There is little doubt that AmericaÃ¢â‚¬s class structure is changing. The decline of the working class has given rise to an incredible concentration of both wealth and disadvantage. But our class structure is not just cleaving along economic lines, but across geographic lines as well. For more and more Americans, our zip codes are our destiny, with our ability to achieve economic mobility, pursue our careers, and afford homes dependent on where we live.
More than a decade ago, in my book WhoÃ¢â‚¬s Your City?, I argued that the knowledge economy is bringing about an epochal shift in our class structure. The old class distinction between the corporate class and workers was giving way to a new geographically based class division. I identified three new classes: Ã¢â‚¬Å“the mobileÃ¢â‚¬ï¿½ who have the means, education, and capability to move to spaces of opportunity; Ã¢â‚¬Å“the stuckÃ¢â‚¬ï¿½ who lack the resources to relocate; and Ã¢â‚¬Å“the rootedÃ¢â‚¬ï¿½ who have the resources to move, but prefer to stay where they are.
But where are these new classes based? Are some places more filled with the mobile, while other places are home to greater concentrations of the stuck and rooted?
To get at this, my colleague Karen King, a demographer at the University of TorontoÃ¢â‚¬s School of Cities, pulled data from the 2017 American Community Survey to chart the share of adults 25 years and older who are currently living in the state where they were born. She did this for all Americans and for different poles of education: Americans who did not complete high school and those who have at least a college degree. My CityLabcolleague David Montgomery made the maps.
The map at the top of the page shows the broad pattern. Nearly six in ten Americans (58.5 percent) currently reside in the state where they were born. There is a minuscule difference between men and women: A slightly higher percentage of men (58.8 percent) lived in theirbirth state compared to women (58.2 percent).
But there is huge variation across states as the map shows. Look at the broad Ã¢â‚¬Å“stuck beltÃ¢â‚¬ï¿½ running across the middle of the country from Iowa, Wisconsin, Michigan, Ohio, and Pennsylvania, down through West Virginia, and into the South in Alabama, Mississippi, and Louisiana where 60 to 74 percent of residents live in the state in which they were born. Louisiana tops the list with nearly three quarters of the population native born, followed by Michigan with 72 percent and Ohio with 71 percent.
The map above tracks the geography of college grads. It shows a similar Stuck Belt spanning the Rustbelt and Deep South. Louisiana again tops the list with nearly two-thirds of its adults who hold a bachelorÃ¢â‚¬s degree or more remaining in the state, followed by Michigan (64 percent), Ohio (63 percent), and Mississippi and Iowa (62 percent).
The third map charts the pattern for Americans who did not complete high school. Now the Stuck Belt has expanded in other parts of the country. Louisiana again tops the list with roughly three-quarters of its adults who did not complete high school native to the state as does West Virginia (75.6 percent), followed by Mississippi (72.9 percent), and Kentucky (72.6 percent).
The geography of the mobile is concentrated in the Sunbelt states (where population has been rapidly increasing) and, to a lesser extent, on the coasts. Nevada stands out with only 10 percent of current adult residents who were born there, followed by Florida (22 percent), Arizona (23 percent) and Alaska (27 percent). Colorado, New Hampshire, Wyoming, Oregon, Delaware, Washington, Idaho, and Maryland all have between 30 and 40 percent. And larger coastal states like California and New Jersey have rates in the low 40 percent range. New York and Illinois have significantly higher shares of people, roughly 55 percent, who were born in those states.
The pattern stands true if we look at only highly educated adults. Less than ten percent of college grads in Nevada were born there, followed by Arizona (17 percent) and Florida (18.5 percent). About four in ten highly educated adults in California were born there, while roughly half in New York (53 percent) and Massachusetts (48 percent) were born in those states.
The last map looks at whoÃ¢â‚¬s more likely to stay in their birth state: college grads or high school dropouts. Blue shows states where college grads are more likely to be born in-state, while beige and maroon indicate where high school dropouts are more likely to have been born in-state. The picture here may not necessarily be in line with what we might think.
For one, New York is dark blue, meaning a higher share of college grads were born there and decided to continue living there. More than half of New YorkÃ¢â‚¬s college grads (53 percent) were born there, compared to a third (33 percent) of the stateÃ¢â‚¬s residents with less than a high school diploma, a 20-point difference. In California, 37.5 percent of highly educated adults were born in-state compared to 20 percent of those without a high school degree, an 18-point differential. The pattern is similar in Texas and Massachusetts.
I identify two possibilities: For one, these states tend to have high immigrant populations. The higher percentage of immigrants without these educational degrees may shift the numbers of the less educated residents who were born in-state. Secondly, the high cost of housing may have pushed out the less educated and less affluent households to find opportunities in other states.
Economic opportunity in America increasingly turns on where we live, and our ability to move. Our class structure is being reshaped by our geography, with a new divide between the mobile versus the stuck and the rooted. The stuck are concentrated in states in the Deep South and the Rustbelt. The mobile are located in Sunbelt states like Nevada, Arizona, and Florida, while states like New York and California appear to be shedding less-educated residents. This new geography of class and place is yet another dividing line in our increasingly polarized society.
CityLab editorial fellow Claire Tran contributed research and editorial assistance to this article.
When world leaders, economists, and pundits talk about global economic power, they usually talk about nation-states. ThatÃ¢â‚¬s how we typically tally up economic power, rating and ranking nations on their gross domestic product. Today, economists and business analyststalk about when China will overtake the United States as worldÃ¢â‚¬s largest economy (based on at least one measure of purchasing power parity it already has).
But this obsession with nation-states does not fit the reality of todayÃ¢â‚¬s highly-clustered knowledge economy, centered in and around global cities. And, itÃ¢â‚¬s not just individual cities and metropolitan areas that power the world economy. Increasingly, the real driving force is larger combinations of cities and metro areas called mega-regions.
Back in 1961, the economic geographer Jean Gottmann coined the term Ã¢â‚¬Å“megalopolisÃ¢â‚¬ï¿½ to describe the emerging economic hub that stretched from Boston to Washington, D.C. The term came to be applied to a number of regions in the world, including the vast Midwestern megalopolis that extends from Chicago, through Detroit and Cleveland, and south to Pittsburgh, which Gottmann dubbed Ã¢â‚¬Å“Chi-Pitts.Ã¢â‚¬ï¿½
But mega-regions are hard to identify using traditional data sources. About a decade and a half ago it dawned on me that you can actually see mega-regions like the Boston-New York-Washington corridor when you pass over them in a plane at night. So my colleagues and I undertook a project to identify the worldÃ¢â‚¬s mega-regions from these satellite images of the world at night.
But now, much improved night-light data has become available from satellitesÃ¢â‚¬â€�data that gives us a better look atthe worldÃ¢â‚¬s mega-regions. My colleague Fabio Dias, a computer imaging expert in the University of TorontoÃ¢â‚¬s School of Cities, extracted the improved light data from these new satellites, which we analyzed with our colleague Patrick Adler. The new data, referred to as Visible Infrared Imaging Radiometer Suite (VIIRS) and developed by the National Oceanic and Atmospheric Administration (or NOAA), are a huge advancement compared to older satellite images.
Using this new and improved satellite data from 2015, we define mega-regions as areas of continuous light that contain at least two existing metro areas, have populations of five million or more, and generate economic output of more than $300 billion. (We add up population and economic output data on a purchasing power parity basis for mega-regions using base data from Oxford Economics via BrookingsÃ¢â‚¬ Global Metro Monitor).In two well-known U.S. cases, we form mega-regions out of two relatively distinct satellite footprints: Chi-Pitts, as well as the Texas Triangle of Houston, Dallas, and Austin.
We ultimately identify 29 mega-regions as the real regional powerhouses of the global economy. Eleven are in Asia, 10 are in North America, six are in Europe, one is in Latin America, one is in Africa, and one more straddles Asia and Africa. My CityLab colleague David Montgomery made the maps of these mega-regions.
Bos-Wash, which extends from Boston through New York and Philadelphia down to Washington, D.C., is the worldÃ¢â‚¬s largest mega-region of nearly 50 million people, generating almost $4 trillion in economic output. Ã‚Â If this mega-region were its own country, the economy would be equivalent to the worldÃ¢â‚¬s seventh largest, bigger than the United KingdomÃ¢â‚¬s or BrazilÃ¢â‚¬s.
Par-Am-Mun: Coming in second, this European mega-region spans Paris, Amsterdam, Brussels, and Munich. Home to 44 million people, it generates $2.5 trillion in economic output, about as much as Mexico does and morethan Italy, equivalent to the worldÃ¢â‚¬s sixth largest economy.
Chi-Pitts: In third place is this great heartland mega-region which runs through Minneapolis, Chicago, Indianapolis, Detroit, Cleveland, and Pittsburgh, encompassing 50 metros large and small, in total. With a population of more than 30 million people, this mega-region produces more than $2 trillion in economic output, comparable to South KoreaÃ¢â‚¬s, making it roughly the 14th largest economy in the world.
Greater Tokyo is AsiaÃ¢â‚¬s largest mega-region, generating just under $2 trillion in economic output and home to almost 40 million people. Its economy is comparable to SpainÃ¢â‚¬s and larger than CanadaÃ¢â‚¬s. It would rank as the worldÃ¢â‚¬s 11th largest economy if it was a country of its own.
SoCal,running from Los Angeles to San Diego, is home to more than 20 million people and produces nearly $1.5 trillion in economic output, comparable to the economic output of Australia, and among the worldÃ¢â‚¬s 20 leading economies.
Seoul-San is the worldÃ¢â‚¬s 19th largest mega-region. Running from Seoul to Busan, it is home to the 36th largest economy in the world.
The Texas Triangle spans Dallas, Houston, and San Antonio as well as Austin. It generates $1.2 trillion in economic output while housing just under 20 million people. It would rank as one of the worldÃ¢â‚¬s top 25 economies.
Beijing-Tianjin is ChinaÃ¢â‚¬s largest mega-region. With nearly 40 million residents, it also produces $1.2 trillion in economic output, putting it, too, among the list of the 25 largest economies in the world.
Lon-Leed-Chester: This mega-region which runs from London through Leeds, and Manchester also generates $1.2 trillion in economic output while housing more than 20 million people. It too makes the list of the worldÃ¢â‚¬s top 25 economies.
Hong-Shen combines Hong Kong and Shenzhen, home to 20 million people. The region creates slightly over $1 trillion in economic output, equivalent to the 26th largest economy in the world.
NorCal consists of San Francisco, San Jose, and other Bay Area cities. With more than 10 million residents, it generates almost $1 trillion in economic output, making it the worldÃ¢â‚¬s 27th largest economy.
Shang-zou: Spanning Shanghai and Hangzhou, this mega-region is home to nearly 25 million people and produces nearly $900 billion in economic output, placing it among the top 30 economies in the world.
Here are more details on the full 29 mega-regions.
Economic Output (billions)
New York; Washington, D.C.; Boston
Paris, Amsterdam, Brussels, Munich
Chicago, Detroit, Cleveland, Pittsburgh
Los Angeles, San Diego
Dallas, Houston, San Antonio, Austin
London, Leeds, Manchester
Hong Kong, Shenzhen
San Francisco, San Jose
Rome, Milan, Turin
Singapore, Kuala Lumpur
Cairo, Tel Aviv
Abu Dhabi, Dubai
Toronto, Buffalo, Rochester
New Delhi, Lahore
Jinan, Zibo, Dongying
CityLab editorial fellow Claire Tran contributed research and editorial assistance to this article
Concern about gentrification has grown in the past decade as the affluent and educated have surged back into cities. But can the pace and pattern of future gentrification be predicted? New research by a team of data scientists and geographers says so.
The research, conducted by Jonathan Reades, Jordan De Souza, and Phil Hubbard of Kings College London and published in the Urban Studies journal,uses an artificial intelligence technique called machine learning that essentially trains computer models to learn from past data to predict future patterns. In this case, the research team used data on past gentrification in London to predict where it will next occur.
The study used machine learning to analyze 2001 data in order to “predict” gentrification as it occurred in 2011. Then, using that model, it predicted where gentrification will occur in 2021. It began by measuring socioeconomic status with four key variables: household income, real estate values, occupational share (percentage in the “top” occupational classes) and job qualifications (based on the share of residents achieving a certain level of a national vocational certification).
Once researchers calculated socioeconomic status for each neighborhood, they analyzed how other demographic measures like age and ethnicity correlate with gentrification. The neighborhood data is based on the UK’s Lower Layer Super Output Area (LSOA), an area similar to a U.S. census tract with between 1,000 and 3,000 people.
When the 2001 model predicted what would happen in 2011, it lined up quite accurately with what had occurred in real life. It generated a very close statistical fit, compared to other traditional models like standard regression analysis.
Surprisingly, the study found that key demographic factors, like the presence of “DINKs” (Dual Income, No Kids couples), vehicle ownership, or ethnicity, did not rank strongly on the list of top predictors for gentrification. Immigration did predict gentrification, but only from other members of the EU (as of 2001), the Americas, and Australia and New Zealand. The type of building also had some predictive value, especially for terraced or older buildings. Ultimately, the study found that most of the top predictors reflected occupation: working long hours, skills and qualifications, and job flexibility, such as self-employment or working from home.
The map below charts gentrification in 2011, using change in socioeconomic status since 2001 on the neighborhood level. It shows two key axes of gentrification emanating from Central London, one to the Southwest and another to the Northeast, featuring so-called “Billionaire’s Row.” Dark purple neighborhoods saw a decline in status, while yellow neighborhoods saw an increase. Pale mint represents areas in which the change in status was so small, it may not be statistically relevant. Even well-off neighborhoods experienced change and gentrification according to this model.
Gentrifying Neighborhoods in 2011
The next map shows the predictions for gentrification in 2021, again charting gentrification as change in socioeconomic status within neighborhoods. Here the model predicts gentrification not only in the central neighborhoods of Westminster, Kensington, and Chelsea, but also that it will spread further out to typically working-class boroughs. At the same time, it predicts that gentrification will pass over smaller towns in the outskirts, suggesting that uplift in one neighborhood may be linked to displacement and thus the decline of another neighborhood. Dark purple represents a decrease in status, and yellow represents an increase.
Predictions for Gentrifying Neighborhoods for 2021
The model also predicts that there will eventually be an overall slowing of gentrification. Notably, the neighborhoods that underwent the most drastic change from 2001 to 2011 will show less change from 2011 to 2021. The model finds these neighborhood scores will grow higher over time, but the trend may not accelerate.
In the future, the research team hopes to reexamine their approach but with more timely data, such as real-time property prices through Zoopla (a real estate website, similar to Zillow in the U.S.) or even other cultural consumption markers through Twitter, to predict how and where gentrification will strike well ahead of time.
While events like Brexit and new transit infrastructure projects may alter the course of gentrification, it appears that tools like machine learning can help us understand not only whygentrification has occurred in the past, but also where it is most likely to occur in the future.
CityLab editorial fellow Claire Tran contributed research and editorial assistance to this article.
For all the talk about tech firms heading back downtown, Amazon chose two HQ2 locations more on the fringes of urban centers—Long Island City, Queens, New York (roughly three miles from Midtown Manhattan) and Crystal City, Virginia (about four miles from downtown D.C.) But these are not traditional suburbs by any stretch of the imagination. They are dense, have mixed uses, and are accessible by transit. The tendency for high-growth firms to locate in such places in both urban and suburban areas is the subject of a new study published in The Professional Geographer.
In the study, authors Emil Malizia, of the University of North Carolina at Chapel Hill, and Yasuyuki Motoyama, of the University of Kansas, argue that the debate over whether growing companies prefer cities or suburbs is oversimplified. Despite the recent shift of companies back to urban centers, more Americans continue to live and work in suburbs. And, for decades, suburban hubs like Silicon Valley and Route 128 outside of Boston were the preferred destination for the tech industry. The study examines the degree to which high-growth firms gravitate to more vibrant areas—defined as neighborhoods that are denser, more mixed-use, more transit-accessible, and more walkable—in both cities and suburbs.
Malizia and Motoyama identified firms based on Inc.’s yearly lists of the 5,000 fastest-growing privately-held firms, each taking in at least $2 million in revenue annually. Firms in the study qualified as “high-growth” because they grew by 20 percent each year for three years or had more than 72.8 percent compound growth over three years between 2007 and 2015. The study was not confined to particular industries, but those represented prominently in the sample included tech, advertising and marketing, business products and services, government services, and health.
The sample comprised roughly 6,000 high-growth firms across 30 selected U.S. metro areas (Malizia and Motoyama excluded New York and Los Angeles because those metros are so large that they might have skewed the results).They measured vibrancy according to characteristics such as density and compactness, diversity of land uses, walkability, and accessibility to transit, based on data from the EPA’s Smart Location Database (SLD).
The maps below show the locations of high-growth firms in four metros: Washington, D.C., Boston, Atlanta, and Phoenix.
Notice the cluster of firms in the center of the first map, of greater Washington, in and around downtown D.C. But dots also radiate outward along major highways into the Northern Virginia suburbs, especially Arlington, Alexandria, Falls Church, Tysons Corner, and Reston, and to the north in suburban Montgomery County, Maryland, around the National Institutes of Health in Bethesda up to Rockville and Gaithersburg.
Next, in the map of greater Boston, we again see a dense cluster of dots in Boston proper and in Cambridge (home to MIT and Harvard). Again, the dots radiate out into the western suburbs; then they arc north, along Interstate 95. Historically, high-tech firms in Massachusetts were located along the region’s Route 128, which runs concurrently with I-95 for some distance.
Now look at Atlanta. Its high-growth firms are spread out, extending well into the suburbs along major highways, as you might expect of a Sunbelt city. Yet there’s also a concentration of firms in more central urban neighborhoods, especially around Georgia Tech and downtown.
Finally, the map of Phoenix shows a pattern of dispersed dots and smaller clusters of high-growth firms, with less concentration in and around downtown.
Of the four metros in the study, Boston and Washington, D.C., were found to have the most vibrancy, and also the highest concentrations of high-growth firms in and around the urban center. Atlanta came out as a mixed bag. Phoenix has the lowest level of vibrancy, according to the study, and also has the most spread-out and sprawling pattern of high-growth firm location. That said, all four metros have large clusters of high-growth firms in suburban areas.
Importantly, the study found that vibrancy plays a key role in the location of high-growth firms in the suburbs as well as in and around the urban core. Growing companies do not just choose a location randomly or spread out anywhere along a highway corridor. Rather, they cluster and concentrate in the more vibrant parts of both urban and suburban areas.
Generally, high-growth firms are more likely to cluster in vibrant urban areas than in the suburbs. But those in the suburbs tend to locate in places that are significantly more vibrant than the suburban average. This is true even of suburban office parks: Those situated amid higher density, that offer more amenities and are better connected via transit, tend to contain more high-growth firms. As the authors put it, “high-growth firms cluster in employment centers that are more dense, diverse, walkable, and connected.”
The study adds to our understanding of the richness and complexity of metropolitan areas. America’s metros are not as simple as dense, amenity-rich downtown areas surrounded by faceless, car-oriented suburbs. They are patchworks of different kinds of urban and suburban communities, made up of urban cores, primary downtowns, secondary downtowns, denser inner suburbs, and all sorts of mixed, “in-between” places. Although vibrancy is highest in primary downtowns, secondary downtowns can also be quite vibrant. They are often home to key anchor institutions such as universities and hospitals and connected to the urban center via transit.
Instead of a simple breakpoint between dense cities and sprawling suburbs, our urban and suburban areas form a continuum, with vibrancy being the essential feature of their economic success.
CityLab editorial fellow Nicole Javorsky contributed research and editorial assistance to this article.
The gentrification of cities like New York, San Francisco, Boston, Seattle, and Washington, D.C. has soared in recent years, as the affluent and educated have poured back into them. These superstar cities and tech hubs are epicenters of the “new urban crisis” with high and worsening levels of income inequality, economic segregation, and increasingly unaffordable housing, all of which have disproportionate negative effects on the less advantaged.
But what is actually behind these shifts? One popular explanation is that it is tied to the concentration of high-paying professional jobs in the urban center. Knowledge workers need to be close to these jobs and networks to advance their careers. Others suggest it is driven by the desire of these groups to avoid lengthy car commutes and keep more time for working, leisure, or family. But a growing body of research by leading urban economists provides evidence that behind both the wealthy’s back-to-the-city movement and the spatial inequality it brings are the cluster of high-end amenities—like restaurants, theaters, concert halls, and other institutions that are uniquely available at the urban core of superstar cities.
That’s the big takeaway of a new working paper that examines the factors at work in the increasing spatial sorting of income groups and the growing divide that comes with it. Victor Couture and Cecile Gaubert of the University of California Berkeley, Jessie Handbury of the University of Chicago, and Erik Hurst of the University of Pennsylvania, look closely at the factors that have attracted the affluent and advantaged back to urban centers and the subsequent effects on economic inequality.
The study looks specifically at the role urban amenities play in this process, by using GPS-based location data from cell phones to identify trips to amenity establishments. The study defines the urban center as the census tracts closest to the city center that host ten percent of the total metro population. It examines the distribution and migration of households of different income levels into the urban center of the nation’s 100 largest metro areas from 1970 to 2013.
Gentrifying Tracts in Select Census Bureau Statistical Areas (CBSAs)
First off, take a look at the pattern of gentrification in the maps above which show the gentrifying neighborhoods of six cities: New York, Chicago, San Francisco, Boston, Philadelphia, and Las Vegas. The maps show the clustering of gentrification in and around the urban center of superstar cities and tech hubs like San Francisco; it’s far less concentrated in a less-educated, service-dominated city like Las Vegas.
Downtown Residential Propensity by Income
Next, look at who lives downtown. The chart above depicts a powerful “U-shaped” pattern of downtown residents—a bifurcation of rich and poor. Despite gentrification, the poor remain clustered in the urban core. Low-income families (defined as those earning less than $25,000) were about one and a half to two times more likely to live in the urban core. As incomes rise, families are more likely to live out in the suburbs. But once median family income crosses the $100,000 threshold, families start to locate back downtown, a pattern which grows as incomes rise past $200,000. This U-shaped pattern is not necessarily a new phenomenon, the study notes: It can be seen in 1970, 1990, and 2013, but has become more pronounced over time.
Researchers found that overall, a 10 percent increase in metro income was associated with a 13 percent increase in the share of affluent households living downtown. “While stable in prior years, the relationship between income and the share of households living downtown has become steeper for the richest households by 2013,” the study notes.The urbanization of the affluent is even more pronounced in economically successful metros that have experienced higher-than-average income growth.
The movement of the affluent back downtown has played a significant role in growing urban inequality, according to the study. This migration over the past several decades has compounded the widening gap between the rich and poor in urban areas. Between 1990 to 2013, the gap in incomes between families in the bottom and top 10 percent of the income distributionin large citiesgrew by 18 percentage points: the incomes of the bottom 10 percent fell by one percent, while those of the top 10 percent grew by 17 percent. Spatial sorting—the affluent moving downtown and low-income residents moving out of increasingly unaffordable areas—increased this gap by an additional 2.3 percentage points.
But what is driving the shift of the affluent back to the urban center and the growing inequality that comes with it? The study finds the answer in the unique cluster of high-end luxury amenities that are only available in the downtowns of large cities. Researchers found that as individuals get richer, they are more likely to move closer to such amenities. Residents who lived in more-expensive tracts with more college-educated cohorts were also more likely to visit high-quality amenities, compared to residents who lived in less-expensive tracts. Furthermore, those higher-income residents spent more money on those amenities, like paying for tickets to the theater, sporting events, and other entertainment.
It is access to these amenities—more so than the availability of good high-paying jobs or even reduced commute times—that may bedriving the rich back to the urban center. As the affluent and educated move back downtown to take advantage of this unique bundle of amenities, they drive housing prices up, which hits hardest on the least advantaged. As a consequence, these low-income residents are driven out of these increasingly expensive areas.
This conclusion is in line with research by Stanford economist Rebecca Diamond and my own researchwhich show how rising demand for limited urban space at the heart of superstar cities drives up land and housing prices, causing a sorting process that has the most negative effect on the least well-off. The study by Couture et al., notes that its findings “suggest that increases in the incomes of high-income individuals was a substantive contributor to increased urban neighborhood change during the last 25 years within the U.S. and that the neighborhood change resulting from the increased incomes of the rich did, in fact, make poorer residents worse off.”
The study considers the effectiveness of “anti-gentrification” policies that might seek to counter such mounting divides by raising taxes on upscale urban neighborhoods to subsidize more affordable housing for the less advantaged. It finds that while this policy mitigates urban gentrification to a degree, the ultimate effect of such policies may shift more affluent households back to the suburbs, worsening the economic and fiscal conditions of cities, while doing little to improve the economic conditions of poor urban residents.
Something clearly needs to be done. Superstar cities are already seeing a mounting backlash to increased gentrification and burgeoning class divides. People have a deep attachment to their neighborhoods and communities, and I have long argued that place is replacing the factory floor as the arena of class struggle. A growing number of cities have imposed inclusionary zoning initiatives, which essentially force developers to include a share of affordable units, for example around 20 percent, in their new projects. San Francisco has seen several unsuccessful efforts to ban tech companies from downtown neighborhoods, and the tax incentives offered to Amazon’s HQ2 have met with opposition from local activist groups.
Ifleft unaddressed, the economic divides within cities will only grow, leading to an even greater backlash against developers, tech companies, politicians, and the “urban elite,” which threatens not only urban revitalization, but is also likely to stall the very engine of innovation and economic progress.
It may be the one data point most commonly cited by urbanists: We live in a world that is over 50 percent urban. We crossed the majority threshold in 2008, and have since grown to 55 percent today. The world is projected to be 70 percent by 2050, and ultimately top out at around 85 percent or so a century from now.
But a new report released by researchers at the European Commission (EC) says the current level of global urbanization is much higher: It estimates the world to be 84 percent urban already. The EC research team, led by Lewis Dijkstra, used satellite images to assess the share of the world’s population that is urban. While traditional estimates from the United Nations and elsewhere find Asia to be 50 percent urban, the EC team’s analysis of satellite images finds it to be 90 percent; while the UN estimates Africa’s urban population at 40 percent, the EC research team finds it to be 80 percent.
The European Commission research team contends that these discrepancies stem from the ways the data is traditionallyreported. The 55 percent figure comes from data that is self-reported by nations. The rub is that different nations use different definitions to identify what is “urban.” According to the EC group, about half of countries define urban based on a minimum population size threshold—85 percent of countries use a threshold of 5,000 people or fewer but other countries have dramatically higher requirements, like Mali’s 30,000, Japan’s 50,000, or China’s 100,000. Only a few countries use population density as a measure of urbanization.
Other definitions are even more detailed and highly specific. India classifies places as urban if less than a quarter of working-age men work in agriculture. And in some nations, political considerations muddle definitions further. For example, in some countries, once a place is classified as urban, there are requirements that it must host certain facilities, like courthouses or police stations. Governments may therefore avoid classifying places as urban simply to avoid shelling out for these upgraded public services. Take the case of Egypt, which has said that it is 43 percent urban every year since 1986, despite significant urbanization since then.
But, at least one other leading team of urbanists is not so sure about the EC’s numbers. A report by Shlomo Angel, a leading expert in global urban expansion at New York University’s Marron Institute of Urban Management, finds the 84 percent figure to be far too high, and contends that the conventional 55 percent figure is more on target.
Angel believes the density thresholds the EC team used to interpret the satellite images end up incorrectly classifying broad swaths of rural farmland as urban. Angel and company further ask how 84 percent of the world’s population is urban, when 37 percent of the world’s labor force is employed in agriculture. Angel believes the most effective way to gauge urbanization is not through population or density per se, but by looking at contiguous built-up areas of 100,000 or more people. The group plans to create its own new estimates of global urbanization in the future.
The EC team sees things somewhat differently. Dijkstra believes that Angel and company’s method of looking at built-up areas is skewed toward richer, more developed countries. “The amount of built-up area per capita is much higher in cities in developed countries than in less-developed countries, by a factor of 5 to 10,” Dijkstra wrote in an email. “As a result, this method has a big, built-in, rich country bias.”
The way we estimate global urbanization is not just a debate among researchers, it has huge import for strategies and policies we will use to address urban challenges in the future. If the world is 55 percent urban, the challenge may be to build new and better cities to accommodate the billions of new urbanites, as well as to retrofit and upgrade existing ones. If the world is already almost 85 percent urban, building new cities would seem less efficacious and the priority would fall to upgrading existing urban centers and settlements where people are.
To my mind, this debate illustrates the pressing need to develop better, more robust, more comparable, and more systematic data on cities and urban areas across the world, something I have pressed for for a long time. As an urbanist, it is troubling to me that our existing science cannot identify whether the world is 55 percent or 84 percent urban. This is a very, very large difference. I spoke to a wide range of experts on global urbanization about this discrepancy. They all believe we lack the data to make an accurate assessment to resolve this debate, and thus are unwilling to take sides. They suggested that we need new and better data, and that regardless, we should continue to develop strategies for upgrading and densifying existing cities and metro areas and for building new ones.
The research, data, and urban science we have on the world’s cities and urban areas is troublingly inadequate. If building better, more resilient, more sustainable, and more prosperous cities is key to our future global well-being, it is critical that we do muchbetter.
CityLab editorial fellow Claire Tran contributed research and editorial assistance to this article.
Class remans a signature axis of American life. Writing here last week, I outlined the great “class inversion” of American politics as the locations of the working class have turned red, while the locations of the more advantaged creative class trend blue. But the geography of the service class—which is the largest class, comprising more than 45 percent of American workers—remains up for grabs.
This week, I take a look at the political attitudes of these three major classes. My colleague at the University of Toronto, Patrick Adler, used recent data from panels of the General Social Survey (GSS), a broad survey of America’s attitudes on a wide range of issues. He compared the attitudes of these three classes on issues such as immigration, women’s rights, gay rights, abortion, and unionization. The results show that the service class lines up more closely with the creative class on some issues and with the working class on others.
Generally speaking, the service class tends to align with the creative class on certain cultural issues, including gun control, one of the most controversial issues in American politics today. In fact, a slightly higher share of service-class members support gun permits than their creative-class counterparts. That said, the service class breaks with the creative class on abortion rights. Just 37 percent of its members support a woman’s right to abortion under any circumstances, which is closer to the one-third or so of working-class members who do, and quite a bit less than the more than half of the creative class who support abortion rights.
The service class also lines up with the creative class on gay rights. Over half of service-class members believe gay people should have the right to marry. That is less than the 60-plus percent of creative-class members who say so, but quite a bit more than the 38 percent of the working class who agree.
But the service class tends to align more closely with the working class on bread-and-butter economic issues, like unionization and protection of workers’ interests and rights.
Nearly half of service-class respondents say that “workers need strong trade unions to protect their interests”—about the same as members of the working class, and considerably more than the 38 percent of the creative class that agrees. This is likely related to the fact that most managers are in the creative class.
The service class is out in front on three issues. It’s most likely to say that employers should hire and promote women. This is likely because the service class is disproportionately made up of women. It’s also the class most likely to say that society is “spending too little on childcare.” This probably reflects that members of the service class have lower incomes and a relatively high percentage of female-headed families.
That said, the service class is the least positive on the benefits of immigration, perhaps because immigrants are most likely to compete for service-class jobs. Still, just over half the members of the service class say that “immigrants are good for America.”
In a previous post, I made the case that the Democrats should rethink their strategy of trying to win back the blue-collar working class and its states and instead try to build a new cross-class coalition of the creative and service classes. As I noted, Democratic strategist Stanley Greenberg says it’s time the Democrats begin to think in such terms.
The takeaway for Democrats looking to appeal to those in the service class would be that they should focus their message on social issues and access to the labor market for women and families, opportunity for working women, the availability and affordability of childcare, more affordable housing, and greater assistance to cities.
CityLab editorial fellow Nicole Javorsky contributed research and editorial assistance to this article.
We typically divide the electoral map into red and blue states, and class is a feature, if not the key feature, in that divide. What we are witnessing is nothing less than a great inversion of America’s political geography. Dating back to FDR and the New Deal, the blue-collar working class once provided the backbone of the Democratic electorate, but today, states with larger working-class populations have swung solidly into the Republican camp. And, blue states have become those where the knowledge, professionals, and cultural workers that make up the creative class predominate.
That’s the key takeaway from an analysis of the connection between class and American politics I conducted with Patrick Adler and Charlotta Mellander. Our analysis looked at the role of class (defined as the kinds of work people do) and voting in the last three presidential elections.
We looked at the correlations between the share of workers that make up the three major classes—the blue-collar working class, the knowledge-based creative class, and the even larger service class—and state voting patterns. The table below details the top states (including Washington, D.C.) with the largest share of the three major classes.
Top Five States With the Highest Share of Workers From Each Class
Then, we drilled down further examining the correlations between state-by-state voting patterns and the 22 major occupational groups, and the more than 800 individual occupations that make up these classes. As usual, I note that correlation does not mean causation, but only points to associations between variables. Still, the patterns we document suggest the powerful role of class in defining America’s political geography.
States with larger working-class populations positively correlated with voting for Republican presidents. This correlation saw a sizable jump in 2012 and increased again in 2016, but more modestly. This is the culmination of a long-running shift, first identified in the 1970s, by Republican strategist Kevin Phillip’s identification of the so-called “silent majority” of socially conservative blue-collar voters.
How Occupational Class Has Correlated With Vote in Presidential Elections
State-Level Occupational Class
Creative classes correlate with voting for Democratic presidents across the three most recent presidential elections. Again, we see a big jump in 2012 and a smaller one in 2016. These correlations are similar in strength to other markers of class, like education and income. More affluent states with greater shares of college graduates skew blue, while less economically advantaged states with less-educated populations trend red. This blue, creative-class pattern is in line with John Judis and Ruy Texiera’s idea about the increasingly liberal orientation of “ideopolis” cities in which knowledge workers cluster.
But the pattern for service-class locations is more mixed. The service class is the largest class by far, composed of more than 70 million members—more than 45 percent of the workforce—whose members toil in low-wage, precarious work in retail shops, office work, and food service. States with greater shares of service-class workers lean slightly Democratic, but not nearly to the degree creative-class heavy states fall into the Democratic camp or working-class heavy states line up for the Republicans.
In the 2016 election, for example, the service class of the workforce was much more modestly correlated with Clinton support and more modestly negatively correlated with Trump support. Part of the reason is that service-class jobs are more spread out across the nation, and part of it is that service-class jobs tend to cluster alongside professional and knowledge-based jobs in larger cities and metro areas.
How Occupational Composition Correlated With Political Vote in the 2016 Presidential Election
Business & finance
Arts, design, entertainment, & media
Computers & math
Life, physical, & social science
Community & social services
Architecture & engineering
Education, training, & library
Building & grounds cleaning
Personal care & service
Food preparation & service
Farming, fishing, & forestry
Office & administrative support
Construction & extraction
Transportation & material moving
Installation, maintenance, & repair
America’s class-based political geography comes into sharper view when we look at the 22 major occupational groups that make up these classes. States with large shares of working-class occupations like installation; maintenance and repair; and construction and extraction are solidly red. Interestingly, the occupations which are most closely connected to blue states are among the very highest paying professions, such as business and finance, followed by arts, design, entertainment and media; and computers and math occupations.
Locations with larger shares of working-class occupations again show up solidly in the Republican party. But now a couple of interesting cross-class patterns become apparent. Two service-class occupational geographies line up more modestly in the Democratic column. States with larger shares of higher wage, more unionized occupations like protective services and community and social service occupations, and states with lower-wage, less-unionized jobs like healthcare support and personal care and service occupations, both trend blue. And there is also one creative-class geography that lines up red: doctors and healthcare practitioners. This may reflect their opposition to Obama’s healthcare reforms.
Our class-based political geography becomes even more interesting when we zero in on the more than 800 specific occupations that comprise the U.S. economy. The red state pattern is relatively straightforward. Support for Trump is highly correlated with the state-wide share of blue-collar working class occupations like welders, tractor trailer drivers, bus and truck mechanics, and so on. But Trump support is also correlated with larger shares of service class occupations like cafeteria cooks, parts salespeople, and tellers. Only a few creative-class occupations, such as radiologic technologists and occupational health and safety technicians, correlate with Trump support.
Occupations Most Correlated With Trump Votes
Welders, Cutters, Solderers, and Brazers
Heavy and Tractor-Trailer Truck Drivers
Cooks, Institution and Cafeteria
Industrial Machinery Mechanics
Welding, Soldering, and Brazing Machine Setters, Operators, and Tenders
Tire Repairers and Changers
Bus and Truck Mechanics and Diesel Engine Specialists
Water and Wastewater Treatment Plant and System Operators
Electrical Power-Line Installers and Repairers
Morticians, Undertakers, and Funeral Directors
First-Line Supervisors of Mechanics, Installers, and Repairers
Chemical Equipment Operators and Tenders
Maintenance Workers, Machinery
Electric Motor, Power Tool, and Related Repairers
Occupational Health and Safety Technicians
Meter Readers, Utilities
The pattern for blue states is a bit more mixed. Clinton support was highly correlated with creative class occupations like medical scientists, market researchers, lawyers, and computer and information system managers. But Clinton support also correlated with some service-class occupations like manicurists/pedicurists and preschool teachers. The only working class occupations to be correlated with the Clinton vote are bus drivers and other transit workers.
Occupations Most Correlated with Clinton Votes
Manicurists and Pedicurists
Medical Scientists, Except Epidemiologists
Preschool Teachers, Except Special Education
Parking Lot Attendants
Bus Drivers, Transit and Intercity
Market Research Analysts and Marketing Specialists
Self-Enrichment Education Teachers
Computer and Information Systems Managers
Public Relations and Fundraising Managers
Producers and Directors
Computer Systems Analysts
Financial Specialists, All Other
Software Developers, Systems Software
Personal Financial Advisors
Architects, Except Landscape and Naval
It appears that service-class geographies are most up for grabs politically. Indeed, as states with a large working-class share have largely abandoned it, the Democratic party’s future would seem to lie in a cross-class coalition of the service and creative class areas and voters.
Job categories like retail sales, customer service, personal-care aides, maids and housekeepers, food service workers and more employ millions upon millions of Americans. These jobs are disproportionately held by women, immigrants, and people of color. These are precisely the kinds of occupations and workers that could be galvanized into a Democratic coalition by policies aimed at higher minimum wages, job upgrading, affordable housing, accessible and affordable healthcare, protecting immigrant and minority rights, and a more robust social safety net for less advantaged groups.
The Largest Occupations Not Correlated With Vote Share ( Most Ubiquitous Occupations)
Customer Service Representatives
Secretaries and Administrative Assistants, Except Legal, Medical, and Executive
General and Operations Managers
Stock Clerks and Order Fillers
Personal Care Aides
Secondary School Teachers, Except Special and Career/Technical Education
Middle School Teachers, Except Special and Career/Technical Education
Packaging and Filling Machine Operators and Tenders
Medical and Health Services Managers
Sales Representatives, Wholesale and Manufacturing, Technical and Scientific Products
Production, Planning, and Expediting Clerks
Child, Family, and School Social Workers
Heating, Air Conditioning, and Refrigeration Mechanics and Installers
Educational, Guidance, School, and Vocational Counselors
Forging such cross-class coalitions is an idea that is making headway among some Democratic strategists. Political consultant Stanley Greenberg has pointed to the advantages of using occupation, as opposed to educational level, as a basic building block of a new Democratic electoral coalition. “For the first time, we are asking occupation to try to get at this—and so, I think there really is potential for Democrats to gain here,” he told the New York Times.
When was the last time you heard a major Democratic politician talk about the day-to-day struggles of retail workers, clerical workers, personal care workers, nurses’ aides, orderlies, or bartenders in the same way they talk about the struggles of auto workers or steel workers? Maybe it’s time they should.
CityLab editorial fellow Claire Tran contributed research and editorial assistance to this article.
José Andrés is one of America’s most famous celebrity chefs, but he does more than appear on the Food Network and morning shows (and spar with President Trump). He works to alleviate the toll of disasters such as Hurricane Maria, which devastated Puerto Rico last year, and the more recent storms Florence and Michael. In his book We Fed an Island(co-authored by Richard Wolffe), Andrés recounts how he and hundreds of volunteers made home-cooked meals for Puerto Ricans after Maria struck.
Andrés’s nonprofit, World Central Kitchen, served more than 3 million meals on the island. As the New York Timesnoted in October 2017, “No other single agency—not the Red Cross, the Salvation Army nor any government entity—has fed more people freshly cooked food since the hurricane, or done it in such a nurturing way.” In February, Andrés was named Humanitarian of the Year by the James Beard Foundation.
I corresponded with Andrés via email and asked him about the relationship between food and place, his “recipe” for building better recoveries after disasters, his start in Spain, and his move to D.C. (a city that he, more than anyone else, put on the culinary map). Our conversation has been lightly edited.
Before we get to your work in Puerto Rico, let’s start with a few questions about food and place. You trained under Ferran Adrià at the restaurant El Bulli [in Spain’s Catalonia region]. How did Catalonia shape your approach to cuisine and life?
I grew up in Catalonia, just outside of Barcelona. It’s such a beautiful place. It has its own unique history and culture, and it is located at a crossroads between many different cultures—Castilian, Valencian, Basque, French, Italian—which make it a deeply diverse, rich region. And there is something in the water there that inspires creativity. Just think of all the amazing artists and thinkers, like Ferran, but also Salvador Dalí, Antoni Gaudí, Joan Miró.
Eventually, you moved to Washington, D.C., and established two of your first restaurants in revitalizing neighborhoods: the original Café Atlántico in Adams Morgan and Jaleo in Penn Quarter. Tell us about your connection to Washington, D.C., and its neighborhoods.
I have loved the city of Washington since I moved here in the early 1990s. It had some amazing stars in the food world, even back then, and its location between Maryland, Virginia, and Pennsylvania makes it perfect for the best products. Back then, Penn Quarter and Adams Morgan, maybe they were not a lot to look at, but there was so much potential … which we have seen over the last 25 years.
What motivated you to establish World Central Kitchen to help with disaster relief?
My original inspiration was going to Haiti after the earthquake in 2010. The capital and much of the country were absolutely destroyed. But we started to see what it would mean to build local capacity, to teach the next generation of cooks and bakers, to build school kitchens. It would mean more strength against future disasters, healthier communities, a brighter future.
What role does food play in helping places rebuild after disasters?
Food has a vital role in helping people rebuild—good food is so important to make a person feel human, understood, appreciated, especially in the times of most need. After a disaster, many people who do not have a roof over their head, they might not feel like anyone cares—especially when they are given a sterile packaged meal. But a warm plate of food, that really does help people feel hope again.
You write in your book that after two days of trying to understand the situation in Puerto Rico from afar, you knew you had to take the first flight out there. Can you elaborate on what was going through your mind when you decided to go there in the immediate aftermath of Maria?
It was not a difficult decision to make for me … it was a question of, “How can I help? What can I do?” I am a cook—have trained my whole life to just act, get in the kitchen, start cooking. I knew that my team from World Central Kitchen could activate kitchens on the island, find cooks and volunteers, locate ingredients, and begin cooking.
When you were providing meals after Maria, what did you learn about the state of disaster-relief efforts? What problems did you notice?
The biggest problem to me is bringing outside supplies—like when FEMA sent military meals from the mainland to try to feed people. Look, if I’m trying to get people back on their feet, and the economy working again, but then I dump outside resources and packaged food into the situation, what am I doing? This top-down mentality doesn’t think about the local community and its needs.
What would a better system of disaster relief look like? How can communities and their people become more resilient?
We really need this new model of disaster relief in which we are focusing on building up local capacity. The goal should be to get the local economy back up and running as soon after a disaster as possible, and not keep people relying on outside support. Strengthening the capacity of local farmers and producers can help them recover more quickly after a disaster.
CityLab editorial fellow Nicole Javorsky contributed research and editorial assistance to this article.
By selecting the New York and Washington, D.C., metros, Amazon’s HQ2 process is a telling reminder of the growing gap between a few superstar cities and the rest of the nation. A new Brookings Institution study released today documents this growing divide. The analysis by Clara Hendrickson, Mark Muro, and William Galston shows the deepening economic divergence and worsening spatial inequality that carves America into two separate and distinct nations, shaping the populist backlash. The researchers offer a series of strategies for addressing the gap and beginning to knit the nation back together.
The nation’s 53 large metros (those with more than 1 million people) represent just 2 percent of all places across America, yet accounted for nearly three-quarters of employment growth since the economic crash of 2008. Meanwhile, smaller places across the nation have fallen further and further behind. Since 2010, the more than 200,000 small towns and rural “micro” communities have seen negative economic growth. As the study notes, “nearly a decade after the Great Recession, the outlook for the places that have been left behind appears dim. Employment in many non-metropolitan places remains below its pre-recession level while the longer-term patterns of growth and divergence remain somber.”
Thecharts below, from the study, show the broad trend. The first shows three trend lines, comparing the economic performance of the median metro to the top 2 percent of metros and the bottom third of metros. From 1969 to 1980, all three kinds grew in lockstep for both average annual wages and employment level. Then in the late 1980s, the dark blue line, representing the top 2 percent of metros, begins to pull away from the rest, with the gap growing ever wider in the 2000s and especially in the wake of the economic crisis.
The next chart shows the change in employment since the 2008 recession in large, medium-size, and small metros, and micro areas including rural communities that are both adjacent and not adjacent to metro areas. Large metros have seen the biggest gains, followed by medium-size and small metros. Micro areas, including rural communities that are both adjacent and non-adjacent to metros, have seen negative employment growth over that time.
Still, it’s worth recognizing the considerable variation in economic performance within each of these types of places. As my recent series on the myths and realities of America’s urban and rural divide shows, our economic divides are fractal and cut across all sizes and types of places. A subset of large urban areas is thriving, but many are faltering. Likewise, a subset of smaller rural places—those with universities or knowledge institutions like federal R&D labs, vibrant arts and cultural scenes, or amenities such as outdoor attractions—are doing quite well, even as many others struggle to survive.
That said, the nation’s worsening economic divergence is the consequence of several key factors and forces. One is deindustrialization, which decimated the economies of many industrial places. Another is globalization, which means that many goods and services are now produced off-shore in specialized global production centers. There are also increasing returns to knowledge and skills, so-called “skill-biased technological changes,” which confer outsize economic benefits to more highly educated and skilled groups and places. And of course, innovative, knowledge-based industries require dense clustering in cities and urban areas. Add to this the ways that public policies like deregulation and lax antitrust enforcement have contributed to the growing gaps between places—all of these elements combined shape deepening spatial inequality, or what I call “winner-take-all urbanism.”
Such growing spatial inequality has registered itself in a deepening political divide. Large, thriving superstar cities and tech hubs have turned politically blue, while lagging smaller and medium-sized places have gone red. As Muro, one of the authors of the report, notes in a recent blog post he co-authored with Jacob Whiton, the Democratic seats in the new House of Representatives add up to more than 60 percent of the economy, while the Republican seats represent 38 percent of it. Or as my own research shows, Hillary Clinton won 56 percent of the vote in metros with one million or more people, while Donald Trump won 57 percent of the vote in those with less than 250,000 people.
And the role of population size, density, and knowledge has only increased in importance in presidential elections since 2008. Echoing themes much discussed here at CityLab, the report notes: “During the postwar period, there was no correlation between regions’ population density and their voting patterns. Today, electoral preferences map almost perfectly onto a region’s density.”
To mitigate such growing spatial inequality and start knitting the country back together, the report recommends a series of “place-sensitive” economic strategies and policies. It strikes a useful and important middle ground between the traditional divide between people-based and place-based policies. Here, it follows the lead of economic geographers Simona Iammarino, Andrés Rodríguez-Pose, and Michael Storper, who advocate for a more balanced approach to “distributed development.” While it recognizes that innovation requires clustering in superstar cities and tech hubs, it seeks to stimulate economic development in outlying places by investing in local skills and capabilities, strengthening connections between local universities and industries, and connecting less-advantaged places with thriving ones.
This is similar to the concept of “smart specialization” developed by regional strategists at the European Commission, which seeks to identify unique economic niches for the less advantaged. The Brookings report hits hard at wasteful and ineffective strategies, such as the big incentives packages like those offered to Foxconn, that divert billions of dollars that could otherwise be used to upgrade local skills and capabilities.
While it is useful and important to develop strategies to upgrade lagging places, the reality is that it may be impossible to mitigate these forces and create the kind of economic convergence of the 1950s and 1960s, a period which economists like Paul Krugman call the Great Moderation. That kind of convergence was the product of America’s extreme economic hegemony and of an industrial system which spread its benefits widely across the economic landscape to places small, urban, suburban, and rural alike.
Left to its own devices, America is becoming more geographically unequal, and politically, it is tearing itself apart.Perhaps for today’s economy, the more efficacious approach would be to recognize our differences and move to a more distributed, decentralized, and devolved system. The goal is to enable smaller places to invest in their own unique economic opportunities and ultimately coexist, despite their economic and political differences.
CityLab editorial fellow Claire Tran contributed research and editorial assistance to this article.
When Amazon announced this week that it would locate HQ2 in both New York City and Arlington, Virginia, major media outlets published articlescomplaining that big tech was reinforcing the growing gap between coastal superstar cities and left-behind cities in the middle of the country. Sure, the company announced a smaller facility in Nashville, but that hasn’t muted the outcry of criticism, fueled by nearly $3 billion in taxpayer-funded incentives—one of the biggest incentive hauls in modern memory.
This is all as predicted: A chorus of urbanists including myself have said that the Amazon HQ2 was never about a single HQ2, but rather about crowdsourcing data on sites, talent pools, and local incentives, for future sites.For these and other reasons, since the announcement nearly every media story about Amazon has been negative,as the Washington Post’s Jonathan O’Connell tweeted.Local politicians and activists groups are already speaking out about how Amazon is taking its “winners” to the proverbial cleaners. A growing chorus of pundits and commentators are also calling on Amazon and other corporations to shift their operations away from coastal superstar cities and spur more development in the heartland.
I’m the first to complain about the geographic inequality and winner-take-all urbanism of big tech and the broader tech economy. But a new database of corporate headquarters compiled by my colleague Patrick Adler and our University of Toronto School of Cities research team suggests that America’s corporate headquarters are actually spread much farther and wider across the economic landscape than is big tech. Our ongoing research projects tracks the location and changing economic geography of Fortune 500 headquarters for the past four decades from 1975 to the present. Our research has one caveat: it excludes service firms, because back in 1975, they were not included in Fortune’s list.
New York City may seem like a big winner with Amazon, and Google is expanding its footprint there too. But New York has actually seen a substantial decline in its corporate headquarters since 1975. While it is true that coastal tech hubs like San Francisco, Seattle, and Washington, D.C., have all gained headquarters in recent decades, so have Sunbelt cities like Houston, Dallas, Atlanta, and Miami. Headquarters remain sprinkled across large and small places in the heartland, like tiny Bentonville, Arkansas, where Walmart is quietly building its new “home office.”
The map paints the picture, showing the total change in headquarters from 1975 to 2017 in the top 28 cities. While New York has the largest number of corporate headquarters, that number has fallen from 84 in 1975 to 70 in 2017, a decline of 17 percent. Los Angeles, America’s second largest metro, saw a similar 17 percent decline. And Chicago, the nation’s third largest metro, saw an even bigger decline, 28 percent, losing 19 headquarters since 1975. Despite picking up General Electric, Boston saw a 21 percent decline in its Fortune 500 headquarters between 1975 and 2017.
Big tech has made the San Francisco Bay Area, which spans the San Francisco and San José metros, the nation’s second largest center of corporate headquarters with 35, compared to Chicago’s 33. Indeed, the Bay Area increasingly looks like America’s new “Second City,” at least based on headquarters, a testament to the power of high-tech in the U.S. economy.
Still, other places across the country have posted even faster rates of growth. Out west, Denver and Seattle each added eight headquarters for a 400 percent rate of growth, and San Antonio also saw a 400 percent increase. Washington, D.C., added 13 new headquarters, for a growth rate of better than 300 percent.
The Sunbelt has also seen substantial growth as a headquarters locale. Dallas and Houston rank third and fourth, with 22 and 20 respectively, each posting a 60 percent-plus growth rate. Miami, which has solidified its position as the economic and financial center for Latin America, has seen 200 percent growth in its headquarters, while Atlanta and Nashville have posted 50 percent growth since 1975.
Don’t write off the Rustbelt as a headquarters location: In addition to Chicago’s large headquarters concentration, cities like Minneapolis, Detroit, St. Louis, Pittsburgh, Cleveland, Cincinnati, Columbus, and Milwaukee still number among the nation’s leading centers for corporate headquarters, despite many of them seeing large drops in their numbers.
America’s headquarters geography reflects the substantial variation and specialization of the U.S. economy. New York leads in finance and business services, consumer services, and goods and materials. But Houston leads in energy, San Jose in tech, and Chicago in retail and wholesale. Chicago also ranks second in consumer services, and goods and materials, and Dallas takes third in energy. Other cities like Nashville and Minneapolis take third in consumer services, and goods and materials, respectively.
Share of Fortune 500 Headquarters By Industry
Finance and Business Services
Goods and Materials
Retail and Wholesale
This variegated pattern comes through even more clearly when we look at the location of corporate headquarters across America’s eight major mega-regions. Not surprisingly, the “Bos-Wash” mega-region, the largest in the United States, leads with 137 headquarters, though that number is down 19 percent since 1975.
As for the Rustbelt: Its great “Chi-Pitts” mega-region, stretching from Chicago through Detroit to Pittsburgh is second, with 89 headquarters, though it is also down since 1975.
The Texas Triangle of Dallas, Houston, and Austin takes third place with 53, up substantially since 1975. The Northern California mega-region around the Bay Area is next, followed by Char-lanta in the Sunbelt, SoCal which stretches from Los Angeles to San Diego, Cascadia (Seattle and Portland), and So-Flo (Miami, Orlando, and Tampa) which tripled its number of corporate headquarters.
Change In Share of Fortune 500 Headquarters By Mega-Region
So, let’s save the calls to force big tech to move to the heartland or break up with the big coastal superstar cities. Tech and innovation are the sectors of the economy that most require clustering and agglomeration, and economists like Enrico Morretti and David Albouy show us that America would actually be far more productive in tech if its big cities were even bigger.
On the other hand, America is a big, spread-out economy with large clusters of headquarters in Houston, Dallas, Miami, and Atlanta in the Sunbelt; in Chicago, Detroit, Minneapolis, and Pittsburgh, and in smaller places as well. Compared to just about any other nation, we have many different kinds of cities. America’s five largest metro areas generate about a quarter of our economic output. Compare that to Canada, whose five largest metros generate 50 percent, London alone generates a third of British GDP and Seoul half of South Korea’s.
What’s even more valuable than giant companies like Amazon are the many cities, across the nation, that create them. Let’s use the money that is being wasted on incentives to help build up our cities, our truly greatest resource, instead.
CityLab editorial fellow Claire Tran contributed research and editorial assistance to this article.
When word got out that Amazon is going to split its HQ2 between New York City and greater Washington, D.C., as the company made official today, many saw it as evidence of the widening gap between America’s coastal superstar cities and the heartland. Amazon is expanding from Seattle, a successful tech hub on the West Coast, to the world’s leading global city (New York) and the capital of the most powerful nation on Earth (Washington), both of which sit on the East Coast Acela corridor, equivalent to one of the world’s largest economies. With Google planning yet another expansion in New York, here is another example of winner-take-all urbanism.
While that is certainly true, there are signs that some big tech firms are expanding away from the San Francisco Bay Area, long the dominant player in tech, to smaller and non-coastal places.
The maps below—from a new report by commercial real estate research firm CBRE on recent office expansions of big tech firms outside their home markets—shed light on the complex, evolving geography of big tech.
The first map shows the pattern for Amazon’s home city, Seattle. Big tech firms based there mainly expanded in three other leading tech cities: San Francisco, New York, and Boston. (Interestingly, not greater Washington, D.C., where Amazon will locate a piece of its HQ2.)
Now look at the pattern for big tech firms headquartered in Boston. Their biggest expansions are in San Francisco, followed by New York and Seattle.
Next, consider New York, the nation’s largest headquarters city by far, where half of HQ2 is going. Its big tech firms are expanding in the San Francisco Bay Area, but also in HQ2 losers Austin and Chicago.
The most illuminating map is the one of the San Francisco Bay Area itself. For one thing, there are a lot more expansions outside of home base. Bay Area tech firms are expanding in New York, taking on considerable footprints in Washington, D.C., and Boston, establishing a big presence in L.A. and Southern California, and even more so in the Pacific Northwest.
But San Francisco’s big tech companies are also setting up shop in less obvious cities, such as Phoenix, Chicago, Denver, and Austin. This is likely a reflection of San Francisco’s metastasizing new urban crisis of high housing prices, escalating office rents, increasing competition for talent, and growing inequality.
Big tech may reflect a winner-take-all geography, but some changes are afoot. Take a look at the chart above, which compares high-tech talent to office-market strength in the 30 leading tech hubs in the U.S. and Canada.
The far-right corner shows “Growth Leaders.” Here we find the established tech hubs of Silicon Valley and Seattle, and the smaller hubs of Austin, Raleigh-Durham, Denver, and Portland. None of America’s larger superstar cities make this category. San Francisco is listed in the lower right-hand quadrant labeled “High Potential,” along with Toronto, Montreal, Indianapolis, and St. Louis.
Leading superstar cities (New York, Boston, and D.C.) appear in the bottom-left quadrant, “Lagging,” along with Dallas, Chicago, Philadelphia, and Pittsburgh. In the “Emerging” quadrant above that is Nashville—where Amazon just announced it is opening a center for excellence for operations, with 5,000 jobs.
While it is true that the geography of big tech reflects a winner-take-all pattern, not everything follows the shift of Amazon and Google to East Coast superstar cities. Indeed, tech in San Francisco may be reaching a saturation point as many of its big firms appear to be heading elsewhere, including some smaller, “rise-of-the-rest” locations. I’ll pick up on this theme in a future post where I look at the trend in headquarters locations for Fortune 500 firms.
CityLab editorial fellow Nicole Javorsky contributed research and editorial assistance to this article.
It is rumored that Amazon will split its new HQ2 between Crystal City in the greater Washington, D.C., metro and Long Island City in New York. While the specific locations may come as a surprise, many urbanists, including myself, have been saying all along that this was never about a single HQ2, but instead about Amazon crowdsourcing information for a host of different things in different cities, like a new research hub in Pittsburgh, a major logistics facility in Indianapolis, or a Latin America headquarters in Miami.
I predicted Amazon would select D.C. back in September 2017 when the original request for proposals was issued, citing the region’s exceptional talent base and quality of life, its location in the East-Coast power corridor, and the fact that large-scale investment and tens of thousands of jobs in the nation’s capital might help mitigate any push for antitrust regulation, not to mention Jeff Bezos’ ownership of the Washington Post and a $20 million mansion.
When I interviewed Scott Galloway, the business analyst and expert on Amazon for CityLab back in November, he predicted New York. “Three possibilities: New York, New York, and New York. This entire bidding thing is a ruse. The most important thing for them is the ability to attract and retain the best talent in the world. And the best young tech talent in the world wants to live in either New York or San Francisco. Every other city is a distant third.”
Splitting it between the two cities is especially clever. For one, it gives Amazon a location in the nation’s and the world’s most important global city, New York—the foremost place for high-level business, management, financial, and marketing talent; and a second location in the deep-talent pool that is the nation’s capital. Both cities are part of a large mega-region and connected by frequent train and air service. Not to mention having two finalists gives Amazon the opportunity to continue to negotiate a better deal and more incentives, but also to mitigate any backlash by threatening to move all or part of its headquarters to the other city.
New York and Washington, D.C, play an important role in the analysis of the changing geography of corporate headquarters that my colleague, Patrick Adler at the University of Toronto’s School of Cities and the Rotman School of Management, and I have been conducting with our research team. Our project has put together detailed data on the location of Fortune 500 companies from 1975 to the present.
Check out the table below, which shows the leading cities for corporate headquarter locations today. New York tops the list with 70 corporate headquarters, more than double the amount of the next leading city. Chicago, Dallas, and Houston are next. The Bay Area, including San Francisco and San Jose, are also high up the list. Despite its reputation as a government town, Washington, D.C., ranks sixth.
Top Ten US Cities for Fortune 500 Headquarters
Change Since 1975
D.C. has seen explosive gains as a headquarters town over the past several decades, adding 13 corporate headquarters since 1975, a whopping growth rate of more than 300 percent. Only the Bay Area (both San Francisco and San Jose) gained more over that period, adding 23 headquarters but only at a 200 percent growth rate. In picking New York and greater D.C., Amazon picked the single largest headquarters city and one of the fastest growing locations for headquarters.
Amazon’s split decision makes even more sense when you realize New York and D.C. are part of the same mega-region, the greater Boston-New York-Washington, or Bos-Wash. This is the largest mega-region in North America, with more than 50 million people and $2 trillion in economic output. It is also far and away home to the most Fortune 500 headquarters, with 137, more than a quarter of the total, as the table below shows. Chi-Pitts is a distant second, followed by the Texas Triangle of Dallas, Houston, and Austin; Northern California; and Char-lanta. The Pacific Northwest region of Cascadia and southern Florida (Miami, Orlando, and Tampa) both pull in last, with 11 and six headquarters respectively.
Top Mega-Regions for Fortune 500 Headquarters in 2017
When all is said and done, splitting Amazon HQ2 between New York and D.C. is a telling case of the big getting bigger and the rich getting richer. In fact, headquarters location is itself a reflection of our lopsided winner-take-all urbanism, with the top five cities accounting for a third of all Fortune 500 headquarters and half of their profits, and the top ten leading cities accounting for half of all headquarters and two-thirds of their profits.
Portion of Revenues and Profits By Top Headquarters in 2017
Amazon’s HQ2 search has been filled with twists and turns. Through it all, Amazon has played cities like a fiddle, crowdsourcing reams of information and compiling what is likely North America’s best site-selection database. It has compelled a competition on its own terms.
But America’s mayors and governors—including leading progressive politicians in blue states and cities—are the bigger culprits: Instead of standing firm or banding together, they’ve offered up hundreds of millions and in some cases billions, of taxpayer dollars. This money could be used to fight poverty, improve schools, or build affordable housing instead of being placed in the hands of a trillion-dollar corporation.
About the only hope left is that Amazon wakes up and does the right thing. Realizing that accepting such excessive public funds may create a backlash that could cost its brand dearly, Amazon could reject incentives andpledge with its new and old headquarter cities to address pressing issues and challenges like transit, homelessness, and housing affordability. The gains to its brand would far outstretch the actual monetary value of any incentives offered by the cities which, relatively, scarcely add to Amazon’s massive value and profits, anyway.
The alternative is hard to ponder. Not only will Amazon’s acceptance of such a huge incentive package mortgage away the “winner’s” future, it will unleash a devastating cycle of more competitions and even bigger incentives packages in the future.
CityLab editorial fellow Claire Tran contributed research and editorial assistance to this article.
In the popular imagination, cities are for young, educated single people, who flock there after college seeking fun, other singles, and more abundant job opportunities. There’s even a term for this back-to-the-city movement: “youthification.” Once the singles are married off and kids enter the picture, the popular narrative goes, their priorities change, and these same folks head out to the suburbs for more space, bigger back yards, and better schools.
But in fact, “power couples” are a big factor in the back-to-the-city movement as well. The phrase brings to mind celebrity couples like Beyoncé and Jay-Z, Kim and Kanye, and J-Lo and A-Rod (or some other pair bestowed with a funny moniker). Among researchers, “power couples” refers to a broader phenomenon of well-educated, high-earning pairs, typically defined as couples where both partners hold a college degree. Power couples are associated with the concept of assortative mating, which describes how people of similar backgrounds—and especially those who are better educated and higher-earning—tend to partner with and marry one another.
The urban preference of power couples is not a new phenomenon. A pioneering study of the locational proclivities of power couples, by the academic power couple of Dora Costa and Matthew Kahn, tracked their geography over the 50-year period spanning 1940 and 1990—before the big acceleration in the back-to-the-city trend after the year 2000. It found that power couples increasingly chose to locate in larger cities and large metro areas (those with 2 million or more people). The share of power couples living in large metros rose from a third in 1940 to nearly half (48 percent) by 1990.
A new study brings the numbers up to date, tracking the location of power couples from 2008 to 2014, when the back-to-the-city movement kicked into high gear. The study sheds important new light on the geography of power couples by comparing three kinds of couples: full power couples, in which both spouses have a college degree; couples where only one spouse has a college degree; and couples where neither spouse has a college degree. (The study looks at native-born, male–female married couples only.) Costa and Kahn used even more detailed data on these couples from the Census’s American Community Survey (ACS), covering more than 300 metros for the period 2008 to 2014.
Not surprisingly, full power couples are significantly more likely to live in large, highly educated cities or metro areas than other types of couples. More than 40 percent (42 percent) of these couples are found in large cities, compared to about 30 percent of couples where just the husband (32 percent) or just the wife (31 percent) graduated from college. Only a quarter of couples where neither spouse graduated college lived in large, highly educated cities.
On the flip side, just 2 percent of full power couples lived in the smallest, least-educated cities or metro areas—reflective of the increased geographic sorting and inequality we see across America. (The study measures human capital in cities as the percentage of college graduates.)
Share of Full Power Couples by Cities’ Human Capital and Size
High human capital
Medium human capital
Low human capital
This same general pattern holds for couples that move from one city to another. Roughly a third (32 percent) of full power couples that relocated between 2008 and 2014 moved to the largest and most highly educated metros, compared to about a quarter of husband-only and wife-only power couples, and less than a fifth of couples where neither partner graduated from college. Just 2 percent of full power couples moved to the smallest, least educated places in America.
Share of Full-Power-Couple Movers by Cities’ Human Capital and Size
High Human Capital
Medium Human Capital
Low Human Capital
Furthermore, the study finds consistent evidence that both men and women in full power couples are financially better off in large, educated cities, which offer better jobs and higher wages for them.
But what happens to couples in which just the husband or just the wife has a college degree?
The study finds considerable evidence of a distinct gender bias in how different types of couples fare in large cities. Full power couples do better in larger, more educated cities. Power couples where just the husband has a college degree also are better off in larger, more educated cities.
But the pattern is different when only the wife has a degree—they are not necessarily better off in those cities. Furthermore, when the woman has the degree, its effect on them moving to a large city rather than a small one is not large enough for researchers to conclude it isn’t due to random chance.
There are a number of potential reasons for this. Perhaps it is, as the study conjectures, that larger, more educated cities offer fewer opportunities for less educated men. Or perhaps it is due to the well-documented gender gap in wages for knowledge-based and professional jobs, and the women in some power couples do not earn enough to make it worthwhile to live in bigger, more expensive cities.
It could be that women who choose less educated male partners do so in part because they have different locational preferences than women who marry college-educated men. They may prefer small-town life to big-city living, or want to stay close to family. Or, despite the belief that there is less of a gender-based division of labor among college-educated couples, perhaps it is still the case that a non-trivial number of college-educated women take their locational cues from the male partner’s career.
But any way you slice it, when it comes to the geography of power couples, gender continues to matter.
CityLab editorial fellow Nicole Javorsky contributed research and editorial assistance for this article.
Race and class are seen as key fault lines in America’s deepening political divide. But a new analysis of congressional voting under Trump shows an additional fault line—a divide that falls across the way we live and get to work, between homeowners and drivers on the one hand, and renters and mass transit commuters on the other.
That is the big takeaway from an analysis of voting by congressional representativesduring the Trump administration, conducted by my colleague Patrick Adler of the University of Toronto’s School of Cities.
Adler looked at correlations between the level of support members of the House of Representatives gave to Trump’s legislative agenda (using FiveThirtyEight’s measure) and some 1,300 separate demographic, social, and economic characteristics of their constituents. Many of these were overlapping variables, which he then clustered into a handful of major categories, like homeowner versus renter and car commuter versus transit user, as well as race, education, and family type (married versus single). As usual, I add that correlation is not causation and point only to associations between variables.
Adler’s analysis suggests that these two key dimensions of our daily life—the kind of housing we live in and the way we commute to work—play a powerful role in America’s political divisions, more so than education (measured as the population share of college graduates), about the same as race and marital status, and only slightly less so than family structure.
Representatives from congressional districts with higher levels of homeownership were more likely to support Trump’s legislative agenda, while representatives from districts with higher levels of renting were much more likely to oppose it, an important finding when affordable housing policies are hot topics this election season.
The way we commute to work is another key dimension of Trump support. Representatives from districts where a large share of commuters drive to work alone were more likely to support Trump’s agenda, whereas reps from districts with a greater percentage of mass transit commuters are more likely to be against it. As I have argued previously, the car is increasingly a factor in America’s political divide, especially between urban and rural communities.
Race, even more so than class, has been identified by many as the key factor in Trump’s rise. Though there is a correlation between Trump support and a larger share of white constituents, the correlation with a district that has a greater share of homeownership is just as strong.
Health insurance, or more specifically Obamacare, is another big wedge issue in American politics. Congresspeople representing districts with more uninsured peoplewere more likely to vote against Trump’s legislative agenda, but the correlation was not as strong as that for support of Trump’s agenda andhomeownership; it was about the same as commuting style.
The share of college graduates is seen by many as a key feature of America’s political divide. But the type of housing we live in and the way we commute to work, are even more strongly associated with support for Trump’s legislative agenda than the share of adults that are college graduates.
The only factor more related to America’s legislative divide is family structure—the share of people who are married or not. Representatives from districts with a larger share of married households are significantly more likely to support Trump’s legislative agenda, while those in districts with a large share of single or non-married households are not.
When it comes to Congress, Republicans are the party of homeowners and drivers; Democrats are the party of renters and transit users. Since city-dwellers are more likely to rent and to use mass transit, these findings reflect the powerful role of rural and urban density in shaping America’s congressional lines, especially this midterm election.
In America today, it’s not just class and race, but way we live in and the way we get to work that are key dimensions of our deepening political divide.
CityLab editorial fellow Claire Tran contributed editorial assistance to this article.
This is the fourth in a series of posts that explore the myths and realities of America’s urban-rural divide. This week we focus on trends in wages and salaries across urban and rural places. For an overview of the series and the data and methodology we use, see the first post in this series.
Wages are a key indicator of the productivity and affluence of cities and regions. There is no doubt that wage and salary levels, as well as their growth, have been widely uneven across American communities, with some winners and many losers. But the pattern does not conform to the simple notion of urban success and rural decline.
Wages and salaries are highest in urban areas. In 2016, counties in large and medium-sized metropolitan areas had median wages and salaries above $40,000. This compared to roughly $37,000 for the nation as a whole. Several large urban counties, like New York, Santa Clara, and San Mateo, had median wages and salaries of more than $100,000. All types of urban counties had wages and salaries that were higher than the national median, while only one type of rural county did—large rural counties adjacent to a metro area.
Median salaries and ratio to the national median, 2016
Type of county
Ratio to national median
Urban county, part of large urban metro
Urban county, part of medium urban metro
Urban county, part of small urban metro
Large rural, county adjacent to a metro
Medium rural county, adjacent to a metro
Small rural county, adjacent to a metro
Large rural county, not adjacent to a metro
Medium rural county, not adjacent to a metro
Small rural county, not adjacent to a metro
Interestingly enough, large rural counties—both those that are adjacent to metro areas and those that aren’t—had wages and salaries that were comparable to counties in small metro areas. And some rural counties had median wages and salaries that were quite high. Butte County, Idaho, a small rural county adjacent to a metro, had a median salary of nearly $90,000 ($88,884), or more than twice the national average. This can be traced to high levels of employment in the government sector, particularly in the Idaho National Lab. North Slope Borough County, Alaska, a medium-sized rural county that is not adjacent to a metro area, had a median wage and salary of nearly $100,000 ($99,283), largely because of the oil and natural-gas development there.
There is considerable variation in wages and salaries across all types of places. While the most affluent large urban counties had wages and salaries of around $100,000, the poorest of them had wages of $20,000 or $25,000, a difference of four or five times.
Wage growth is another story entirely. Rural communities have seen significantly faster growth in wages and salaries than their urban counterparts. The map below, charting wage and salary growth across American counties for the period 2001 to 2016, illustrates this unevenness. The size of the dots indicates the change in wages and salaries. Purple dots designate the most urban counties, while the lightest blue dots are the most rural ones.
County wage and salary growth, 2001–2016
Between 2001 and 2016, wages grew by roughly 50 percent across all counties. Most types of rural counties saw wage growth above the national average, while all types of urban counties had below-average gains. In fact, the smallest and most isolated rural places—small rural counties that are not adjacent to a major metro area—posted the highest wage growth of all, nearly 60 percent.
The performance of rural counties becomes even clearer when we look at the top 10 percent of U.S. counties on wage growth. Nearly 275 rural counties are in this top 10 percent (including more that 100 small rural counties that are not adjacent to metro areas), compared to just 41 urban counties.
Likewise, more than a quarter of all small rural counties not adjacent to a metro area rank in the top 10 percent of all counties on wage growth. So do 18 percent of small rural counties that are adjacent to a metro, and 11 percent of medium-size rural counties that are not adjacent to a metro. This compares to just 4 percent of urban counties in large metros, 2 percent of urban counties in medium-sized metros, and 5 percent of counties in small metros.
The same basic pattern comes through when we look at the share of counties that are in the bottom 10 percent on wage gains.
Urban counties in large metros had the largest share (13 percent) in the bottom 10 percent, followed by urban counties in small metros. Large rural counties that are not adjacent to a metro area had the smallest share of wage loss (under 4 percent).
Wages and wage growth across America are uneven. While wages and salaries are higher in urban places, generally speaking, wage growth has been larger in rural America. The narrative of successful urban places and declining rural ones belies a larger reality of winners and losers across all types of places.
The EIG report, released earlier this week, uses data on income, jobs, business dynamism, educational attainment, unemployment, vacant housing, and poverty, to track the performance of thousands of zip codes across America over two periods, 2007 to 2011 (defined by the study as the recession)and 2012 to 2016 (recovery). They combine these key measures into a Distressed Communities Index (DCI).
Their analysis confirms the decline of America’s once-sturdy middle-class neighborhoods, and the splitting of the nation into areas of concentrated advantage, juxtaposed with areas of concentrated disadvantage.
Fewer than 40 percent of Americans, 120 million or so, live in middle-class neighborhoods which the study’s authorsclassify as “comfortable” and “mid-tier.” Another third, 106 million people, live in “at-risk” or “distressed” communities. An advantaged quarter or so of Americans, 86 million, live in affluent, “prosperous” neighborhoods. Furthermore, the gap between advantaged and disadvantaged neighborhoods has increased in the past decade or so.
America’s rising neighborhood inequality is etched into its broader economic geography. The greatest concentrations of affluent neighborhoods were found in the North and East, especially around bicoastal superstar cities, with the South and East being home to the largest concentrations of distressed communities. Utah had the largest percentage of its population living in a prosperous neighborhood—about half—while California saw the greatest upward shift, with the number of residents in prosperous neighborhoods growing by more than three million.
Meanwhile, Alabama, Arkansas, Mississippi, Louisiana, New Mexico, and West Virginia, had the largest concentrations of distressed neighborhoods, with the latter three seeing the greatest rise in the share of their populations living in distressed communities.
The change in the prosperity of a zip codeover the periods studied, also varied by whether the area was rural, suburban, or urban. Suburban neighborhoods were the most stable, with 61 percent of suburban zip codes experiencing no change in their DCI quintile over the two periods studied. Slightly fewer than a fifth of suburban neighborhoods moved downward, and 21 percent were upwardly mobile.
A slightly smaller share of urban neighborhoods had no change, with an even greater share being upwardly mobile (a reflection of the back-to-the-city movement and commensurate with gentrification) and an even smaller share was downwardly mobile. Rural areas were the most unstable or volatile: 30 percent experienced downward mobility, 27 percent experienced upward mobility, and just roughly 40 percent were stable. Here the study notes that, “the rungs on the ladder of community well-being are farther apart for city and suburban zip codes; for rural zip codes, they may be closer together—but they are also more slippery.”
Spatial inequality in American reflects its widening class and racial divides. Prosperous neighborhoods have larger concentrations of the creative class, while distressed neighborhoods have much larger concentrations of blue-collar workers. Distressed neighborhoods had much larger concentrations of racial minorities. As the study notes, non-white groups made up more than 55 percent of the population of distressed neighborhoods while making up less than 40 percent of the population as a whole. Whites, on the other hand, make up roughly three-quarters of the residents of prosperous neighborhoods.
A recent study from The Hamilton Project of Brookings Institution, sounds similar themes. It tracks spatial inequalityover the years 1960 to 2016. To do so, it examines the performance of all 3,000-plus U.S. counties on indicators of income, poverty, life expectancy, vacant housing, and more, which it combines into an overall “Vitality Index” of its own.
Taking an even longer view, the study shows how the economic performance of different parts of the United States has diverged in recent years. In other words, after years of richer and poorer areas edging closer to each other in terms of economic performance, the trend has reversed since 1980. You can see the lines of the chart come together between 1960 to 1980 and then grow apart thereafter. This is true of each and every region of the country, east coast and west coast, Rust Belt and Sun Belt, and parts in between.
The result is a resurgence ofgeographic inequality. Today, median household income for the top 20 percent of America’s counties is more than twice as high as the median household income of the bottom 20 percent, while poverty rates are roughly three times greater in the poorest 20 percent of counties, compared to the most affluent 20 percent.
This economic divide can be clearly seen in their map which charts the changes in the overall Vitality Index over the past three-and-a-half decades. Blue indicates high levels of economic performance while orange reflects low levels of economic performance. The picture it shows is one of an economically divided nation, with areas high on the Vitality Index in and around the Boston-New York-Washington D.C. Corridor, in the San Francisco Bay Area and Los Angeles, and the Pacific Northwest, as well as places like Denver, Raleigh, Austin, and Atlanta in the Sun Belt. On the other side of the equation, huge areas of the Midwest, Southwest, and South, lag far behind economically
In some respects, economic vitality has simply compounded over this time. If a county was doing well 30 or 40 years ago, it tends to do well today. Almost 60 percent of counties that ranked in the top 20 percent on economic performance in 1980 did so in 2016, and nearly 90 percent of the top 40 percent of high-performing counties are the same today as they were back in 1980. If a county was doing poorly in 1980 then it also tends to perform poorly today. More than 70 percent of counties in the bottom 20 percent on economic performance in 1980 remained there in 2016, and more than 90 percent of the poorest 40 percent of counties stayed there as well. And the counties that have done well have been those with more college grads and more highly educated populations, higher levels of density, higher rates of innovation, and less dependence on manufacturing back in 1980.
But economic performance has also varied significantly across the various regions of the country. In addition to increasing vitality in the big cities and knowledge hubs of the coasts, the energy-producing regions of Texas and the Dakotas saw significant improvements in economic vitality. The Rust Belt-particular counties in Michigan, Ohio, Pennsylvania, and Indiana, saw significant declines in economic vitality. Generally speaking, Rust Belt counties saw their economic performance fall from around the national average for vitality in 1980, to far below it by 2016.
The Brookings Institution research also reinforces the role of race in America’s growing spatial inequality. A second and related study uses data on economic mobilityinU.S. counties from Raj Chetty and his colleagues to trace the connection between race and spatial inequality.
The study documents particularly low levels of economic mobility for black children who grow up in predominantly black communities. While black households tend to be located in low-income places, these places also have lower levels of economic mobility, which can intensify regional inequality from one generation to the next. This is in line with findings from a huge body ofliterature in sociology from scholars like William Julius Wilson, Robert Sampson, and Patrick Sharkey, that document the way in which many black children and families live in areas of concentrated poverty and disadvantage.
And these racial differences in inequality and mobility are compounded by significant differences in where black and white people live across the various regions of the country. Across the country, whites are over-represented in smaller metros and rural areas, while blacks are over-represented in the urban center of large cities and metro areas. But, these patterns differ markedly by region of the country. Metropolitan areas are home to 99 percent of blacks in the Northeast and 96 percent of blacks in Midwest. But in the South, white and black households are about equally likely to live in metropolitan areas. Furthermore, the disproportionate concentration of black households in the urbanized manufacturing areas of the Midwest and South, have disproportionately hurt their economic prospects.
Indeed, the connection between race and spatial inequality has long historical roots. The study points out that the same counties with high concentrations of the black population today are virtually the same counties that had large concentrations of the black population before the Civil War. Spatial inequality thus reflects the long history of racial subjugation running from slavery, Reconstruction, and Jim Crow, to the present.
America is not only economically unequal: Its inequality cuts sharply across geographic lines. We are becoming a country of have and have-nots that turns on where we were born or where we are able to live. And this worsening winner-take-all geography is bound up with, and reflects, our long running divides of race and class. Increasingly, our neighborhood, and our zip code, is our economic destiny.
CityLab editorial fellow Nicole Javorsky provided research and editorial assistance for this article.
Lately, there’s been talk of a shift in innovation and high-tech startups from expensive, increasingly unaffordable hubs like Silicon Valley to more affordable, up-and-coming locales such as Pittsburgh, Detroit, Cincinnati, and Nashville. I’m all for it: Having spent nearly two decades in Pittsburgh at Carnegie Mellon University, I have long been a fan of the incredible innovation capacity and entrepreneurial potential of that great city.
But according to new data I analyzed with my colleague and collaborator Ian Hathaway (a leading expert in entrepreneurship and venture capital), the more troubling reality for the United States is that an even bigger “rise of the rest” is occurring in cities in Asia, Europe, and elsewhere in the world. Our report released on Friday compiles the most detailed data yet on global startup cities, tracking venture-capital investment in nations and cities around the world. Using data from PitchBook, a leading source of information on venture-capital investment, it tracks that investment in more than 100,000 startup companies in 300-plus global cities over the period 2005 to 2017.
Up until very recently, the U.S. was far and away the dominant player in high technology backed by venture capital. Game-changing companies like Intel, Apple, Microsoft, Google, Genentech, Amazon, Facebook, Twitter, Netflix, Uber, Airbnb, and WeWork are just a few well-known examples of venture-backed companies that have introduced new technologies and spurred the rise of whole new industries.
But America’s long-standing lead in VC-backed high tech is now in jeopardy, according to our analysis. About two-and-a-half decades ago, the U.S. was home to more than 95 percent of global startup and venture-capital activity. Today, that share has been cut to a little more than one-half. And the pace of that decline is accelerating, with more than half of the fall occurring in just the past five years.
While it is true that venture-capital investment in the U.S. continues to rise, having reached more than $90 billion in 2017, such investment is growing even faster in other parts of the world, expanding by nearly 375 percent—more than twice the 160-percent increase here. China saw the largest jump, its share expanding from 4 percent of global venture investment in 2005 to a nearly a quarter of it by 2017. But it’s more than China. Nations including India, Singapore, Japan, the United Kingdom, Germany, France, Sweden, Israel, and Canada have all seen substantial increases in venture-capital investment in their startup companies.
When it comes to high-tech innovation and startups, the real action happens in tight clusters of activity within cities and urban centers. And here, the relative decline of the U.S. and the rise of the global rest is, if anything, even more palpable. The San Francisco Bay Area remains the world’s leading startup city, with roughly 20 percent of global VC investment. But a growing number of global cities are gaining ground, and quickly.
Beijing and London have joined the Bay Area, New York, and Los Angeles in the club of what Hathaway and I term Superstar startup cities. In our second tier, Elite hubs, Shanghai, Singapore, Bangalore, Delhi, Mumbai, Berlin, Paris, and Stockholm join Austin, Seattle, San Diego, and Chicago. And in the third tier, Advanced global startup cities, Toronto, Sydney, Dublin, Barcelona, Amsterdam, and Hong Kong join Raleigh-Durham, Miami, Denver, and D.C.
Of America’s rise-of-the-rest cities, only two or three—Pittsburgh, Baltimore, and Minneapolis—make the list of the world’s 60 or so established startup cities. The majority of them, such as Nashville, Detroit, Indianapolis, Columbus, and Cincinnati, are part of a separate group of 40 or so emerging tech hubs, alongside smaller U.S. college towns like Ann Arbor, Madison, and Bozeman, and rapidly growing Asian hubs like Bangkok, Ho Chi Minh City, Calcutta, and Manila.
Ultimately, the global geography of startup cities remains incredibly clustered, concentrated, and spiky. Just the top six—San Francisco and nearby Silicon Valley, New York, Boston, Beijing, and Shanghai—attract more than half of all venture-capital investment, despite being home to only 1 percent of the global population. And just four of those cities—San Francisco, New York, Beijing, and Shanghai—accounted for half of the global increase in VC investment in the past half-decade.
The pattern is clear: The rise of the rest is occurring, and it is mainly occurring in cities outside the United States. Across the world, innovators and entrepreneurs are increasingly realizing that they no longer have to come to Silicon Valley or elsewhere in the U.S. to launch their startup, and they are more often starting their new companies at home.
As Hathaway and I outlined in the Wall Street Journal this past weekend, part of the reason is that other nations and global cities have gotten wise to America’s long-term advantage and increased their investments in universities and R&D; made their cities denser and more attractive; and worked hard to retain their talent at home and opened their borders to global talent. The eroding advantage of the U.S. is partly self-inflicted, because it has clamped down on immigration and become far less open to foreign entrepreneurs and innovators.
The U.S. still accounts for roughly half of all venture-capital investment in high-tech startups. But if the trend continues as it has, it is more likely than ever that the next game-changing innovation will be launched not in Silicon Valley, Boston, Seattle, or New York, but in Shanghai, Bangalore, London, Berlin, or Tel Aviv.
CityLab editorial fellow Nicole Javorsky provided editorial assistance with this article.
This is the third of a series of posts that explore the myths and realities of America’s urban-rural divide.This one reviews recent research on the economic mobility of children who grow up in rural and urban areas. For an overview of the series and the data and methodology we use, see the first post in this series.
When it comes to economic mobility, the image that comes to mind is one of savvy, ambitious kids from the cities and suburbs of large superstar metro areas like New York, Boston, and San Francisco getting ahead, while children from more isolated, rural areas fall further and further behind.
But this narrative is not borne out by the data. Actually, according to several new studies, kids who grow up in rural areas have a better shot at upward mobility than their peers who live in larger, denser urban areas.
The new Opportunity Atlasreleased earlier this week by economist Raj Chetty and his team at Opportunity Insights, with researchers from the U.S. Census Bureau, finds that the economic mobility of Americans is tied to the neighborhoods in which they live. This updates their earlier work which found that economic mobility varies considerably across metro areas. One of the least appreciated and least talked about findings of that earlier research is that low-income youth growing up in rural areas have a better chance at upward mobility than their urban counterparts. Or as the authors put it: “Opportunities for upward mobility are not necessarily better for children growing up in cities rather than in rural areas.”
Two recentstudies from a team of researchers led by Bruce Weber of Oregon State University use Chetty’s data on economic mobility to zero in more closely on what accounts for the differences in upward mobility between urban and rural areas. Chetty’s team looked at metropolitan-level data in their earlier study; Weber and his team used that as a basis to then examine finer-grained county-level data. Their study explores the economic mobility of kids growing up in three types of counties: urban “metropolitan” counties; rural “non-metropolitan” non-core counties; and rural “micropolitan” counties—counties that have an urban cluster of between 10,000 and 50,000. There are roughly 1,200 urban counties in metropolitan areas—these make up almost 40 percent of all counties and contain about 85 percent of the total U.S. population. The roughly 650 rural counties in micropolitan areas contain a bit less than 10 percent of the population. And there are about 1,300 more isolated non-core, non-metropolitan counties holding 6 percent of the population.
Their research reinforces the conclusion that the rural counties are better for upward mobility than urban counties. Using Chetty’s original measure of absolute upward mobility, Weber’s more granular examination finds a higher rate of upward mobility in rural counties (44.1) compared to urban counties (42.1)—a gap of 2.0. This is slightly less than Chetty’s earlier finding of higher mobility in rural commuting zones (45.8) compared to urban (41.7)—a gap of 4.1.
What matters is not just whether a county is urban or rural but how far it is from a large urban or metropolitan center. Yet, close proximity to an urban center does not increase economic mobility, Weber, et al. found.Instead, upward mobility declines with proximity to a major urban center. And, the further away a place is, the higher the economic mobility. In other words, upward mobility is a function of remoteness.
In an email conversation with me, Nathan Hendren, one of Chetty’s collaborators and coauthors, pointed out that, “In general, we do find that youngsters growing up in rural areas tend to have higher rates of upward economic mobility. This tends to be the general pattern across the U.S.” He added that, “It’s interesting that distance from a metro area is positively correlated with mobility.”
A number of other factors bear on the upward economic mobility of children from urban and rural areas. For one, living closer to work, reflected in short commutes (of less than 15 minutes), has a much bigger effect on upward mobility in rural than in urban counties. This may be because jobs and economic opportunities are more spread out in rural areas, and families living in them may benefit from living closer to those opportunities.
Even the smallest amount of income inequality leads to a large decrease in upward mobility for urban youth. For rural youth, that impact is twice as large, even though income inequality is greater in urban areas. And growing up in a home headed by a single mother has a more adverse effect on kids from rural counties than ones in urban counties. The disproportionate role these two factors have on upward mobility in rural communities may reflect the fact that these areas lack the social services and safety nets available in urban areas. Conversely, the high school dropout rate has much less of an effect on mobility in rural areas than on urban counterparts.
Based on their broader analysis, which controls for these various factors, Weber and company conclude that if factors like income inequality and high school dropout rates were the same in urban and rural counties, upward mobility would be even higher for rural places, pointing out that the higher upward mobility of rural places is likely due to their “more favorable conditions” on factors like family structure, inequality, commuting, and social capital.
The newest work from Chetty’s team, released this week, adds additional nuance by zeroing in on the effects of census tracts (an even smaller geographic unit than counties) on economic mobility. Co-authored with Hendren of Harvard, John Friedman of Brown, and Maggie Jones and Sonya Porter of the U.S. Census Bureau, it echoes the findings of sociologists like Robert Sampson of Harvard University and Patrick Sharkey of New York University, on the power of “neighborhood effects.”
Chetty and his collaborators find that it is not just the metro or county a child grows up in, but the specific neighborhood that matters. The prospects for economic mobility can and do vary greatly from neighborhood to neighborhood, even within the same metro or county. As they put it: “The sharply divergent patterns of opportunity across the country suggest that the underlying drivers (as well as potential policy solutions) may also vary greatly from place to place.”
Furthermore, the differences in upward mobility between urban and rural neighborhoods tend to differ across various parts of the country. David Williams, policy director for Opportunity Insights, points to the differences between the Midwest and the South as examples of this pattern. In Iowa and the Dakotas, rural areas afford much greater prospects for upward mobility compared to urban centers. But in Georgia and the Carolinas, rural areas offer much less opportunity for economic mobility than urban centers.
“It kind of complicates our narrative of the urban-rural divide,” says Williams. “There are good and bad things happening in both urban and rural places across the country,” he says, noting that these differences may offer a way both to learn what works and what doesn’t, in order to help bridge our political divide.
The reality of economic mobility defies what we think of as our urban-rural divide. It may even offer an opportunity for each type of place to begin to learn from the other.
CityLab editorial fellow Claire Tran contributed research and editorial assistance to this article.
When wealthy elites embrace issues such as inequality, poverty, climate change, women’s empowerment, and LGBTQ rights, are they spurring change—or reinforcing the status quo? In his new book Winners Take All, the writer Anand Giridharadas says it’s the latter. Giridharadas persuasively argues that when they adopt pressing social and economic issues as causes, plutocrats simply reinforce their position atop the hierarchy.
I spoke with Giridharadas by phone about how members of the global elite came to see themselves (and be seen as) the people who could remedy systemic societal problems, and where we go from here. Our conversation has been lightly edited.
How do we get to the point in our society when the elite take it upon themselves to be, and are seen as, the remediators of our social problems?
It’s a testament to just how unequal and angry and, in many ways, decadent the United States is. We have this situation where the extreme inequities of our time, on one hand, inspire elites to step up, do more, solve social problems. But at the same time, those same inequities have an equal and opposite consequence.
In a highly inequitable society, the price of not being on the top grows much higher. When the middle is not a nice place to be, you don’t want to fall from the top. That’s actually a very common culture in developing countries, where there’s this real clinging to privilege and there’s no middle, and either you’re rich or you’re poor. That has started to happen here. It’s that strange dualism of wanting to make a difference and wanting to cling to one’s privileges.
You point to elite gatherings such as Davos, Aspen, South by Southwest, the Clinton Global Initiative. What do you see as the problem with them?
What is particularly worrisome to me is that when elites get together in our time, they don’t just get together because they enjoy hanging out. This is a coming together of elites in a kind of gated way to discuss how to change the world. While that sounds great, it also means the priority-setting of how the world is changed—the discourse of world-changing itself, the theories of how to go about change—become hugely shaped by these confabs, by the people who attend them, and their network. What you end up having is rich people not just joining the effort to change the world, but conquering the effort to change the world.
A lot of what we see around us is an altered discourse about what change is, and that trickles down. Like low-income or middle-class college students who now speak in this kind of billionaire, Aspen-Davos discourse of how to change the world: “It must be a win-win,” et cetera, et cetera.
Wow, what a remarkable payoff: You get together in these confabs, you coordinate and disseminate this language that causes us to think about changing the world in this more winner-friendly way. Then, people around the world start to imbibe these ideas and talk about change in the way that you talk about it. The next thing you know, you’ve neutered a lot of the threat of class resentment and actual popular organizing against you. It’s actually a brilliant move.
You title one of your chapters “Arsonists Make the Best Firefighters.” Why?
One of the things that’s important to understand is that inequality and the erosion of the American Dream is not a naturally occurring phenomenon like the weather. It is an engineered phenomenon. When the wealthy fought for an anti-tax movement to reduce taxation starting in the ’70s, they knew the effect that would have. When they pushed for deregulation and other aspects of their pro-business, pro-wealth agenda, they succeeded, despite knowing that real people would be hurt by underfunded programs and absent regulations.
So the winners of our age have engineered a world in which they, the winners, take all—in which any of the rest of us are more defenseless than before and find it harder to achieve the American Dream.
Those same winners have done an amazing job of rebranding themselves as the solutions to the problem. They are the ones who are going to sit on the boards of foundations and advise on how to reduce inequality. They are the ones who are going to start impact investment funds to solve the issue.
The other day, I saw a webinar trying to teach people about impact investing to address the opioid crisis. The opioid crisis was literally caused by an excess of the profit motive. And now it’s going to be solved by tapping into the profit motive. When the winners get to redress the injustices they helped to create, they gain a veto for any kind of solutions to those problems that would threaten them.
Do you think it’s all a charade?
It’s a mix. There’s a spectrum, from the naive to the shrewd. There’s a version of this that’s Wall Street, that’s totally about making money and greed and understands that a certain amount of structured giving can lubricate the continued taking. I wrote a piece in TheNew Yorker not long ago about an email where Goldman Sachs was literally talking about, “Ah, terrible stories coming at us about our role in the mortgage crisis. What if we tried to pitch this story of GS Gives, this new program?”
But you also have this phenomenon of the naive rather than the shrewd, who I think are genuinely motivated by making the world better. I don’t think Mark Zuckerberg is driven by money. I think Mark Zuckerberg would be much less of a problem if he were driven by money. He’s an idealist who thinks he got lucky and alighted on a set of tools that are truly going to empower humankind, and that anything that you and I and government do to stop him—slow him down, ask questions, regulate him—are basically stopping and slowing the liberation of humanity.
Therefore, you have this mix of people who are cynically trying to use a little bit of doing good to protect the right to do well, but you also have people who are blind to ways in which they might not actually be the humanitarians they think they are.
Billionaires like Jeff Bezos, George Soros, and Mike Bloomberg (who is allegedly considering running for president) position themselves as the antidote to Trump. But you see them and Trump more as flip sides of the same coin. Why is that?
One of the things that became very clear to me as I reported this is: It’s not about billionaires we like versus billionaires we don’t like. This is not about rich people who do the right thing versus rich people who do the wrong thing. This is a fundamental question of, why do rich people have so much power over public life in America?
Donald Trump is an easy villain, but people we are inclined to like, who share [socially liberal] views on women and immigration and fighting to help the DREAMers and who hate Trump, a lot of those people are complicit in why we are in this moment. And we have to have an honest conversation about that.
I think these philanthro-capitalists laid the ground for Trump in two ways. First, for years, they allowed problems to fester—real problems like declining social mobility, what trade was doing to America, issues around cities and gentrification. Every time you say Lean In is going to fix gender equality, or one charter school in Bed-Stuy is going to solve education, or you’re going to have some kind of tote bag that saves the environment—every time we were promulgating phony change, that is not doing real change. It is crowding out real change and redefining change so we cannot do more ambitious change. This has led to a situation where many of our biggest problems are being led to fester instead of being solved.
I don’t think you can understand the rise of Trump without understanding all of the oxygen that that gave him, the oxygen of unsolved problems en masse. The guy got an enormous amount of oxygen from the very real feeling out there that elites didn’t have regular people’s backs. And I think the philanthro-capitalists played a big part in that. While we were distracted by the smokescreen of changing the world, there was real harm being committed.
Second, a lot of the language and intellectual moves that philanthro-capitalists made gave Trump his talking points. When Donald Trump says, “I alone can fix it”—that’s not a move that started with him. The philanthro-capitalists have been saying that all along. Again, that’s the idea: that the arsonists should be the firefighters, that the people who caused the problem are in the best position to solve it. That’s the same notion as Goldman Sachs partners are the best at sitting on anti-poverty organization boards. Donald Trump rode in on the coattails of the philanthro-capitalists, partly by borrowing their intellectual maneuvers, and partly by exploiting the very real problems that they fake-solved.
What do you make of Amazon HQ2—of Jeff Bezos, the world’s richest man and head of a trillion-dollar company, asking cities and states to cough up billions of dollars in incentives for his company’s new headquarters?
You have Amazon engaged in a race to the bottom of cities throwing away tax revenue in order to attract it. That is money cities could spend on homelessness or education. Then you have the founder of Amazon giving away $2 billion to solve supposedly the very issues that cities will be less able to solve, because they’re giving money as a tax break. Then you’ve got the employment practices of the company, the amount of pay, the volatility of seasonal employment, its own tax practices.
You end up having this complicated mix where that company and the people who work for it are fighting on both sides of the war. And at one level they’re trying to do good, whether it’s Jeff Bezos giving a lot of money away, or the fact that every time I log into Amazon, there’s some pop-up that asks me to give money to charity. On the other hand, given their employment practices, given the desire for tax breaks, what they’re doing in their workday activities is not only undermining what they’re doing charitably, but overwhelming it.
It’s the same thing with these banks that are doing urban revitalization things right now. They are one of the primary reasons for all the foreclosures across this country, and the harm they cause is on a much vaster scale. We would be much better off with a J.P. Morgan that didn’t help cause a financial crisis and then need to revitalize what they helped to kill. Don’t kill things and then revitalize them. Just avoid killing them. It’s so much more efficient. These people love efficiency, so here I come with news of how to be more efficient.
Wealthy people have always funded pet projects in cities, like museum buildings and symphony halls. Now they have taken to funding city-government positions. One Midwestern city has donors that want to endow local government.
That kind of giving still tends to confer power. The fact that we are moving toward a society in which rich people are reaping these outsized profits because of how little they pay people and how little tax they pay [means] something is broken. Just pay your damn taxes.
What does it mean when the media is dependent on wealthy people?
Should billionaires really own our means of discovering the truth? (Hi Laurene, nice to have you reading this article!) This is part of a loop. First, business interests over the last 30 to 40 years have waged a war on government, stripping down taxes, stripping down regulations, and they built a winners-take-all economy. Then, after bludgeoning government and shrinking it, they waged a war to control political power and to make sure they’re the ones who write bills.
What I write about in my book is third-stage, where you’ve got the conquest of the economy by the winners, you’ve got the conquest of political life by the winners, and you have the privatization of social change. What you’re getting at is a fourth stage.
What is the best prospect even if the first three institutions fall to moneyed interests? If you have a press that’s free and investigates, you still have some hope of exposing the other three and keeping them relatively honest. When the winners, a small handful of them, basically own all of the major publications of quality and Facebook, Google, and Twitter, which are responsible for whether that journalism is visible to people, you’ve closed the loop.
I’m not sure what other institutional valves there are to fix the situation we are in. We have to get over our temptation to ask whether the billionaire who owns The Atlantic is a better or worse billionaire than the billionaire who owns The Washington Post or the billionaire who sold TIME magazine to another billionaire. We have to ask why so much of our society is owned by billionaires.
Are there any alternative ways we can avoid relying on billionaires’ leftovers? Or if not, are there mechanisms to better use their philanthropic funds?
There’s an opportunity in this era for rich people to become traitors to their class and to give in ways that actually pull up the ladder, so no one can become rich in the way they did—to actually break down the system atop which they stand. There is the opportunity to do the kind of giving that would not aggrandize them and their power, that would actually level the playing field in ways that threaten their fellow plutocrats. It’s important that they’re not gaining power over these movements, but supporting others.
What does the future of labor solidarity look like? If you were to give $1 billion to that, you would not be acting in your own self-interest. How come we don’t have plutocrats donating money to those who are cracking down on hidden, offshore money? That’d be a great philanthropic cause: going after your own privilege and the privilege of people like you.
Another thing rich people can do is they can learn from what was done 100 years ago, when people like [Andrew] Carnegie required states to take over the maintenance of a library he created privately and agree to fund it through taxpayer dollars. Not because he couldn’t afford it, but because he wanted to use donations to develop a public habit.
The more important thing that needs to happen is not from rich people—it’s from the rest of us, who need to learn to take change back. That means as an individual, asking, “What can I do?” My simple answer is, next time you see a problem, don’t start a cupcake company that gives back—just solve it. When you see a problem, think of a solution that is public, democratic, institutional, and universal. Think of a solution that solves the problem for everybody at the root. And then build movements.
We are not a country of movements anymore. Even in the anguish that President Trump has caused, how many billionaires have Democrats considered as the salvation? Howard Schultz, Zuckerberg, Bloomberg, Oprah, Tom Steyer. Look at ourselves for a second. Why is it that we keep turning to billionaire sugar daddies and sugar mommies to rescue us from a phony billionaire sugar daddy?
There is something in our culture that doesn’t trust ourselves in our collective capacity as people to organize and build movements, and we need to learn how to do that again. People at events ask, “How do I change the world?” No. How do we change the world? Stop asking what you can do as an individual. If we could actually reacquire the habit of joining things, not tweeting and liking and poking but actually being part of movements—sustained communities of people that fight for things together—I think we can rebalance where power is in our society.
The irony is, the tools of our time make it easier than ever for regular people to organize. Yet those tools have so far produced the opposite outcome, which is more power concentration than we’ve had in 100 years. It’s time to reverse that.
Are you optimistic or pessimistic that this will happen?
I think Donald Trump has done us the enormous favor of discrediting the billionaire change agent now and forever. There is a chance that we will actually have one of those inflection points, just as the Gilded Age gave way to an age of reform 100 years ago. We are overdue for an age of reform in America, an age that will have a public-spirited nature rather than a private-spirited nature, an age whose emphasis will be building and rebuilding what we share in common.
Who better to flamboyantly discredit the idea of rich people saying they were fighting for us while enriching themselves than Donald J. Trump?
CityLab editorial fellow Nicole Javorsky contributed editorial assistance to this article.