segregation in the United States.  His thesis was that white residents were willing to live in a mixed race neighborhood, but only when whites were still a comfortable majority of its population. Above some level–the tipping point–whites would continue to live in a mixed race neighborhood only when whites remained a comfortable majority of its residents.  The notion of a tipping point has a dour implication for neighborhood change, it implies that mixed race neighborhoods, when they occur, are unstable and temporary transitional states between longer and more durable periods of segregation.

A paper published earlier this year by Kwan Ok Lee in the Journal of Urban AffairsTemporal Dynamics of Racial Segregation in the United States: An Analysis of Household Residential Mobility–looks at the processes of neighborhood change by race in the United States over the past four decades to see whether the instability of integrated neighborhoods implied by the “tipping point” theory is actually borne out in practice.  The results are surprising.

Lee’s paper looks at data on the racial and ethnic composition of census tracts in the United States.  Tracts are neighborhood-sized units developed by the Census Bureau that have an average population of about 4,000 persons.  Lee classified each of these census tracts according to the race and ethnicity of its population into one of six groups (predominantly white, predominantly black, predominantly other, black-white, white-other, black-other and multiethnic).  The exact definitions are complicated, but in general tracts with more than 80 percent of the population in one group were classified as predominantly in that group; multi-ethnic neighborhoods were those where no one group was a majority of the tract’s population (more details below). Lee’s paper traces neighborhood change in each of these tracts over two 20-year periods, 1970 to 1990 and 1990 to 2010. There’s a lot in this paper, but we think there are three particularly interesting findings.

First, the data show the growing diversity and modestly declining segregation of US neighborhoods.  The share of all neighborhoods that were predominantly white in the US declined from 67 percent in the 1970-1990 period to 57 percent in the 1990-2010 period.  Over this time period, the pace of transition to more racially mixed neighborhoods accelerated.  One in four predominantly white neighborhoods in 1970 became racially mixed over the next two decades; in 1990 one in three of predominantly white neighborhoods became racially mixed.   Similarly, the rate of transition in predominantly black neighborhoods also accelerated; about 19 percent of predominantly black neighborhoods in 1970 became racially mixed over the next 20 years; that fraction increased to about 24 percent between 1990 and 2010, as illustrated on the following chart.

lee_transition

Second, black-white neighborhoods became much more stable.  Black-white neighborhoods were those between 10% and 50% non-Hispanic black, and less than 10% Hispanic or non-Hispanic Asian. Of black-white neighborhoods in 1970, forty percent transitioned away from being racially mixed in the 20 years between 1970 and 1990.  Of the black-white neighborhoods in 1990, only 20 percent transitioned away from being racially mixed between 1990 and 2010; in effect, the rate of “tipping out” of integration declined by half.

Third, the number of truly multi-ethnic neighborhoods nearly doubled, from about 1.6 percent of all neighborhoods in 1970-1990 to about 3 percent of all neighborhoods in 1990-2010.  The definition of multiethnic is tracts that were at least 10% non-Hispanic black, at least 10 percent Hispanic or non-Hispanic Asian, and at least 40 percent non-Hispanic white. Once they became multi-ethnic, from 1990 to 2010, about 90 percent of them remained multi-ethnic for the next twenty years.

In all, its now the case that predominantly white neighborhoods are more likely to become racially mixed (one in three) than racially-mixed neighborhoods are likely to become dominated by a single racial/ethnic group (one in five).  And though they constitute a small share of the total, multi-ethnic neighborhoods are growing, and, once-established, persistent.

Lee also used data from the Panel Survey of Income Dynamics to follow the actual moves of thousands of families over several decades.  She found that once families moved into racially mixed neighborhoods, they tended to stay in those neighborhoods, or when they moved, they moved to other racially mixed neighborhoods.  She found that about 68 percent to 86 percent of black and white movers residing in racially mixed neighborhoods moved within their current neighborhoods or moved to other mixed neighborhoods during 1991–2009.

While much of our nation remains substantially segregated by race, Lee’s analysis points to at least a couple of hopeful signs.  The pace of desegregation, as measured by the transition of neighborhoods from predominantly black or predominantly white to a more multi-racial mix has accelerated. And once established, it appears that multi-racial neighborhoods tend to stay that way, and that few households in such neighborhoods make subsequent moves that lead to re-segregation.

 

Where are African-American entrepreneurs?

Entrepreneurship is both a key driver of economic activity and an essential path to economic opportunity for millions of Americans. Historically, discrimination and lower levels of wealth and income have been barriers to entrepreneurship by African-Americans, but that’s begun to change. According to newly released data from the Census Bureau, its now estimated that there are more than 108,000 African-American owned businesses with a payroll in the U.S.

The new survey, conducted by the Census Bureau, in cooperation with the Ewing Marion Kauffman Foundation, provides a rich source of data about the economic contributions of African-American-owned businesses. Called the Annual Survey of Entrepreneurship, this is the first iteration of a survey that gathers data which asks detailed questions about key demographic characteristics of business owners, including gender, race and ethnicity, and veteran’s status. And unlike other business data, the entrepreneurship survey reports data by age of business, allowing us to examine separately the economic contributions of newly formed businesses.

The survey focuses on businesses with paid employees, and so generally excludes self-employed individuals working on their own. In 2014, the survey reports that there were more than 5.4 million businesses with a payroll in the United States. Of these, about 270,000 businesses were public corporations (or other business entities for which the gender or other demographic characteristics of owners could not be ascertained). These large corporate businesses employed almost 60 million workers (52 percent of total payroll employment).  The remaining 5.1 million firms with identifiable owners employed about 55 million workers.

The survey concludes that about 108,000 businesses, or roughly two percent of those businesses with individually identifiable owners, were owned exclusively by African-Americans. Together these businesses employed more than 1 million workers nationally.  On average, African-American owned businesses are younger than other businesses; about 14.1 percent of these African-American-owned businesses had started in the past two years, compared to about 8.9 percent of all employer firms. Africanowned businesses are found in all economic sectors, but are disproportionately represented in  health and social services.  About 28 percent of African-American owned businesses are engaged in health and social services, compared to about 12 percent of all individually owned businesses.

The report also offers data on business ownership patterns for the 50 largest US metropolitan areas.   We thought it would be interesting to see how different areas ranked in terms of the share of all businesses with employment that were owned by African-Americans.

Here’s a listing of the number of African-American owned businesses per 1,000 African-Americans in the population in each of the fifty largest US metropolitan areas. Think of this as an indicator of the likelihood that an African-American owns a business with a payroll in each of these places. Overall, about three in one thousand African-Americans in these fifty large metropolitan areas own a business.

Among the cities with the highest proportions of business owners among the African-American population are San Jose, St. Louis, Denver and Seattle. Each of these cities has about six or seven African-American entrepreneurs per 1,000 African-American residents. San Jose is famously the capital of Silicon Valley, which may explain why such a relatively high fraction of its African-American residents own businesses with a payroll. In contrast, Louisville, Buffalo, Memphis and Cleveland have much lower rates of African-American entrepreneurship, each of these metro areas has fewer than two African-American entrepreneurs per 1,000 African-American residents.

Another way to think about this data is to compare the share of the population in each metropolitan area that is African American with the share of entrepreneurs who are African American. The following chart shows this information. As one would expect, as the share of the African-American population increases, so too does the fraction of entrepreneurs who are African-American. There are some clear outliers. As shown on the chart, St. Louis has somewhat more African-American entrepreneurs than one would expect, given the size of is African-American population, and conversely, New Orleans has fewer. But on average, entrepreneurship is much less common among African-Americans than the overall population, in every metro area. On average, the share of the African-Americans who are entrepreneurs is about one-fifth their share of the population of a given metropolitan area.

In a previous post, we examined the geography of women-owned businesses.   The Census plans to conduct its new survey of entrepreneurs on an annual basis. This promises to be a useful was of benchmarking efforts to draw more Americans of every stripe into business ownership.

Homeownership can exacerbate inequality

In yesterday’s post, we described why homeownership is such a risky financial proposition for low income households, who tend to be disproportionately people of color. From a wealth-building standpoint, lower income households tend to buy homes at the wrong time, in the wrong place, face higher financing costs, and have less financial resilience to withstand the fluctuations of housing and economic markets. Yet we continue to persist the the belief that homeownership is a universal elixir for wealth building. In fact, there’s some strong evidence that our excessive investment in housing–and our subsidies for homeownership have worsened our income inequality problems. This suggests it might be time to rethink our national outlook on housing and wealth building.

Has Homeownership Actually Heightened Inequality?

New research from Zillow’s Svenja Gudell shows that the collapse of the housing bubble actually worsened inequality. Modestly priced homes saw the biggest price declines, and the households who owned these homes lacked the equity to cope with the downturn, and were much more likely to be foreclosed upon: “When the bubble popped, less-expensive homes—often bought by low-income homeowners—were more likely to be foreclosed on than higher-end homes.”

In many important respects, the case for home-ownership as wealth creation is a circular argument: We proclaim that housing is a great investment, and encourage families to go heavily into debt to purchase homes, and then use the fact that so much household wealth is tied up in housing to justify additional subsidies and regulations to drive up home values. These regulations include local zoning (which limits the supply of housing, helping drive up prices or as it’s usually expressed “to protect property values”), but go much further. The federal government directly or indirectly provides or guarantees most home mortgages (and prices lower and terms more favorable that would be the case in a purely private market). And the federal tax code provides something on the order of a quarter of a trillion dollars in annual subsidies to homeownership. If homeownership is a good investment, it’s substantially because government policies have made sure that it pays off.

From a distributional standpoint, it’s clear that the emphasis on homeownership has actually led to a greater concentration of wealth, and not greater equality. As Matthew Rognlie showed virtually all of the increase in wealth inequality in the United States in the past four decades is accounted for by the increase in the share of capital in housing. Mian and Sufi plotted the ratio of the amount of home equity owned by the highest income quintile compared to the middle quintile of the US population. In the 1990s, a household in the highest income quintile had about 5 times as much housing equity as the average, middle quintile. By 2010, this difference had nearly doubled: to 9 times as much housing equity.

Particularly over the past decade, housing has a poor record as a wealth creator. Overall, homeowners collectively lost something on the order of $7 trillion in the collapse of the housing bubble. To put that number in some perspective, consider the average home equity of a household in the middle of the income distribution, with a household head aged 35 to 44 years. Data compiled from the Fed’s Survey of Consumer Finance by David Rosnick and Dean Baker shot that while inflation-adjusted home equity for this group grew from 1992 through 2007, since then it has fallen sharply; today the households in the middle quintile of this age group have less than half as much home equity as in 2007.

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Time to Rethink Homeownership?

The collapse of the housing bubble erased all of the growth in the homeonwership rate in the United States since 1980. On the upswing, the bubble generated lots of (paper) wealth, and drew millions of households into ownership. The homeownership rate peaked at more than 69 percent in 2007, then plunged to less than 64 percent, as millions of households lost their homes.

The aftermath of the bubble should remind us that homeownership is a risky endeavor, and that for a substantial portion of the population, it’s not a feasible or prudent strategy for trying to build wealth. It’s time to re-think the role of homeownership in promoting wealth, especially for the poor. There are three big takeaways here:

  1. Pushing homeownership as a universal wealth building strategy for the poor, is a snare and a delusion. Its likely to hurt many families. Policies that lower the bar for home purchases, like very low down payment loans, may actually expose those least able to handle the risks of homeownership to even greater probability of loss.
  2. The efforts to extend homeownership down the economic spectrum in many ways simply constitute a way of providing political cover for subsidies like the mortgage interest deduction that chiefly benefit upper income households, thus actually worsening income inequality.
  3. As a nation, we have no substantial policy for helping renters build wealth. More than a third of our population, including its youngest, poorest, and people of color are, will continue to be renters. We might, for example, consider repurposing some of the $250 billion annually in federal tax subsidies to homeownership to help reduce rental costs or subsidize savings programs for renters.

Three challenges for the civic commons

In Philadelphia last week, the Gehl Institute convened Act Urban—a global group of leaders and practitioners in the field of the civic commons. After three days of fieldwork and observation, expert presentations and intense discussion, I was asked, along with other panelists to sum up what we’d heard and what the challenges are for this emerging field going forward. Here’s an abbreviated summary of what I had to say.

Philadelphia's Chinatown. Credit: Mumu Matryoshka, Flickr
Philadelphia’s Chinatown. Credit: Mumu Matryoshka, Flickr

 

Like most of the attendees I spoke with, I found it hopeful and encouraging to see the breadth and impact of the projects underway in Philadelphia. As a regular visitor to the city over the past couple of decades, it’s evident that change is very much in the air, and that in important respects, the fabric of the city is beginning to be re-woven in ways that promises to bring Philadelphians closer together. Over the course of three days we saw numerous examples of a range of institutions re-thinking their roles and facilities to promote greater civic involvement and to cross, if not erase, long-established boundaries that divided the community.

From the presentations and discussions, we know that what’s happening in Philadelphia is starting to happen in other cities as well, both in the US and in other countries. All of this is exciting and encouraging.

But, in my view, three big challenges stand directly ahead.

They are the three “M’s”: Moving from micro to macro; Markets; and Metrics. I’ll address each of them in turn.

Micro to macro

Economists routinely make a distinction between micro-economics and macro-economics. Micro is the observation of a single facet of the economy. It’s about learning from and describing the nature of a small, bounded and usually partial segment of the economy. Macroeconomics is the converse—it is the economy of the globe and nations, about how all kinds of small actions and activities add up at a large scale.

The field of civic engagement is strongly grounded in its “micro” phase. It’s about learning how to craft individual pieces of the public realm so that they function better, whether that’s parks, streets, libraries, swimming pools, or other public spaces. This is a logical starting place; it’s easier to mobilize, secure resources, make progress, learn from mistakes and move forward with small scale investment. And the success stories—many of which we heard described in Philadelphia—help inform practice and spread the message about the opportunities and merits of civic engagement.

Pop-up protected bike lane, Minneapolis. Credit: nickfalbo, Flickr
Pop-up protected bike lane, Minneapolis. Credit: nickfalbo, Flickr

 

But at some point, the civic commons has to explicitly aim to achieve scale. Instead of being exceptional and innovative, changing and challenging the status quo, it has to come to define the regular way of doing things. This is the challenge of moving from micro to macro, of moving from projects to policies and institutions, and moving from tactical urbanism to a broader strategy.

Philadelphia’s decision to impose a tax on soda and to use the proceeds to help fund a multi-year bond to pay for capital improvements to parks, libraries and public spaces is an example of how to transition from micro to macro. Not only does this measure provide the resources to greatly increase the scale of activities, the funding mechanism—which is visible and broad-based—means that every citizen will know that they’re making a contribution, and that they have a stake in these investments.

Markets

The second “M” is markets. It may seem odd to invoke markets in the context of public space, but they matter a lot, and they’re telling us something important. When we speak of the civic commons and public realm, we tend to frame it as a largely government-led or public sector function. Municipal governments are primarily responsible for building, financing, operating and regulating public spaces. But viewed more closely, there’s an ambiguity and an interdependence between the public and private sectors on the ground in cities.

At the street level, great urban spaces are formed by mutually reinforcing public and private investments. Great streets, squares, and public spaces attract people, and the flow of people stimulates commerce. And the nearby presence of businesses—shops, bars, cafes, restaurants—reinforces the activity in the public realm. As we showed with our recent Storefront Index (which measures the number and concentration of customer-facing retail and service businesses in cities), the difference between an under-utilized park and an activated one is substantially explained by the presence and density of adjacent storefronts.

At a larger scale, it’s readily apparent that there’s a growing demand of great urban environments. Somewhat paradoxically, just as technology has, at least in theory, freed us from the need to be physically present in a particular place to work, or access information, or have easy access to a wide range of goods, people seem to be craving the opportunity to live in places that afford a wide range of opportunities for easy personal interaction. The rows of people in coffee shops, independently working at their laptop computers, signals a strong desire to be in the public realm, at the same time they are connected to the Internet.

Credit: brewbooks, Flickr
Credit: brewbooks, Flickr

 

Just as technology is freeing us from place, there’s a growing demand to live and work in cities. Well-educated young adults are disproportionately moving to cities. Companies that hire these workers are moving to cities as well. The rent premium for central locations relative to suburbs has increased sharply in the past decade. All of these trends are a sign that there’s strong market demand for urbanity. At City Observatory, we’ve called this “the shortage of cities” because the demand for urban space and urban living is increasing far faster than we’ve been able to increase the supply. And a key element of the supply of cities is the public realm that makes city living and city neighborhoods so appealing. So as we think about how to expand the civic commons and activate public spaces, we should do so with a clear recognition that this is something that the market demands.

Metrics

My third “M” is metrics: how we measure the extent and activation of the public realm. The ability to measure the health and extent of public spaces and the activity that occurs within them is important both to designing great spaces, to moving from “micro” to “macro” and harnessing the growing market demand for the civic commons.

Many of the obstacles we face in promoting the public realm are due to the fact that we face a severe disparity in the kinds of things we measure. Some disciplines and some sets of investments have well-developed sets of metrics and copious statistics that make the strong case for their interests. This is very clear in the case of automobile transportation: every city has detailed measures of traffic volumes, vehicle speeds, vehicle delay times and the like. Almost no city has good data on the number of pedestrians, their convenience or comfort, or even good data on the use of parks or public spaces.

In public policy, it’s often the case that what counts is what gets counted. And the effect in the public realm is that great emphasis gets put on what we can count (the number and speed of vehicles moving through a place) and very little emphasis gets put on how many people actually use or inhabit spaces. In essence we often prioritize “traveling through” rather than “being in” urban environments.

The way to change this is to develop a range of metrics of the quality and use of public spaces. New data and new technology make possible a range of new metrics. In the past few years, Walk Score has emerged as a convenient, ubiquitous and easily understood tool for measuring the walkability of urban spaces. At City Observatory, we’ve developed the Storefront Index, which measure the number and concentration of customer-facing retail and service businesses that help frame walkable commercial neighborhoods. New technology lets us count the number of people walking in or using public spaces. We’re just in the infancy of these measures, but they can be useful tools for planning, and for elevating the health and use of the public realm in policy discussions.

Moving from exceptional innovation to commonplace adoption

One of the exciting things about visiting projects that are transforming neighborhoods and urban spaces is seeing the insight and creativity that designers, community groups and enlightened leaders have brought to bear on improving the public realm. Part of the sense of accomplishment from this kind of innovation comes from challenging the accepted norms, bending or negotiating the rules and doing something that hasn’t been done—or that people thought was impossible. While we should always continue to be innovative, the next big challenge for those with an interest in building cities by strengthening the public realm is to transform innovative breakthroughs into accepted, even commonplace practice. The keys to doing this will be to build on the visible evidence of success in particular projects, and use that to leverage institutional change: not breaking the rules for one project, but re-writing the rules for all projects. That’s why the three “M’s” are important: moving to system level change will require thinking about the “macro” rather than just the micro, harnessing the growing market demand for great urban places, and developing metrics that build a strong case for policy and investment.

Joe Cortright presented these remarks to the closing session of the Act Urban convening in Philadelphia on June 17, 2016. For more information about the Act Urban project, visit its website.

Neighborhood change in Philadelphia

Last week, the Pew Charitable Trusts released a fascinating report detailing neighborhood change in Philadelphia over the past decade and a half. “Philadelphia’s Changing Neighborhoods” combines a careful, region-wide analysis of income trends with detailed profiles of individual neighborhoods.

Using tract-level income data, Pew researchers classified Philadelphia neighborhoods according to their median income in 2000 and the increase in their median income between 2000 and the five-year 2010-2014 American Community Survey.

A tract counted as “gentrifying” if its income was below 80 percent of the regionwide average in 2000, but grew by at least 10 percent in real terms by 2014, and its income was then in the top half of all the neighborhoods in the city of Philadelphia.

Credit: Tom Ipri, Flickr
Credit: Tom Ipri, Flickr

 

A couple of key conclusions emerge from this work.

Though it gets a lot of press attention and generates controversy, gentrification in Philadelphia has been rare, and is concentrated in just a few neighborhoods. By Pew’s reckoning, just 15 of the region’s 371 Census tracts (or about four percent) experienced gentrification.

For low-income neighborhoods, a continuing decline in income was a far more common outcome. In Philadelphia, ten times as many poor neighborhoods (164) experienced real declines in income as experienced gentrification since 2000.

These findings for Philadelphia echo our own analysis of neighborhood change from 1970 through 2010, presented in our report “Lost in Place.” (Lost in Place used poverty rates to identify low income neighborhoods and identified gentrification as a decline in poverty rates to below the national average in formerly high poverty neighborhoods.) Our key conclusion—that gentrification affected just five percent of those living in high poverty neighborhoods, and that most place over high poverty remained poor for decades—is very similar to Pew’s Philadelphia analysis.

Much of the controversy surrounding gentrification stems from the widespread belief that gentrification automatically results in the displacement of long-time neighborhood residents. Implicitly, many people seem to visualize neighborhood change as a kind of zero-sum game: each new resident moving in must mean that one previous resident moved out. The published academic literature, however, mostly fails to find widespread displacement. While the Pew study doesn’t address displacement directly, their research provides an interesting sidelight to this question.

The authors of the study also graciously provided us with unpublished data on the population levels for each of the Census tracts in their study, with data sorted according to their classification of neighborhood change. Like many cities, since 2000 Philadelphia has begun to experience a population increase. Gentrifying neighborhoods played an outsized role in contributing to city population growth. Between 2000 and 2014, the 15 gentrifying neighborhoods grew by 13.4 percent, adding 7,000 new residents. Citywide, the population increase was only 2 percent. These 15 tracts accounted for 22 percent of citywide population growth.

Meanwhile, poor neighborhoods that didn’t gentrify only managed to tread water in terms of population levels. Overall, population in these neighborhoods increased only 0.2 percent between 2000 and 2014; some 40 percent of all poor neighborhoods lost population. The different growth trajectories of poor neighborhoods that don’t gentrify compared to those that do is a good reminder that neighborhood change is seldom a zero-sum game.

Special thanks to Emily Dowdall for sharing the tract level data.

Schools and economic integration

There’s a growing body of evidence that economic integration—avoiding the separation of rich and poor into distinct neighborhoods—is an important ingredient in promoting widely shared opportunity. The work of Raj Chetty and his colleagues shows that poor kids who grow up in mixed income communities experience far higher rates of economic success than those who live in neighborhoods of concentrated poverty.

We know that one of the principal channels through which this process works is the quality of local schools. Schools in mixed-income neighborhoods tend to have students from both high-income and low-income strata, and benefit from the generally higher levels of parental involvement and resources that higher-income families are able to lavish on schools. Massey and Rothwell have shown that one’s neighbors educational level is nearly half as powerful as one’s own parent’s level of educational attainment in explaining children’s long term economic success, and they hypothesize that much of this effect is transmitted through the school system.

At the same time, the composition and quality of urban schools has been a critical challenge for cities around the country. For decades, as higher income families decamped cities for the suburbs—in part to get access to what were perceived as better schools—urban school districts have faced a triple whammy of declining enrollments, a growing concentration of students from poor families, and declining fiscal resources. The results are chronicled in a new Government Accountability Office report.

GAO compiled data from the National Center for Educational Statistics, and classified schools as low poverty, high poverty, and all other based on the fraction of students in each school eligible for free and reduced-price school lunches.  (Low poverty schools were those where no more than 25 percent of students were eligible; high poverty schools had at least 75 percent of students eligible.  The data show that in little more than a decade the number of students enrolled in low poverty schools has fallen by half (from 39 percent to 20 percent), while the number of students in high poverty schools has increased from 14 percent to 25 percent.  As the GAO report details, students of color are much more likely to attend high poverty schools; 48 percent of black students and 48 percent of Latino students attend high poverty schools, compared to only 8 percent of white students.

Share of K-12 Students Enrolled by Poverty Status of School

2000-01 2005-06 2010-11 2013-14 Change, 2000-01 to 2013-14
Low Poverty 39% 33% 24% 20% -19%
All Other 47% 51% 56% 54% 8%
High Poverty 14% 16% 20% 25% 11%

Source:  GAO, K-12 EDUCATION: Better Use of Information Could Help Agencies Identify Disparities and Address Racial Discrimination, April 2016, GAO-16-345.

 

In the past couple of decades, as we’ve long noted, there’s been a revival in the fortunes of urban centers. In many cities, population growth has been rekindled, particularly by the movement of well-educated young adults into urban centers. But the long-term resilience of this trend depends on whether young adults will stay in cities once they start having children, a question that hinges directly on the quality of urban schools.

Against this backdrop comes news that test scores in the Washington, DC school system have chalked up some impressive gains in recent years. According to the National Assessment of Educational Progress (NAEP), reading and math scores for fourth and eighth graders have seen significant increases.

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As the population of the District of Columbia has changed in recent years, that’s begun to alter the demographic characteristics of the students in DC schools. More kids are from wealthier and whiter families, fewer are from poor families, immigrant households and families of color. But as we’ve written, the growing wealth of urban centers has not yet entirely converted them into the sort of playgrounds for the white and wealthy that is sometimes supposed: it’s still the case that two out of every three school age kids in the District of Columbia are black, and an even higher fraction of public and charter school students. Some have feared that the increase in test scores is solely a result of these demographic changes—that scores are higher simply because different students are taking the tests.

A new analysis from the Urban Institute challenges that view. After controlling for changes in the race and ethnicity of the student body, they find that scores have increased much faster than can be explained by demographic changes. The analysis also concludes that the gains in test scores can’t be explained solely by changes in parental educational levels—one key measure of socioeconomic status. The data show gains in scores in both conventional public schools, and also in charter schools. The Urban Institute findings echo an earlier analysis by District of Columbia’s Office of Revenue Analysis, that shows that adjusting for race and ethnicity does little to change increase in test scores.

While the Urban Institute study confirms that test score increases are real, it doesn’t answer the question of why scores improved. There have been a series of changes enacted in the District in the past decade: new educational management under Michelle Rhee and her successors, a stronger Mayor role in school governance, and increased resources and more widespread adoption of charters.

And while the increase in educational scores isn’t simply a product of the district’s changing population, it could well be that gentrification in the district has a synergistic or interactive effect with these other forces. Education reform measures in the district may be more successful if they’re undertaken with a slightly different mix of students, in schools where a higher fraction of families have the resources to support learning and engage in the schools. They may also be more politically effective in holding the city and schools accountable for results.

While the data gathered so far can’t definitively answer these questions, the noticeable improvement in educational results in the District of Columbia is an encouraging sign for the city’s future growth. It suggests that city schools can improve, and that, in turn, makes the city a more attractive home for young families who might have felt compelled to move to suburbs to get better school quality. And for city students who otherwise might have been isolated in economically segregated, under-performing schools, it means that they have better educational, and in the long run economic opportunities. We’re looking forward to future research that can help measure and sort out these explanations.

Sprawl, segregation, and mobility

This is the fourth in an ongoing series of posts about income segregation, urban planning, and economic opportunity. In the first, we examined three different ways of looking at income segregation: the proportion of people living in low-income neighborhoods, high-income neighborhoods, or both “extremes.” In the second, we looked at another kind of income segregation, measured along the entire income spectrum, and distinguished between segregation and inequality. In the third, we examined how income segregation has changed, both since 1970 and since the Great Recession.


Over the last two weeks, we’ve written about how income segregation is really many different kinds of sorting; how to measure several of the most important kinds; how and why to distinguish between segregation per se and inequality; and how income segregation has changed over the last 40 years.

But we’re not just interested in analyzing and diagnosing income segregation for its own sake. We’re interested in how it intersects with outcomes we care about—notably economic opportunity for people with low incomes, and the strength of common civic culture—as well as policy levers that cities and other governments can use to improve those outcomes.

That makes a recent study led by Reid Ewing of the University of Utah particularly valuable. Ewing et al’s paper is one of the first to rigorously analyze the relationship between economic mobility, income segregation, urban sprawl, inequality, and other potential correlates of economic mobility.

The study builds off of an innovative “compactness index” developed by Ewing and Shima Hamidi to compare levels of sprawl between metropolitan areas. Previously, researchers like Raj Chetty and his team at the Equality of Opportunity Project had used commute times as a proxy for sprawl, but that’s obviously related to a number of other factors beyond the spread-out-ness of the urban environment.

The big takeaway from Reid’s study is that in metropolitan areas that are more compact—that is, less sprawl-y—children born into the lowest fifth of the income distribution are much, much more likely to reach the top fifth as adults. On average, if City A is twice as compact as City B, low-income children in City A will be 41 percent more likely to reach the top fifth of the income distribution than low-income children from City B. (In the real world, “twice as compact” is roughly the difference between sprawl-y metropolitan Nashville and less sprawl-y metropolitan St. Louis.)

St. Louis. Credit: Ron Reiring, Flickr
St. Louis. Credit: Ron Reiring, Flickr

 

Reid et al also find that lower levels of income segregation—specifically, segregation of the poor—is associated with greater mobility, confirming the findings of other researchers.

But when it comes to the interaction of sprawl with income segregation, things are a little more ambiguous. The study actually finds a negative relationship between compactness and segregation—that is, more compact metropolitan areas tend to be somewhat more segregated by income.

That raises a few questions. First, if compactness is associated with more income segregation, and more income segregation is associated with lower mobility, then how can compactness itself be associated with higher mobility? The answer to that, according to Reid et al, is that other effects—perhaps most notably access to jobs—overwhelm the income segregation effect and cause the net effect to be positive.

Second, why would more compact metro areas tend to be more segregated? There are a number of possibilities here. One is that there is some inherent connection between relatively dense built environment and segregation—but it’s not totally clear what the mechanism there would be. On the other hand, there are a number of possible third factors that might lead to the appearance of such a relationship. One is that in the US, more compact urban areas tend to be older urban areas—and older urban areas are ones where larger proportions of neighborhoods were around to be affected by the more blatant policies that promoted racial segregation, including redlining, restrictive covenants, and widespread racist violence. These historically racially segregated neighborhoods are, today, very disproportionately likely to be areas of concentrated poverty. In that way, the mechanism wouldn’t be that compact development leads to segregation, but that older cities are both more compact and more segregated.

Another possibility is the “modifiable aerial unit problem.” Reid et al measure segregation by Census tracts, which are drawn to have very roughly equal numbers of residents. That means that they’re much smaller in denser cities—and, perhaps, more sensitive to block-by-block sorting than in more sprawling regions, where tracts can include many different blocks that aren’t really in the same neighborhood.

Census Tracts at the same scale in Brooklyn and suburban New Jersey. Credit: Social Explorer

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Census Tracts at the same scale in Brooklyn and suburban New Jersey. Credit: Social Explorer

 

Or perhaps there’s something distinct that Reid’s compactness index is picking up that other definitions of sprawl don’t include. After all, previous research has in fact found a connection between looser zoning regulations—that is, ones that allow more compact multi-family development—and less income and racial segregation, presumably because a wider variety of housing types is more likely to include a wider variety of housing prices.

But in any case, clearly more research is needed. In the meanwhile, Reid’s study provides more evidence both that more compact urban areas provide more economic opportunity to the low-income, and that income segregation is a key lever of opportunity as well.

How economically integrated is your city?

Last week, we looked at some of the growing body of academic evidence that shows that mixed income neighborhoods play a key role in helping create an environment where kids from poor families can achieve economic success.

One of our key urban problems is that economically, we’ve grown more segregated over time:  the poor tend to live in neighborhoods that are substantially poor, and the better off live in neighborhoods with few poor residents.  As a result, one of the key metrics we ought to be paying attention to  the level and change in economic segregation in our metropolitan areas.  

There are a variety of different facets to economic segregation.  It encompasses the segregation of poverty (the concentration of the poor in predominantly poor neighborhoods), the segregation of affluence (enclaves of high income households) and the separation of the middle class from high income and low income households. Also, in any metropolitan area, segregation levels will be influenced by the degree of overall income inequality.

The most comprehensive analyses of trends in economic segregation come from the outstanding research by Kendra Bischoff and Sean Reardon, whose report is worth diving into if you want more details.

Over the past four decades, economic segregation trends are extremely easy to summarize: they’re up. American cities are far more segregated by income today than they were in 1970 by every measure we’re aware of, indicating more “secession of the successful,” more concentrated poverty, and even more sorting among the lower-middle and upper-middle income tiers.

Credit: Kendra Bischoff and Sean Reardon
Credit: Kendra Bischoff and Sean Reardon

 

In large metro areas, in 1970, just 5.5 percent of families lived in “poor” neighborhoods (where median income is below 67 percent of the regional median), and 4.4 percent lived in “affluent” neighborhoods (where median income is more than 150 percent of the regional median). By 2012, those figures had both more than doubled, to 13.1 and 8.5 percent, respectively—meaning that over a fifth of all families lived in either poor or wealthy neighborhoods, as opposed to one in ten in 1970.

So that’s how things have changed over the last 40 years. What about the last five?  In their most recent paper, Bischoff and Reardon focus on changes between 2007 and 2012. (For sticklers, these are actually averages of 5-year American Community Survey results from 2005-09 and 2010-2014). Over that period, income segregation has continued its rise, but the trends look somewhat different than they have over the longer term.

A neighborhood in Atlanta. Credit: Chris Yunker, Flickr
A neighborhood in Atlanta. Credit: Chris Yunker, Flickr

 

Over the last five years, the proportion of families in low- and high-income neighborhoods has continued to increase—but a more sophisticated look at the numbers suggest that’s more about changing income than actual segregation. Rather, Bischoff and Reardon show that most of the rise in income segregation between 2007 and 2012 came from the increasing segregation of lower-middle-income families (those between the 10th and 50th percentile of income) and upper-middle-income families (those between the 50th and 90th percentiles). The growing inequality of income overall is one factor fueling economic segregation.

There are several different ways to measure economic segregation–and the Bischoff and Reardon paper has measures for the segregation of the poor from everyone else, the segregation of the rich, and a combined measure showing how much the rich and poor are segregated from the middle class. Their most comprehensive measure of aggregate segregation is an indicator called “H”, which is an entropy index that captures the degree of dispersion from an even distribution at all income levels.  We use this measure as the single best indicator of overall levels of income segregation. While values of H don’t have a simple intuitive description, higher levels correspond to greater segregation; lower values correspond to less segregation.

Using Reardon and Bischoff data, we’ve ranked all of the 51 largest US metropolitan areas according to their degree of income segregation from 1970 to 2012.  The most segregated areas are shown at the top of the table (you can use tools in the table to re-sort rankings for different years).  The final column in the table shows the change in the value of “H” for each metro area between 1970 and 2012.

Several findings stand out.  First, income segregation increased almost everywhere.  Only two of the 51 largest metro areas–Raleigh and New Orleans–didn’t experience an increase in income segregation over the past four decades. In addition, the rankings of metro areas are relatively stable over time–income segregation is an enduring and slowly changing feature of the built environment.  Among the metro areas with the highest levels of income segregation are Dallas-Fort Worth, Philadelphia and New York.  The three metros with the lowest levels of income segregation are Portland, Orlando and Minneapolis-St. Paul

To see how an individual metropolitan area has performed over time, you can also select it on the following chart.  The chart shows graphically, the value of H and other segregation indicators for a single metropolitan area for each of the years in the Bischoff Reardon database.  In addition to H (blue), the chart illustrates the percent of population in poor neighborhoods (red), the percent in high income neighborhoods (green) and the combined percent in high income and poor neighborhoods (orange). For each indicator, higher values indicate greater segregation.  These other measures help show the extent to which segregation in any place is driven more by concentration of poverty or secession of the successful.

The Bischoff and Reardon data confirm both the prevalence and growth of income segregation in American metropolitan areas. This data is an important tool urban leaders can use to understand how their region performs on this important dimension, and also lets us see which communities might be good places to examine to understand the policies and characteristics that have fostered higher levels of economic integration.

The rising tide of economic segregation

Last week, we argued that the problem called “income segregation” is actually several problems, and broke it down with the help of different measurements designed to capture different aspects of the issue.

In particular, we pointed out the need to distinguish between 1) the segregation of poverty, 2) the segregation of affluence, and 3) the segregation of the middle—and 4) the difference between income inequality and income segregation per se.

And—although we’ll go into more detail on why economic segregation matters in a subsequent post—recall that we care enough to dive into this because both a large body of empirical research and on-the-ground experience suggests that economic integration has a major positive influence on economic opportunity, especially for people with low incomes.

Today, we’ll briefly cover how each of these problems has evolved in American cities—both over the long term, and more recently. As before, we’re largely working off of outstanding research by Kendra Bischoff and Sean Reardon, whose report is worth diving into if you want more details.

At the highest level, economic segregation trends are extremely easy to summarize: they’re up. American cities are far more segregated by income today than they were in 1970 by every measure we’re aware of, indicating more “secession of the wealthy,” more concentrated poverty, and even more sorting among the lower-middle and upper-middle income tiers.

Credit: Kendra Bischoff and Sean Reardon
Credit: Kendra Bischoff and Sean Reardon

 

In 1970, just 8.6 percent of families lived in “poor” neighborhoods (where median income is below 67 percent of the regional median), and 6.6 lived in “affluent” neighborhoods (where median income is more than 150 percent of the regional median). By 2012, those figures had both more than doubled, to 18.6 and 15.7 percent, respectively—meaning that over a third of all families lived in either poor or wealthy neighborhoods, as opposed to just over one in seven in 1970.

But it’s not just segregation of poverty and affluence that have increased substantially over the last few generations. Bischoff and Reardon’s “H” score—which, if you remember, measures segregation along the entire spectrum of income—has also increased, from 0.115 to 0.146. In today’s terms, a shift that size is the equivalent of moving from a metro area of average segregation almost all the way to one in the top ten percent of most segregated metro areas. In other words, it’s a lot.

And what about the question of inequality versus segregation? Is this increase in segregation simply a result of greater gaps in earnings between rich or poor, or are people actually being sorted to live with more people at their relative level? Well, recall that last time we said that the H index was valuable in part because it measures segregation by people’s income rank, rather than absolute level of income—meaning it filters out a lot of the effects of rising inequality. Since it has increased substantially, we can conclude that rising income segregation is not just a result of rising inequality. Rather, there are other factors at work promoting segregation—including some we’ve talked about at City Observatory, like zoning laws that prohibit a mix of housing types in the same neighborhood.

So while rising inequality is correlated with rising segregation, it’s not the whole story. In a previous report, in fact, Bischoff and Reardon found that inequality is mostly associated with the segregation of affluence, and less strongly correlated with the segregation of people with lower incomes.

So that’s how things have changed over the last 40 years. What about the last five?

In their most recent paper, Bischoff and Reardon focus on changes between 2007 and 2012. (For sticklers, these are actually averages of 5-year American Community Survey results from 2005-09 and 2010-2014). Over that period, income segregation has continued its rise, but the trends look somewhat different than they have over the longer term.

A neighborhood in Atlanta. Credit: Chris Yunker, Flickr
A neighborhood in Atlanta. Credit: Chris Yunker, Flickr

 

Over the last five years, the proportion of families in low- and high-income neighborhoods has continued to increase—but a more sophisticated look at the numbers suggest that’s more about changing income than actual segregation. Rather, Bischoff and Reardon show that most of the rise in income segregation between 2007 and 2012 came from the increasing segregation of lower-middle-income families (those between the 10th and 50th percentile of income) and upper-middle-income families (those between the 50th and 90th percentiles).

How much does that matter? It’s hard to say. Most of the research on the effects of neighborhood income on individuals’ economic, academic, or health outcomes have focused on very poor neighborhoods, or areas of concentrated poverty. But if this trend continues, it may also be worth further investigating the effects of segregation on lower-middle-income, or working class, neighborhoods as well.

Our next installment in this series will examine the relationship between income segregation, urban built form, and sprawl—and how better urban planning might mitigate some of the trends towards increasing segregation that we’ve just discussed.

Until then, you can look up how your region has fared on the interactive tool we’ve created below, based on data from Bischoff and Reardon.

 

The positive feedback loop of integration

Yesterday, we critiqued a study that claimed to show that the benefits of putting low-income housing in very low-income neighborhoods greatly exceeded the benefits of putting it in higher-income neighborhoods—especially higher-income and predominantly white neighborhoods—where it might have more of a pro-integration effect.

Among the several points of our critique was that the study severely under-measured the benefits of integration.* While its cost-benefit analysis only counted the income gains based on estimates from Raj Chetty et al’s work, we pointed out that there are many other benefits you might expect from integration: better mental health, school performance, safety from crime, and so on.

But there’s an even bigger issue here that goes beyond any of these discrete benefits. Which is: there is evidence that integration creates positive feedback loops that change the fundamental dynamics of neighborhood change.

Somerville, MA. Credit: Tim Sackton, Flickr
Somerville, MA. Credit: Tim Sackton, Flickr

 

After all, the study’s authors calculated that a major cost of putting low-income housing in higher-income neighborhoods was that the property values in those neighborhoods declined as a result—not, apparently, because of any problems the new housing caused, as crime did not increase, but simply because their neighbors preferred not to live around the kinds of people who live in low-income housing.

But in many ways, that effect—and those preferences—depend on a steady supply of neighborhoods without any low-income housing. In part, this is the sort of “prisoner’s dilemma” that we’ve talked about before: in a policy context in which segregation creates resource-rich winners and resource-poor losers, any hint that your neighborhood or municipality might be going towards the resource-poor loser end of the spectrum is cause for alarm. The issue isn’t one or two low-income buildings per se—it’s the possibility that once one or two come in, the segregating dynamics of the housing market will bring in so many more that the area will become very disproportionately low-income. And even where the issue is one or two buildings—because a given homeowner happens to just have discriminatory preferences—that homeowner can only act on their preferences and leave the neighborhood if there are other neighborhoods without any low-income housing for them to flee to.

But what if every neighborhood had some minimum level of low-income housing? What if there were a metropolitan area with a regional integration plan that eliminated the option of living in a totally segregated higher-income neighborhood, protected by exclusionary zoning and other anti-poor policies?

Well, we don’t know for sure, because no such metropolitan area really exists. But there are urban regions that have instituted integration policies for public schools. And the evidence from those is pretty encouraging.

Take this 2012 report from the University of Minnesota’s Institute on Metropolitan Opportunity. In it, Myron Orfield and Thomas Luce  look at the trajectories of suburban neighborhoods between 1980 and 2009—asking, for example, how likely it is that a mixed-race community will end up resegregating. The overall numbers are not great: if a Census tract was 23 percent or more people of color in 1980, it was more likely to resegregate than remain diverse by 2009. (The report defined “resegregate” as become less than 40 percent white. Obviously there’s no objective threshold, but the general pattern holds regardless of where you draw the line.) Interestingly—and similar to the results in our “Lost in Place” report—a vanishingly small number of integrated suburban neighborhoods resegregated as white.

Credit: Institute on Metropolitan Opportunity
Credit: Institute on Metropolitan Opportunity

 

But the report reran these same numbers for 15 metropolitan areas with regional school desegregation initiatives. In other words, these are places where the connection between the demographics of your neighborhood and the demographic of your public school was, to some extent, broken. If a new low-income housing project was built on your wealthy block, then, that wouldn’t necessarily change the demographics of your children’s school, because the desegregation initiative would have already introduced low-income students from other neighborhoods. And by the same token, you wouldn’t necessarily be able to “escape” lower-income students by moving to another neighborhood.

So what’s the result? Well, diverse neighborhoods in these metropolitan areas were much, much less likely to resegregate than similar neighborhoods in regions without school desegregation initiatives. Neighborhoods up to about 37 percent people of color were more likely to remain diverse than to resegregate—and even neighborhoods that were 50 percent people of color in 1980 were only slightly more likely to resegregate, as opposed to having a roughly 75 percent chance of resegregating in regions without school desegregation initiatives.

Credit: Institute on Metropolitan Opportunity
Credit: Institute on Metropolitan Opportunity

 

And this difference is associated just with ending the “prisoner’s dilemma” in schools, not neighborhoods. That is, even if white people are able to access segregated housing, they appear much less likely to want it if that housing won’t guarantee segregated schools. Imagine, then, what might be possible if segregated housing itself were much harder to come by.


* To be fair, the study’s authors acknowledged at one point that they were doing this. Nevertheless, they didn’t change their cost-benefit analysis or conclusions as a result, so our criticism stands.

Income segregation along the whole spectrum

Yesterday, we introduced three kinds of economic segregation, and how you might measure each: the proportion of people in high-income neighborhoods; the proportion of people in low-income neighborhoods; and the proportion of people in either high- or low-income neighborhoods.

Each says something important about how people are sorted by income in a metropolitan area. But these measures also miss some things. For one, they don’t reflect how segregated people in the middle of the income spectrum are—whether working-class and upper-middle-class people live in the same neighborhoods, for example.

More subtly, but importantly, these measurements are also very sensitive to changes in inequality, even if segregation per se doesn’t change. Imagine that in your metropolitan area, you doubled the income of the richest 20 percent of families, and cut in half the income of the poorest 20 percent. All of a sudden, many more neighborhoods would meet the definitions of “rich” or “poor” according to these measurements, and so they would tell you that segregation had increased. But in fact, nobody actually moved—and the likelihood that, say, someone in the 10th percentile of income was living in the same neighborhood as someone in the 90th percentile didn’t change at all. What changed wasn’t segregation, but inequality.

Which brings us to…

The H index

So Riordan and Bischoff created an index, called H, that takes into account everyone. Unfortunately, unlike the previous three indicators, it doesn’t have an easy lay-person interpretation: it’s just a number, varying between 0 and 1, with larger numbers indicating more segregation. Basically, it works by ranking each family household by income across an entire metropolitan area, and then comparing the distributions of rankings in each neighborhood.

An important thing about H is that it is insensitive to changes in inequality. That is, because it depends on ranks and not actual incomes, if the top 10 percent of families doubled their income, or the bottom 10 percent of families cut their income in half, H would not change, even though there would be important implications for economic segregation. (Presumably we care more about high-end segregation, say, if wealthy people are really wealthy. The less different rich people are from everyone else, the less their separation matters.) In part, this is helpful: it means that an increase in H really tells us something about how people are being sorted into different neighborhoods, and not just a change in income inequality in general. But it also leaves out an important part of the story—just how different high-ranked families are from low-ranked families.

How does H differ from High + Low?

In reality, these two measures are highly correlated, as we might expect. But there are some notable differences. In both San Francisco and Milwaukee, for example, about 35 percent of families live in neighborhoods that are either low-income or high-income. But SF’s H index is 0.14, and Milwaukee’s is 0.19—a very significant jump from the least-segregated third of cities to the most-segregated third. That suggests much more sorting of relatively middle-income families in Milwaukee than San Francisco. It may also suggest that part of San Francisco’s bad showing on the High + Low score is about inequality, rather than segregation—which makes sense if we think that, say, there are many more very high-earning families in the Bay Area than in Milwaukee.

Similarly, Cincinnati and New Orleans score almost identically on the H index, at 0.15. But New Orleans has dramatically more people living in high- or low-income neighborhoods, 39 percent, versus 27 percent in Cincinnati. This is likely because New Orleans has more income inequality than Cincinnati.

In a way, you can think of the H index as a sort of pure description of segregation, while the “High + Low” score captures both segregation and inequality. While we presented that combination as a drawback at the top of this post, it might actually better reflect what many people have in mind when they think about the negative consequences of economic segregation. If harm is caused by extreme neighborhoods—both resource-hoarding rich neighborhoods and opportunity-scarce poor neighborhoods—then it matters how rich the rich are and how poor are the poor.

An increase in the H index might not directly translate to those sorts of ills—rather, it likely suggests that people are increasingly living among people with similar incomes to their own, whether or not they’re creating more rich or poor neighborhoods in the process. That distinction will turn out to be important in our next post about economic segregation: how it’s changing in America today.

There’s more than one kind of income segregation

Much of the conversation about urban inequality today—from Raj Chetty’s work on intergenerational economic mobility, to issues of concentrated poverty and gentrification—is framed in terms of economic segregation. But it turns out that “economic segregation” isn’t just one thing, and what we mean by the phrase, and how we choose to measure it, has serious implications both for our understanding of urban inequality and the kinds of policies we might design to fix it.

The basic issue is that unlike racial segregation, which has a few (ostensibly) discrete categories into which people fall, income segregation has to divide people based on a continuous spectrum with no obvious objective cutoffs, or even number of categories. Social scientists have come up with a number of different approaches to this problem; in this post, we’ll go through several of the most common and explain why they matter, with the goal of leaving you more able to engage in detailed, thoughtful conversations about inequality, segregation, and opportunity in your own city and beyond. (The examples will be based on work by Sean Riordan and Kendra Bischoff, whose papers on measuring economic segregation over the last several years have been excellent.)

 

High-income segregation

One approach is to measure how separate upper-income people are from everyone else. You might focus on this if you believe that, especially when the rich have a greater share of total income than they have in generations, what Robert Reich has called the “secession of the successful” threatens to keep an enormous share of society’s resources out of reach of everyone else. You can think of this as a sort of Mossack Fonseca problem: like offshoring wealth to avoid federal taxes, forming clusters of exclusive communities is a way of ensuring that money that might otherwise be used to pay for society-wide benefits will instead be spent disproportionately on the wealthy people themselves.

Riordan and Bischoff measure this by counting the proportion of people in a metropolitan area who live in neighborhoods where the median family income is more than 1.5 times higher than the median family income of the region as a whole. So, for example, the median family income in the Boston metro area is about $96,000 in the 2014 1-year American Community Survey; for that year, this measure would count the number of people living in neighborhoods where the median family income was at least $144,000. Because they use the median, and not the average, this will only capture neighborhoods where at least half of all families meet that threshold of disproportionate income; and because they measure families, it corrects for some of the differences between neighborhoods that result from age differences.

Low-income segregation

Another approach is to measure how separate low-income people are. You might focus on this version if you believe that the main threat from economic segregation is concentrated poverty; indeed, much of the research on economic segregation has focused on the problems associated with neighborhoods where a very large proportion of residents are low-income, including worse economic mobility, educational, and health outcomes.

Riordan and Bischoff’s measure for this mirrors their high-income segregation indicator: the proportion of people in a metropolitan area who live in a neighborhood where the median family income is at least 33 percent below the median income of the region as a whole. So, going back to Boston, this measure would count the number of people living in neighborhoods where the median family income is less than about $64,000.

High + Low

Perhaps you are interested in both of these aspects of economic segregation. An easy way to add them together is to…add them together. Another Riordan-Bischoff index is simply the proportion of people who live in high-income neighborhoods or low-income neighborhoods. This makes sense if you want to see how typical it is for someone to live in a community that is on some extreme, as opposed to being middle- or mixed-income. It makes for a good, quick, intuitive number that captures both high-end and low-end segregation.

On the other hand, it doesn’t necessarily tell you which of these is a problem, or in which proportions. Perhaps one city has a huge problem with concentrated poverty, while another’s issue is concentrated wealth. The policy response would not necessarily be the same to both.

How much of a difference does it make?

So how much do we gain by breaking down these different kinds of income segregation? Quite a bit, actually.

 

Comparing the prevalence of high-income and low-income neighborhoods by metro area, we can see quite a range of differences. In most cities, these numbers are roughly proportional. In Richmond, VA, 14 percent of families live in high-income neighborhoods, and 16 percent live in low-income neighborhoods. In San Diego, it’s 18 percent in high-income areas, and 21 percent in low-income areas. But in other cities, one side clearly dominates.

In some cities, many more people live in low-income neighborhoods than upper-income ones. In New Haven, for example, 16 percent of families live in high-income neighborhoods—but 25 percent live in low-income ones. In Milwaukee, it’s 13 percent and 22 percent; in Providence, it’s 10 percent and 20 percent. In these areas, concentrated poverty appears to be an even larger problem than in a typical metro area.

Interestingly, there really aren’t many cities where people in wealthy neighborhoods outnumber people in low-income neighborhoods. Partly, that reflects an argument we’ve been making for a while about the relative importance of the issues of gentrification by upper-income people and concentrated poverty. But it’s also, of course, a reflection of the income cutoffs chosen by Bischoff and Reardon. It might be useful if we had a fourth measurement—one that took into account the entire spectrum of income, and didn’t depend on arbitrary categorizations?

Tomorrow, we’ll introduce you to Bischoff and Reardon’s “H Index,” which does just that.

Successful cities and the civic commons

 

At City Observatory, we’ve been bullish on cities. There’s a strong economic case to be made that successful cities play an essential role in driving national economic prosperity. As we increasingly become a knowledge-driven economy, it turns out that cities are very good at creating the new ideas of all kinds that propel economic progress.

But cities aren’t simply economic machines. Nor are they merely large and efficient collections of buildings, pipes, wires, asphalt and concrete. Cities are importantly a collective social endeavor, what Ed Glaeser calls mankind’s greatest invention. It is the opportunity for interaction with others in cities that is their special power and attraction. Some of those interactions are purely economic, but they are deeply embedded in a much wider set of connections and relationships.

Despite all the strength and energy of cities today, there’s growing evidence that the web of social connections that ties cities together, and that underpins much of their economic importance, is coming apart at the seams. For decades cities have been pulling apart: sprawl has moved us physically further from one another, and within metropolitan areas, our neighborhoods have become more segregated by income.

As we pointed out in our CityReport, Less in Common, the shared opportunities and spaces that let Americans of all different backgrounds easily interact with one another have been steadily eroded. We spend more time alone, we socialize with others less, we’re segregated from one another by income, and we generally spend less time in public or in the company of those who are different from us.

 

Click to see the full infographic.

Click to see the full infographic.

 

 These trends are mirrored in how we get around, relying more and more on cars cars as a mode of transportation, replacing walking and public transit—modes in which, outside a sealed, private machine, you might actually interact with neighbors or others. In fact, while about 30 percent of Americans reported spending time with their neighbors in 1970, that number was down to about 20 percent today.

This privatizing of our once public lives has also manifested itself in further segregation of neighborhoods by economic status, a trend that has been well documented, and which we have explored at length at City Observatory.  Rich and poor Americans have become more spatially divided as we sort into high income and low income neighborhoods. While only 15 percent of Americans lived in rich or poor neighborhoods in 1970, by 2012, that figure was up to 34 percent.

The good news is that there’s a growing recognition of this challenge, and many people and communities are actively looking for ways to rekindle public life. There’s some compelling evidence that the move back to cities is propelled, at least in part, by a hunger for greater opportunities to interact with one another, to—as our friend Carol Coletta puts it—“to live life in public.” This shows up in the growing popularity of  “third places”—coffee shops, bars, farmers markets, and other settings where people gather away from home and work.

Its exciting that Knight Foundation and four other foundations have launched their new initiative “Reimagining the Civic Commons.” Over the next five years, these foundations will fund a $40 million grant program to promote innovative projects in five cities: Akron, Chicago, Detroit, Memphis and Philadelphia. The project aims to fund a series of experiments that consider how we might better use a range of public spaces–parks, libraries, and even sidewalks–to foster greater civic engagement. This could turn out to be a vital bit of public-minded venture capital that can help further illustrate—and develop—the vital role that public spaces play in underpinning a successful city.

Fundamentally, this strategy makes sense because of the strong interdependence of the social and economic network effects in cities. Economists portray city economies as being driven by agglomeration effects–the increased intensity and productivity that occurs when large numbers of people can interact (especially when they have diverse knowledge and backgrounds). As Jane Jacobs argued, diverse, well-connected cities produce the “new work” that propels economic progress. But this diversity and connectedness pays dividends in the form of widely shared opportunity. Raj Chetty and his colleagues have shown that cities with lower levels of racial and economic segregation–where its easier for people of different backgrounds to connect–have higher levels of intergenerational mobility, especially for children of low income families.

At their root, as Ed Glaeser has argued, cities are about the absence of distance between people, and that’s not simply physical distance, but social distance as well. Having a civic realm that works well, where people from throughout the city can easily interact is not a mere public amenity, but a vital component of a successful city.

 

Our infographic for thinking about the civic commons

City Observatory is about cities, and while much of the discussion of urban policy surrounds the physical and built environment, ultimately cities are about people. When cities work well, they bring people together. Conversely, when cities experience problems, its often because we’re separated from one another or driven apart.  

A critical feature of cities is how people experience their neighborhoods as communities—as places where people gather, interact, and enrich each others’ lives. In our 2015 report “Less in Common,” we explored the ways in which increasing auto-centric development has degraded this aspect of our urban life. Now, as we did with our report “Lost in Place,” City Observatory and Brink Communication have put together an infographic to make these important ideas easy to share—and as always, this and all of our work is licensed under Creative Commons-Attribution, so feel free to incorporate it in your own presentations or reports.

The infographic illustrates many of the key findings of “Less in Common,” which illustrate ways in which increasing sprawl has weakened our communities, and show how a broader trend of Americans living more widely separated private lives has created a space for smart urban planning to strengthen the public realm.

Click to see the full infographic.
Click to see the full infographic.

 

Perhaps one of the clearest connections is in recreation: While Americans who went swimming in 1950 would probably go to a community pool, since then, the number of private, in-ground pools has increased from 2,500 to 5.2 million in 2009, as large-lot zoning and the construction of highways far into the suburban periphery has essentially subsidized the consumption of private land, at the expense of public facilities. These trends are mirrored in how we get around, relying more and more on cars cars as a mode of transportation, replacing walking and public transit—modes in which, outside a sealed, private machine, you might actually interact with neighbors or others. In fact, while about 30 percent of Americans reported spending time with their neighbors in 1970, that number was down to about 20 percent today.

This privatizing of public life has also encouraged further segregation of neighborhoods by economic status, a trend that has been well documented, and which we have explored at length at City Observatory.  Rich and poor Americans have become more spatially divided as we sort into high income and low income neighborhoods. While only 15 percent of Americans lived in rich or poor neighborhoods in 1970, by 2012, that figure was up to 34 percent.

The erosion of the civic commons also has a profound impact on economic opportunity: In regions with more economic segregation, children from low-income households are much less likely to be able to improve their income status as adults.

As the rapper Ice Cube told National Public Radio earlier this year, reflecting on the school integration policies of his childhood:

I liked it because I was being bused with a lot of my homies. So we was, like, all going out there, and then it was a lot of different neighborhoods. So it was, like, buses from all these different neighborhoods all converging on this white school. And it was kind of cool because we had a chance to see different things, different people, have different conversations, hear different music and just get a chance to see that the world was bigger than Compton, South Central or, you know, whatever. You know, so we had a chance to really kind of open our horizons…

In other words, the strength of our public spaces and institutions is crucial both for educational and economic opportunity, as well as expanding our sense of collective potential and identities. That’s something we should all be able to get behind.

Click here to see the full infographic.

What it means to be in common

When we talk about the costs and consequences of car-dependent urban development, we often talk about hard economics and climate science. Spread-out neighborhoods divided by big, pedestrian-hostile roads force people to spend more on transportation than they would in a place where many trips could be taken by foot or transit. In high-demand cities, relatively lower-density development can lead to a “shortage of cities” that pushes housing prices up, encourages economic segregation, and leads to lower intergenerational economic mobility. And these urban forms are also highly correlated with more greenhouse gas emissions, worsening the threat of climate change.

But people also experience their neighborhoods as communities—as places where people gather, interact, and enrich each others’ lives. In our 2015 report “Less in Common,” we explored the ways in which increasing auto-centric development has degraded this aspect of our urban life. Now, as we did with our report “Lost in Place,” City Observatory and Brink Communication have put together an infographic to make these important ideas easy to share—and as always, this and all of our work is licensed under Creative Commons-Attribution, so feel free to incorporate it in your own presentations or reports.

The infographic illustrates many of the key findings of “Less in Common,” which illustrate ways in which increasing sprawl has weakened our communities, and show how a broader trend of Americans living more widely separated private lives has created a space for smart urban planning to strengthen the public realm.

Click to see the full infographic.
Click to see the full infographic.

 

Perhaps one of the clearest connections is in recreation: While Americans who went swimming in 1950 would probably go to a community pool, since then, the number of private, in-ground pools has increased from 2,500 to 5.2 million in 2009, as large-lot zoning and the construction of highways far into the suburban periphery has essentially subsidized the consumption of private land, at the expense of public facilities. These trends are mirrored in how we get around, relying more and more on cars cars as a mode of transportation, replacing walking and public transit—modes in which, outside a sealed, private machine, you might actually interact with neighbors or others. In fact, while about 30 percent of Americans reported spending time with their neighbors in 1970, that number was down to about 20 percent today.

This privatizing of public life has also encouraged further segregation of neighborhoods by economic status, a trend that has been well documented, and which we have explored at length at City Observatory.  Rich and poor Americans have become more spatially divided as we sort into high income and low income neighborhoods. While only 15 percent of Americans lived in rich or poor neighborhoods in 1970, by 2012, that figure was up to 34 percent.

The erosion of the civic commons also has a profound impact on economic opportunity: In regions with more economic segregation, children from low-income households are much less likely to be able to improve their income status as adults.

As the rapper Ice Cube told National Public Radio earlier this year, reflecting on the school integration policies of his childhood:

I liked it because I was being bused with a lot of my homies. So we was, like, all going out there, and then it was a lot of different neighborhoods. So it was, like, buses from all these different neighborhoods all converging on this white school. And it was kind of cool because we had a chance to see different things, different people, have different conversations, hear different music and just get a chance to see that the world was bigger than Compton, South Central or, you know, whatever. You know, so we had a chance to really kind of open our horizons…

In other words, the strength of our public spaces and institutions is crucial both for educational and economic opportunity, as well as expanding our sense of collective potential and identities. That’s something we should all be able to get behind.

Click here to see the full infographic.

An infographic summarizing neighborhood change

One of City Observatory’s major reports is “Lost in Place,” which chronicles the change in high-poverty neighborhoods since 1970. In it, you’ll find a rich array of data at the neighborhood level showing how and where concentrated poverty grew.

We know it’s a complex and wonky set of data, so we’ve worked with our colleagues at Brink Communication to develop a compact graphic summary of some of our key findings. We’re proud to present that here. And like all material on City Observatory, it’s available for your free use under a Creative Commons-Attribution license, so feel free to incorporate it in your own presentations, email, and social media to help explain the processes of neighborhood change in your city.

You might find it especially useful paired with more local-specific content from “Lost in Place,” such as these interactive city-by-city, neighborhood-by-neighborhood maps. Further down this page, you can also find an interactive dashboard with full statistics for your city, including trends in high-poverty, low-poverty, rebounding, and “fallen star” neighborhoods, and the total number of people living in high-poverty neighborhoods from 1970 to 2010.

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Click the thumbnail below for the full infographic. We’ve also included some further narrative context below.

Click for full infographic.
Click for full infographic.

 

Neighborhood change has been a hot topic in many American cities—and, increasingly, on the national stage—for a number of years. At City Observatory, we’re especially interested in shifting community demographics as they relate to economic and racial integration, which have been shown to have profound impacts on people’s class mobility, longevity, and more.

But while most of the focus has been on gentrification—the process of middle- and upper-income people moving into lower-income neighborhoods—our own research shows that low-income communities are much more likely to suffer from the opposite problem: increasing poverty and severe population decline. Three-quarters of neighborhoods with a poverty rate twice the national average in 1970 still had very high levels of poverty in 2010, and had lost an average of 40 percent of their population. That represents a much larger number of people who have been “displaced” by a lack of opportunity or high-quality public services than have been displaced by gentrification.

Our perceptions of neighborhood change are often shaped by those places that are experiencing the greatest pace of change.  The data in “Lost in Place”—available for all of the nation’s 50 largest metro areas—lets anyone look to see how poverty has changed and spread in their city since 1970. And our new infographic helps explain the major components of change. We invite you to use these tools to explore and discuss the process of neighborhood change in your city.

A new look at neighborhood change

One of City Observatory’s major reports is “Lost in Place,” which chronicles the change in high-poverty neighborhoods since 1970. In it, you’ll find a rich array of data at the neighborhood level showing how and where concentrated poverty grew.

We know it’s a complex and wonky set of data, so we’ve worked with our colleagues at Brink Communication to develop a compact graphic summary of some of our key findings. We’re proud to present that here. And like all material on City Observatory, it’s available for your free use under a Creative Commons-Attribution license, so feel free to incorporate it in your own presentations, email, and social media to help explain the processes of neighborhood change in your city.

You might find it especially useful paired with more local-specific content from “Lost in Place,” such as these interactive city-by-city, neighborhood-by-neighborhood maps. Further down this page, you can also find an interactive dashboard with full statistics for your city, including trends in high-poverty, low-poverty, rebounding, and “fallen star” neighborhoods, and the total number of people living in high-poverty neighborhoods from 1970 to 2010.

Screen Shot 2016-04-18 at 2.39.55 PM

Screen Shot 2016-04-18 at 2.44.38 PM

 

Click the thumbnail below for the full infographic. We’ve also included some further narrative context below.

Click for full infographic.
Click for full infographic.

 

Neighborhood change has been a hot topic in many American cities—and, increasingly, on the national stage—for a number of years. At City Observatory, we’re especially interested in shifting community demographics as they relate to economic and racial integration, which have been shown to have profound impacts on people’s class mobility, longevity, and more.

But while most of the focus has been on gentrification—the process of middle- and upper-income people moving into lower-income neighborhoods—our own research shows that low-income communities are much more likely to suffer from the opposite problem: increasing poverty and severe population decline. Three-quarters of neighborhoods with a poverty rate twice the national average in 1970 still had very high levels of poverty in 2010, and had lost an average of 40 percent of their population. That represents a much larger number of people who have been “displaced” by a lack of opportunity or high-quality public services than have been displaced by gentrification.

Our perceptions of neighborhood change are often shaped by those places that are experiencing the greatest pace of change.  The data in “Lost in Place”—available for all of the nation’s 50 largest metro areas—lets anyone look to see how poverty has changed and spread in their city since 1970. And our new infographic helps explain the major components of change. We invite you to use these tools to explore and discuss the process of neighborhood change in your city.

Daytime and nighttime segregation

In cities, you’ll sometimes hear people talk about a “daytime population”: not how many people live in a place, but how many gather there regularly during their waking hours. So while 1.6 million people may actually live in Manhattan, there are nearly twice that many people on the island during a given workday.

Most studies on segregation deal with what you might call the “nighttime population,” or actual locations of residence. And of course, that kind of segregation has been shown to have significant negative effects. But it’s also in large part a matter of convenience: the Census means that we have detailed data on where people live. It’s harder to get data on where they happen to spend their time when they’re not at home.

But a fascinating study asks whether, and how, waking mobility affects patterns of segregation. The authors—Taylor Shelton, Ate Poorthuis, and Matthew Zook—used geotagged Twitter and Foursquare data in Louisville, KY to determine whether users likely lived in that city’s West End (predominantly black) or East End (predominantly white). Then they mapped the ratio of the number of tweets by East End residents to the number of tweets by West End residents all across the city.

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The results are striking: While the West End is visible as a block of nearly solid purple, indicating virtually no tweets from East End residents, Louisville’s East End appears as various splotches of orange, grey, and purple—indicating a much greater mix of East and West End residents.

The implication is that West End residents, who are mostly black, are much more likely to cross boundaries of segregation than East End residents, who are mostly white. In part, that may be a matter of necessity, as the wealthier East End has more jobs, stores and services.

But it also fits in a pattern of racial stigma and avoidance described by other studies as well. What’s ironic here is that while racial segregation is often described as a limitation on the movement of the disadvantaged population—and in many important ways, from health outcomes to employment, it is—in terms of physical mobility, it turns out that the driver of the West End’s isolation isn’t that West Enders never leave, but that East Enders never visit.

A view of 9th Street, which divides the East and West Ends of Louisville, KY. Credit: Google Maps
A view of 9th Street, which divides the East and West Ends of Louisville, KY. Credit: Google Maps

 

That dovetails with research by Ed Glaeser, who suggests that since around 1970, the persistence of “nighttime” residential segregation has been driven primarily by whites’ decisions to avoid neighborhoods that have a significant black population, and to leave their own neighborhoods when blacks move in. It also resonates with research from Robert Sampson, who found a significant stigma attached to predominantly black neighborhoods, and Maria Krysan, who found that while blacks’ knowledge about predominantly white neighborhoods in Chicago depended on their distance and economic class, whites were much more likely to describe themselves as knowing nothing about black neighborhoods, regardless of other factors.

Shelton, Poorthuis, and Zook did find that a few specific activities could draw East End residents west: a cluster appeared near the Churchill Downs racetrack during horse racing season, but then disappeared when the season ended. In other words, while this is a hopeful sign that some kinds of activities clearly generate geographic crossover, these kinds of visits appeared to be to few or limited to have any wider spillover effects in increasing even daytime integration elsewhere in the West End.

That suggests the remedy for this kind of separation will have to go deeper than just an occasional event that draws people from around the city for a few hours. But this paper helps underscore that when we think about segregation, we need to think about more than just where people sleep at night.

How great cities enable you to live longer

Low income people live longer in dense, well-educated, immigrant-friendly cities

Some of the most provocative social science research in the past decade has come from the Equality of Opportunity Project, led by Stanford economist Raj Chetty. The project’s major work looks at the factors contributing to intergenerational economic mobility–the extent to which different communities actually enable the American dream of people in the lowest income groups being able to move up economically. In another research project, Chetty and his colleagues have looked at how life expectancy varies by community.  

The bulk of the paper concerns the relationship between longevity and income, and has been well-reported elsewhere. It highlights patterns that anyone following issues of inequality in the US would have long suspected to be true—that life expectancy is strongly correlated with income, and that the gap in life expectancy between high- and low-income people has grown—but which are now confirmed, in detail, in hard numbers.

But because Chetty et al also analyzed their data by commuting zone (akin to a metropolitan area) and county, we can also draw important conclusions about the link between place and life expectancy, just as their earlier research linked place and economic opportunity. And it appears that strong urban environments can boost their residents’ longevity—especially for the low-income.

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This wide variation in life expectancy by region provides some insights into the community characteristics that are most closely associated with longer lives. Here we’ve reproduced a key chart from Chetty’s paper, which shows the correlation between a series of regional characteristics and the life expectancy of people in the bottom income quartile.

Credit: Chetty et al
Credit: Chetty et al

 

Dots correspond to the point estimate, lines represent the 95 percent confidence interval of the estimate. Positive values indicate that life expectancy increases with increases in the local characteristic; negative values indicate that life expectancy decreases as the value of the local characteristic increases.

In part, these statistics, affirm what we already know:  Places where people smoke more and where obesity is more prevalent have shorter life expectancies; places where people exercise more have longer life expectancies. Regional variations in key health behaviors are reflected directly in the life expectancy of the poor. And, the report casts some doubt on some other factors that people think influence health and mortality.  Chetty et al looked at the role of a range of health care measures, the presence of social capital, and the role of inequality and of unemployment and found that regional variations in these characteristics had weak, if any correlation with regional variations in life expectancy.

The unexpected importance of place.

The most interesting result of this paper is the strong, consistent positive contribution of several community level variables to life expectancy.  P, poor people tend to live longer in places with more immigrants, more expensive housing, higher local government spending, more density, and a better educated population. Consider each of the five characteristics in the category “Other Variables” at the bottom of Chetty, et al’s Figure 8.

What these data show are a string of strong positive correlations. Places with more immigrants have longer life expectancy for the poor. The same holds for places with more expensive housing: here, too, the poor live longer. The poor also live longer in places with high levels of government spending, more density, and a better educated population. Taken together, these correlations suggest the importance of positive spillover effects from healthy urban places. Large cities tend to have higher levels of density. The most successful cities tend to attract more immigrants, have more expensive housing, and a better educated population. These data suggest that the poor have longer life expectancies in thriving cities.

The authors explain that their data make a strong case for a relationship between cities and greater longevity of the poor:

. . . the strongest pattern in the data was that low-income individuals tend to live longest (and have more healthful behaviors) in cities with highly educated populations, high incomes, and high levels of government expenditures, such as New York, New York, and San Francisco, California. In these cities, life expectancy for individuals in the bottom 5% of the income distribution was approximately 80 years. In contrast, in cities such as Gary, Indiana, and Detroit, Michigan, the expected age at death for individuals in the bottom 5% of the income distribution was approximately 75 years. Low-income individuals living in cities with highly educated populations and high incomes also experienced the largest gains in life expectancy during the 2000s.

As noted, these correlations don’t show causation; some of the effect may have something to do with those—like immigrants—who self-select to move to cities. But the strength of these correlations (and their absence for other variables like access to medical care) signals a need for further scrutiny. As always, this kind of broad statistical work comes with caveats: the paper takes only a first-pass, high-level look at correlations between geographic variables and life expectancy. This analysis shows the simple and direct relationship between each tested variable and life expectancy—but doesn’t measure any interactions among variables. And the standard caveat applies: correlation doesn’t prove causation. Still, by examining the correlation between selected local characteristics and life expectancy, we can begin to answer some of our questions about what aspects of place affect this aspect of quality of life.

There’s long been a good body of circumstantial evidence to support the proposition that cities are healthier. We know the people in cities and denser environments tend to walk more, a key factor associated with longevity. They also tend to drive less, and suffer less from the toll of crashes and the sedentary life styles associated with car dependent living. We know that cities promote higher levels of innovation and productivity, and that city economic success is correlated with education, but these data suggest that there may be important spillover benefits in terms of life expectancy even for those who are relatively low income.

“Live long and prosper” was Spock’s famous admonition in Star Trek. Together with the earlier research on the connections between place and inter-generatinal mobility, this new work highlighting the role of community characteristics in influencing life expectancy signals that successful cities may be an important contributor to realizing those twin goals.

A surprising message about the connection between place and life expectancy

There aren’t many economists whose research findings are routinely reported in the New York Times and Washington Post. But Raj Chetty—and his colleagues around the country—have a justly earned reputation for clearly presented analyses with detailed findings and direct policy relevance. Last year, they released the most detailed study yet on how place affects intergenerational mobility. And the paper they released Monday is the latest to draw a link between the qualities of urban spaces and the most profound issues of opportunity—in this case, life expectancy.

The bulk of the paper concerns the relationship between longevity and income, and has been well-reported elsewhere. It highlights patterns that anyone following issues of inequality in the US would have long suspected to be true—that life expectancy is strongly correlated with income, and that the gap in life expectancy between high- and low-income people has grown—but which are now confirmed, in detail, in hard numbers.

But because Chetty et al also analyzed their data by commuting zone (akin to a metropolitan area) and county, we can also draw important conclusions about the link between place and life expectancy, just as their earlier research linked place and economic opportunity. And it appears that strong urban environments can boost their residents’ longevity—especially for the low-income.

Before exploring the details, an important note: the Chetty paper takes only a first-pass, high-level look at correlations between geographic variables and life expectancy. This analysis shows the simple and direct relationship between each tested variable and life expectancy—but doesn’t measure any interactions among variables. And the standard caveat applies: correlation doesn’t prove causation. Still, by examining the correlation between selected local characteristics and life expectancy, we can begin to answer some of our questions about what aspects of place affect this aspect of quality of life.

Screen Shot 2016-04-13 at 9.53.25 AM

Credit: Chetty et al
Credit: Chetty et al

 

Here we’ve reproduced a key chart from Chetty’s paper, which shows the correlation between a series of regional characteristics and the life expectancy of people in the bottom income quartile.

Credit: Chetty et al
Credit: Chetty et al

 

Dots correspond to the point estimate, lines represent the 95 percent confidence interval of the estimate. Positive values indicate that life expectancy increases with increases in the local characteristic; negative values indicate that life expectancy decreases as the value of the local characteristic increases.

We can split these findings into three categories:

Confirming the obvious. places where people smoke more and where obesity is more prevalent have shorter life expectancies; places where people exercise more have longer life expectancies. Regional variations in key health behaviors are reflected directly in the life expectancy of the poor.

Little evidence for the expected. Chetty et al looked at the role of a range of health care measures, the presence of social capital, and the role of inequality and of unemployment and found that regional variations in these characteristics had weak, if any correlation with regional variations in life expectancy.

Unexpected importance of place. Strikingly, poor people tend to live longer in places with more immigrants, more expensive housing, higher local government spending, more density, and a better educated population. Consider each of the five characteristics in the category “Other Variables” at the bottom of Chetty, et al’s Figure 8.

What these data show are a string of strong positive correlations. Places with more immigrants have longer life expectancy for the poor. The same holds for places with more expensive housing: here, too, the poor live longer. The poor also live longer in places with high levels of government spending, more density, and a better educated population. Taken together, these correlations suggest the importance of positive spillover effects from healthy urban places. Large cities tend to have higher levels of density. The most successful cities tend to attract more immigrants, have more expensive housing, and a better educated population. These data suggest that the poor have longer life expectancies in thriving cities.

The authors explain that their data make a strong case for a relationship between cities and greater longevity of the poor:

. . . the strongest pattern in the data was that low-income individuals tend to live longest (and have more healthful behaviors) in cities with highly educated populations, high incomes, and high levels of government expenditures, such as New York, New York, and San Francisco, California. In these cities, life expectancy for individuals in the bottom 5% of the income distribution was approximately 80 years. In contrast, in cities such as Gary, Indiana, and Detroit, Michigan, the expected age at death for individuals in the bottom 5% of the income distribution was approximately 75 years. Low-income individuals living in cities with highly educated populations and high incomes also experienced the largest gains in life expectancy during the 2000s.

As noted, these correlations don’t show causation; some of the effect may have something to do with those—like immigrants—who self-select to move to cities. But the strength of these correlations (and their absence for other variables like access to medical care) signals a need for further scrutiny.

There’s long been a good body of circumstantial evidence to support the proposition that cities are healthier. We know the people in cities and denser environments tend to walk more, a key factor associated with longevity. They also tend to drive less, and suffer less from the toll of crashes and the sedentary life styles associated with car dependent living.

“Live long and prosper” was Spock’s famous admonition in Star Trek. Together with the earlier research on the connections between place and inter-generatinal mobility, this new work highlighting the role of community characteristics in influencing life expectancy signals that successful cities may be an important contributor to realizing those twin goals.

Why mixed-income neighborhoods matter: lifting kids out of poverty

There’s a hopeful new sign that how we build our cities, and specifically, how good a job we do of building mixed income neighborhoods that are open to everyone can play a key role in reducing poverty and promoting equity. New research shows that neighborhood effects—the impact of peers, the local environment, neighbors—contribute significantly to success later in life. Poor kids who grow up in more mixed income neighborhoods have better lifetime economic results. This signals that an important strategy for addressing poverty is building cities where mixed income neighborhoods are the norm, rather than the exception. And this strategy can be implemented in a number of ways—not just by relocating the poor to better neighborhoods, but by actively promoting greater income integration in the neighborhoods, mostly in cities, that have higher than average poverty rates.

In the New York Times, economist Justin Wolfers reports on groundbreaking work by Eric Chyn of the University of Michigan that found previous research may have understated the effect of neighborhoods on lifetime earnings and employment. The paper shows that moving low-income children in very poor neighborhoods to less poor neighborhoods can have a major positive effect on their life chances.

Most media outlets have covered this story as reinforcing the importance of “mobility programs”: that is, policies that encourage residents of very low-income neighborhoods to move to more economically integrated areas, usually with some form of direct housing assistance like vouchers. And the ability to move to neighborhoods with good amenities and access to jobs, without having to pay unsustainable amounts for housing or transportation, is a crucial part of creating more equitable, opportunity-rich cities.

But the coverage may be missing the other half of the policy equation: Chyn’s paper adds to the evidence about the value of mixed-income neighborhoods in general, not just mobility. That means it’s just as important that cities find a way to invest in low-income neighborhoods to bring opportunity to them, rather than simply trying to move everyone out.

Why the new research is so important

The results of the voucher demonstration illustrate that there can be large benefits from even modest changes in economic integration. The average household moved about 2 miles from their previous public housing location, and still lived in a neighborhood that had a higher than average poverty rate. Chyn’s results show the effects of moving from neighborhoods dominated by public housing (where the poverty rate was 78% on average), to neighborhoods that had poverty rates initially 25 percentage points lower, on average. Most participants still lived in neighborhoods with far higher levels of poverty than the typical American neighborhood. But compared to their peers who remained in high poverty neighborhoods, they enjoyed better economic results later in life.

This chart shows that children who moved out of very low-income neighborhoods were about 5-10 percentage points more likely to be employed as adults.
This chart shows that children who moved out of very low-income neighborhoods were about 5-10 percentage points more likely to be employed as adults.

 

In this chart, you can see the growing earnings benefit to children who left very low-income neighborhoods in their adult years.
In this chart, you can see the growing earnings benefit to children who left very low-income neighborhoods in their adult years.

 

This study—on the heels of a widely-cited study led by Harvard economist Raj Chettyreleased last year—adds even more heft to the growing body of evidence that helping people with lower incomes move to mixed-income neighborhoods can play a huge role in spreading economic opportunity.

The new research improves on older studies by getting rid of an important confounding factor that affected some earlier research by more closely replicating a true “natural experiment.”

The experiment was made possible by the decision to demolish large scale public housing in Chicago in the early 1990s. The families dislocated from the old style public housing—which were in neighborhoods of extremely concentrated poverty—had to find new housing. The Chicago Housing Authority (CHA) provided the families with vouchers to move to privately operated rental housing, typically in neighborhoods with far lower levels of poverty. The kids who moved to new lower-poverty neighborhoods saw a significant increase in their lifetime earnings compared to otherwise similar kids who remained in the public housing that wasn’t torn down.

This natural experiment has an important advantage over the “Moving to Opportunity” (MTO) housing experiment conducted by the federal government in the 1990s. In MTO, public housing households had to apply for a voucher lottery. This created the possibility that the people who had applied were particularly motivated and able to make the transition to a new neighborhood. That would mean that even those households that lost the lottery might have better-than-average outcomes, reducing the gap between those who moved and those who didn’t, and making the effect of moving appear smaller than it really was.

But unlike MTO, the participants in the CHA relocation program were not self-selected. They represented a more or less random cross-section of public housing residents, and so the differences between the outcomes of treatment groups (those who got vouchers) and those who didn’t (control groups) could be treated as purely the result of the voucher program.

The policy implication: Mixed-income neighborhoods promote opportunity

But it’s important to put this finding in a broader context. Evidence about mobility programs, in turn, are part of a larger body of research that neighborhoods matter for economic opportunity. While the focus has been helping people leave neighborhoods with high concentrations of poverty, it’s also possible to bring investments and resources to these communities.

Of course, when that happens, it often happens in conjunction with—or even because of—a return of middle- and upper-income people to the neighborhood. In other words, gentrification.

For some, that’s enough to reject that policy avenue. But some research suggests we ought to give it another look. While news from neighborhoods in San Francisco and Brooklyn, where incredibly high levels of demand and tight supply have led to spiraling housing costs, makes it sound like gentrification inevitably and utterly displaces all a neighborhood’s residents, other research suggests that displacement is far less widespread than commonly thought. While housing costs can be an issue, a recent study from the Philadelphia Federal Reserve suggests that displacement is much less common than we might expect—and another study of New York public housing residents in gentrifying areas showed an increase in earnings and school test scores.

This research also occurs against a backdrop of widening inequality and economic segregation. And inequality has an important spatial dimension: low-income and high-income households are increasingly segregated from one another in separate neighborhoods. As we’ve documented in our research at City Observatory, the effects of this segregation on the poor, in the form of the growing concentration of poverty, are devastating, and the number of Americans living in neighborhoods of concentrated poverty in large metropolitan areas hasmore than doubled since 1970, from 2 million to 4 million.

While the spatial response, as we’ve said, has focused on mobility, enabling the poor to move to higher income neighborhoods is challenging for a number of reasons. The raison d’etre of many suburbs is exclusion—using zoning requirements to make it essentially impossible for low income households to afford housing—and efforts by outside organizations or governments to reduce these barriers have been difficult. If we want to make the biggest difference in economic integration, we need to try to integrate low-income neighborhoods as well as high-income neighborhoods.

Neighborhoods for everyone

Taken together, the new Chyn results add to the growing body of literature on neighborhood effects and strongly suggest that we ought to be looking for all kinds of opportunities, large and small, to promote more mixed-income neighborhoods. Even the small steps—like lowering the poverty rate in a kid’s neighborhood from 75 percent to less than half—pays clear economic dividends.

But we also need to remember that integration isn’t just about moving around people with low incomes. We can reinvest in neighborhoods of concentrated poverty in ways that improve quality of life and enhance opportunity in place.

What is an “unequal” city?

Why does economic inequality—as opposed to just poverty—matter? There are a lot of reasons, but a big one is that higher levels of inequality make it harder to improve your economic position. As Federal Reserve Chair Janet Yellen has argued, the bigger the gap between rich and poor, the harder it is for the children of rich and poor to have equal opportunities to make their lives what they would like. So at the national level, more inequality means less economic mobility.

But at more local levels, the story is more complicated. The massive study on economic mobility carried out by Raj Chetty and his colleagues and released last year found that more unequal metropolitan areas were, in fact, worse for mobility.

The relationship between economic mobility and metro area (CZ) economic segregation. Credit: Chetty et al, "Where is the Land of Opportunity?"
The relationship between economic mobility and metro area (CZ) economic segregation. Credit: Chetty et al, “Where is the Land of Opportunity?”

 

But they also found that in more economically segregated metropolitan areas, low-income children were less likely to make it out of poverty. In part, that makes sense: in segregated regions, poor children are more likely to grow up in neighborhoods, or municipalities, with high concentrations of poverty—which in turn leaves fewer resources for the kinds of public and private amenities, from schools to stores, that help people improve their quality of life.  

What does that mean for inequality? Well, high levels of segregation are created by economically homogenous neighborhoods:  rich people live near other rich people, poor people live in neighborhoods of high poverty. In other words, more segregation equals more neighborhood-level equality. A wealthy suburb where everyone earns six figures is going to have very low levels of income inequality; ditto a neighborhood where almost everyone is economically struggling. In that sense, given high levels of regional inequality, sub-regional equality—in a neighborhood or municipality—isn’t really a good thing.

That’s an important caveat to coverage of urban inequality, like this one from Fast Company, covering a report from the Brookings Institution. At a national and regional level, high levels of inequality are very bad. But local policy is mostly made by municipalities at a sub-regional level. And the only way for municipalities to pursue more equality is, in effect, by pursuing economic segregation.

Boston, the city where Brookings reported inequality has grown the most. Credit: bill_comstock, Flickr
Boston, the city where Fast Company reported inequality has grown the most. Credit: bill_comstock, Flickr

 

As we’ve written before, this is admittedly counterintuitive. With most other national issues—poverty, say, or school segregation—we can take a problem we know to exist across the country and drill down to see which regions, cities, and neighborhoods are most badly affected. But doing the same with inequality ends up being quite misleading. It means something very different for a city or neighborhood to have a high 95/20 ratio than for the country as a whole. It would be a great thing for national inequality levels to fall; but does anyone think Chelsea in New York City would be a more “equal” place if its public housing residents were all removed, leaving only the wealthy and reducing its 95/20 ratio? Would it be unprogressive to create affordable housing in a place like Winnetka, Illinois, increasing local inequality thereby making it a place where both rich and poor could live?

This is not to dispute, as Brookings argues, that there are trade-offs: Low-income households in economically integrated neighborhoods—that is, neighborhoods with high levels of economic inequality—may face higher prices for some goods and services. But on net, there’s little evidence that they’d be better off if all the rich people decamped for some exclusionary community. (As they are, in fact, increasingly doing.) Ironically, then, if local officials want to fight national inequality, they ought to focus on creating integrated, affordable neighborhoods—even if that means those neighborhoods appear more “unequal.”

Engaged communities, civic participation, and democracy

Today we’re publishing an edited version of a speech given by Carol Coletta, VP of Community and National Initiatives at the Knight Foundation, last month in Portland, OR.


Informed and engaged communities are fundamental to a strong democracy. But many of the signs of those communities are not encouraging:

Newspaper readership has plummeted in recent years. It is a particular problem with local papers. More depressing, no one believes there is yet a business model that will support robust local reporting.

Distrust among Americans is increasing. The share of the population that believes “most people can be trusted” has fallen from a majority in the 1970s, to about one-third today.

Economic segregation has gone up while middle-income neighborhoods decline. Between 1970 and 2009 the proportion of families living either in predominantly poor or predominantly affluent neighborhoods doubled from 15 percent to 33 percent.

Politically, we have sorted ourselves into like-minded geographies.  Nearly two-thirds (63 percent) of consistent conservatives and about half (49 percent) of consistent liberals say most of their close friends share their political views.

Credit: hjl, Flickr
Credit: hjl, Flickr

 

Portland State’s own Phil Keisling and Jason Jurjevich looked at voting behavior in local elections for Knight and found that turnout in most cities is abysmally low—typically hovering around 20 percent.

They also found that the age of those who do cast ballots was anywhere from 13 to 17 years higher than the citywide median age of the adult population. In other words, as Jason put it, “18-34 year-olds are almost entirely abdicating to their grandparents’ generation the key decision of who should actually govern them.”

Phil and Jason also found many “voting deserts” in cities—places where the percentage of people voting is half or less than the already-terrible overall rates—as low as half a percent! It’s tough to have informed and engaged communities when the trends are working so powerfully against them. On the other hand, there are a few hopeful signs.

As cities cut staff and services to parks and recreation, libraries, and public works in recent years, frustrated citizens have begun taking matters into their own hands. Do-it-yourself efforts to improve cities—sometimes known as “tactical urbanism”—continue to heat up, fueled by the massive move of young adults to core cities.

And while Millennials may not be reading newspapers, they lead the country in the civic use of social media. Pew found that half of 18-to-29 year olds decide to learn more about political or social issues because of what they read on social networking sites. Fifty-seven percent engage in political activity on social networking sites and nowhere else.

If only they could be convinced to vote.

Voting isn’t the only means of civic participation, of course. But a high and sustained voter turnout rate is the best single measure of whether all the other things we are doing to promote engagement are working—and that’s why we make increased local voter turnout the North Star for our efforts.

But here’s a conundrum: Knight, as a foundation, doesn’t support “get out of the vote” efforts. We don’t support voter registration. We don’t support efforts to overturn voter suppression.

So what’s left that looks promising?

To get the answer, we turned to Portland. Last year, thanks to my friend and colleague Ethan Seltzer, I had the opportunity to interview the founders of modern day Portland. They told me how they reclaimed power from the small group of elected elites who used to wield their influence from the basement of a downtown hotel.

They decided that if they were to engage Portlanders in the civic life of their community, they had to be convinced to “live life in public.” In other words, they had to be lured from the comfort and privacy of their living rooms and backyards and share public life in the company of strangers.

At the time, there were, they told us, a lot of impediments to doing that. There was, for instance, a prohibition against playing music in the park. Sidewalk cafes were illegal.

So they set out to eliminate as many of the things that discouraged public life as they could. And today, you have a wonderfully rich public realm and many signs of robust public life. Like the founders of modern day Portland, we believe that public life—or “living life in public,” as they put it—is critical to civic engagement.

And yet, many of our so-called “engagement processes” that are codified into law are clearly deficient. There is the requisite three-minute public comment period in every public meeting. Architects and planners must have engagement “specialists” on their teams. App developers have had a field day with their attempts to induce civic engagement via smart phones (many of them supported by Knight funding).

But our efforts are failing miserably. Just look at the rate of voting in local elections. Most Millennials don’t see voting in local elections as a way to express their values, nor do they see local government as a way to get things done. And apparently, a lot of other Americans agree.

As long as opportunities for civic engagement are episodic, tucked away, or on a schedule, I’m not sure we will ever have the broad engagement we need to make our communities successful. We don’t need the occasional well-attended community meeting with 100 people in the audience. We need thousands of people engaged every day in the civic life of their city. And we believe that the places we inhabit everyday can be a far more powerful way to stimulate a culture of engagement than any process or any app.

I want to offer up one very humble example of what’s possible. It’s called the Pop-up Pool in Philadelphia’s Francisville neighborhood, between a very poor neighborhood and one that is seeing a lot of new investment. It is the product of Ben Bryant, who submitted his idea to the Knight Cities Challenge.

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Public pools, as you may know, have tortured racial histories; and as private pools have proliferated, support for public pools has waned, leaving a customer base consisting of those with no other options—in Philadelphia, that means usually poor, usually black, and in the case of Philly’s pools, usually under 18.

Ben took a look at Philly’s pools and saw the potential for a much more dynamic neighborhood asset, one that could attract people from the two very different neighborhoods that bordered it.

How to do that? Pretty simple.

  • Add seating where there was none.
  • Add a few palm trees to amp up the emotional resonance of the pool as a stay-cation.
  • Add water Zumba classes that everyone could enjoy while looking slightly dorky doing;
  • Change the rules so moms could enter the pool with street clothes on vs. a bathing suit.
  • Promote it daily on social media in ways the city never would.

It was an immediate hit. The pool suffered no loss of existing patrons, but it gained new popularity with residents who discovered the pool for the first time. People of different economic status, different ages, and groups of singles along with parents and children were happily sharing the pool together.

It took less than a week for the City to announce that it planned to convert all of its pools to the pop-up pool model.

Writing in The New Yorker last month, Adam Gopnik described the mixing we need this way: “Cities…shine by bringing like-minded people in from the hinterland (gays, geeks, Jews, artists, bohemians), but they thrive by asking unlike-minded people to live together in the enveloping metropolis…. While the clumping is fun, the coexistence is the greater social miracle.”

If we can crack that one, we can unlock enormous opportunity for Americans.

The future of a city is not made with a few broad strokes by a few key people. The future is made by thousands of people making small decisions every day about what they believe about the future and their role in it. By building up the civic commons—parks, recreation centers, libraries, cultural centers—to support the active sharing of public spaces and activities by a wide mix of people of different economic statuses, different ages, we can encourage people to make those decisions in a way that builds more informed, engaged communities, and a stronger democracy.

 

What’s really going on in gentrifying neighborhoods?

Yesterday, we wrote about the Chelsea neighborhood in Manhattan, which is in the unique position of being one of the wealthiest urban communities in the nation, and also having almost a third of its housing be public or otherwise subsidized. The question was, what happens to the residents of public housing in a place like Chelsea when it gentrifies?

The answer was mixed, but mostly positive. A study from the well-respected Furman Institute at NYU showed that residents of public housing in wealthy or “increasing income” neighborhoods earned substantially more, on average, than public housing residents in low-income neighborhoods. Moreover, they experienced less violent crime and their children went to better public schools—and, likely as a result, did better in school themselves. While low income residents of gentrifying neighborhoods cited problems finding affordable local retail and a sense of alienation from the businesses and institutions catering to their very different neighbors, on balance, many thought their neighborhoods had changed for the better.

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Credit: Furman Center

 

But New York City is unique in having such huge concentrations of public and subsidized housing in many affluent or “increasing income” neighborhoods. In most places, low-income residents in low-income communities live in market rate housing that’s affordable because there is so little demand for it from middle class and upper-income households. In those cases, you would expect that as demand increases, those residents would be priced out, excluding them from the benefits of an increasingly resource-rich community.

A new study from the Federal Reserve Bank of Philadelphia challenges this narrative. The authors (Lei Ding and Eileen Divringi of the Fed, and Jackelyn Hwang of Princeton, who has written other notable studies on gentrification) tracked movement in and out of urban neighborhoods in Philadelphia from 2002 to 2014, and look at the effect of rising rents and incomes on existing residents. From our perspective, there are three big takeaways.

First, demographic change in gentrifying neighborhoods doesn’t happen the way most people think it does. Ding et al find that in gentrifying neighborhoods, existing residents are just 0.4 percentage points more likely to move out in a given year than they would be in a non-gentrifying neighborhood. (As a baseline, just over 10 percent of all residents moved in a given year.) Even in those neighborhoods with the most rapid increases in rents and income, existing residents are just 3.6 percentage points more likely to move. Moreover, because the authors are interested in involuntary displacement, presumably for economic reasons, they look at whether people who leave gentrifying neighborhoods are more likely to move to poorer communities. In most cases, the answer seems to be no.

Credit: Federal Reserve Bank of Philadelphia
Credit: Federal Reserve Bank of Philadelphia

 

So what explains the changing income and ethnic backgrounds of residents in these communities? In short, it’s not who’s moving out; it’s who’s moving in. People who would have left anyway are replaced by a whiter, more affluent group of people; most neighborhoods have turnover rates high enough that this dynamic alone can change the makeup of a neighborhood’s residents quite quickly. To quote the study: “Overall, the results are more consistent with the notion that changes in the characteristics of inmovers are a more important force in determining the demographic changes in gentrifying neighborhoods.”

Second, the Philadelphia Fed joins the Furman Center in finding that those residents who remain in gentrifying communities see some economic gain. In this case, the authors measure financial health through credit scores. Remaining in a gentrifying neighborhood is worth, on average, a gain of 11 credit score points over three years, compared to a situation in which the resident’s neighborhood did not see rising income and rent levels. It’s not entirely clear why this happens, but rising income (as suggested in the Furman report), or improved access to credit, might be a part of it. Complicating those results, residents with particularly bad credit saw relatively larger increases in their scores, though living in a gentrifying neighborhood was associated with slightly smaller increases than if they had lived in a nongentrifying area.

Finally, while many narratives about gentrification suggest that the choice is between neighborhood change versus stability, that is not what the Fed study depicts. While the average gentrifying neighborhood in Philadelphia saw its median income increase by nearly 42 percent between 2000 and 2013, nongentrifying neighborhoods did not remain the same—their median income fell by almost 20 percent, with an almost five percentage point increase in the poverty rate. Moreover, while gentrifying neighborhoods’ population grew by an average of 2.3 percent, nongentrifying neighborhoods lost nearly two percent of their population, driven not only by an exodus of non-Hispanic whites, but also a decline of almost five percent in the population of non-Hispanic blacks. Perhaps most amazingly, the proportion of residents who were housing-cost-burdened increased by over 10 percentage points in neighborhoods that didn’t gentrify—probably because of falling incomes.

We should pause for a moment to note that while the Philadelphia Fed study is valuable, in part because it uses a more detailed data set, none of its results are really new. As Lei et al acknowledge, studies of gentrifying neighborhoods (most famously Lance Freeman’s) have generally failed to find higher rates of outmoving among existing residents, compared to otherwise similar nongentrifying neighborhoods. The finding that residents who stay in gentrifying neighborhoods see some economic benefits, as we’ve already pointed out, echoes what the Furman Center reported in its study of public housing residents in New York, and a Cleveland Fed study from 2013. And the fact that low-income neighborhoods that don’t gentrify also don’t remain the same—that the alternative to a lack of reinvestment is a persistent pattern of economic and demographic decline—is what we found in our own study, “Lost in Place.”

"Lost in Place" found that neighborhoods that remained poor lost about 40 percent of their population from 1970 to 2010.
“Lost in Place” found that neighborhoods that remained poor lost about 40 percent of their population from 1970 to 2010.

 

The point here is not that everything in gentrifying neighborhoods is peachy. After all, there’s very little displacement of economically vulnerable people in exclusionary high-income neighborhoods, because there are no economically vulnerable people to begin with. But when there are problems, they’re less likely to be existing residents forced out by rising rents, and more likely to be potential residents who are turned away before they even arrive. The challenge, in neighborhoods that are becoming more affluent as well as ones that already are, is to make sure that there is a sufficient stock of affordable housing (both subsidized and “naturally occurring” at market rate) to accommodate people of whatever means who want to move in. The best way to do that remains to make sure there isn’t an overall shortage of housing, and that there’s a variety of housing types, from single family homes to apartments of various sizes; and to remain friendly to developers of subsidized housing and people holding housing vouchers.

But the fact that rigorous studies of neighborhood change consistently produce results that, at least, complicate widely repeated narratives about gentrification ought to give us pause. While there are certainly people who are forced out of their homes by rising rents, it’s curious that the focus on those cases tends to crowd out attention on people who would like to move to a neighborhood but can’t afford it, a situation that appears to be more widespread. Similarly, the implicit assumption of most gentrification coverage—that the absence of gentrification would result in the preservation of the existing neighborhood as is—clearly needs to be reassessed. We’ll write more about this media issue tomorrow.

Higher-inequality neighborhoods reduce inequality

A few weeks ago, in a post about what income inequality means in an urban (rather than national) context, we contrasted images of a lower Manhattan neighborhood with a Dallas suburb. The Manhattan street had subsidized housing on one side and very expensive homes on the other; the Dallas suburb just had the expensive homes. Our point was that putting affordable housing in otherwise affluent neighborhoods makes for high Gini coefficients, but is also better policy than the alternative.

 

Last week, the New York Times took a deeper look at what such a contrast actually looks like on the ground, reporting on the lives of public housing residents in Chelsea, Manhattan, one of the city’s wealthiest areas to feature large numbers of public housing units. The long article highlights both the benefits and challenges of such mixed neighborhoods.

Start with the good news. The Times references a May study by the New York City Housing Authority (NYCHA) and NYU’s Furman Center that compared outcomes for public housing residents in three kinds of neighborhoods: persistently low-income, persistently high-income, and “increasing income” (or gentrifying). Residents of public housing projects in wealthy and increasing income neighborhoods showed dramatically better economic and quality-of-life outcomes than those in low-income neighborhoods—even though their racial, ethnic, and age demographics weren’t significantly different. Annual household income for public housing residents was roughly $4,500 higher in more affluent neighborhoods, and $3,000 higher for those in increasing income ones. Predictably, violent crime rates are also significantly lower. And perhaps most encouraging, children of NYCHA residents in affluent and increasing income neighborhoods not only go to schools with much better test results, but score much higher themselves on reading and math.

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In many ways, the study is yet another confirmation of earlier work by researchers like Raj Chetty and Patrick Sharkey, showing that being poor in a poor neighborhood is usually much worse than being poor in a middle-class or affluent neighborhood. While the juxtaposition of very high income households and very low income households in neighborhoods like Chelsea brings the problem of inequality into sharp relief, places that are home to a mix of household types neither cause income inequality, nor do they make the effects of poverty worse for the poor. If anything, these sorts of mixed-income neighborhoods actually work to reduce inequality—or at least improve mobility between income groups—compared to places that are more “comfortably” homogeneous.

There are also, however, some problems. One of them has to do with a gap in the housing market. New York is fortunate to have a large stock of relatively well-maintained public housing in many of its more affluent neighborhoods—in Chelsea, a full 29 percent of all homes are in public housing or other income-restricted units—but, as some of the most in-demand real estate in the world, the homes whose prices are set by the market are almost uniformly very expensive. That leads to a neighborhood where low income people can live in public housing, and rich people can live in market-rate housing, but there aren’t necessarily many places to go if you’re middle class.

But this may be one of those times when extreme cases make for bad policy. In most places, the solution to a missing middle is to build missing middle housing. Chelsea, and other Manhattan neighborhoods, however, combine some of the highest density in the country with some of the highest housing demand in the world; its affordable housing solutions are necessarily going to look very different than those of most American neighborhoods. (That is, building small apartment buildings and granny flats is unlikely to help anything in Manhattan.) While it’s unclear what to do about middle class housing in Chelsea, this problem is unlikely to be intractable in many other places. Or if it is intractable, it would be for political, rather than economic, reasons.

Another problem has to do with the fact that housing makes up only part of what makes a neighborhood “affordable.” In New York City, the second-biggest household expense—transportation—isn’t so much of an issue, since most of the city has excellent low-cost transit access, and few residents would need a car. But in some neighborhoods, like Chelsea, non-public housing residents are so wealthy (the largest single income category on one block the Times looked at was those making over $200,000 a year) that groceries and other retail may be priced at levels above what a very low income household can afford. As a result, those residents may have to travel farther to do their shopping, and may feel alienated from their own neighborhood if they feel like most of the businesses around their home aren’t “for them.”

Both of those are genuine issues, and policymakers ought to be aware of them. We at City Observatory have been particularly concerned with the latter; our “Less in Common” report focused on the decline in public amenities where people of different backgrounds can interact and build a sense of community. Making sure that mixed income neighborhoods have such public amenities—parks, swimming pools, markets, and so on—that people of all backgrounds feel welcome and attracted to is a crucial part of building successful urban communities.

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But it’s hard to read the report and not conclude that, on balance, the “gentrification” of neighborhoods around public housing is a win for its residents. The poor still face real problems living in neighborhoods with rich neighbors, but these problems—more expensive local shops, a need to travel further for some bargains—are of a different kind than the high crime, limited economic opportunity, and poor schools that are the rule in neighborhoods of concentrated poverty. Even the Times story, which foregrounds the challenges, gets around (in the very last paragraph) to quoting one of its subjects saying, “I’d rather have Chelsea as it is today…. There’s more people. It’s brighter, it’s beautiful, it’s more inviting than it used to be. We’re very lucky to be able to stay in housing that hopefully will not disappear.”

Perhaps our policy goal should be that everyone ought to have access to such luck, in the sense of living in affordably-priced housing in an economically healthy neighborhood with lots of educational and employment opportunities. That means both bringing back economic vibrancy to places that have lost it, and making sure that places that already have it provide both subsidized and market rate housing whose cost allows people with a full range of incomes to live there. (To be clear, by “economically healthy” we have in mind something slightly less excessive than the concentration of wealth in Chelsea—but that’s also not a concentration that most places have to worry about.)

At this point, some readers will be thinking: Sure, this works when low-income people live in public housing, whose affordability isn’t going away. What about neighborhoods where most low-income people live in homes whose prices are set by the market, and which may price them out if more middle- and upper-income people move in? A recent study has some encouraging news on that front as well. Look for us to take it up soon.

Our birthday wish: Cities for everyone

Two years and two days ago–on October 15th, 2014–we launched City Observatory, a data-driven voice on what makes for successful cities.  Since then, we’ve weighed in daily on a whole series of policies issues set in and around urban spaces. So today, we’re taking a few moments to celebrate our birthday, reflect back on the past year, and plot a course forward.

Many happy returns! (Flickr: Daniel Nelson)
City Observatory turns 2. Many happy returns! (Flickr: Daniel Nelson)

Its a tremendously encouraging time to be working in cities.   After decades of disinvestment and the out-migration of people and jobs, cities, particularly city centers, are on the comeback.  With each passing day, the evidence that cities are leading our nation’s economy becomes more compelling: City home values are rising much faster than in their surrounding suburbs, an indicator we call the “Dow of Cities.” City center job growth is outpacing that in the suburbs for the first time in decades, and the expansion of large metro economies is driving the national economic expansion. More investment is flowing into downtown areas.  As we’ve chronicled, more people, particularly well-educated young adults, are increasingly choosing to live in close-in urban neighborhoods.

The return to urban living has the potential to help create a healthier planet, as people drive less and use less energy to maintain relatively smaller urban homes. It can help make people healthier, too, by allowing them to walk more, and reducing their exposure to one of the greatest causes of death and serious injury in the country: car crashes. Finally, overwhelming evidence shows that bringing people with social capital and middle-class incomes back into urban neighborhoods that had lacked those things for years creates more social equity and economic mobility.

While we’re fundamentally optimistic about cities, and we see them as essential to tackling many of the nation’s most pressing problems, we also recognize that cities are the epicenter of some serious challenges.

The immediate effect of this recent surge of interest, investment and migration is a shortage of cities.  More people now want to live in great urban neighborhoods than ever before. And the demand for urban living has grown far more rapidly than the supply of great urban places. Unfortunately, too many policies made it difficult to build additional housing in the most desirable neighborhoods. This mismatch is accentuated by the  temporal imbalance between fast-changing demand and slow-changing supply and has manifested itself in the form of higher rents and real estate values in urban centers. While higher rents are an important indicator of a turnaround–and the market signal that will help alleviate this shortage–higher rents pose major problems for many urban residents, particularly low income households.

In our view, the solution to this affordability problem will come from increasing housing supply and from building more great urban neighborhoods.  This is a matter of supply and demand:  as long as the demand exceeds the supply, prices (and rents) will go up.  But its also a matter of arithmetic:  If more people want to live in a neighborhood than their are houses to hold them, then some people who would like to live their will end up living somewhere else. And given the penurious nature of our housing support for the poor, it means low income households will be those disproportionately disadvantaged.

The movement back to the cities is an unparalleled opportunity to tackle one of the most persistent and destructive problems confronting our nation, the growth of economic segregation. We know that as bad as it is to be poor, it’s worse to have to live in a neighborhood where a large fraction of your neighbors are also poor: concentrated poverty amplifies all of the negative effects of poverty and it results in a permanently limited lifetime opportunities. Our work, and that of our colleagues at the Brookings Institution shows that despite all of the focus on an urban renaissance, neighborhoods of concentrated poverty are actually growing, and they are still disproportionately in urban centers.

Too often, unfortunately, discussions of cities get framed as a zero-sum game:  If we make the city or neighborhood better for some group or person, we’re somehow making it worse for everyone else. Many resist any change, for fear that they will end up worse off.

The challenge in our view is to look for ways to turn the revitalization of our cities into a win-win experience for all.  How do we leverage the growth and investment in cities in a way that promotes and expands their cultural, economic, and racial/ethnic diversity?  How do we build cities for everyone? There are promising efforts in many cities, as exemplified by the YIMBY–“Yes in my back yard“–movement that is growing, and which now has friends in very high places. In Seattle that city’s HALA “Housing Affordability and Livability Agenda”  has inspired some provocative conversations that are reshaping the contours of the city’s political scene. Environmental, social justice and housing affordability advocates in Portland have started a “Portland for Everyone” organization to advocate for more supply.

These efforts are hopeful signs that we can take the energy and momentum that is building in favor of urban living, and use that force to help propel efforts to build more diverse and inclusive communities. In our third year, City Observatory will focus on the challenge of building cities for everyone. We hope you’ll join jus.

 

Talent, opportunity, and engagement are essential to successful cities

We’re very excited to spread the news that this fall, our partners and supporters at the Knight Foundation are reprising their wildly successful “Knight Cities Challenge.” Last year, Knight chose 32 winners out of more than 7,200 project proposals from people in cities all over the country, awarding them the resources and support they needed to jumpstart their ideas about how to improve their communities. 

This year, submissions are open until noon EST on October 27—and the application requires less than 300 words. If you or someone you know has an idea for your city, check it out!

Yesterday, we shared some thoughts about what goes into making a “successful city” on Knight’s blog. We’ve republished them below.


What makes a successful city? Maybe it’s easiest to start with an image of a successful neighborhood. Picture a place where people can get together—a small park, say. There are children playing in one area as their parents and grandparents watch from benches. Near the street, there’s a small market—an art fair, say, to raise money for a community group—and neighbors are perusing the stalls and making conversation, running into friends and acquaintances.

Near the park are some shops, which keep a steady stream of people walking by, so the park never feels vacant or forbidding. The people from the neighborhood can walk, bike or drive a very short distance to these shops—a hardware store, a cafe, a small grocery, a barber, a post office—to take care of most of their daily needs. Not only can they get a gallon of milk or mail a package without making a big inconvenient trip, but they know they might run into a friendly face, exchanging quick smiles or stopping for a chat as time permits.

The homes in the neighborhood fit the full range of people’s needs. There are houses with yards and large apartments for growing families; smaller apartments and backyard cottages for young people without children, and for the elderly who no longer want, or are able, to maintain a large home. The range of housing types means there’s a range of housing prices, so nobody is excluded from living here, and the neighborhood is a place where people who don’t earn much can hope to get a foothold towards a better life. The mix of single-family homes and apartments also means there are enough people around to support the community business district without huge parking lots meant to draw people from miles away.

The neighborhood is close to jobs. There are local jobs in the shops and the neighborhood school—also walking distance from most of its students’ homes—but it’s also a quick commute to downtown, via a reliable and frequent public bus, or a safe bike ride, or a fairly short drive. Traffic on local streets doesn’t zoom by at 45 miles per hour—the neighbors wouldn’t want that, because it wouldn’t be safe—but because the community is centrally located they don’t have to travel at highway speeds to get there fast. The neighbors are able to spend far less on transportation than people who live farther out, because they can take transit or walk for so many trips, and when they do drive they don’t have to go far. With extra room in their household budgets, they invest in home repairs, or better food for their families, or chip in to the local school.

This is a sunny picture, but it’s not unrealistic. If this, or something like it, is your vision of a successful neighborhood—and a successful city—the good news is that we have some idea of how to get closer to it. Vibrant, attractive community spaces, especially near hubs of activity like shops or transit stations, can help create places where neighbors can meet. Opening up our zoning laws (which have made places like the one I just described illegal in most American cities) can open the door to neighborhoods where you can run basic errands without getting in your car, and where a large range of housing types means there are homes for a large range of people, not just a few. Building a transportation system around access—the ability to actually get to the stores, schools and jobs that people need—rather than pure driving speed is crucial to safer, more humane and more affordable commutes. And attracting talent, especially people with college degrees, can help build a powerful local economy and grow jobs.

The Knight Cities Challenge is so exciting because it funds projects, and people, who are building great communities through many of these avenues. Even better, it empowers the people who know their neighborhoods best to create bottom-up change that’s distinctive to a given place, rather than trying to dictate a rigid one-size-fits-all program. Here at City Observatory, where we spend all day thinking about how to build more successful cities, we can’t wait to see what the next class of challenge winners comes up with.

Want to learn more about the Knight Cities Challenge? Attend a community information event or virtual office hours. Here is a schedule of what’s comingYou can follow the challenge at #knightcities on Twitter or sign up for our email newsletter. You can send questions to citieschallenge@knightfoundation.org.

One of the biggest myths about cities: Crime is rising

There’s a lot happening in American cities these days, which means that there’s a lot to read about! Even for those of us at City Observatory, sometimes good, important articles slip through the cracks. In recognition of that, periodically, we’ll dig back into our archives to republish a piece that we think deserves another go-around.

This time, it’s a post from last October about the myth of rising urban crime rates. Since then, there’s been even more talk about this, fueled in part by fear of Black Lives Matter-related protests.

This persistent alarmist meme about “rising urban crime” got a big boost two weeks ago with an article in the New York Times pointing to a number of examples of higher murder rates in some US cities compared to a year ago.  While the Times analysis was thorough debunked by FiveThirtyEight (absolute must read article here), the more widely read Times piece no doubt gave new life to this discredited old saw about cities—which is why we thought it was timely to recall our earlier analysis of crime rate trends. (Also see this piece from CityLab on the pernicious effects of high-crime myths.)


robocop watch
Credit: Danni Naeil, Flickr

The Myth: Crime in cities is on the rise

The Reality: Cities are getting safer

For decades, the common perception about cities is that they were dangerous, dirty, and crowded. A look at the facts tells a different story: our cities are cleaner, safer, quicker, and healthier than ever. Today I’ll take a look at how urban neighborhoods have become safer despite public attitudes to the contrary.

On the whole, violent crime is declining in the Unites States. The overall murder rate has dropped by more than half since 1991 and property crimes like burglary have been on the decline. As a result, American concern about crime has ebbed: in 1994 a majority of Americans told Gallup crime was the nation’s most pressing issue; only 1 percent gave that answer in 2011. Even though we individually regard crime as less of a problem, people still tend to think of big cities as somehow dangerous. Consider the New York paradox: According to YouGov, Americans who have never been to the Big Apple are evenly divided on whether its safe or not, while those who have traveled their regard it as safe by a two-to-one margin.

This drop in crime has been greatest in the nation’s largest cities. Violent crimes of all kinds declined 29 percent in the central cities of the nation’s 100 largest metropolitan areas — a significantly steeper decline than in the nation’s suburbs (down 7 percent). Property crimes in central cities fell even more — down 46 percent, compared to a 31 percent decrease in suburbs.

Survey evidence demonstrates that the drop in crime is not widely understood by the general public. A September 2014 survey by YouGov found that most Americans believe crime rates have increased over the past two decades. Their data show that 50 percent of Americans think crime rates are up; 22 percent think they are down, 15 percent think crime rates are unchanged, and 13 percent don’t know.

Hollywood continues to peddle the storyline of cities of the future as savage, crime-ridden dystopias (see for example this year’s remake of Robocop). Meanwhile the good news about safer cities goes almost unnoticed. A 2011 study by the Brookings Institution pointing to significant declines in 80 of the nation’s 100 largest cities has gone practically unnoticed, garnering just seven citations in other work, according to Google Scholar. (Google Scholar, August 19, 2014).

While crime has dropped, it’s not the only factor making cities better places to live. Wednesday, I’ll conclude the series by showing how traffic jams aren’t actually as bad as they used to be.

What do we know about neighborhood change, gentrification, and displacement?

In last Friday’s The Week Observed, we flagged an exhaustive literature review from the Federal Reserve Bank of San Francisco, summarizing what we know about gentrification and neighborhood change over about 40 pages. We focused on one of the takeaways Richard Florida picked out in his article about the study in CityLab, on the connection between public transit and gentrification, but it would be a shame to reduce an amazingly wide-ranging study to that one issue. (Florida has since published a second piece on the connection between displacement and gentrification.)

A building under renovation in Cincinnati's Over the Rhine neighborhood. Credit: David Brossard, Flickr
A building under renovation in Cincinnati’s Over the Rhine neighborhood. Credit: David Brossard, Flickr

 

So on the off chance that your post-Labor Day plans don’t involve wading through 45-page Fed Reserve PDFs, we’ve pulled out four of the most interesting findings from the report’s coverage of the academic literature on neighborhood change and gentrification.

  1. Even as racial segregation declines, income segregation continues to grow, driven by the “secession of the rich.”

From 1980 to 2010, the proportion of lower-income households living in majority low-income Census tracts increased modestly, from 23 to 25 percent. But the proportion of upper-income households living in majority upper-income Census tracts doubled, from 9 to 18 percent. Moreover, the rich aren’t just moving to wealthy neighborhoods: they’re increasingly moving to their own suburban jurisdictions.

One bright spot is that the trend towards revitalization of central cities may be some kind of counterweight to the “secession of the rich.” In cities where upper-income households tend to live in the suburban periphery, like Houston or Dallas, the rich are more segregated than in denser cities with wealthier cores, like Philadelphia or Seattle.

  1. In most contexts, people value public transit accessibility enough to pay a premium for it. (And its value can partially or entirely offset higher rents.)

The value of living near a transit station depends heavily on how useful your city’s transit network is. That, in turn, depends on a) how extensive, efficient, and reliable transit service is, and b) how (in)convenient and expensive driving is. But in most cases, the value of living near transit is clearly reflected in the fact that people are willing to pay more for that access.

This does not mean, though, that we ought to avoid making transit investments for fear of provoking “gentrification.” In fact, that’s the opposite of what a broad reading of the research suggests. To begin with, transportation costs associated with driving can eat up well over 20 or 25 percent of low-income households’ budgets—meaning that low-income residents who are able to switch to using transit may be able to absorb some rising home values and still save significant amounts of money. In fact, those savings seem to be a major reason that many low-income households choose to live in transit-served neighborhoods.

But the literature also suggests that, in general, the effect of investments like transit on housing costs and displacement depends to a huge extent on the local housing market. These sorts of gentrification-related problems are mostly an issue in places with extremely tight markets, where lots of people are competing for relatively small numbers of homes. In other words, the problem, once again, is a shortage of cities and the kind of neighborhoods that many people want to live in. The solution isn’t to further restrict the growth of these neighborhoods—it’s to make sure we have as many as we need to allow everyone who wants to live in them to do so, and to use subsidized housing to fill in the gaps.

A light rail train in Charlotte. Credit: James Willamor, Flickr
A light rail train in Charlotte. Credit: James Willamor, Flickr

 

  1. Despite decades of research, we are nowhere close to a consensus understanding among academics of what exactly displacement is, how to measure it, or how big of a problem it is.

Displacement has been a serious topic of research since the urban renewal programs of the 1950s, and received another big boost as gentrification became a major concern beginning in the 1990s. But conflicting definitions of such key terms as “gentrification” and “displacement,” along with varying approaches to interpreting the “significance” of findings, makes it extremely difficult to summarize any findings across the many papers on the subject.

One big issue, for example, is how to measure a counterfactual: What would have happened to people in a neighborhood without any gentrification pressures? For people like Lance Freeman, the correct comparison is poor neighborhoods that don’t gentrify. Those types of communities tend to have high levels of mobility, which makes the number of potentially displacement-related moves in gentrifying areas look smaller than if you use city-wide averages or less-mobile neighborhoods as the baseline.

Researchers do agree that rising rents predict increased displacement, though some studies suggest that the effect is lessened because existing residents are willing to pay more in rent as their neighborhood gains gentrification-related amenities. And, partly as a result of the issues outlined above, researchers differ wildly on how much displacement actually happens, and to what extent it’s a problem. Freeman’s national study in 2005 found that a household in a gentrifying neighborhood had only a 1.3 percent chance of being displaced nationally, while Kathe Newman and Elvin Wyly, using a different baseline of comparison, concluded that as many as 10 percent of all moves in New York City between 1989 and 2002 were a result of displacement. (The fact that these two measures are not actually directly comparable reflects another serious, yet typical, problem in trying to synthesize the research on gentrification.)

  1. Changing in-migration patterns can transform neighborhoods quickly, even without displacement pressures.

As we’ve written about previously, neighborhood change happens because of two types of moves: people moving in and people moving out. Often, we conceive of gentrification as reducing the number of low-income people in a neighborhood mainly because the number of people moving out increases dramatically as housing prices increase.

But a shift in the kinds of people moving into a neighborhood can also create major demographic change, even if the number of people moving out stays relatively stable. A paper by Freeman and Branconi suggests that it’s theoretically possible for a neighborhood to reduce its poverty rate from 30 percent to 12 percent in just ten years, without any “displacement,” if the pool of people wanting to enter the community changes dramatically and low-income households who would have left anyway are replaced by higher-income residents. That explains how it’s possible, as they found, that low-income households in gentrifying New York City neighborhoods are actually less likely to move than in high-poverty neighborhoods that aren’t gentrifying.

In practice, of course, it’s unlikely that there would be an influx of middle- or upper-income residents to a neighborhood without some effect on housing prices. But especially in cities with looser housing markets, or in cases of neighborhood change that don’t involve rising incomes, this suggests that we ought to be very wary of assuming that demographic shifts are always the result of displacement.

Looking at housing injustice requires a broad lens

What does it mean for someone to be displaced by gentrification? And in a just world, what do our cities’ neighborhoods look like?

As reported by Next City, a team of researchers at the University of California-Berkeley has put together a an analysis that probes just those questions. But the stilted answers they come up with get at the heart of one of the greatest contradictions in the debate over where America’s cities are going, and where they ought to be.

Let’s back up for just a moment. The Urban Displacement Project, led by Miriam Zuk, wants to use predictive modeling to peer into the future of neighborhood change in the Bay Area. Drawing on previous theory, Zuk and her colleagues categorized every Census tract in the region into one of eight stages of gentrification, and used previous trends to project how much further gentrification might go.

So far, so good. But digging into the Urban Displacement Project’s methodology reveals a curious choice.

In their course of their analysis, Zuk and her team try to quantify the number of people who have been or might be “displaced” as a result of gentrification. How do you know if someone has been displaced? As the authors point out, previous researchers have generally cast a wide net: displacement might occur as a result of any number of factors, from disinvestment that makes a home unpleasant or dangerous to rising prices that force tenants to look for more affordable housing elsewhere.

But rather than attempt to sort through different types of change, the Urban Displacement Project simply assumes that any reduction in the number of low-income people in a neighborhood is a result of displacement. As Zuk acknowledges, this is rather extreme: it doesn’t leave room for someone to move away simply because, as people do, they decide that they would be happier living somewhere else; and it would define a low-income person who remained exactly in the same place but got a better-paying job as a “displaced person.” It’s unlikely that, in the teeth of the recession, this last number is particularly high—but unless we believe that economic progress in poor neighborhoods is impossible, it’s hard to see how this could be a viable assumption going forward.

This notion of displacement also seems to imply that neighborhoods are, or ought to be, entirely static and that their residents never move. We know that this isn’t true for neighborhoods in general, and poor neighborhoods in particular. As the Urban Institute has shown, one of the main ways that poor families improve their economic situation, get access to better schools and reduce their risks of crime is to move to different neighborhoods. As the Berkeley team writes, it’s probably not fair to describe those sorts of moves as totally “voluntary,” since the movers are reacting to deficits beyond their control in communities they may otherwise want to remain in. But in a context that’s heavily focused on gentrification, rolling this other kind of “displacement” may obscure more than it illuminates.

Neighborhood demographic change, after all, mostly depends on two things: who’s moving in, and who’s moving out. That means a neighborhood’s poverty rate will remain the same only if the poverty rate for each of those two groups is exactly the same. It also means that this balance can be thrown off by a change in either group. If the people moving in become less poor, the neighborhood’s poverty rate will fall even if nothing has changed about the people moving out. Even if, that is, people are not being displaced at any greater speed than they were before.

Much of the research on gentrification has focused on trying to untangle these two threads. In doing so, it has often found that neighborhood change is driven as much or more by a change in the “move-in” group than the “move-out” group. One of the most famous studies, by Columbia’s Lance Freeman, found that low-income residents of gentrifying neighborhoods were only somewhat more likely to move than low-income residents of non-gentrifying neighborhoods: the real difference was who was coming in to replace them. The Urban Displacement Project counts this shift in in-migration as displacement—which doesn’t leave room to interpret, say, a decline in neighborhood stigma that causes middle-income people to stop shunning a poor neighborhood as a positive development.

But the most fundamental problem with this definition of displacement is what it implies about the kinds of neighborhoods we want in our cities. If every reduction in the number of poor people in a neighborhood is “displacement”—and we agree that displacement is something to be avoided—then the only conclusion is that every neighborhood must remain exactly as poor as it is now.

Claiming that every neighborhood ought to stay as it is with respect to poverty—or, for that matter, racial segregation, although Zuk et al are mostly focused on economics—would be one thing in a country where the status quo was relatively egalitarian, and where the neighborhoods that people lived in reflected their own agency in a just, fair, and free society. But obviously wherever that country is, we do not live in it. When the status quo is the result of generations of discrimination and cruelty, taking Zuk’s position is chaining ourselves to a continuing legacy of terrible injustice.

In fact, a full 88 of the 129 tracts that the Urban Displacement Project identify as “gentrifying” are still poorer than the Bay Area as a whole. Obviously that doesn’t mean they’ll stay that way; gentrification and displacement is a process along a spectrum. But it does demonstrate how much room there is in many American neighborhoods to reduce the poverty rate without becoming an exclusionary bastion of privilege. The question is whether, once the process has begun, it can be arrested before housing prices push out all or nearly all of the original community of residents. The answer to that is not obvious everywhere, though our report, Lost in Place, showed that nationally, instances in which this process of improvement managed to reduce the poverty rate in previously high poverty neighborhoods are extremely rare. Despite high-profile examples of neighborhood change in some coastal cities, it’s not at all clear that there’s necessarily a gentrification “tipping point” that leads inexorably to the exclusion of all the poor.

None of this is to minimize the fact that, especially in places like San Francisco, rising home prices often constitute an injustice. Clearly, being forced to leave your home is wrong, even if you live in a neighborhood that has been a target of economic or racial segregation. But we need a framework for understanding housing and neighborhood change that allows us to talk about and address the full range of challenges our cities face. A definition of “displacement” that implicitly rejects any change to the status quo is not up to that task.

Instead, we need to ask what will lead to more open, diverse and integrated neighborhoods in our metropolitan areas. Just as it’s reasonable and necessary to ask how we’ll promote greater housing choices in the nation’s wealthy suburbs (a discussion provoked by the Obama administration’s Affirmatively Furthering Fair Housing rule), it’s necessary to think about what rectifying the pattern of segregation in America’s poor neighborhoods would look like as well.

New Orleans’ missing black middle class

Washed away?  Or moved to the suburbs?

At FiveThirtyEight, Ben Casselman writes: “Katrina Washed Away New Orleans’s Black Middle Class.” It’s a provocative piece showing the sharp decline in the black population of the city of New Orleans, particularly the city’s black middle class. While the city has rebounded in many ways since Katrina, the city’s black population has recovered more slowly, and middle-income blacks especially so. While the white, non-Hispanic population of the city is still below pre-Katrina levels, it has rebounded faster than the black population.

Casselman alludes to the diaspora of the city’s African American population, which is down by nearly 100,000 from pre-Katrina levels. His analysis shows that the black middle class has recovered far more slowly than other demographic groups, but doesn’t say where they might have moved to. And the analysis conspicuously omits one major factor shaping population trends in New Orleans (and for that matter, other U.S cities): the suburbanization of the black middle class. The word “suburb” doesn’t appear in Casselman’s piece.

Casselman is clear that his analysis covers just the city limits of New Orleans, or Orleans Parish. But there’s much more to metro New Orleans than Orleans Parish. Like most US metros, a majority of the region’s population—and most of its population growth—has been in its suburbs. Suburbanization has accelerated post-Katrina. The city’s population is only about 30 percent of the New Orleans metropolitan area, down from about from 36 percent of the metro total in 2000.

The suburbanization of blacks in New Orleans

Those who follow New Orleans closely know that the area’s black population has grown increasingly suburban. The Data Center, a New Orleans-based independent research organization, has tracked the region’s changes before and after Katrina on its website and in a recent report “Who Lives in New Orleans and Metro Parishes Now?” Their data shows the makeup of the metropolitan area according to its constituent parishes for the years 2000 and 2013. Their findings show a stark gap between demographic trends in the city and surrounding suburbs:

While the city has 97,395 fewer black residents, the metro area as a whole has only 66,752 fewer black residents, revealing that the suburban parishes have gained more than 30,000 blacks. Moreover, the metro area as whole has had a net loss of 75,228 white residents. In short, the metro area as a whole is increasingly diverse with gains in blacks, Hispanics, and Asians and losses of white residents in nearly every parish.

While the black population is increasing in suburban parishes, the reverse is true for the white population, according to The Data Center’s report. The white, non-Hispanic population of the suburban parishes has decreased 11 percent, slightly faster than in Orleans parish, compared to an increase the black population of the suburban parishes of 17 percent. (We’ve reproduced the data from the center’s 2014 report below in the Appendix.)

The movement of black Americans to the suburbs is a widespread trend. According to the Brookings Institution’s Bill Frey, the black population of many central cities is decreasing (including in nine of the ten largest cities), and the black population of the suburbs is increasing almost everywhere, with 96 of the 100 largest metropolitan areas recording increasing in their suburban black population. This movement is propelled by the black middle class; Frey notes that the black movement to the suburbs is led by the young, those with higher education, and married couples with children. As Pete Saunders has written, suburban living is still aspirational for many blacks.

Black middle class growing in New Orleans suburbs

But the growing black population in New Orleans’ suburbs is not a representative sample of the region’s African American residents. Rather, Census data suggest that it largely reflects an increase in middle-income and upper-income black households. Data tabulated by the Census Bureau’s American Community Survey show the relative income levels of black households living in Orleans Parish compared to suburban areas. We’ve extracted data from American Fact Finder for 2005 and 2012. (The 2013 data are available, but reflect a change in metro area boundaries, so we use the earlier data for comparability.) While the Census Bureau reports data in the same income ranges in each year, the dollar figures are not directly comparable between 2005 and 2012 due to inflation over that time period. In metropolitan New Orleans, higher income black households are more likely to live in the suburban parishes than are low income blacks. As of 2012, about 72 percent of blacks with incomes under $15,000 live in Orleans Parish, while about 53 percent of blacks with incomes over $35,000 live in one of the surrounding suburban parishes. Comparing the income distribution data for black households in Orleans Parish with those for suburban parishes in 2005 and 2012 shows that while lower income black households have become more heavily concentrated in the city, middle- and upper-income black households have become more likely to live in the suburbs. Here we’ve divided all black households in metro New Orleans into three roughly equal groups based on household income, and reported the share of the metro total in each income group that resides in Orleans Parish in 2005 and 2012. In 2006, about 63.4 percent of the region’s black middle-income households lived in Orleans Parish. This declined to 54.4 percent in 2012. The share of the region’s poorest black households living in Orleans Parish actually increased from about 68 percent to 72 percent. The location of the highest earning third (those with incomes of $40,000 or more) shifted from a majority in Orleans Parish (54 percent in 2005) to a majority living in the suburban parishes in 2012.  

 

A more integrated New Orleans

Overall, the metropolitan New Orleans region has become more integrated. During the decade of the 1990s, black/white segregation in metropolitan New Orleans was actually increasing—a pattern that ran contrary to the national trend. But between 2000 and 2010, the New Orleans metropolitan area recorded a sharp decrease in segregation as measured by the black/white dissimilarity index. According to William H. Frey at the University of Michigan Population Studies Center, the black-white dissimilarity index for metropolitan New Orleans has fallen from 68.3 in 1990 and 69.2 in 2000 to 63.9 in 2010.

A racial dot map of metropolitan New Orleans, showing the state of segregation in 2010. Credit: Cooper Center Dot Map
A racial dot map of metropolitan New Orleans, showing the state of segregation in 2010. Credit: Cooper Center Dot Map

One of the keys to addressing the black-white earnings disparity is reducing segregation. As we wrote earlier this year, metropolitan areas with higher levels of segregation have, on average, much higher black-white earnings gaps. Similarly, as the work of Raj Chetty and his colleagues has shown, income and racial segregation is a powerful correlate of impaired economic mobility. The problem is exceptionally acute in New Orleans, which ranks 99th of the 100 largest metropolitan areas on Chetty’s index of intergenerational economic mobility.

As New Orleans rebuilds, it has an opportunity to address the historic patterns of segregation that have aggravated the economic plight of the area’s African-American population. It appears that it is making some progress on this front.

It’s fair, as FiveThirtyEight has done, to acknowledge the significant demographic changes that have taken place in New Orleans. Unquestionably, Katrina has had an enormous impact. But the decline of the black middle class in New Orleans also reflects two well-established trends: the national decline of segregation in housing and the movement of higher income blacks to the nation’s suburbs. Fewer blacks live in New Orleans, and more live in its suburbs. While the white non-Hispanic share of the city’s population has increased—at xx percent it’s still a minority—the white, non-Hispanic population of the area’s suburbs has decreased even faster. In the wake of Katrina, metro New Orleans is gradually becoming a more integrated city.

Appendix:

Population Change, by Race & Ethnicity,

Metropolitan New Orleans, 2000 to 2013

Orleans Parish
2000 2013 Chg. %Chg
White, Non-Hispanic 128,871 117,377 -11,494 -9%
Black 323,392 223,742 -99,650 -31%
Hispanic 14,826 20,849 6,023 41%
Asian 11,007 11,356 349 3%
Other 6,578 5,391 -1,187 -18%
Total 484,674 378,715 -105,959 -22%
Balance of MSA
2000 2013 Chg. %Chg
White, Non-Hispanic 602,643 536,753 -65,890 -11%
Black 175,177 204,179 29,002 17%
Hispanic 43,719 82,212 38,493 88%
Asian 17,621 23,882 6,261 36%
Other 13,892 15,236 1,344 10%
Total 853,052 862,262 9,210 1%
New Orleans MSA
2000 2013 Chg. %Chg
White, Non-Hispanic 731,514 654,130 -77,384 -11%
Black 498,569 427,921 -70,648 -14%
Hispanic 58,545 103,061 44,516 76%
Asian 28,628 35,238 6,610 23%
Other 20,470 20,627 157 1%
Total 1,337,726 1,240,977 -96,749 -7%

 

Source: http://www.datacenterresearch.org/data-resources/who-lives-in-new-orleans-now/

Are racial “tipping points” overblown?

Why are America’s neighborhoods so segregated? For a lot of people, the answer requires reaching deep into history: explaining the rise of the subsidized mortgage market and redlining; racial violence in towns from Cicero, Illinois to Charleston, South Carolina; restrictive racial covenants; blockbusting; and on and on.

But back in 1971, a professor named Thomas Schelling proposed a much simpler answer. It was called the “tipping point” model, and it suggested that near-total segregation might be the inevitable outcome of even very modest preferences for neighbors who look like you.

The intuition is very simple: imagine an all-white neighborhood where white people have a range of preferences about the racial makeup of their block. One day, a black family moves in. All the white families are okay with having a black family on their block—except for one of them, which moves away and is replaced by another black family. Now all the white families are okay with having two black families on their block, except for one of them, which moves away and is replaced with a black family. And so on, until there are no white families left.

Late last year, the tipping point model got a renewed burst of attention thanks to an incredibly well-made interactive web page called the “Parable of the Polygons,” which explained Schelling’s theory with anthropomorphic rectangles and triangles. The “Parable” was linked to across the Internet, with headlines like “An Immersive Game Shows How Easily Segregation Arises,” and “Math Explains Segregation.”

The evidence for tipping in American history…

But while tipping makes for elegant theory, it’s less clear that it actually works to describe what happened—or is happening—in American cities. The first study to search for empirical evidence of tipping looked at how the racial composition of neighborhoods changed depending on its starting point—and found something that looked very much like Schelling’s tipping point:

Screen Shot 2015-08-05 at 6.06.24 PM

This graph compares the percentage of a neighborhood’s population that was non-white in 1970 with racial change over the next decade. What you want to look at is the vertical line at about 5% minority in 1970. If there were no tipping point, you would expect the trend lines on either side of that vertical line to match up. Instead, they’re extremely far apart, suggesting that something happened when a neighborhood hit about 5% black and Hispanic that caused it to suddenly lose lots and lots of its white residents. In other words, a tipping point.

…and the evidence against tipping

But a follow-up study by William Easterly put some limitations on those findings. Instead of simply asking whether tipping points exist at all, it asked whether their results were as extreme as predicted by Schelling. Remember that in the original model, a small number of new black or Hispanic households could tip a neighborhood to being almost entirely black and Hispanic. That’s important for explaining American residential segregation, because many segregated neighborhoods do, in fact, have virtually no white residents.

But that’s not what the Easterly study found. Instead, while most neighborhoods became less white between 1970 and 2000—which makes sense, since the country as a whole did, too—there was very little evidence of a tipping point that led to all-black and Hispanic communities. While racial change did take place, with about 10% of all neighborhoods switching from majority-white to majority-black or Hispanic over that time period, they didn’t follow the pattern predicted by Schelling.

Screen Shot 2015-08-05 at 6.08.51 PM

For one, even nearly all-white neighborhoods—the ones on the far side of the “tipping point” that should be racially stable—became much less white over that time period. Even more surprising, neighborhoods where more than 75% of residents were black or Latino actually became whiter from 1970 to 2000. In a way, that makes sense, since we know that racial segregation is slowly declining; but it’s very confusing from a “tipping point” perspective, since even much smaller proportions of people of color are supposed to send white residents running.

So neighborhoods at the two extremes of segregation didn’t behave as predicted by the Schelling model. What about relatively integrated neighborhoods? According to tipping theory, these should be the least racially stable, quickly becoming dominated by one group or another. Instead, they were among the most stable: the typical neighborhood that was 50% white in 1970 changed less than neighborhoods that were 100% black or 100% white.

Why it matters

Now, there are important caveats to all this. This doesn’t at all contradict the large amounts of evidence that Americans, and especially white Americans, have pronounced racial preferences with regard to their neighbors, or that those preferences have major consequences. (Recall the Ed Glaeser and Jacob Vigdor study that explains lower property values in black neighborhoods, everything else being equal, by the artificial drop in demand that results from whites’ avoidance.) Also, because the available data only begins in 1970, we can’t totally rule out that something like the more extreme version of Schelling’s tipping point model actually did lead to segregation before 1970.

Still, in putting limits on Schelling’s model, Easterly’s study helps shine light on some of the dangers of relying too heavily on “tipping” to explain segregation. First, the tipping point model can be seductive because it tells a story about segregation in which there are no real villains: in a world with very mild racial preferences, it’s amazing “how easily segregation arises”—it’s “math”!

But we know that’s a very selective reading of the evidence. The historical record of racial segregation in American cities is clear, and there are lots of villains. They include the federal, state, and local governments, and the white voters that elected them; the realtors and bankers who enforced discriminatory real estate practices; and the many regular, everyday people who reacted to new black neighbors by throwing bricks or burning crosses, and the even greater number who failed to stand up to them.

But the fact that tipping points aren’t nearly as extreme as they’re often made out is also cause for optimism. Taken literally, the tipping point theory would lead to a pretty fatalistic view about the persistence of segregation: if only very small differences in preferences necessarily lead to widespread segregation, then it may be nearly impossible to make progress.  In the wake of the Obama Administration’s announcement that it would be more aggressively pursuing the mandate to “affirmatively further fair housing” in the 1968 Fair Housing Act, many skeptical commenters suggested that any attempt to introduce racial or economic diversity in privileged white communities would turn those neighborhoods into “Detroit”—the country’s poster child for white flight.

That’s a theory that’s more than a little self-interested, since many of the people arguing it live in exactly the kinds of communities that have “benefited” from exclusionary practices for decades. Look, for instance, at the outcry in Westchester County, NY, when HUD suggested that some of the policies that had kept many of its towns so privileged, like a refusal to allow subsidized housing or multi-family buildings, ought to change. Or the people who stood up at a public meeting outside St. Louis to insist that, while they personally didn’t have anything against allowing lower-income black students from a nearby district coming to enjoy the high quality of their schools, other people would probably pick up and leave, and soon the whole district would be poor and black.

But its plausibility comes from the broad acceptance of something like the “tipping point” idea. Easterly’s study shows that the breakdown of one kind of segregation doesn’t automatically lead to segregation at the other extreme—and that’s a thin reed of hope that less divided, and less unequal, cities might be more realistic than we think.

Playing together is getting harder to do

 

In our CityReport, Less in Common, we explored a key symptom of the decline in social capital: Americans seem to be spending less time playing together. One major driver of this trend is a dramatic privatization of leisure space. Instead of getting together in public parks and pools (or just playing in the street), more of our recreation takes place in private backyards, private pools, and private gyms. Prior to World War II, for example, there were fewer than 2,500 homes in the US with in-ground private pools. Today, there are more than five million.

Everyone's got a pool in this southern California subdivision. Credit: Google Maps
Everyone’s got a pool in this southern California subdivision. Credit: Google Maps

 

While that may not seem like a big deal – isn’t it a good thing if people can swim in their own backyards? – pools are a particularly good example of the ways that the privatization of leisure space is tied up with the history of sprawl and racial segregation. When prohibiting black swimmers from enjoying public pools became illegal, many of them lost all or nearly all of their white patrons, or simply shut down. Their replacements sprouted in places where exclusion was easier: behind the fences of private yards or gated communities. The stakes were demonstrated as recently as last week, when white residents of a gated community in McKinney, Texas objected to black residents and their friends in their community pool. The police officers who showed up handcuffed, manhandled, and pulled a gun on the unarmed teens – many, if not all, of whom had a right to be there – all in the name of keeping their pool exclusive.

And what’s true of swimming pools is true of many other kinds of recreation: we’re spending more time playing apart in private places than playing together in public ones. For example, while regular exercise has become an important priority for many Americans, we increasingly exercise in private facilities, rather than public parks or community centers. The membership of private gyms has increased from 13 million in 1980 to more than 50 million today. While It’s great that more people are working out, the membership of private gyms skews heavily towards younger, wealthier and better educated demographics.

Moreover, the pattern of privatized recreation starts at an early age. Opportunities for children to serendipitously engage in unstructured (and largely unsupervised) play have diminished.

One of the iconic images of recreation in the U.S. is the pickup game, whether it’s half-court hoops on a public playground, or baseball in the proverbial sandlot, or soccer on a grassy field. Whoever shows up can play, and the games have their own, largely self-organizing and self-regulating character. While there’s no published data on this kind of informal activity,the privatization of recreational space means these games are harder and harder to play – and when they do happen, the players are more likely to be of the same racial or economic background. Even participation in organized sports has been in decline. The number of 6- to 17-year olds participating in the four most popular team sports – baseball, basketball, football and soccer – has declined 4 percent nationally in the past five years.

Credit: Shad A Hall, Flickr
Credit: Shad A Hall, Flickr

 

More generally, it has become increasingly rare for younger children to walk or cycle away from their homes and away from constant parental supervision. The few parents who promote greater independence are treated as eccentric for raising “free range kids.” Recently, in the suburbs of Washington DC, parents were taken to court for letting their children ages 6 and 10, walk three blocks to a local park unaccompanied. The combination of physical distance and paranoia limit the amount of time kids spend unsupervised in the public realm.

And helicopter parents don’t have helicopters – they rely on SUVs to haul children from place to place. The result is that parents spent a not inconsiderable amount of time and money transporting children to and from school, play dates and other social activities – the kinds of trips that in earlier times (and denser communities) kids could have taken on their own. Todd Litman estimates the “chauffeuring burden” accounts for between 5 and 15 percent of all vehicle travel, and imposes costs greater than the high end estimates of congestion time loss. Plus, the added time spent traveling in private cars is time spent cocooned in a vehicle, out of the public realm.

In sprawling suburbs, the closest park or schoolyard may be too far away to walk or bike. The tendency toward building larger elementary and secondary schools, coupled with lower residential densities means schools are further from the average household (even though they may have ample open space).

The average size of an elementary school has increased about 20 percent in the past two decades, chiefly as older, smaller schools are closed, and newer schools tend to be larger.

In 1982 the average elementary school had 399 students, but by 2010 had grown to 470.

This pattern has been reinforced by policies that make it hard to retain or renovate small schools, and which produce bigger schools on the urban fringe. Nationally adopted standards for school size mandate that new elementary schools have a minimum of ten acres, with the result that fewer, bigger schools are built, typically on the periphery of communities where large sites are available, and where land is cheap.

The lessened ability of kids to bike and walk to school, and to travel independently and their growing dependence on adults to be their chauffeurs, chaperones and social directors has been identified as a major contributing factor to the rapid growth of childhood obesity. But it seems equally likely that inactivity and isolation is also contributing to the widespread malady of decreased social capital.

As one old saying goes, “the family that plays together, stays together.” The same might well be said of communities. Looking for opportunity to create more ways that we can play together in the public realm is likely to be an important strategy for reducing the erosion of our shared social capital.

Playing Apart

Our City Observatory report, Less in Common, catalogs the ways that we as a nation have been growing increasingly separated from one another.  Changes in technology, the economy and society have all coalesced to create more fragmentation and division.

As Robert Putnam described this trend in his 2000 book, we are “Bowling Alone.”  And while work, housing and shopping have become more stratified and dispersed, there still ought to be the opportunity for us to play together. Sports fandom is one of the few countervailing trends: within metropolitan areas popular support for the “home team” whether in pro-sports or college athletics is cuts across demographic and geographic boundaries.

But in our personal lives our recreation is becoming more isolated, chiefly through the privatization of leisure.

Consider:  instead of going to public parks and playgrounds, more children play in the copious backyards of suburban homes. This trend is amplified by helicopter parents.  Free range children are an anomaly, and the combination of sprawl and insecurity adds to the chauffering burden of adults–which in turn means spending more time in cocooned private vehicles. And as we know, the decline in physical exercise among the nations children has been a key factor in the explosive growth of juvenile obesity.

One of the hallmarks of the decline in the public recreational commons is swimming.  In the early part of the 20th century, swimming pools were almost exclusively in the public domain.  Prior to World War II it was estimated that there were fewer than 2,500 homes with private, in-ground swimming pools.  Today, there are more than 5 million.

That’s one of the reasons we found Samsung’s television commercial “A Perfect Day” so compelling. It highlighted the adventures of a group of kids, cycling around New York City, and ending up spending time at a public pool.  Its encouraging that a private company can make our aspirations for living life in public a central part of its marketing message.

That’s certainly a contrast to the trend of commoditization of leisure. Increasingly, we pay to play, and play in the private realm.  The number of persons who belong to private gyms has increased from about 13 million in 1981 to more than 50 million today.  While gyms provide a great experience for those who join, they tend to draw disproportionately from wealthier and younger demographic groups–again contributing to our self-segregation by common background and interest.

Over just the past five years, the number of Americans classified as “physically inactive”–not participating in sports, recreation or exercise, has increased from 75 million to 83 million, according to the Physical Activity Council.  And youth participation in the most common team sports — soccer, basketball, football and baseball — has declined 4 percent since 2008.

As we think about ways to strengthen and restore the civic commons, we will probably want to place special emphasis on parks and recreation.  Public parks are one of the places where people of different races, ethnicities and incomes can come together and share experiences.

Is gentrification a rare big city malady?

  • Gentrification is a big issue in a few places, and not an issue at all elsewhere.
  • Big cities with expensive housing are the flashpoint for gentrification.

The city-policy-sphere is rife with debate on gentrification. Just in the past weeks, we have a French sociologist’s indictment of bourgeois movement to the central city, the Mayor of Washington and the Secretary of Housing and Urban Development pointing to 300 new units of affordable rental housing as a bulwark against gentrification in DC’s fast-changing Shaw neighborhood, and continued debate over the merits of a moratorium on new housing development as a means of stemming change in San Francisco’s Mission District.

Most of the stated concern about gentrification revolves around the belief that neighborhood improvement automatically produces widespread displacement of the existing population.  But how widespread is the problem?

Outside a big cities with tight housing markets, the effects of gentrification may be much more benign. In an essay entitled “Is gentrification different in legacy cities?” Todd Swanstrom argues that in most of the nation’s metros, the effects of gentrification are more muted, and on balance positive. Because housing prices are low and there is a lot of slack in the housing market, the movement of better educated and higher income people into cities is far less likely to result in the displacement of the existing population.

The variety of opinions about the effects of gentrification are apparent when one talks to mayors. Consider the results of a 2014 survey of the nation’s mayors undertaken by Boston University. The survey explored mayoral attitudes about gentrification, asking them whether they agreed, disagreed or neither agreed nor disagreed with the proposition that “rising property values are good for a neighborhood.” Overall, of the 70 mayors surveyed, 45 percent agreed and 30 percent disagreed with this statement. The pattern of responses is highly correlated with property values: mayors of cities with median home values in the bottom and middle third of the national distribution agreed by a more than two to one margin that rising values are good, compared to only about 20 percent of the mayors of cities with the most expensive homes.

Mayoral Opinion on “Rising Property Values”

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Source: Boston University Initiative on Cities, Mayor’s Leadership Survey

 

Another way of tracking public awareness of the issue is through data on Internet searches. Google data confirm that public interest in gentrification is increasing. The increase has mostly been strong and steady, with a very strong spike coinciding with Spike Lee’s famous anti-gentrification rant at the Pratt Institute in Brooklyn in February, 2014.

The Google data also show a distinctive geographic pattern to the interest in gentrification. Google Trends reports the metropolitan areas with the greatest relative propensity to search for specific terms, including gentrification. Searches for gentrification come disproportionately from a handful of large metropolitan areas, corresponding to some the nation’s largest and most liberal cities: New York, Austin, Chicago, San Francisco, and Washington head the list. And 32 of the 51 largest US metropolitan areas have reported values of zero for searches related to gentrification.

Top Metropolitan Areas for Gentrification Searches

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Source: Google Trends, Page Rank Index for “Gentrification” Relative to Top Metro (New York).  Metros color-coded based on statewide presidential vote in 2012, blue-democratic, red-republican.

All of the other metropolitan areas in the country have an index value of zero in Google Trends, indicating almost no interest in the subject.

Fifteen of the nineteen metropolitan areas on this list, including 12 of the top 13 are located in blue states (based on statewide vote for president in 2012). It’s been argued elsewhere that liberals have done a lousy job of fighting gentrification, and these data at least superficially support this argument.

A common factor in gentrification is a surging demand for urban living in the face of a limited supply of urban housing.  We haven’t undertaken a detailed analysis of the housing markets in the cities where gentrification interest is strongest, but they map to largest cities with robust housing markets (with the exception of Detroit).  In smaller markets, and where housing is relatively inexpensive, gentrification doesn’t seem to register as an issue, as measured either by Google Trends or mayoral opinion.

Its instructive to look at the relationship between metro area population, housing prices and interest in gentrification.  The following table stratifies the nation’s 51 largest metropolitan areas – all those with a population of one million or more – by population size, and looks at average home prices (reported by Zillow) in metros with, and without, a reported interest in gentrification (as indicated by the Google Trends data discussed above).

Several findings stand out.  First, interest in gentrification is universal among the 12 largest metropolitan areas, but decreases rapidly as metro area population falls:  half of the second quartile of large metros, two the next quartile and none of the last quartile had a measurable interest in gentrification, according to the Google search results.  Second, home values tend to be much higher in metros with an interest in gentrification:  average prices are about 50 percent higher in the second quartile, and about three times higher in the third quartile.   As the final column suggests, home prices tend to be higher in larger metros, but the smaller metros that have an interest in gentrification have average home prices that are higher than in the largest 12 markets.  Interest in gentrification is strongly related to market size and to high home pricesAs John Buntin speculated in Slate, the high interest in gentrification in pricey coastal real estate markets may have more to do with middle class concerns about affording real estate than about the displacement of the poor.

Average Home Value in Markets by Interest in Gentrification
Average Home Value
Market Size Number Interested Interested Not Interested Value All Markets
12 Largest 12 297,267 NA 297,267
13th-24th 6 308,850 205,467 257,158
25th-36th 2 550,600 154,720 220,700
37th-51st 0 NA 164,513 164,513
Source:  Google (Gentrification Interest), Zillow (Home Prices)

Most cities have strong limitations on dense development, particularly in the most desirable neighborhoods, so when housing demand surges, it leads to price increases and development pressure that is felt in lower income neighborhoods.  In sprawling markets where the housing supply is relatively elastic (Atlanta, Houston, Dallas) gentrification is far less of an issue; and as noted above, it seems to be a non-issue in most Sunbelt cities (for example Phoenix, Jacksonville, Tampa, Nashville, Charlotte).

These data show that interest in gentrification, while growing is still a highly localized issue:  it tends to be a concern in large cities, not small; in expensive housing markets, not affordable ones, and is disproportionately of interest in common in blue states, and relatively rare in red ones.

 

New evidence on integration and economic mobility

It’s unusual to flag an economics article as a “must-read” for general audiences: but if you care about cities and place, and about the prospects for the American Dream in the 21st Century, you owe it to yourself to read this new article by Raj Chetty, Nathaniel Hendren, “The Impacts of Neighborhoods on Intergenerational Mobility: Childhood Exposure Effects and County-Level Estimates.”  (The Executive Summary is just six pages long—you can download it here.)

This work strongly confirms the growing belief that the kind of community you grow up in has a huge impact on your lifetime economic opportunities.  Specifically, the Chetty-Hendren study shows that some communities do a much better job of helping kids from low income families achieve economic success than do others.  And these communities tend to be ones that have low levels of economic and racial segregation, better schools, less violent crime, and fewer single-parent families.  An important part of how we assure opportunity to all hinges on how we build communities.

Seattle ranks as one of the most mobility-friendly metropolitan areas. Credit: Jonathan Miske, Flickr
Seattle ranks as one of the most mobility-friendly metropolitan areas. Credit: Jonathan Miske, Flickr

This study is remarkable for a number of reasons:  it’s clearly and simply presented, based on an extraordinarily large and powerful database, provides detailed findings (down to the county level), and provides strong evidence that its findings are cause-and-effect, not mere correlation.

One of the big bugaboos of economic research is that, unlike other scientific inquiries, economists are not generally allowed to run random selection controlled experiments on human beings—which we would all probably agree is a good idea.  Economic research, like most social science, typically must rely on statistical inference from sample data often gathered for other purposes, with its attendant margins of error.  And secondary statistical data make it especially difficult to make definitive cause-and-effect statements: For example, did a community’s environment cause children to have particularly high levels of economic mobility, or did the unseen choices of some parents to move in and out of particular neighborhoods lead “natural” high achievers to locate in some places and “natural” low achievers to locate elsewhere.

Using the combination of a massive, long-term longitudinal data set (created from anonymized tax return data), and data from the Federal Government’s quasi-experimental “Moving to Opportunity” program which gave low income families vouchers to enable them to move to non-poor neighborhoods.

It’s highly unusual in the world of economics to use the word “causal” to describe one’s reported findings, but in this new report you’ll see this term used early and often to describe the findings.  The use of data on siblings and exploiting the differential effects for boys and girls is clever and impressive.  One of the criticisms levied of other work is that it can’t control for the fact that intergenerational mobility for some families may represent selection effects: the most energetic, ambitious families are more likely to move away from worse environments and to better ones.  It is very rare in social science to be able to make this kind of strong claims about causality.

And New Orleans ranks as one of the worst. Credit: Chuck Coker, Flickr
And New Orleans ranks as one of the worst. Credit: Chuck Coker, Flickr

 

The great thing about the Chetty-Hendren research is that you can drill down to the county level to see what impact the local community has on economic outcomes for kids.  And the measure of success couldn’t be clearer: they show how much each additional year spent growing up in a particular neighborhood is likely to influence a child’s income as an adult.  As they explain in their report:

Every extra year spent in the city of Baltimore reduces a child’s earnings by 0.86% per year of exposure, generating a total earnings penalty of approximately 17% for children who grow up there from birth.

The differences among metropolitan areas are substantial: a poor child growing up in Seattle would be expected to earn about $29,000 (about $3,000 or 12 percent more than the national average for children in the bottom quintile of the population), while a poor child growing up in New Orleans would be expected to earn a little more than $22,000 at the same age, ($3,800 or almost 15 percent less than the national average.)  You can see data for individual counties and for commuting zones (metropolitan areas and their surrounding hinterlands) at the New York Times website.

To provide a quick snapshot for large metropolitan areas, we’ve created a graphic showing the Chetty-Hendren estimates for central counties (the county that includes the first-named city in a metropolitan area) and for the surrounding commuting zone.  These data show how much more (or less) than the national average a child in a family in the lowest quartile of the income distribution growing up in the central county or commuting area would make at age 26.  (Orange dots represent commuting zones; blue dots represent central counties.)

A couple of patterns are apparent: in general, central counties have lower rates of economic mobility for poor children than in commuting zones.  Central counties tend, on average, to have more concentrated poverty, lower-performing schools, and higher rates of single-head households—all of which are correlates of low economic mobility.

In a companion paper, Chetty and Hendren and Harvard Economist Larry Katz re-examine an important, and previously discouraging set of findings from the Moving to Opportunity (MTO) project.  MTO was a federal project that gave poor families vouchers to move from poor neighborhoods to middle income neighborhoods.  The previously reported results found that the moves produced little economic improvement for adults, and modest results for children.  In their re-analysis, Chetty, Hendren and Katz, show that when children moved made a huge difference:  those who moved as very young children (under age five) showed significant gains, while those who moved at an older age showed few if any gains.  Consistent with their larger analysis of inter-neighborhood moves, the gains to moving to better neighborhoods were directly correlated to how long children were exposed to better conditions.  (For a more detailed review of these studies and their import, it’s worth reading Justin Wolfers’ commentary.)

At City Observatory, we think these findings are the strongest evidence yet that addressing neighborhood and urban development is critical to promoting equal opportunity for all.  As Chetty and Hendren conclude that while the evidence shows that some children can gain opportunity by moving to a new, better neighborhood that this isn’t a scaleable solution for everyone; as a result:

. . . one must also find methods of improving neighborhood environments in areas that currently generate low levels of mobility. . . our findings provide support for policies that reduce segregation and concentrated poverty in cities (e.g., affordable housing subsidies or changes in zoning laws) as well as efforts to improve public schools.

The Civic Commons & City Success

Urban housing sprawl.

Why we wrote “Less in Common,” our latest CityReport.

We’ve come increasingly to understand the role of social capital in the effective function of cities and urban economies.  The success of both local and national economies hinges not just on machines and equipment, skilled workers, a financial system and the rule of law, but also on widespread norms of reciprocity and a sense of connectedness and mutual obligation and respect —  a combination of factors that has come to be called “social capital.”

Robert Putnam’s work—Making Democracy Work, Bowling Alone, and most recently Our Kids—deserves considerable credit for popularizing the term social capital.  Nobel economist Douglass North argued that social capital is one of the keys to the adaptive efficiency that enables economies to progress.  In his book Triumph of the City, Ed Glaeser describes how this process plays out in particular places: “Humans,” he says, “are a social species, and our greatest achievements are all collaborative. Cities are machines for making collaboration easier.”

The latest research from Raj Chetty, Nathan Hendren, and their colleagues, reinforces the critical role of place-based social ties in shaping the economic opportunities of the poor.  They found that metro areas with high levels of racial and economic segregation—a key correlate of declining social capital—also had far lower rates of economic mobility for the children of the poor.

The stakes are high.  In the pursuit of overall economic well-being and widespread opportunity for success, social capital in cities is critical.

Less in Common explores the ways in which the social fabric—the network of connections that tie us together in communities—has become generally thinner and more frayed over the past several decades.

Less_in_Common_Cover

In assembling this report, we purposely set out to be eclectic both in our scope and in the kinds of data and indicators we assembled.  Here you’ll find measures of everything from stated social trust, to the numbers of security guards, to the numbers of library books we borrow, to the numbers of swimming pools and farmers markets in our neighborhoods.  Unlike Gross Domestic Product, measures of social capital don’t have a single common denominator that enable them to easily be summed and compared.  Many, if not all, of these trends play out in cities, and have profound implications for city success.

While there are some counter-currents, the overall pattern of change is an ominous one.  Stated trust is declining.  Income segregation is increasing.  We are more isolated individually, and our governments and civic institutions are more fragmented and balkanized.  We spend less time in the shared public spaces that are open to people different from ourselves.

There is compelling evidence that the connective tissue that binds us together in cities is coming apart.   As we’ve spent more time in isolation and less time socializing with our neighbors, participation in the civic commons has suffered. Rebuilding social capital in America will require innovative approaches to spur community engagement.

How do we reinvigorate the civic commons?  While some solutions may be national in scope, many of the best opportunities for strengthening social capital will be in individual neighborhoods.

There’s no single-minded policy solution that can accomplish the task alone — no –whether infrastructure improvement, human capital investment, regulation or deregulation, tax or tax break –that can easily or comprehensively address the problem.  There are many facets to this issue and consequently many dimensions along which we can pursue solutions.

We offer Less in Common as a rough portrait of some of the trends that have been playing out, and as one contribution to the discussion about how we can strengthen and rebuild social capital in our neighborhoods, our cities and our nation.  We look forward to the conversation.

Baltimore’s problems belong to 2015, not 1968

In the wake of violent protests against yet another apparent police killing in Baltimore, variations of this meme spread rapidly in certain corners of social media. Their message went something like this: Pundits and politicians may think Baltimore’s crisis began with the first brick that hit a window at CVS, but we – the people who live there – know the crisis goes back much further, and much deeper.

With this in mind, there’s some irony to the spate of columnists warning that the disturbances in Baltimore mark a return to the “bad old days” of the mid-to-late 1960s, when a series of violent protests in America’s black neighborhoods held the nation riveted. Those riots, too, were treated as a crisis by pundits who had not applied the term to decades of housing discrimination, or illegal violence on the part of police officers and white civilians.

But using violent protests as a point of analytic departure – rather than the underlying crises that provoked them – doesn’t just (unintentionally) reveal one of the similarities between 1968 and 2015. It also misses a lot of the major differences.

At the Washington Post, Radley Balko has covered some of the ways in which things are much better now. For one, the wave of violent crime that plagued American neighborhoods – and especially ones whose residents were predominantly low-income and black or brown – has receded, even if it remains far too high. Balko reminds us that the U.S. saw 500 fewer murders in 2013 than in 1969, despite the fact that there were over 100 million more people.

In other ways, things are much worse. As we’ve covered here at City Observatory, concentrated poverty has exploded since 1970, with three times more neighborhoods with poverty rates of at least 30 percent in 2010. And even as crime has declined, the incarceration rate – particularly among black men – has skyrocketed, disrupting millions of lives and leaving communities across the country with millions of “missing” men.

From our "Lost in Place" report.
From our “Lost in Place” report.

But especially when it comes to the role of cities, the comparisons to 1968 miss the very, very different trend lines of that era. In the 1960s, America was decades into – and had decades remaining in – a period in which wealth, and anyone who had enough of it, was moving as far as it could from urban centers.

For a variety of reasons, those trends have now reversed. The geography of metropolitan wealth is now double-peaked: downtowns and surrounding neighborhoods are catching up to rich outer suburban neighborhoods, and the ring of poverty that separates them is moving further out into outer city neighborhoods and inner-ring suburbs.

Houston shows a typical pattern, with growing wealth in the center and an economically shrinking middle ring.
Houston shows a typical pattern, with growing wealth in the center and an economically shrinking middle ring.

Importantly, this movement of middle- and upper-income people and jobs back to center cities has taken place despite the presence, and growth, of intensely concentrated poverty within those very same cities. That is, the prediction of people like Joel Kotkin that the problems of poor neighborhoods in Baltimore will hold back this trend just doesn’t appear to match the evidence.

It is true, of course, that so far reinvestments in places like Baltimore’s Inner Harbor have mostly not translated to meaningful improvements in the lives of residents in struggling neighborhoods. But we believe that the return of wealth to America’s inner cities at least provides a valuable opportunity to make those improvements.

That’s because broadly speaking, the built form of cities – defined as anything from a dense Manhattan neighborhood to the “streetcar suburban” communities in Midtown Memphis – really is a better arena for seeking social justice than most automobile-oriented suburbs.

For one, urban neighborhoods tend to offer a larger proportion of public, rather than private, space. This can take the form of anything from public parks, libraries, and civic centers, to simply a sidewalk. For people who are low-income, that means access to services, amenities, and cultural life that might be denied to them in the suburbs: think of the difference between a city with public pools and a suburb where people only go swimming in their backyards, private athletic centers, or a shared private pool in a gated community.

Second, because urban neighborhoods tend to offer a wider range of transportation options – including walking, biking, and public transit – transportation costs are much, much lower. In a city, you can save thousands of dollars a year by not owning a car – money that would be better spent on higher-quality food, your kids’ education, or retirement savings, but which in a typical suburb would instead go to insurance and gas. Even where a family doesn’t want to give up car ownership entirely, moving from one car per adult to one car per household can mean significant savings.

As you move away from the heart of metropolitan areas, transportation costs skyrocket. From the Center for Neighborhood Technology's "H+T Index."
As you move away from the heart of metropolitan areas, transportation costs skyrocket. From the Center for Neighborhood Technology’s “H+T Index.”

Finally, urban neighborhoods tend to be located in larger municipalities. (This isn’t always true, of course – there are urban neighborhoods in small suburbs and very “suburban” neighborhoods in large cities – but for historical reasons, it generally is.) That means city services can draw on taxes from both high-income and low-income neighborhoods – as opposed to smaller suburban municipalities, where high tax revenues from rich areas feed back into high-quality services for the wealthy, while low-income municipalities struggle to fund services for their poorer residents.

The return of wealth to central cities has the potential to enhance all three of these factors: balancing tax rolls in historically disproportionately poor cities allows those governments to provide better public services and amenities, while creating jobs in central cities, rather than suburbs, allows more people to get to work via affordable transit. On top of that, attracting middle class households back to cities that have gone decades without them creates the possibility of more economically integrated neighborhoods – which, as recent research shows, can lead to more social mobility for low-income residents.

So far, of course, we’re very far from realizing that potential. But that’s a very different failure than the situation cities like Baltimore found themselves in fifty years ago.

On Baltimore: Concentrated Poverty, Segregation, and Inequality

Yet again, a black citizen dies at the hands of the police. This event and the ensuing riots in Baltimore are a painful reminder of the deep divisions that cleave our cities.  There’s little we can add to this debate, except perhaps to say that there’s a strong evidence for a point made by Richard Florida:

The real problem in Baltimore is race & class division – persistent concentrated poverty.

We’ve chronicled the persistence and spread of concentrated poverty in our recent reports and blog posts at City Observatory.  Our Lost in Place report tracked the change in neighborhoods of concentrated poverty in the nation’s largest metro areas over the past four decades.  Our dashboard for Baltimore shows that the number of high poverty neighborhoods in Baltimore increased from 38 in 1970 to 55 in 2010.  And high poverty neighborhoods have hemorrhaged population.  Only one census tract in Baltimore saw its poverty rate fall from above 30 percent in 1970 to less than 15 percent in 2010.

Balt_Map

 

And as our map shows, the Baltimore has experienced persistent–and growing–concentrated poverty in many of its urban neighborhoods.  Concentrated poverty remains rooted in the neighborhoods adjacent to the central business district–and has spread outward in the decades since 1970.

Baltimore_Map

Earlier this month, we highlighted the connection between racial segregation and black white income disparities in the nation’s cities.  Those places with the greatest levels of segregation regularly also had the biggest differences in incomes between black and white households.  Segregation appears to be an important contributor to racial income disparities.  These data show that Baltimore is somewhat more segregated than the typical large US metro, with a black-white dissimilarity index of 64, ranking about 20st highest (most segregated) of the largest metropolitan areas in the country.  And on average black incomes in Baltimore were about 28 percent lower than white incomes, a slightly greater disparity than in the typical large metropolitan area.  So while somewhat more severe than average, the levels of racial segregation and income differentials in Baltimore are hardly unusual in large metro areas.

Sadly, concentrated poverty is a problem which only becomes visible to many Americans when it erupts in the violence we’ve seen in the past few days in Baltimore.  We hope the data provided here give everyone a sense of the depth and seriousness of the problem.

How we measure segregation depends on why we care

Segregation is complicated and multi-dimensional, and measuring it isn’t easy

In 2014, NYU’s Furman Center hosted a roundtable of essays on “The Problem of Integration.” Northwestern sociologist Mary Pattillo kicked it off:

I must begin by stating that I am by no means against integration…. My comments are not to promote racial separatism, nor to argue that people of the same “race”–-and we must always signal just how time- and place-specific “race” is–“naturally” want to be around each other….

Instead, my point is simply to identify the following conundrum of integration politics: Promoting integration as the means to improve the lives of Blacks stigmatizes Black people and Black spaces and valorizes Whiteness as both the symbol of opportunity and the measuring stick for equality.  In turn, such stigmatization of Blacks and Black spaces is precisely what foils efforts toward integration. After all, why would anyone else want to live around or interact with a group that is discouraged from being around itself?

I thought of this problem, and this roundtable, while reading about FiveThirtyEight’s fascinating metric for residential racial segregation in American cities. By creating “diversity indices” at both the metro area level and neighborhood level, Nate Silver was able to distinguish between two relevant kinds of racial makeup, and – by subtracting one from the other – create a segregation statistic that takes into account the fact that Salt Lake City’s overall population does not look like Atlanta’s. In doing so, Silver came to the (perhaps not so surprising) conclusion that more diverse cities also tend to be more segregated.

Screen Shot 2015-05-04 at 10.38.35 AM

But another number jumped out at me from this analysis. Or rather, the lack of one: New York City did not appear on the “most segregated” list. Why was this? After all, on what is probably the most-cited measure of segregation, the dissimilarity index, metropolitan New York City ranks as the third-worst city in the country for black-white segregation (behind only Detroit and Milwaukee), and second-worst for Hispanic-white segregation (behind only Los Angeles).

(New Yorkers, of course, are frequently surprised to hear that their city is so divided. Then again, they only recently discovered that Los Angeles has art galleries, so.)

The answer, of course, has to do with the difference between Nate Silver’s segregation index and the dissimilarity index. Silver’s measure looks at whether people of any racial or ethnic group live in neighborhoods where they’re as likely to run into people of other groups as they would be in their metropolitan area as a whole. That is, you get a really bad score for having neighborhoods that are made up overwhelmingly of a single racial group.

On that measure, New York is clearly less segregated than, say, Chicago, to take the worst city by Silver’s count. Here, for example, are the areas in New York where 90% or more of the residents are black:

And here it is in Chicago:

In New York, this kind of single-race neighborhood is relatively rare, while in Chicago it’s almost the norm on the South and West Sides. Why, then, does the dissimilarity index suggest that New York is so segregated?

It’s because the dissimilarity index asks not just about any racial mixing – it usually asks specifically about whether blacks and whites, or Hispanics and whites, live in the same neighborhoods. And if that’s the question, then New York looks much, much worse. In fact, if you look for places where both blacks and whites make up at least 10% of the population, it turns out only a small minority of New York neighborhoods meet that criteria. (New York City overall is about 35% non-Hispanic white and 25% black.*)

NYCSeg

So which measure is “right”? Both of them, of course – they just answer different questions.

But to bring us back to the Furman Center’s roundtable, I think at this point it’s useful to ask why we think segregation matters. If the answer is that we think that our lives are enriched by being in proximity to – and, hopefully, forming meaningful friendships with – people of other backgrounds, then Silver’s index makes sense.

But one of Furman’s respondents, the sociologist Patrick Sharkey, suggested another, perhaps weightier reason. Segregation is important not just because it troubles our dreams of a country where people of different backgrounds can all get along, Sharkey wrote; it matters because segregation is how deep racial inequalities get reproduced from generation to generation:

Living in predominantly black neighborhoods affects the life chances of black Americans…because black neighborhoods have been the object of sustained disinvestment and punitive social policy since the emergence of racially segregated urban communities in the early part of the 20th Century. Residential segregation has been used consistently over time as a means of distributing and hoarding resources and opportunities among white Americans and restricting resources and opportunities from black Americans. Racially segregated communities provide one of several mechanisms through which racial inequality is made durable.

That is, it’s easier to send black children to inferior schools if their schools are all on one side of town, and white schools are on the other. It’s easier to target housing and mortgage discrimination against blacks – one of the most important causes of the wealth gap – if all the black-owned houses are in one area. It’s easier to unleash abusive policing and incarceration practices on black communities without disturbing – or even attracting the attention – of whites for decades if whites and blacks don’t live in the same neighborhoods.

The New York Times‘ visualization on how the neighborhood a child grows up in affects their future earnings reinforces this idea. As Yonah Freemark pointed out, a map of counties where social mobility is worst looks pretty similar to a map of counties with large black – or Native American – populations.

And that includes the large black populations in the New York metro area.

If this is why we care about segregation, then Silver’s measure – which doesn’t care which racial groups are mixing, as long as there is some mixing going on – is less useful. What matters then isn’t just integration: what matters is that privileged groups live in the same places as traditionally oppressed groups, so that place-based discrimination is made more difficult. In the United States, that means whites and people of color living in the same neighborhoods. Where that doesn’t happen – even if an area is integrated with, say, blacks and Latinos – then place-based discrimination is still viable, and it will be much easier to reproduce racial inequality.

This is also at least a partial resolution to Mary Pattillo’s concern that wringing our hands about the problem of segregation could, effectively, be implying that black people and their neighborhoods are inferior. Instead, focusing on place-based discrimination underlines that segregated neighborhoods aren’t inferior – they’re just more vulnerable to discrimination from more powerful groups whose members don’t live there. That, of course, is not the end of the story, and there are other tradeoffs involved in integration. But focusing on this rationale provides some clarity both to conversations about, and measures of, what continues to be one of the defining traits of the American city.

* Here, for the sake of convenience, I’ve switched to city, rather than metro-area, numbers, but I promise it looks pretty much the same at the larger scale, too.

Want to close the Black/White Income Gap? Work to Reduce Segregation.

 

Nationally, the average black household has an income 42 percent lower than average white household. But that figure masks huge differences from one metropolitan area to another. And though any number of factors may influence the size of a place’s racial income gap, just one of them – residential segregation – allows you to predict as much as 60 percent of all variation in the income gap  from city to city. Although income gaps between whites and blacks are large and persistent across the country, they are much smaller in more integrated metropolitan areas and larger in more segregated metropolitan areas.  The strength of this relationship strongly suggests that reducing the income gap will necessarily require reducing racial segregation.

To get a picture of this relationship, we’ve assembled data on segregation and the black/white earnings gap for the largest U.S. metropolitan areas. The following chart shows the relationship between the black/white earnings disparity (on the vertical axis), and the degree of black/white segregation (on the horizontal axis).   Here, segregation is measured with something called the dissimilarity index, which essentially measures what percent of each group would have to move to create a completely integrated region. (Higher numbers therefore indicate more segregated places.) To measure the black-white income gap, we first calculated per capita black income as a percentage of per capita white income, and then took the difference from 100. (A metropolitan area where black income was 100% of white income would have no racial income gap, and would receive a score of zero; a metro area where black income was 90% of white income would receive a score of 10.)

The positive slope to the line indicates that as segregation increases, the gap between black income and white incomes grows as black incomes fall relative to white incomes. On average, each five-percentage-point decline in the dissimilarity index is associated with an three-percentage-point decline in the racial income gap (The r2 for this relationship is .59, suggesting a close relationship between relative income and segregation).

What’s less clear is which way the causality goes, or in what proportions. That is to say: there are good reasons to believe that high levels of segregation impair the relative economic opportunities available to black Americans. Segregation may have the effect of limiting an individual’s social networks, lowering the quality of public services, decreasing access to good schools, and increasing risk of exposure to crime, all of which may limit or reduce economic success.  This is especially true in neighborhoods of concentrated poverty, which tend to be disproportionately neighborhoods of color.

But there are also good reasons to believe that in places where black residents have relatively fewer economic opportunities, they will end up more segregated than in places where there are more opportunities. Relatively less income means less buying power when it comes to real estate, and less access to the wealthier neighborhoods that, in a metropolitan area with a large racial income gap, will be disproportionately white. A large difference between white and black earnings may also suggest related problems – like a particularly hostile white population – that would also lead to more segregation.

The data shown here is consistent with earlier and more recent research of the negative effects of segregation.  Glaeser and Cutler found that higher levels of segregation were correlated with worse economic outcomes for blacks.   Likewise, racial and income segregation was one of several factors that Raj Chetty and his colleagues found were strongly correlated with lower levels of inter-generational economic mobility at the metropolitan level.

Implications

To get a sense of how this relationship plays out in particular places, consider the difference between two Southern metropolitan areas: Birmingham and Raleigh.  Birmingham is more segregated (dissimilarity 65) than Raleigh (dissimilarity 41).  The black white income gap is significantly smaller in Raleigh (blacks earn 17 percent less than whites) than it is in Birmingham (blacks earn 29 percent less than whites).

The size and strength of this relationship point up the high stakes in continuing to make progress in reducing segregation as a means of reducing the racial income gap.   If Detroit had the same levels of segregation as the typical large metro (with an dissimilarity index of 60, instead of 80), you would expect its racial gap to be  12 percentage points smaller, which translates to $3,000 more in annual income for the average black resident.

These data presented here and the other research cited are a strong reminder that if we’re going to address the persistent racial gap in income, we’ll most likely need to make further progress in reducing racial segregation in the nation’s cities.

The correlations shown here don’t dispose of the question of causality:  this cross sectional evidence doesn’t prove that segregation causes a higher black-white income gap.  It is entirely possible that the reverse is true:  that places with smaller income gaps between blacks and whites have less segregation, in part because higher relative incomes for blacks afford them greater choices in metropolitan housing markets.  It may be the case that causation runs in both directions.   In the US, there are few examples of places that stay segregated that manage to close the income gap; there are few places that have closed the income gap that have not experienced dramatically lower levels of segregation.   Increased racial integration appears to be at least a corollary, if not a cause of reduced levels of income disparity between blacks and whites in US metropolitan areas.

If we’re concerned about the impacts of gentrification on the well-being of the nation’s African American population, we should recognize that anything that promotes greater racial integration in metropolitan areas is likely to be associated with a reduction in the black-white income gap; and conversely, maintaining segregation is likely to be an obstacle to diminishing this gap.

Though provocative, these data don’t control for a host of other factors that we know are likely to influence the economic outcomes of individuals, including the local industrial base and educational attainment.  It would be helpful to have a regression analysis that estimated the relationship between the black white earnings gap and education.  It may be the case that the smaller racial income gap in less segregated cities may be attributable to higher rates of black educational attainment in those cities.  For example, the industry mix in Raleigh may have lower levels of racial pay disparities and employment patterns than the mix of industries in Birmingham.  But even the industry mix may be influenced by the segregation pattern of cities; firms that have more equitable practices may gravitate towards, or grow more rapidly in communities with lower levels of segregation.

Brief Background on Racial Income Gaps and Segregation

Two enduring hallmarks of race in America are racial segregation and a persistent gap between the incomes of whites and blacks.  In 2011, median household income for White, Non-Hispanic Households was $55,412; for Blacks $32,366 (Census Bureau, Income, Poverty, and Health Insurance Coverage in the United States: 2011, Table A-1).  For households, the racial income gap between blacks and whites is 42 percent.  Census Bureau data shows on average, black men have per capita incomes that are about 64 percent that of Non-Hispanic White men.  This gap has narrowed only slightly over the past four decades: in the early 1980s the income of black men was about 59 percent that of Non-Hispanic whites.

Because the advantage of whites’ higher annual incomes compounds over time, racial wealth disparities are even greater than disparities in earnings.  Lifetime earnings for African-Americans are about 25 percent less than for similarly aged Non-Hispanic White Americans.   The Urban Institute estimated that the net present value of lifetime earnings for a non-hispanic white person born in late 1940s would be about $2 million compared to just $1.5 million for an African-American born the same year.

In the past half century, segregation has declined significantly.  Nationally, the black/non-black dissimilarity index has fallen from an all-time high of 80 in 1970 to 55 in 2010, according to Glaeser and Vigdor .  The number of all-white census tracts has declined from one in five to one in 427. Since 1960, the share of African-Americans living in majority-non-black areas increased from less than 30 percent to almost 60 percent.  Still, as noted in our chart, their are wide variations among metropolitan areas, many of which remain highly segregated.

Technical Notes

We measure the racial income gap by comparing the per capita income of blacks in each metropolitan area with the per capita income of whites in that same metropolitan area.  These data are from Brown University’s US 2010 project, and have been compiled from the 2005-09 American Community Survey.  The Brown researchers compiled this data separately for the metropolitan divisions that make up several large metropolitan areas (New York, Chicago, Miami, Philadelphia, San Francisco, Seattle, Dallas and others).  For these tabulations we report the segregation and racial income gaps reported for the most populous metropolitan division in each metropolitan area.

How important is proximity to jobs for the poor?

More jobs are close at hand in cities.  And on average the poor live closer to jobs than the non-poor.

One of the most enduring explanations for urban poverty is the “spatial mismatch hypothesis” promulgated by John Kain in the 1960s.  Briefly, the hypothesis holds that as jobs have increasingly suburbanized, job opportunities are moving further and further away from the inner city neighborhoods that house most of the poor. In theory, the fact that jobs are becoming more remote may make them more difficult to get, especially for the unemployed. How important is proximity to getting and keeping a job?

A new Brookings Institution report from Elizabeth Kneebone and Natalie Holmes, The Growing Distance Between People and Jobs  sheds some light on this old question.  Their data show that between 2000 and 2012, jobs generally decentralized in U.S. metropolitan areas, with the result that on average, people live further from jobs than they did a decade ago.  Put another way:  there are fewer jobs within the average commute distance of the typical metropolitan resident.

While job access has diminished for most Americans, the report notes that the declines in job access have been somewhat greater for the poor and for racial and ethnic minorities than for non-poor and white metropolitan residents.  This, in the report’s view, has exacerbated the spatial mismatch between the poor and jobs.

The Kneebone/Holmes findings emphasize the change in job access over time.  As jobs decentralized, the average American had about 7 percent fewer jobs within a typical commuting radius in 2012 than in 2000.  But its illuminating to look at the level of job access.  Certain patterns emerge:

People who live in large metropolitan areas have access to many, many more jobs, than do residents of smaller metropolitan areas.  The typical New Yorker is has just shy of a million  jobs within commuting distance; the typical Memphian, only 150,000.  This is what economists are talking about when they describe “thick” urban labor markets.

Dig deeper, and it turns out that within metropolitan areas, cities have much better job access than suburbs.  We’ve taken the Brookings data for 2012 and computed the relative job accessibility of cities compared to their to suburbs for each of the nation’s 50 largest metro areas.  For example, an average city resident in Charlotte has about 320,000 jobs within typical commuting distance.  The average suburban resident in the Charlotte metro has just 70,000.  (Metro level data are shown in the table below).  This means that a Charlotte city resident has about 4.6 times as many jobs within commuting distance of her home than does her suburban counterpart.  For the typical large metro area, city residents have about 2.4 times as many jobs within commuting distances as their suburban neighbors.  This pattern of higher job accessibility in cities holds for every large metro area in the country–save one:  Las Vegas.

At first this may seem counter-intuitive, but consider:  even though jobs may have been decentralizing, central locations are often better able to access jobs in any part of the region.  Its also the case that despite decentralization, job density–the number of jobs per square mile–still tends to be noticeably higher in urban centers than on the fringe.  Its also interesting to note that the difference in job accessibility between cities and suburbs (+140 percent) dwarfs the average decline in job accessibility (-7%) over the past decade.  While aggregate job accessibility may have decreased slightly, individuals have wide opportunity to influence their access to jobs in every metropolitan area based on whether they choose to live in cities or suburbs.

Perhaps even more surprisingly, on average the poor and ethnic minorities generally are closer to jobs than their white and non-poor counterparts.  We can do the same computation to compare relative job accessibility within each metro area for poor and non-poor populations, and to compare job accessibility for blacks and whites.  Despite job decentralization, and the fact that poorer neighborhoods often themselves support fewer local businesses and jobs, the poor residents of the typical large metropolitan area have about 20 percent more jobs within typical commuting distance than do their non-poor counterparts.  The black residents of large U.S. metropolitan areas are have on average about 50 percent more jobs within typical commuting distance than their white counterparts in the same metropolitan area.  Again, this pattern holds for virtually all large metropolitan areas.  Data showing relative job accessibility for poor and non-poor persons and black and white persons by metropolitan area are shown in the two right hand columns of the table above.

Of course, a pure distance-based measure of job accessibility may not fully reflect the transportation accessibility to particular jobs–especially for poor persons who are disproportionately more likely to not have access to automobiles for commute trips.  But the data show that city residents have strikingly better access to a large number of jobs, and other forms of transportation–transit, cycling and walking–generally work better in cities.  The density and proximity of jobs in cities, plus the availability of transit is one reason why poor persons disproportionately concentrate in cities, according to research by Ed Glaeser and his colleagues.

The very much higher level of physical job accessibility in cities, and the relative proximity that poor people and black Americans enjoy to employment opportunities is a signal that physical employment mismatch is at best only a partial explanation for persistent urban poverty.  Other important barriers, particularly a lack of education, concentrated poverty, and continued discrimination are also important factors.

We’re deeply appreciative of our friends at Brookings undertaking this analysis, and making their methodology and findings accessible and transparent.  The metro-by-metro data they present add a new dimension to our understanding of urban land use and evolving labor markets.  While we strongly encourage everyone to explore this data, we offer an observation. In measuring job accessibility, Kneebone and Holmes chose to use separate and locally customized estimates of local commute distance.  For example, the average intra-metropolitan commute (according to data from the LEHD program) in Houston is 12.2 miles, while in New Orleans it is 6.2 miles.  This means that a big part of the difference in measured job accessibility between these two metropolitan areas reflects the fact the typical commute shed for Houston cover a far larger area than for New Orleans.  While this may be an accurate reflection of typical commuting behavior in each cities, it makes direct comparisons between different metropolitan areas problematic.

How is economic mobility related to entrepreneurship? (Part 2: Small Business)

We recently featured a post regarding how venture capital is associated with economic mobility. We know that these are strongly correlated—and that, if we are concerned with the ability of children today to obtain ‘The American Dream,’ we should be concerned with how to increase economic mobility.

To understand more about how cities can increase intergenerational economic mobility, we wanted to take a look at another measure of entrepreneurship: small businesses per capita.

We follow Glaeser, et al, and measure the number of businesses with 20 or fewer employees per 1,000 population in each of the nation’s largest metropolitan areas. As in the previous post, we measure economic mobility as the probability that children born in the bottom quintile rise to the top quintile as adults.

The chart below shows the results: cities with a larger number of small businesses per capita have higher rates of economic mobility. This relationship is positive, but statistically less strong a fit (R-squared: .16) than venture capital.

The data from this post and the previous one suggest that there a positive relationship between entrepreneurship and higher levels of economic mobility, particularly that economic mobility is somewhat correlated with higher numbers of small businesses and more strongly correlated with venture capital.

This analysis is both partial and preliminary. We know from Chetty, et al, that there are other factors (segregation, schools, family structure) that influence economic mobility. A more comprehensive analysis would consider whether or not after controlling for the variation explained by these other factors there was any remaining variation explained by entrepreneurship. Moreover, these relationships are simple correlations, and do not necessarily indicate cause and effect. For example, it could be that economic mobility causes entrepreneurship. Furthermore, our data on small businesses and venture capital are taken from recent years; a more rigorous analysis would look to see whether small business and venture capital levels of two or more decades ago were correlated with economic mobility over the succeeding time period.

Still, taken as a whole, the data suggest that more entrepreneurial places have higher levels of economic mobility. Why this relationship exists and what implications it may have for policy are questions worthy of further research.

To learn more about innovation and entrepreneurship from a metro perspective, go to our cards here. (We also feature information on economic mobility and opportunity, economic segregation, and more here.)

Less in Common

The essence of cities is bringing people—from all walks of life—together in one place.  Social interaction and a robust mixing of people from different backgrounds, of different ages, with different incomes and interests is part of the secret sauce that enables progress and creates opportunity.  This ease of exchange underpins important aspects of our personal lives, civic effectiveness and economic development.

But over the past several decades, a number of trends–some social, some economic, some political, and others technological–have interacted to dramatically change the ways, the places, and the amounts of interaction between different groups in our society.  By many measures, we now spend less time in social settings, and are less likely to regularly interact with people whose experiences are different from our own.  In our schools, communities, work, shopping and personal activities, we’re increasingly separated from one another.

Our new report, Less in Common, surveys a wide range of measures of how Americans have grown apart from one another over the past several decades.  We’ve intentionally drawn promiscuously from a variety of fields to illustrate the breadth and variety of ways in which this trend seems to be unfolding.

Many of these changes are reflected in the physical landscape of our cities.  In North America, development patterns, particularly the growth of suburbs after World War II, diminished access to an easily shared urban life.  Space and experiences became more private, fueled by suburban expansion, large lots, and the predominance of single-family homes. These development patterns have resulted in Americans having “less in common.”  This phenomenon appears to play out in many different ways:

Distrust among Americans is increasing.  A key marker of social capital that is regularly used in comparing nations and tracking trends over time is the generalized feeling of trust.  The General Social Survey reports that the share of the population that says “most people can be trusted” has fallen from a majority in the 1970s, to about one-third today.

Americans spend significantly less time with their neighbors.  In the 1970s, nearly 30 percent of Americans frequently spent time with neighbors, and only 20 percent had no interactions with them.  Today, those proportions are reversed.

The biggest portion of our leisure time is spent watching television.  TV watching is up to 19 hours per week today compared to about 10 hours in the 1960s.  We spend less time socializing and communicating.

Our recreation is increasingly privatized.  Since 1980, the number of members of private health clubs have quadrupled to more than 50 million.  We used to swim together—prior to World War II, almost all pools were public.  Today, we swim alone in the 5 million or so private swimming pools compared to just a few thousand public ones.

Driving alone has become the norm, with transit reserved for the poor. Today, 85 percent of American commuters travel to work in private automobiles, up from 63 percent in 1960.  Carpooling has fallen by half since 1980, and the share who commute via transit has declined from 12 percent in 1960 to less than 5 percent today.

Economic segregation trends upward as middle-income neighborhoods decline. High-income and low-income Americans have become more geographically separated within metropolitan areas. Between 1970 and 2009 the proportion of families living either in predominantly poor or predominantly affluent neighborhoods doubled from 15 percent to 33 percent.

Many of us live in gated communities. By 1997 it was estimated that there were more than 20,000 gated community developments of 3,000 or more residents. By design, gated communities restrict access and carefully control who is allowed into a community to separate residents from outsiders.

Politically, America sorts itself into like-minded geographies.  Nearly two-thirds (63 percent) of consistent conservatives and about half (49 percent) of consistent liberals say most of their close friends share their political views.

There are some counter-trends to the general pattern of isolation and separation.  Racial segregation, though still high, has declined steadily for decades. New community spaces—like farmer’s markets—have grown rapidly.  Widespread availability of the Internet combined with social media has made it easier and more democratic to connect with others and with all forms of information.

A broadly shared sense of common interest, anchored in a society that promotes social mobility and easy interaction, is a vital underpinning of effective political institutions and the economy.  If we’re going to make progress in tackling a range of our nation’s challenges, and live up to our full potential, we need to reinvigorate the civic commons.

You can also see the findings in the form of an easy-to-share infographic:

Click to see the full infographic.
Click to see the full infographic.

America’s Most Diverse Mixed Income Neighborhoods

In a nation increasingly divided by race and economic status, where our life prospects are increasingly de ned by the wealth of our zip codes, some American neighborhoods are bucking the trend.

These neighborhoods—which we call America’s most diverse, mixed-income neighborhoods—have high levels of racial, ethnic and income diversity. This report identifies, maps and counts the nation’s most diverse mixed-income neighborhoods. In these neighborhoods, residents are much more likely than the average American to have neighbors from different racial/ethnic groups than themselves, and neighbors with different levels of income. We find that:

  • Nearly 7 million Americans live in neighborhoods with both high levels of racial/ethnic and economic diversity.
  • Roughly half of these neighborhoods are found in three of the nation’s largest, most diverse metropolitan areas: New York, Los Angeles and San Francisco.
  • Most large metropolitan areas have several neighborhoods that are among the nation’s most diverse and mixed income. Forty-four of the nation’s 52 largest metro areas have at least one diverse, mixed-income neighborhood.
  • The racial and ethnic diversity of a metropolitan area sets the context for having diverse, mixed income neighborhoods. Whether metropolitan diversity is reflected in the lived experience in the typical neighborhood depends on how segregated a metropolitan area is by race, ethnicity and class.
  • Some metropolitan areas come much closer to realizing their potential for neighborhood racial/ethnic diversity, given their metropolitan demographic composition.

We identified the nation’s most diverse, mixed income neighborhoods using Census data on the race, ethnicity and household income of neighborhood residents. For each of more than 31,000 urban neighborhoods, we computed a Racial and Ethnic Diversity Index (REDI), which corresponds to the probability that any two randomly selected individuals in a neighborhood would be from different racial/ethnic categories. (Using Census data, we tabulated the number of white, black, Asian, Latino and all other persons in each neighborhood). We used a similar approach to compute an Income Diversity Index (IDI) which measures the variety of household incomes. Neighborhoods that ranked in the top 20 percent of all urban neighborhoods nationally on both of these measures were classified as diverse mixed income neighborhoods.

Which cities have the highest levels of diversity and mixed income?

Nearly all of the nation’s largest cities have at least one neighborhood that meets our definition as being both racially and ethnically diverse and mixed income. Three large cities–New York, Los Angeles and San Francisco account for nearly half such neighborhoods, but some smaller cities also rank high in the fraction of their population living in these diverse, mixed income neighborhoods.

Which cities are performing up to their potential?

Whether a city has many diverse, mixed income neighborhoods depends directly on the demographics of the metropolitan area in which it is located. There is still a wide range of racial and ethnic diversity among metropolitan areas. The following chart shows the relationship between a metropolitan area’s overall racial and ethnic diversity (shown on the horizontal axis) and the percentage of that region’s population that lives in diverse, mixed income neighborhoods. More diverse metros generally have a larger share of their population living in diverse, mixed income neighborhoods. The regression line shows the typical relationship between metro diversity and the share of population living in diverse, mixed income neighborhoods. Cities above that line are performing better, on average, than one would expect based on their diversity; cities below that line are performing less well.

Some cities do a better job of realizing their diversity at a neighborhood level, than others. For each large metropolitan area we’ve computed the racial and ethnic diversity of the median neighborhood–reflecting lived experience of the typical resident. We’ve then compared that with the racial and ethnic diversity of the metropolitan area to see how closely the experience of the typical neighborhood resident comes to matching the diversity of the metropolitan area in which they live. Cities at the top of the list have neighborhood diversity that closely resembles metro diversity; those at the bottom are much more segregated, and don’t experience at the neighborhood level much of the diversity of their region.

Where are the most diverse, mixed income neighborhoods?

We’ve mapped the locations of the most racially and ethnically diverse and most mixed income neighborhoods in each of the nation’s 52 largest metropolitan areas. The map for San Francisco–one of the higher ranking metro areas–shows strong concentrations of diverse, mixed income neighborhoods in the City of San Francisco and the East Bay.

Detailed maps of the location of diverse, mixed income neighborhoods for each of the nation’s 52 largest metropolitan areas are available here. These on-line maps enable you to see the patterns of diversity in each metro area, and drill-down to the census tract level to inspect data for individual neighborhoods.

Why integration matters

A growing body of social science research confirms the importance of diversity to economic success. Greater socioeconomic mixing is facilitated in neighborhoods that re ect America’s racial and ethnic diversity, and which offer housing that is affordable to people with a range of incomes. In a series of studies led by Stanford’s Raj Chetty and his colleagues at the Equality of Opportunity Project, racial and economic segregation have been shown to reduce intergenerational economic mobility (the probability that children of low income families will, as adults, earn higher incomes than their parents). A recent post at City Observatory presents a synopsis of the literature on this subject, with citations to key works.

For a long time, we’ve known that neighborhoods of concentrated poverty are toxic to the life prospects of children who grow up there. Rothwell and Massey have shown that your neighbors’ educational attainment is nearly half as large as your parents’ educational attainment in shaping your life prospects. Living in a neighborhood with greater diversity and a mix of incomes generally means that families enjoy better-resourced public services and civic assets (including schools, parks and libraries) and develop stronger, more diverse social networks. Diverse, mixed-income neighborhoods are a platform for helping kids from lower-income families to escape poverty and realize the American dream.

Want to know more?

We’ve laid out our data, methodology and more detailed findings on our analyses of racial and ethnic diversity, and of income diversity in our technical report “Identifying America’s Most Diverse Inclusive Neighborhoods.”

Lost in Place

Lost in Place: Why the persistence and spread of concentrated poverty–not gentrification–is our biggest urban challenge.

A close look at population change in our poorest urban neighborhoods over the past four decades shows that the concentration of poverty is growing and that gentrification is rare.

While media attention often focuses on those few places that are witnessing a transformation, there are two more potent and less mentioned storylines. The first is the persistence of chronic poverty. Three-quarters of 1970 high-poverty urban neighborhoods in the U.S. are still poor today. The second is the spread of concentrated poverty: three times as many urban neighborhoods have poverty rates exceeding 30 percent as was true in 1970 and the number of poor people living in these neighborhoods has doubled.

The result of these trends is that the poor in the nation’s metropolitan areas are increasingly segregated into neighborhoods of concentrated poverty. In 1970, 28 percent of the urban poor lived in a neighborhood with a poverty rate of 30 percent or more; by 2010, 39 percent of the urban poor lived in such high-poverty neighborhoods. The data, methodology and results of our study are spelled out in our full report, available in PDF format here. The highlights are as follows:

  • High poverty is highly persistent. Of the 1,100 urban census tracts with high poverty in 1970, 750 still had poverty rates double that of the national average four decades later.
  • Though poverty persisted, these high-poverty neighborhoods were not stable—in the aggregate they lost population, with chronic high-poverty neighborhoods losing 40 percent of their population over four decades.
  • Moreover, few high-poverty neighborhoods saw significant reductions in poverty. Between 1970 and 2010, only about 100 of the 1,100 high-poverty urban neighborhoods experienced a reduction in poverty rates to below the national average. These 100 formerly high-poverty census tracts accounted for about five percent of the 1970 high-poverty neighborhood population. In contrast to chronically high-poverty neighborhoods, which lost population, these “rebounding” neighborhoods recorded an aggregate 30 percent increase in population.
  • Urban high-poverty neighborhoods proliferated between 1970 and 2010. The number of high-poverty neighborhoods in the core of metropolitan areas has tripled and their population has doubled in the past four decades. A majority of the increase in high-poverty neighborhoods has been accounted for by “fallen stars”—places that in 1970 had poverty rates below 15 percent, but which today have poverty rates in excess of 30 percent.
  • The growth in the number of poor persons living in “fallen star” neighborhoods dwarfs the decrease in the poverty population in “rebounding” neighborhoods. Since 1970, the poor population in rebounding neighborhoods fell by 67,000 while the number of poor persons living in fallen star neighborhoods increased by 1.25 million.
  • The data presented here suggest an “up or out” dynamic for high-poverty areas. A few places have gentrified, experienced a reduction in poverty, and generated net population growth. But those areas that don’t rebound don’t remain stable: they deteriorate, lose population, and overwhelmingly remain high-poverty neighborhoods. Meanwhile, we are continually creating new high-poverty neighborhoods.

To be poor anywhere is difficult enough, but a growing body of evidence shows the negative effects of poverty are amplified for those who live in high-poverty neighborhoods—places where 30 percent or more of the population live below the poverty line. Quality of life is worse, crime is higher, public services are weaker, and economic opportunity more distant in concentrated poverty neighborhoods. Critically, concentrated poverty figures prominently in the inter-generational transmission of inequality: children growing up in neighborhoods of concentrated poverty have permanently impaired economic prospects.

Our analysis focuses on the 51 largest US metropolitan areas–all those with a population of 1 million or more in the latest Census. The following tables summarize, by metro area, the key variables in our research–the number of high poverty neighborhoods in 1970 and 2010, and the numbers of neighborhoods transitioning between various categories over time.

Listen to the author speak about the report on Think Out Loud, Oregon Public Broadcasting, December 9, 2014:

The Strong Towns Podcast also had the author on to speak about the report.

 

 

For people interested in tracking the performance of a single metropolitan area across all of our measures of concentrated poverty, we offer a Metro-level dashboard. You can select an individual metropolitan area and see how it performs on each of our indicators.

Finally, you can drill down to the level of individual census tracts to examine population change, and the change in the number of persons living in poverty in each metropolitan area covered in our report. (See the full-sized version here)

You can also see the findings in this easy-to-share infographic:

Click for full infographic.
Click for full infographic.

How segregation limits opportunity

The more segregated an metro area is, the worse the economic prospects of the poor and people of color

Our City Observatory report, Lost in Place, closely tracks the growth of concentrated poverty in the nation’s cities; this is particularly important because of the widespread evidence of the permanent damage high-poverty neighborhoods do to children of poor families.

Two recent studies shed additional light on the importance of economic and racial integration to the life chances of students from low income families and children of color.

Writing in the journal Social Problems, Lincoln Quillan explores the question “Does Segregation Create Winners and Losers?” Quillian uses data from the Panel Study of Income Dynamics, a federal survey program that gathers longitudinal data on a representative group of Americans over several decades.

Quillian shows that increases in segregation at the metropolitan level are associated with lower rates of high school completion for poor and black students. Poor and black students that live in more segregated metropolitan areas are less likely to graduate from high school after controlling for other observable factors that influence individual success, such as the level of their parents’ education. Significantly, higher rates of segregation do not appear to have any statistically significant effects on the high school completion rates of whites or the non-poor. Taken together, these findings suggest that increasing racial and economic integration improves the educational outcomes for black and poor students without any negative effect on the educational outcomes of white and non-poor students.

This is important. If increased economic integration does not affect educational prospects for higher-income students, then the myth that having more integrated neighborhoods will “drag down” the potential success for the current residents is just that: a false myth. The implication of this research for housing policy is particularly salient.

In another article, published in the Annals of the American Academy of Political and Social Science, Sean Reardon, Lindsay Fox, and Joseph Townsend look at the trends in income segregation. Using data from the American Community Survey, they look at the trends behind the growing overall levels of income segregation in most metropolitan areas.

Their analysis finds that aggregate household income segregation has increased mostly because of the increasing isolation of the highest income households from low- and moderate-income households. This is what Robert Reich famously labeled “the secession of the successful.” Higher-income households are more likely to live in neighborhoods with other high-income households than was true two three decades ago. The authors also estimate changes in income segregation for each of the 50 largest metropolitan areas in the nation. They point out wide variations across the country.

Differences in income levels and residential segregation patterns among metropolitan areas produce very different experiences for the urban poor in different metros. In some higher income metro areas with less segregation, the poorest residents live in neighborhoods with noticeably higher incomes than the poorest residents of poorer, more segregated metros. For example, those in the tenth percentile of household income in Washington D.C. and Minneapolis live in neighborhoods that have average household incomes equal to the levels experienced by the median-income households in Atlanta and Los Angeles. You can see these differences in the figure below, excerpted from the paper:

Reardon Figure 4

This plots household income against neighborhood income. Most metros are similar, with the typical low-income family living in a neighborhood with a median income of $45K. Washington and Minneapolis have higher average incomes and are more economically integrated than other large metropolitan areas. Families in the lowest 25th percentile in these cities live in neighborhoods with median incomes of $60,000 (Minneapolis) and $70,000 (Washington). In the typical large metro area, you have to have an income of $75,000 (or more) to have such well-to-do neighbors.

Finally, this paper also presents major findings on racial integration and associated effects on economic integration. Black and Hispanic households tend to be highly concentrated into black and Hispanic neighborhoods, which has implications for poverty and economic mobility that we outline in our report here and blog post here. Most importantly, households with the same yearly income live in very different neighborhoods depending on their race:

“Black middle-class households (with incomes of roughly $55-$60,000), for example, typically live in neighborhoods with median incomes similar to those of very poor white households (those with incomes of roughly $12,000). For Hispanic households the disparity is only slightly smaller. Moreover, even high-income black and Hispanic households do not achieve neighborhood income parity with similar-income white households.”

While the growing gap between rich and poor is capturing greater policy attention, these two studies remind us that the spatial patterns of integration within metropolitan areas have a big impact on the quality of life and life prospects, especially of low-income households. It also indicates that how we build and inhabit our cities influences educational attainment and economic success, have an important role in ameliorating the effects of income inequality, which can have long-lasting impacts on city-wide educational attainment and economic success.

A hat tip to City Observatory’s friend Bridget Marquis for flagging these articles.

New Findings on Economic Opportunity (that you should know)

Our recent report, Lost in Place, closely tracks the growth of concentrated poverty in the nation’s cities; this is particularly important because of the widespread evidence of the permanent damage high-poverty neighborhoods do to children of poor families.

Two new studies shed additional light on the importance of economic and racial integration to the life chances of poor students and children of color.

Writing in the journal Social Problems, Lincoln Quillan explores the question “Does Segregation Create Winners and Losers?”

Quillian uses data from the Panel Study of Income Dynamics, a federal survey program that gathers longitudinal data on a representative group of Americans over several decades.

Quillian shows that increases in segregation at the metropolitan level are associated with lower rates of high school completion for poor and black students. Poor and black students that live in more segregated metropolitan areas are less likely to graduate from high school after controlling for other observable factors that influence individual success, such as the level of their parents’ education. Significantly, higher rates of segregation do not appear to have any statistically significant effects on the high school completion rates of whites or the non-poor. Taken together, these findings suggest that increasing racial and economic integration improves the educational outcomes for black and poor students without any negative effect on the educational outcomes of white and non-poor students.

This is important. If increased economic integration does not affect educational prospects for higher-income students, then the myth that having more integrated neighborhoods will “drag down” the potential success for the current residents is just that: a false myth. The implication of this research for housing policy is particularly salient.

In another article, due for publication in a forthcoming issue of the Annals of the American Academy of Political and Social Science, Sean Reardon, Lindsay Fox, and Joseph Townsend look at the trends in income segregation. Using data from the American Community Survey, they look at the trends behind the growing overall levels of income segregation in most metropolitan areas.

Their analysis finds that aggregate household income segregation has increased mostly because of the increasing isolation of the highest income households from low- and moderate-income households. Higher-income households are more likely to live in neighborhoods with other high-income households than was true two three decades ago. The authors also estimate changes in income segregation for each of the 50 largest metropolitan areas in the nation. They point out wide variations across the country.

Differences in income levels and residential segregation patterns among metropolitan areas produce very different experiences for the urban poor in different metros. In some higher income metro areas with less segregation, the poorest residents live in neighborhoods with noticeably higher incomes than the poorest residents of poorer, more segregated metros. For example, those in the tenth percentile of household income in Washington D.C. and Minneapolis live in neighborhoods that have average household incomes equal to the levels experienced by the median-income households in Atlanta and Los Angeles. You can see these differences in the figure below, excerpted from the paper:

Reardon Figure 4

This plots household income against neighborhood income. Most metros are similar, with the typical low-income family living in a neighborhood with a median income of $45K. Washington and Minneapolis have higher average incomes and are more economically integrated than other large metropolitan areas. Families in the lowest 25th percentile in these cities live in neighborhoods with median incomes of $60,000 (Minneapolis) and $70,000 (Washington). In the typical large metro area, you have to have an income of $75,000 (or more) to have such well-to-do neighbors.

Finally, this paper also presents major findings on racial integration and associated effects on economic integration. Black and Hispanic households tend to be highly concentrated into black and hispanic neighborhoods, which has implications for poverty and economic mobility that we outline in our report here and blog post here. Most importantly, households with the same yearly income live in very different neighborhoods depending on their race: “Black middle-class households (with incomes of roughly $55-$60,000), for example, typically live in neighborhoods with median incomes similar to those of very poor white households (those with incomes of roughly $12,000). For Hispanic households the disparity is only slightly smaller. Moreover, even high-income black and Hispanic households do not achieve neighborhood income parity with similar-income white households.”

While the growing gap between rich and poor is capturing greater policy attention, these two studies remind us that the spatial patterns of integration within metropolitan areas have a big impact on the quality of life and life prospects, especially of low-income households. It also indicates that how we build and inhabit our cities influences educational attainment and economic success, have an important role in ameliorating the effects of income inequality, which can have long-lasting impacts on city-wide educational attainment and economic success.

A hat tip to City Observatory’s friend Bridget Marquis for flagging these articles.

Why integration matters

Socioeconomic mixing, in neighborhoods that are diverse in race, ethnicity and income, benefits everyone

To some extent, we take for granted that integration and equal opportunity should be valued for their own sake. But its worth noting that achieving greater integration along both racial/ethnic and income dimensions is important to achieving more widespread prosperity and combatting poverty.

A growing body of sociological and economic research have demonstrated the high costs associated with racial and income segregation. While a comprehensive review of this literature is beyond the scope of this paper, we highlight here some of the key research findings that bear on the economic consequences of neighborhood diversity. Neighborhoods of concentrated disadvantage are not simply places where many households suffer from their own individual problems. The segregation of poverty (or a marginalized racial group) creates its own additional, collective burden on residents of these communities.

Galster and Sharkey undertake an extensive literature review of data on neighborhood effects of poverty. They find that segregation is associated with lower cognitive development and weaker academic performance, greater likelihood of teen pregnancy and risky behaviors, reduced physical and mental health, lower incomes and lower probability of employment, greater likelihood of being affected by or engaged in crime. Looking at more than 100 studies which they regard as quantitatively rigorous they conclude:

. . . the findings on the number of (methodologically rigorous) studies that have found substantial, statistically significant effects of spatial context (for at least some set of individuals) and those that have not, by outcome domain. The tally makes it clear that the preponderance of evidence in every outcome domain is that multiple aspects of spatial context exert important causal influences over a wide range of outcomes related to socioeconomic opportunity, though which aspects are most powerful depends on the outcome and the gender and ethnicity of the individuals in question.
(Galster & Sharkey, 2017)

Part of this burden is evident in day-to-day quality of life issues, such as greater exposure to crime. Studies of the “Moving to Opportunity” program, in which families were given assistance to move from low-income to middle-income neighborhoods, showed a marked improvement in self-reported well-being. Moving to a neighborhood whose poverty rate was 13 percentage points lower was associated with an increase in self-reported quality of life equivalent to an increase of $13,000 in household income (Ludwig et al., 2012). But perhaps the most serious effects of concentrated disadvantage are the ways in which it acts to reproduce inequality and quash economic opportunity and mobility—the very promise of the American dream.

High-poverty neighborhoods put their residents at a significant and immediate economic disadvantage. They typically have fewer local jobs than other neighborhoods, and often are distant from, or poorly connected to, other major job centers. These communities also often lack social networks that allow residents to find job openings (Bayer, Ross, & Topa, 2004).

For these and other reasons, people who grow up in high-poverty neighborhoods, on average, have worse economic outcomes than people who grow up in other kinds of neighborhoods, even if their family backgrounds are identical. The Equality of Opportunity Project has shown that inter-generational income mobility is significantly higher in metropolitan areas with lower levels of income segregation(Chetty, Hendren, Kline, & Saez, 2014)). The effect is so strong that, for children whose families move from high-segregation to low-segregation metropolitan areas, each additional year spent in the high-segregation region before the move is associated with less income as an adult.

Chetty and Hendren find that across metropolitan areas both income and racial ethnic segregation have a negative effect on children’s income as adults (Chetty & Hendren, 2016) (Chetty & Hendren, 2016)

“. . . our analysis strongly supports the hypothesis that growing up in a more segregated area – that is, in a neighborhood with concentrated poverty – is detrimental for disadvantaged youth. “

But they go on to say that it’s not because of their parents access to jobs, but because of the children’s exposure to a different set of peers.

“Areas with less concentrated poverty, less income inequality, better schools, a larger share of two-parent families, and lower crime rates tend to produce better outcomes for children in poor families. Boys’ outcomes vary more across areas than girls’ outcomes, and boys have especially negative outcomes in highly segregated areas. One-fifth of the black-white income gap can be explained by differences in the counties in which black and white children grow up.”

Other studies have found similar effects. For example, black children who grow up in high-poverty neighborhoods that transition to low levels of poverty have incomes that are 30 to 40 percent higher than black children with similar backgrounds who grow up in neighborhoods that remain at high levels of poverty (Sharkey, 2013) Observing the results of a natural experiment that relocated families from public housing in Chicago, Eric Chyn found that children who moved even relatively short distances to neighborhoods with somewhat lower poverty rates also experienced noticeable gains in earnings (Chyn, 2016)

Another analysis suggests that the educational level of ones neighbors has an effect on a child’s economic future nearly as large as that of the educational level of a child’s own parents. The effect of neighborhood educational level on children’s future earnings have been estimated to be two-thirds as powerful as the influence of the children’s own parental educaton (Rothwell & Massey, 2014).

The effects that are observed at the neighborhood level appear to compound to produce the variations in economic results we observe across metropolitan areas. Quillian shows that increases in segregation at the metropolitan level are associated with lower rates of high school completion for poor and black students. (Quillian, 2014) Quillian uses data from the Panel Study of Income Dynamics, a federal survey program that gathers longitudinal data on a representative group of Americans over several decades. Poor and black students that live in more segregated metropolitan areas are less likely to graduate from high school after controlling for other observable factors that influence individual success, such as the level of their parents’ education. Significantly, higher rates of segregation do not appear to have any statistically significant effects on the high school completion rates of whites or the non-poor. Taken together, these findings suggest that increasing racial and economic integration improves the educational outcomes for black and poor students without any negative effect on the educational outcomes of white and non-poor students.

A recent study prepared by the Urban Institute and the Metropolitan Policy Center estimated the cumulative economic and social costs associated with segregation in that metropolitan area. They found that the annual estimated cost of segregation in Chicago was more than $4 billion annually in lost income, and meant that fewer residents achieved a college education, while more were victims of crime, including homicide. (Acs, Pendall, Treskon, & Khare, 2017)

Taken together, the weight of social science evidence shows that racial/ethnic and economic segregation have profound consequences for individuals, for neighborhoods and entire cities. Much of the persistence and severity of poverty is due to the continued segregation. More integrated neighborhoods and more integrated cities enjoy better economic results, and produce better lifetime opportunities for their children. These findings point up the critical importance of the role of the nation’s racially and ethnically diverse, mixed income neighborhoods.

References

Acs, G., Pendall, R., Treskon, M., & Khare, A. (2017). The Cost of Segregation: National Trends and the Case of Chicago, 1990–2010. Washington, DC: Urban Institute. Retrieved from http://www. urban. org/research/publication/cost-segregation.

Bayer, P., Ross, S. L., & Topa, G. (2004). Place of Work and Place of Residence: Informal Hiring Networks and Labor Market Outcomes (Working paper No. 2004–07). University of Connecticut, Department of Economics. Retrieved from https://ideas.repec.org/p/uct/uconnp/2004-07.html

Chetty, R., & Hendren, N. (2016). The impacts of neighborhoods on intergenerational mobility ii: County-level estimates. National Bureau of Economic Research. Retrieved from http://www.nber.org/papers/w23002

Chetty, R., Hendren, N., Kline, P., & Saez, E. (2014). Where is the Land of Opportunity? The Geography of Intergenerational Mobility in the United States. National Bureau of Economic Research. Retrieved from http://www.nber.org/papers/w19843

Chyn, E. (2016). Moved to opportunity: The long-run effect of public housing demolition on labor market outcomes of children. Unpublished Paper. University of Michigan, Ann Arbor.

Galster, G., & Sharkey, P. (2017). Spatial Foundations of Inequality: A Conceptual Model and Empirical Overview. RSF, 3(2), 1–33. https://doi.org/10.7758/RSF.2017.3.2.01

Ludwig, J., Duncan, G. J., Gennetian, L. A., Katz, L. F., Kessler, R. C., Kling, J. R., & Sanbonmatsu, L. (2012). Neighborhood effects on the long-term well-being of low-income adults. Science, 337(6101), 1505–1510.

Quillian, L. (2014). Does Segregation Create Winners and Losers? Residential Segregation and Inequality in Educational Attainment. Social Problems, 61(3), 402–426.

Rothwell, J. T., & Massey, D. S. (2014). Geographic Effects on Intergenerational Income Mobility. Economic Geography, n/a-n/a. https://doi.org/10.1111/ecge.12072

Sharkey, P. (2013). Stuck in place: Urban neighborhoods and the end of progress toward racial equality. University of Chicago Press. Retrieved from http://books.google.com/books?hl=en&lr=&id=R-b_NlPJeuUC&oi=fnd&pg=PR5&dq=patrick+sharkey+stuck+in+place&ots=xJkeq39Kje&sig=0lmKDBM6OxHGMNk0jBga4EtDFqM

 

Is your city or neighborhood poorer than 40 years ago?

We recently released our latest report, Lost in Place: Why the persistence and spread of concentrated poverty–not gentrification–is our biggest urban challenge. It speaks to a national trend that’s been largely ignored– that urban poor are being concentrated into poorer neighborhoods, and that those neighborhoods are increasing in number. We speak here about some of those implications, and here and here we provide references to what others are saying about gentrification and poverty; here we address economic segregation.

Neighborhood change is by definition a highly local process, and everyone wants to know how their city is performing:  What about their city? Their neighborhood?  Nationally, the number of high-poverty neighborhoods tripled, and the number of people in poverty in those neighborhoods have doubled, but this is not the pattern in every city. In Detroit, the numbers are even more staggering–the population living in poverty is more than 228,000, from less than 40,000 40 years previous.  A few places like Virginia Beach saw an actual decline in concentrated poverty.  Rebounding neighborhoods have been more common in some metros like New York and Chicago.

If you want to see the data for individual metros, we’ve created a city-specific dashboard. Just select the city of interest, and you’ll see a comprehensive set of indicators showing how your metro performed between 1970 and 2010.

As you look at individual cities, keep these overall trends in mind:

  • Most cities only had 1 or 2 “rebounding” neighborhoods, or neighborhoods that were previously high poverty, and by 2010 were below the national average rate of poverty (15%).
  • Nationally, the number of high-poverty tracts tripled.
  • Overall, the number of poor people in those high-poverty tracts doubled.
  • High-poverty neighborhoods that didn’t rebound weren’t stable: they lost, on average, 40 percent of their population over 40 years (both of poor and non-poor persons). This means most “chronic high poverty” neighborhoods saw a dramatic reduction in population by 2010.
  • The majority of the increase in those living in high poverty were in newly poor or “fallen star” neighborhoods. (Fallen stars are neighborhoods that had poverty rates below the national average in 1970, but have poverty rates of 30 percent or higher today).  The number of fallen stars exceeded the number of rebounding neighborhoods 12 to 1.

The process of neighborhood change is often difficult and disruptive, and poverty and gentrification are sensitive topics. Each city is different and has unique challenges; however, most cities follow the national trend of increasing concentrated poverty.  If we are serious about bettering the lives of the poor (and we should be), we need to carefully examine the data about change and look for solutions that are fully grounded in the facts of neighborhood change.

If you want to look at each city’s specific tract-level data, go to the report here and scroll to the maps. We will also be sharing an informational post about how these were made soon- check back in a couple days!

 

Measuring “anti-social” capital

The number of security guards is a good measure of a city’s level of “anti-social” capital

In his book Bowling Alone, Robert Putnam popularized the term “social capital.” Putnam also developed a clever series of statistics for measuring social capital. He looked at survey data about interpersonal trust (can most people be trusted?) as well as behavioral data (do people regularly visit neighbors, attend public meetings, belong to civic organizations?). Putnam’s measures try to capture the extent to which social interaction is underpinned by widely shared norms of openness and reciprocity.

It seems logical to assume that there are some characteristics of place which signify the absence of social capital. One of these is the amount of effort that people spend to protect their lives and property. In a trusting utopia, we might give little thought to locking our doors or thinking about a “safe” route to travel. In a more troubled community, we have to devote more of our time, energy, and work to looking over our shoulders and protecting what we have.

The presence of security guards in a place is arguably a good indicator of this “negative social capital.” Guards are needed because a place otherwise lacks the norms of reciprocity that are needed to assure good order and behavior. The steady increase in the number of security guards and the number of places (apartments, dormitories, public buildings) to which access is secured by guards indicates the absence of trust.

The number of security guards in the United States has increased from about 600,000 in 1980 to more than 1,000,000 in 2000 (Strom et al., 2010). These figures represent a steep increase from earlier years. In 1960, there were only about 250,000 guards, watchmen and doormen, according to the Census (which used a different occupational classification scheme than is used today). The Bureau of Labor Statistics reports that the number of US security guards has increased by almost 100,000 since 2010, to a total of more than 1.1 million. As a measure of how paranoid and unwelcoming we are as a nation, security guards outnumber receptionists by more than 100,000 workers nationally.

Sam Bowles and Arjun Jayadev argue that we have become “one nation under guard” and say that the growth of guard labor is symptomatic of growing inequality. The U.S. has the dubious distinction of employing a larger share of its workers as guards than other industrialized nations and there seems to be a correlation between national income inequality and guard labor.

Just as the U.S. has a higher fraction of security guards than other nations, some cities have more security guards than others. To understand these patterns, we’ve compiled Bureau of Labor Statistics data from the Occupational Employment Survey on private security guards. BLS defines security guards as persons who guard, patrol, or monitor premises to prevent theft, violence, or infractions of rules, and whom may operate x-ray and metal detector equipment. (The definition excludes TSA airport security workers).

This occupational data reports the number of security guards in every large metropolitan area in the country. Adjusting these counts by the size of the workforce in each metro area tells us which places have proportionately the most security guards–which are arguably the least trusting–and which places have the fewest security guards, which may tend to indicate higher levels of social trust. We rank metropolitan areas by the BLS estimates of the number of security guards per 1,000 workers.  For particularly large metro areas, we report BLS estimates for the largest metropolitan division in a metro area.)

Security Guards per 1,000 Workers, 2017

At the top of the list is Las Vegas. While the typical large metro area has about 8 security guards per 1,000 workers, Las Vegas has 19 per 1,000.  Miami ranks second, with more than  twice as many (18 per 1000) as the average large metro. Other cities with high ratios of security guards to population are Memphis, New Orleans, Miami and Baltimore. Washington D.C., with its high concentration of government offices, defense and intelligence agencies, and federal contractors, also has a high proportion of security guards.

At the other end of the spectrum are a number of cities in which the ratio of security guards to workforce is one-third lower than in the typical metro area. At the bottom of the list are Minneapolis-St. Paul, Grand Rapids and Portland, all with fewer than six security guards per 1,000 workers. (The Twin Cities and Portland also do well on most of Putnam’s measures of social capital)

It seems somewhat paradoxical, but the salaries paid to security guards get treated as a net contribution to gross domestic product. Yet, in many important senses, security guards don’t add to the overall value of goods and services so much as they serve to keep the ownership of those goods and services from being rearranged. As Nobel prize winning economist Douglass North has argued, we ought to view the cost of enforcing property rights as a “transaction cost.” In that sense, cities that require lots of guards to assure that property isn’t stolen or damaged and that residents, workers, or customers aren’t victimized, actually have higher costs of living and doing business than other places. These limits on easy interaction may stifle some of the key advantages to being in cities.

Anti-Social Capital?

In his book Bowling Alone, Robert Putnam popularized the term “social capital.” Putnam also developed a clever series of statistics for measuring social capital. He looked at survey data about interpersonal trust (can most people be trusted?) as well as behavioral data (do people regularly visit neighbors, attend public meetings, belong to civic organizations?). Putnam’s measures try to capture the extent to which social interaction is underpinned by widely shared norms of openness and reciprocity.

It seems logical to assume that there are some characteristics of place which signify the absence of social capital. One of these is the amount of effort that people spend to protect their lives and property. In a trusting utopia, we might give little thought to locking our doors or thinking about a “safe” route to travel. In a more troubled community, we have to devote more of our time, energy, and work to looking over our shoulders and protecting what we have.

The presence of security guards in a place is arguably a good indicator of this “negative social capital.” Guards are needed because a place otherwise lacks the norms of reciprocity that are needed to assure good order and behavior. The steady increase in the number of security guards and the number of places (apartments, dormitories, public buildings) to which access is secured by guards indicates the absence of trust.

The number of security guards in the United States has increased from about 600,000 in 1980 to more than 1,000,000 in 2000 (Strom et al., 2010). These figures represent a steep increase from earlier years. In 1960, there were only about 250,000 guards, watchmen and doormen, according to the Census (which used a different occupational classification scheme than is used today).

This trend has led Sam Bowles and Arjun Jayadev to argue that we have become “one nation under guard” and that the growth of guard labor is symptomatic of growing inequality. The U.S. has the dubious distinction of employing a larger share of its workers as guards than other industrialized nations and there seems to be a correlation between national income inequality and guard labor.

Just as the U.S. has a higher fraction of security guards than other nations, some cities have more security guards than others. To understand these patterns, we’ve compiled Bureau of Labor Statistics data from the Occupational Employment Survey on private security guards. BLS defines security guards as persons who guard, patrol, or monitor premises to prevent theft, violence, or infractions of rules, and whom may operate x-ray and metal detector equipment. (The definition excludes TSA airport security workers). In 2015, there were more than 1,050,000 security guards in the US.

This occupational data reports the number of security guards in every large metropolitan area in the country. Adjusting these counts by the size of the population in each metro area tells us which places have proportionately the most security guards– which are arguably the least trusting, and which places have the fewest security guards. This may be an indicator of higher levels of social trust.

Here are the data:

At the top of the list is Las Vegas. While the typical large metro area has about 39 security guards per 10,000 population, these Miami has more than  twice as many (86 per 10,000). Other cities with high ratios of security guards to population are Memphis, New Orleans, Miami and Baltimore. Washington D.C., with its high concentration of government offices, defense and intelligence agencies, and federal contractors, also has a high proportion of security guards.

At the other end of the spectrum are a number of cities in which the ratio of security guards to population is one-third lower than in the typical metro area. At the bottom of the list are Minneapolis-St. Paul, Providence and Portland. (The Twin Cities and Portland also do well on most of Putnam’s measures of social capital)

It seems somewhat paradoxical, but the salaries paid to security guards get treated as a net contribution to gross domestic product. Yet, in many important senses, security guards don’t add to the overall value of goods and services so much as they serve to keep the ownership of those goods and services from being rearranged. As Nobel prize winning economist Douglass North has argued, we ought to view the cost of enforcing property rights as a “transaction cost.” In that sense, cities that require lots of guards to assure that property isn’t stolen or damaged and that residents, workers, or customers aren’t victimized, actually have higher costs of living and doing business than other places. These limits on easy interaction may stifle some of the key advantages to being in cities.

Anti-Social Capital?

In his book Bowling Alone, Robert Putnam popularized the term “social capital.” Putnam also developed a clever series of statistics for measuring social capital. He looked at survey data about interpersonal trust (can most people be trusted?) as well as behavioral data (do people regularly visit neighbors, attend public meetings, belong to civic organizations?). Putnam’s measures try to capture the extent to which social interaction is underpinned by widely shared norms of openness and reciprocity.

It seems logical to assume that there are some characteristics of place which signify the absence of social capital. One of these is the amount of effort that people spend to protect their lives and property. In a trusting utopia, we might give little thought to locking our doors or thinking about a “safe” route to travel. In a more troubled community, we have to devote more of our time, energy, and work to looking over our shoulders and protecting what we have.

The presence of security guards in a place is arguably a good indicator of this “negative social capital.” Guards are needed because a place otherwise lacks the norms of reciprocity that are needed to assure good order and behavior. The steady increase in the number of security guards and the number of places (apartments, dormitories, public buildings) to which access is secured by guards indicates the absence of trust.

The number of security guards in the United States has increased from about 600,000 in 1980 to more than 1,000,000 in 2000 (Strom et al., 2010). These figures represent a steep increase from earlier years. In 1960, there were only about 250,000 guards, watchmen and doormen, according to the Census (which used a different occupational classification scheme than is used today).

This trend has led Sam Bowles and Arjun Jayadev to argue that we have become “one nation under guard” and that the growth of guard labor is symptomatic of growing inequality. The U.S. has the dubious distinction of employing a larger share of its workers as guards than other industrialized nations and there seems to be a correlation between national income inequality and guard labor.

Just as the U.S. has a higher fraction of security guards than other nations, some cities have more security guards than others. To understand these patterns, we’ve compiled Bureau of Labor Statistics data from the Occupational Employment Survey on private security guards. BLS defines security guards as persons who guard, patrol, or monitor premises to prevent theft, violence, or infractions of rules, and whom may operate x-ray and metal detector equipment. (The definition excludes TSA airport security workers).

This occupational data reports the number of security guards in every large metropolitan area in the country. Adjusting these counts by the size of the workforce in each metro area tells us which places have proportionately the most security guards– which are arguably the least trusting, and which places have the fewest security guards. This may be an indicator of higher levels of social trust.

Here are the data:

At the top of the list are Las Vegas and Miami. While the typical large metro area has about 9 security guards per 1,000 workers, these two cities have roughly twice as many (Las Vegas as 21 per 1,000; Miami 17 per 1,000. Washington D.C., with its high concentration of government offices, defense and intelligence agencies, and federal contractors, also has a high proportion of security guards.

At the other end of the spectrum are a number of cities in which security guards make up about one-third less of the workforce than in the typical metro area. At the bottom of the list are Minneapolis-St. Paul, Providence and Portland. (The Twin Cities and Portland also do well on most of Putnam’s measures of social capital)

It seems somewhat paradoxical, but the salaries paid to security guards get treated as a net contribution to gross domestic product. Yet, in many important senses, security guards don’t add to the overall value of goods and services so much as they serve to keep the ownership of those goods and services from being rearranged. As Nobel prize winning economist Douglass North has argued, we ought to view the cost of enforcing property rights as a “transaction cost.” In that sense, cities that require lots of guards to assure that property isn’t stolen or damaged and that residents, workers, or customers aren’t victimized, actually have higher costs of living and doing business than other places. These limits on easy interaction may stifle some of the key advantages to being in cities.