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.

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.

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.

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.

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 Affairs —Temporal 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.

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.

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 Affairs —Temporal 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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

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.

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.

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.)

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

 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.

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.

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.

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.

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.

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.

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

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.

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.

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

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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 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.

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.

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.

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.”

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.

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.

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.

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.

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.

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.”

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.

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.

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.

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.

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.

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.

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 coming. You can follow the challenge at #knightcities on Twitter or sign up for our email newsletter. You can send questions to citieschallenge@knightfoundation.org.

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.)

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.

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.)

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.

2. 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.

3. 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.)

4. 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.

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.

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.

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

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

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:

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.

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.

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.

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.

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.

• 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”

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

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 prices.  As 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.

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.

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.

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.

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.

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.

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.

Think riots destroyed #Baltimore? Entire blocks boarded up. pic.twitter.com/OKSnHXMb9f

— Michael Kaplan (@MichaelD_Kaplan) May 1, 2015

Look what the riots did to Baltimore! Oh wait no…These were taken before the riots. Oops. @MayorSRB pic.twitter.com/2iTsnVDf6G

— Chels (@BEautifully_C) April 30, 2015

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.

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.

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.

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.

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.

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.

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.

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.

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.*)

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.

NYT analysis of income mobility is interesting, but it's a map of black & native American pop. http://t.co/aXZQGVrRfG pic.twitter.com/DKoA0NA467

— Yonah Freemark (@yfreemark) May 4, 2015

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.