New York

COVID Work Trip Reduction Estimates: CSAs with Transit Legacy Cities

Wendell Cox — Thu, 13 Aug 2020 20:29:01 -0400

America’s elite central business districts have symbolized the ascendency of big cities, epitomized by soaring office towers. But today, due the COVID-19 pandemic, so much office work performed in these CBDs can be done remotely, that their future seems far less towering than in the past. In contrast, less dense areas, notably exurbs, appear to have suffered less loss in their employment patterns.

This is not because these functions are unnecessary, but they do not need nearly the office space once imagined. much of their economic activity has continued outside the office. A Stanford University survey indicates that 42 percent of the workforce is working remotely — about 8 times the normal rate reported by the American Community Survey for 2018.

This article describes the reduction in work visits, by counties within the six combined statistical areas (CSAs) that have the nation’s six “transit legacy cities” (the municipalities of New York, Chicago, Philadelphia, San Francisco, Boston and Washington) at their cores. These cities (which are to be contrasted with metropolitan areas) accounted for 58% of transit commuting destinations in 2018 and have the six largest downtowns (central business districts or CBDs) in the United States. These dense developments function as the urban cores of the larger labor market CSA definition by the Office of Management and Budget. Each of these CSAs well served by lengthy commuter rail systems, as well as radial express freeways.

The Google Mobility Reports

The data is from the “Google COVID-19 Community Mobility Reports,” which estimate trips by people compared to a base of for various activities, including trips to the workplace. According to Google:

The reports use aggregated, anonymized data to chart movement trends over time by geography, across different high-level categories of places such as retail and recreation, groceries and pharmacies, parks, transit stations, workplaces, and residential. We’ll show trends over several weeks, with the most recent information representing 48-to-72 hours prior. While we display a percentage point increase or decrease in visits, we do not share the absolute number of visits. To protect people’s privacy, no personally identifiable information, like an individual’s location, contacts or movement, is made available at any point.

Trip rates are compared to a baseline of January 3 to February 6, 2020. The period covered is the month of June, when many parts of the nation were “locked down.” We have used the Google daily data, developing daily averages compared to the baseline period of January 3 to February 6, 2020.

These data reflect the economic disruption that COVID-19 has unleashed upon not only the CSAs, but their suburban and exurban ring components. They capture a measure of the decline in trips to work by people now working from home and by people who have lost their jobs, either permanently or temporarily. Both of these factors have created economic disruption. The first, working at home, has severely impacted businesses, for example downtown restaurants, bars and hotels that rely on work location driven commerce. Many are now deserted, and, like empty offices, could impact commercial real estate in the longer term, depending on how permanent is the current increase in remote working. The more challenging economic prospects of those who have been laid off is perhaps the most important disruption.

The discussion below describes the classification into CSA sectors (generally core, suburban and outside the principal metropolitan area), weighting the Google work visit figures by the workplace employment in each county (using ACS 2014-2018 work place data). These data are provided as very general estimates.

New York CSA: The 41 county New York CSA (12,400 square miles, 33,100 square kilometers) extends beyond the metropolitan area to include counties such as Fairfield (Bridgeport-Stamford) and New Haven in Connecticut, Mercer (Trenton) in New Jersey, Dutchess and Orange in New York and Monroe in Pennsylvania. New York is the largest CSA and has a sufficient number of counties to produce a more fine-grained analysis. Further, the New York CSA has the most diverse in urban form in the nation, with by far the nation’s densest central business district and the highest density neighborhoods as well as overall suburban densities in the New York barely one half that of Los Angeles suburbs, and smaller urban area densities that are one-third to one-fifth that of Los Angeles suburbs (New Haven and Poughkeepsie).

The largest percentage loss in workplace visits was (Brooklyn Queens and Hudson [Jersey City]). New York City’s four outer boroughs experienced a loss of 42.5% workplace visits. The inner suburban counties, all of which are adjacent to the city of New York, had an average loss of 38.2%. The outer suburban counties, which include all other counties in the metropolitan area, lost a considerably lower drop of 32.8%. The counties in the adjacent metropolitan area (in the CSA, but not in the MSA) lost 31.4%. Overall, workplace visits were down 41.1% in the New York CSA (Figure 1).

San Francisco Bay CSA: The 14 county San Francisco Bay CSA (10,600 square miles, 27,500 square kilometers) extends beyond the 5-county MSA to include counties such as Santa Clara, Sonoma, Solano and three in the San Joaquin Valley (San Joaquin, Stanislaus and Merced).

The city of San Francisco equaled Manhattan in its drop in workplace visitations, at 55.0%. Santa Clara County (San Jose), the center of Silicon Valley, but outside the San Francisco metropolitan area, suffered a drop of 48%, while suburban San Mateo County lost 46%. Overall, the employment weighted loss in the suburban counties of the MSA was 42.1%, while the loss in the CSA counties outside the MSA such as San Joaquin and Merced was 37.4%. The overall CSA loss was 41.9% (Figure 2).

Washington CSA: The 41-county Washington-Baltimore CSA (13,600 square miles, 35,100 square kilometers) was created by the merger of two of the nation’s largest metropolitan area. It extends to counties in Pennsylvania and West Virginia. This analysis separates the city of Baltimore, rather than considering it a suburb of Washington.

The city of Washington experienced the third worst workplace visitation loss among the six CSAs, at 50%. The largest loss in the CSA was in Arlington County, VA, adjacent to the District of Columbia, at 51.0%. The city of Baltimore, which is not a transit legacy city, has a smaller downtown and less reliance on transit had a loss of 35.6%. The other counties of the MSA lost 42.2%. The counties outside the MSA lost 33.2%. The overall CSA loss was 40.7% (Figure 3).

Philadelphia CSA: The 16-county Philadelphia CSA (7,300 square miles, 19,000 square kilometers) includes additional counties in Pennsylvania, such as Atlantic (Atlantic City) and Berks (Reading). The city of Philadelphia lost 39.0% of its workplace visits. The suburban counties declined 35.2%, while the counties outside the MSA lost 28.9%. The overall CSA loss was 35.2% (Figure 4).

Boston CSA: Unlike the CSA’s above, the 19-county Boston CSA (9,700 square miles, 25,100 square kilometers) does not have a separate county equivalent jurisdiction for its core city (Boston). The core Suffolk County is largely dominated by Boston, and which experienced a workplace visit loss of 41.7%.Suburban MSA counties lost 39.0%, while counties outside the CSA lost 29.3%. Overall, the Boston CSA loss was 36.9% (Figure 5).

Chicago CSA: The 19-county Chicago CSA ((8,900 square miles, 23.000 square kilometers) core county is Cook, includes the city of Chicago and a large suburban population. Cook County’s workplace visits were down 37.2%. It is likely that the city of Chicago’s workplace visits were down more, because it represents a smaller percentage of the central county than in the other five CSAs. The suburban counties had a loss of 30.9%, while the counties outside the MSA lost 20.2%. The overall loss was 33.9% (Figure 6).

Denser Areas Subject to Greater Economic Disruption

Just as denser areas of urbanization have borne the brunt of Covid-19 fatalities, they have also absorbed a disproportionate share of lost economic activity. Overall the workplace visit loss in counties with transit legacy cities reaches 46.7 percent while those outside, with their lower urban densities and greater dispersion of jobs fell by 32.9percent. All economies have proven vulnerable to the pandemic, but certain areas, particularly those with the transit legacy cities, with the largest CBDs and most transit dependency, have suffered markedly worse.

Wendell Cox is principal of Demographia, an international public policy firm located in the St. Louis metropolitan area. He is a founding senior fellow at the Urban Reform Institute, Houston and a member of the Advisory Board of the Center for Demographics and Policy at Chapman University in Orange, California. He has served as a visiting professor at the Conservatoire National des Arts et Metiers in Paris. His principal interests are economics, poverty alleviation, demographics, urban policy and transport. He is co-author of the annual Demographia International Housing Affordability Survey and author of Demographia World Urban Areas.

Mayor Tom Bradley appointed him to three terms on the Los Angeles County Transportation Commission (1977-1985) and Speaker of the House Newt Gingrich appointed him to the Amtrak Reform Council, to complete the unexpired term of New Jersey Governor Christine Todd Whitman (1999-2002). He is author of War on the Dream: How Anti-Sprawl Policy Threatens the Quality of Life and Toward More Prosperous Cities: A Framing Essay on Urban Areas, Transport, Planning and the Dimensions of Sustainability.

Photo: Chicago Loop (downtown), by author.

America's Long Suffering Rail Commuters

Wendell Cox — Fri, 31 Jul 2020 20:29:01 -0400

The long, streaking commuter trains (suburban rail) carrying workers mostly into and out of downtown every day may give the impression of “rapid transit.” However, regardless of the top speeds they reach, the average suburban rail rider spends far more time traveling to work than those using other modes of getting to work (Figure 1). They spend far longer than the majority of commuters, who drive alone. Even in the New York combined statistical area (CSA), with the largest suburban rail network a majority drive to work (Figure 2).

Suburban rail travel times are extraordinarily long. The average one-way commute is 71 minutes, nearly 20 minutes longer than the average transit commute. Suburban rail comes closest to matching average transit times in Philadelphia, where its riders spend about 11 minutes longer traveling each way. Suburban rail trips are 28 minutes longer in Washington-Baltimore and 25 minutes longer in Los Angeles (Figure 3).

This is indicated by American Community Survey (ACS) commuting data for 2018. ACS asks for information on how people commute to work (Figure 4). The 2018 question (#31) was “How did this person usually get to work LAST WEEK?” with this clarification “If this person usually used more than one method of transportation during the trip, mark (X) the box of the one used for most of the distance. Question #31 asks: “How many minutes did it usually take this person to get from home to work LAST WEEK?”

This article reviews work trip travel times for the “railroad” mode in the CSA’s with the seven largest networks in the United States (Note). The larger labor markets that comprise CSA’s are used because the railroad networks are not confined to the core metropolitan areas. The largest suburban rail network is in New York, which carried 460,000 daily one-way commuters on three systems (Long Island Rail Road, Metro-North Railroad and New Jersey Transit). The smallest network, in Los Angeles carried 30,000 one-way commuters daily (Figure 5).

Comparing Suburban rail to All Transit Modes

The seven CSAs also the largest number of transit commuters, as well and the largest transit systems. They include the transit “legacy” cities of New York, Chicago, Philadelphia, Boston, San Francisco and Washington, as well as Los Angeles. These CSAs have the most comprehensive transit systems in the nation. Yet, the average one-way commute time is more than two-thirds greater than that of driving alone (51.0 minutes on transit and 30.2 minutes driving alone).

Comparing Suburban Rail to Driving Alone

Commuter rail riders have one-way trips that average 2.3 times that of those who drive alone in the seven CSA’s. Suburban rail commutes in Philadelphia compare best to driving alone, at just more than double (30 minutes more). Suburban rail riders have average trips 2.5 times as long as driving alone in New York, Washington-Baltimore and Los Angeles (Figure 6). Assuming travel to work and back home, the average suburban rail commuter in New York commutes for 90 more minutes than the average solo driver. The difference is even greater in Washington-Baltimore (94 minutes) and Los Angeles (96 minutes).

Most Long - Suffering Commuters

The longest suburban rail commutes, by county are shown below:

Harford County, Maryland (Washington-Baltimore CSA) report the longest suburban rail commutes, at 107.3 minutes. This is 3.4 times the average commute of Harford County residents driving alone.
Orange County, New York has the longest suburban rail commutes in the New York CSA, at 106.8 minutes. This is 3.3 times that of the average Orange County commuter who drives alone.
San Joaquin County, California suburban rail commuters spend an average of 106.1 minutes traveling to work, the longest in the San Francisco Bay CSA. This is 3.1 times that average commute time for San Joaquin County residents driving alone.
Kenosha County, Wisconsin suburban rail commuters travel the longest in the Chicago CSA, at 106.1 minutes. This is 3.7 times than average commute time for fellow county residents driving alone.
San Bernardino County, California suburban rail commuters travel the longest in the Los Angeles CSA, at 94.8 minutes. This is 3.1 times the average commute time for fellow residents driving alone.
Providence County, Rhode Island County suburban rail commuters travel the longest in the Boston-Providence CSA, at 86.2 minutes. This is 3.6 times than the average commute time for fellow residents driving alone.
Bucks County, Pennsylvania suburban rail commuters travel the longest in the Philadelphia CSA, at 84.4 minutes --- 2.8 times the 29.7 minutes of Bucks residents who drive alone.

Where Suburban Rail Reigns: New York

As Figure 5 (above) indicates, nearly one-half of the nation’s suburban rail ridership is in New York. The top five counties with the largest number of suburban rail commuters are in New York, and seven of the top 10 (Figure 7):

Nassau County, New York (located on New York’s Long Island Rail Road) has the largest number of suburban rail commuters, at 88,900 daily and has an average commute time of 72.4 minutes. This is 2.4 times the travel time of drive alone commuters. Suffolk County is also on the Long Island, with the fifth most suburban rail commuters, but the longest travel time among the strongest 10 counties, at 96.5 minutes. Queens County, ranked 9th, is also on the Long Island.
Westchester County, New York, is served by the Metro North Railroad and has the third highest number of suburban rail commuters, at 81,700 daily and a travel time of 68.3 minutes. Across the Connecticut border, Fairfield County ranks fourth in daily suburban rail commuters, with a travel time of 84.3 minutes.
Essex County, New Jersey, served by New Jersey Transit (NJT) ranks 7th, with a travel time of 64.6 minutes. Middlesex County, New Jersey also has NJT service and ranks 8th with a travel time of 79.5 minutes

Impact of Covid-19 on Suburban Rail

During the Covid-19 pandemic, there has been a massive increase in remote working.

This is most evident in New York, where the New York Metropolitan Transportation Authority (MTA) reports weekday ridership down on the Long Island Rail Road 79% and on Metro-North Railroad 83% from typical levels.

Much has been written about the large increase in working at home during the pandemic and the likelihood that a portion of this increase could be retained after the health threat passes. A permanent increase seems likely among New York suburban rail commuters. Regardless of how they previously traveled, many other commuters around the country have found working at home preferable to the crowded trains and crowded highways that may have needlessly consumed so much of their time.

Note: The American Community Survey does not differentiate between “railroad” and “ferry boat” travel times. Most of these commuters are on suburban rail systems.

Photograph: Approaching the Hudson River Tunnel in New Jersey toward New York’s Penn Station (by author).

Perspective: U. S. COVID-19 Deaths and Urban Population Density

Wendell Cox — Fri, 10 Jul 2020 20:29:01 -0400

There is wide consensus that the COVID-19 virus spreads person-to-person, especially in confined spaces that are insufficiently ventilated. It is exacerbated by prolonged proximity, which John Brooks, the Centers for Disease Control and Prevention’s chief medical officer indicates is 15 minutes or more of unprotected contact with someone less than 6 feet away. Avoiding such proximity is the justification for social distancing and face masks and the lockdowns that have been implemented around the world.

Preventing infection means minimizing exposure density, which is the duration of risky contacts that can be estimated using factors from population density to much more precise measures of crowding that vary by personal lifestyles and living conditions.

The best (lowest) geographic level for which there is comprehensive national COVID-19 death data available now is for counties. It may be tempting to evaluate death rates using county population densities, which are readily available. However, many counties have very large rural areas that drive down overall densities and can mask significant urban densities.

By definition, a disease spread by personal contact is likely to be associated densities the reflect the risk of exposure — the sum of exposure densities, duration weighted. Exposure density is far greater in urban areas than in rural areas and strongly associated with higher urban densities, as indicated below.

This article is provided from a perspective of COVID-19 death rates by categories of county urban, rather than total county densities, through July 8, 2020, based on data published by USAFacts.org.

The Influence of Large Rural Areas on County Densities

Overall county densities differ markedly from their urban densities.

The ultimate example may be a comparison between Cuyahoga County Ohio (Cleveland) and San Bernardino County (in the Los Angeles combined statistical area).

In 2010 Cuyahoga County’s 1.2 million residents lived at an overall population density of 2,800 per square mile and an urban density of 3,063 per square mile. In other words, Cuyahoga’s urban density was about 9% denser than its overall density.

In 2010 San Bernardino County’s 2.2 million residents lived at an overall population density of 101 per square mile and an urban density of 3,090 per square mile. San Bernardino’s urban density was about 3,000% more (30 times more) than its overall density.

San Bernardino’s huge rural area drives down the overall density. For the purposes of COVID-19 analysis, the urban density is more appropriate than the total density, because the urban density is a better surrogate for the proximities that can are associated with infection.

This applies to much more than Cleveland and San Bernardino. For example, other million-plus counties with under 1,000 per square mile densities include Maricopa (Phoenix), fifth most populous county in the nation, San Diego, Riverside (California) and Palm Beach (Florida). Their much higher urban densities are substantially diluted because their rural areas are so large.

This problem was identified by the Census Bureau with respect to metropolitan areas in 2012: “Overall densities … can be heavily affected by the size of the geographic units for which they are calculated.” Counties are the building blocks of metropolitan areas, and their often-overwhelming rural land areas create this difficulty.

Perspective: County Urban Densities

County densities can differ materially from county urban densities.

There are 955 counties with urban densities below 1,000 per square mile. This is less than one-third of the nation’s counties. By contrast, 95% of US counties (2,996) have densities below 1,000 per square mile (Figure 1).

Counties with urban densities under 1,000 per square mile (including counties without urban areas) are estimated to have 17 million residents. By comparison, using total density, counties with under 1,000 per square mile are estimated to have 204 million residents --- 12 times as many (Figure 2). The analysis below describes reallocating more than 185 million US residents from counties with overall densities of less than 1,000 to the urban density categories from 1,000 to 7,499 per square mile. All of this reallocation is within the suburban range (under 7,500) as defined in the City Sector Model (See “Population Growth Concentrated in Auto Oriented Suburbs and Metropolitan Areas”).

Using urban densities rather than total densities reclassifies counties with overwhelming urban populations to higher density categories. For example, Los Angeles, San Bernardino, Maricopa, and Miami-Dade counties move up two density categories. King County, Washington (Seattle) moves up one category. Other highly urbanized counties with smaller rural land areas remain in the same density categories, such as Cuyahoga County, Dallas County, Texas, Harris (Houston), Hennepin County (Minneapolis) Cook County (Chicago), Mecklenburg County (Charlotte) and Franklin County, Ohio (Columbus).

Coincidentally, the US Census Bureau generally considers 1,000 per square mile to be the minimum urban density in its urban area criteria.

County Death Rates by Urban Density Categories

Counties with higher urban densities have a far higher percentage of the COVID-19 deaths, as is illustrated in Figures 3 and 4. Further, the higher urban density counties tend to have proportionally more deaths than their share of the national population (Figure 5). With just 4.3% of the nation’s population (Figure 6), counties with 7,500 per square mile urban population densities have 21.7% of the COVID-19 deaths (Figure 7) as of July 8, 2020.

New York County (Manhattan), the nation’s densest and the only county with greater than 50,000 urban density is estimated to have a death rate of 1.906 per 1,000 population. Manhattan has 4.8 times its proportional share of deaths, with 2.4% of the nation’s deaths. However, Manhattan’s death rate is lower than the 2nd highest urban density category (25,000 to 49,999), and is discussed further in “The Manhattan Anomaly,” below.
The highest death rate is among counties with from 25,000 to 49,999 urban densities, at 3.000 per 1,000 population. This includes two counties, both in New York city, Bronx County and Kings County (Brooklyn). These two counties have 7.5 times their proportional share of deaths, with 9.1% of deaths and only 1.2% of the population.
The counties with from 10,000 to 24,999 urban densities are estimated to have a death rate of 1.814 per 1,000 population. These include Queens (New York city), San Francisco, Hudson (which includes Jersey City), Suffolk (which includes Boston) and Philadelphia. These counties have 4.6 times their proportional share of deaths, with 8.6% of deaths and only 1.9% of the population.
The 7,500 to 9,999 density category is estimated to have a death rate of 1.000 per 1,000 population. These include 5 county-equivalent jurisdictions, the District of Columbia (Washington) the city of Alexandria, VA, Richmond County, VA, Arlington County, VA and the city of Baltimore. These counties have 2.5 times their proportional share of deaths, with 1.7% of deaths and only 0.7% of the population.
The 5,000 to 7,499 density category (similar to the highest density suburban areas) is estimated to have a death rate of 0.589 per 1,000 population. This includes 15 counties, such as Los Angeles County, Essex County, NJ (Newark) Miami-Dade County (Florida), Cook County, IL (Chicago), Orange County. CA (Anaheim & Santa Ana), Alameda County CA (Oakland), and Denver County, CO. These counties have 1.5 times their proportional share of deaths, with 13.4% of deaths and only 9.1% of the population.
The 2,500 to 4,999 density category is estimated to have a death rate of 0.318 per 1,000 population. These counties have less than their proportional share of deaths (0.8), with 22.4% of deaths and 28.1% of the population.
The 1,000 to 2,499 density category is estimated to have a death rate of 0.301 per 1,000 population. These counties have less than their proportional share of deaths (0.8), with 40.3% of deaths and 53.3% of the population.
The under 1,000 urban density category is estimated to have a death rate of 0.161 per 1,000 population. These counties also have proportionately fewer deaths (0.4), with 5.2% of deaths and 2.1% of the population.

The use of urban densities reduces the death rates in the lower density categories, by transferring counties with substantial masked urbanization into their urban density categories. There are no changes, however, among the counties with 7,500 or higher density since their rural influence is virtually zero (Figure 8).

The Manhattan Anomaly

There has been surprise that the nation’s densest county, New York County (Manhattan) has a lower death rate than other boroughs (counties) with lower densities: The Bronx, Queens, and Brooklyn (Kings County). Manhattan has advantages that are associated with lower exposure densities, such as:

Manhattan residents are exposed to considerably less prolonged risk of infection on transit than in the outer boroughs. The average Manhattan transit commuter has a 37.5-minute one-way work trip. In contrast, the average one-way transit work trip for residents of the Bronx and Queens is 18 minutes longer. Brooklyn transit work trips are 13 minutes longer and Staten Island work trips are 36 minutes longer.
Higher rates of poverty have been associated with higher death rates, while higher incomes are associated with less infection risk. Median household incomes in Manhattan are 55% higher than in The Bronx, 44% higher than in Brooklyn, 27% higher than in Staten Island and 19% higher than Queens.

Even so, Manhattan’s COVID-19 deaths rate are 4.8 times its share of the national population.

The “Surge” in Context

A number of recent articles have described a “surge” in infections; However, the surge has not produced a surge in deaths. Comparing A comparison of the last three weeks indicates reveals about 10% reductions in hard hit New York City, with the three counties that comprise the two highest urban density categories (New York, Kings and Bronx). The New York City declines have been attributed to social distancing and a strict lockdown, both of which required a more substantial change in behavior due to the city’s much higher density and transit ridership than anywhere else in the nation.

There were smaller reductions in the 7,500 to 9,999 and 10,000 to 24,999 urban density categories. There were small increases in the lower urban population density categories. The strong relationship between higher death rates and higher urban density categories remains (Figure 9).

The good news is that death rates have dropped substantially. During the first eight days of July, daily deaths declined 73% from the peak daily rate of April 16-30 (Figure 10). What seems clear is although the virus has spread widely, its most lethal impacts have, for the most part, been felt where urban densities are greater.

Methodology Note:

The analysis uses death data as allocated to counties on a daily basis by USA Facts. County urban population density is estimated from 2010 Census data and scaled to 2019 using the rate of change in the total populations. This is by no means the optimal method for analyzing the association between COVID-19 deaths and urban densities. But it is about as far as such analysis can go at the national level until more local COVID-19 death data is available.

Photograph: The Bronx (foreground), Queens and Manhattan (background), by author.

Wendell Cox is principal of Demographia, an international public policy and demographics firm. He is a Senior Fellow of the Center for Opportunity Urbanism (US), Senior Fellow for Housing Affordability and Municipal Policy for the Frontier Centre for Public Policy (Canada), and a member of the Board of Advisors of the Center for Demographics and Policy at Chapman University (California). He is co-author of the "Demographia International Housing Affordability Survey" and author of "Demographia World Urban Areas" and "War on the Dream: How Anti-Sprawl Policy Threatens the Quality of Life." He was appointed by Mayor Tom Bradley to three terms on the Los Angeles County Transportation Commission, where he served with the leading city and county leadership as the only non-elected member. Speaker of the House of Representatives appointed him to the Amtrak Reform Council. He served as a visiting professor at the Conservatoire National des Arts et Metiers, a national university in Paris.

Storied Cities

Aaron M. Renn — Tue, 07 Jul 2020 20:29:01 -0400

Athens is the birthplace of Western culture, with the physical ruins of its classical age still visibly present as a perpetual reminder. Virgil composed his epic poem, The Aeneid, recounting the mythic flight of Aeneas from defeated Troy to Italy, becoming the forbear of Rome. New York sees itself as unique center of commerce, founded when the Dutch (not the English) bought Manhattan for beads in the city’s first hustle. Nashville needs no reminder that it’s the center of country music, nor Detroit that it is the Motor City. In Indianapolis and Louisville, the deep traditions and rituals associated with their marquee events, the Indianapolis 500 auto race and the Kentucky Derby horse race, embody and define local culture.

It’s been widely observed that there’s an increasing sameness to cities today, a sort of neoliberal urban monoculture that’s swept the globe. Visit any city in the world and see the same boutique hotels, swank restaurants, outposts of global luxury brands, and so on. The travel guides published by the über-swank magazine Wallpaper are great for the cosmopolitan traveller, but also eerily similar from place to place. At the website The Verge, Kyle Chayka has written about the rise of what he calls “AirSpace,” a sort of uniform global aesthetic promoted especially by AirBnB listings. In the United States, after visiting cities in all fifty states, journalist Orianna Schwindt wrote in New York magazine about the “unbearable sameness of cities,” in which every coffee shop seemed to feature the exact same décor, right down to their Ikea lights.

There is truth in this claim of uniformity. A global economy demands globally standardized products that can be graded and traded. It demands a frictionless environment for business that requires places to present themselves as familiar to whomever from around the globe happens to arrive in them.

Yet beneath this veneer of sameness, beyond the Edison bulbs and Emeco chairs, cities are actually very different from one another, even within the same region of the country. This is true even if it can be difficult to articulate exactly what makes them unique. Some cities—London, Paris, New York, Los Angeles, and many more—have larger-than-life identities that even people who’ve never visited them know about. But even places where the local identity is not top of mind, the reality of that uniqueness is still there. Cincinnati, Cleveland, and Columbus are all in the state of Ohio. But even if people can’t articulate what their local identity is, anyone visiting these three places can’t help but be immediately struck by how radically different they all are from each other.

This unique identity or personality of a place emerges from roots deep in their past. For Old World cities whose foundations are in the dim mists of history, this can only be recapitulated as myth. In the case of Rome, this is a myth as we traditionally understand it, as in The Aeneid. But for other places, the civic myths are of different form and origin. For those in the New World, civic identity is often being forged during the foundation generations of a place, of which we often possess contemporary records and so can study them historically in a different way. Who settles and initially governs a place matters a great deal to what a city is long afterward, even when the initial settlers are no longer present. The way this is uncovered and presented often comes in the form of history, but it has a mythic component as well, one that sometimes self-consciously seeks to justify a particular conception of a city.

We see this in the origin in New York, where the initial founding and governance by the open-minded, tolerant, and commercially oriented Dutch established a character that still inhabits the city today, despite the near absence of anyone of Dutch ancestry. While the Italians, Jews, and many, many other immigrants to New York profoundly shaped the city over time, the original Dutch ethos of the city also shaped them in return.

Though urban cultures are often protean and dynamic, consistent threads often run through them for the long haul. Sociologist E. Digby Baltzell puts it like this: “As the twig is bent, so the tree inclines. Or, as Freud was to teach us, our adult lives largely repeat the emotional and intellectual responses established in early childhood. So in history the formative experiences of civilizations set patterns which successful generations forever seem to follow.”

The local founding and history has not just cultural but economic consequences. In her seminal comparison of the Bay Area and Boston examining how Silicon Valley came to dominate the American high-tech industry, vanquishing the Route 128–corridor cluster that had been the early leader and its onetime rival, AnnaLee Saxenian alights on culture as the distinguishing factor. In her book Regional Advantage: Culture and Competition in Silicon Valley and Route 128, she contrasts the hierarchical, autarkic, firm-based business culture of Boston with the network-based collaborative business culture of Silicon Valley. Boston’s business culture derived from hundreds of years of local culture extending back to the Puritans. Indeed, the founder of Route 128’s most important tech company was called “a modern-day Puritan.” But Silicon Valley’s business culture emerged from the group of transplants from elsewhere who arrived in California having rejected the old ways of doing business from where they came from back East.

And as researcher Sean Safford noted in his comparison of the different fortunes of the superficially similar communities of Allentown and Youngstown in the wake of their respective steel-industry collapses, “Culture does not determine one’s actions, but it may limit the possibilities.” In Allentown, the history of the community created civic relationships that gave local leaders the capability to organize themselves to respond to the crisis after economic networks collapsed. This was not the case in Youngstown. These differences extended back to the founding of these places.

Pure economic history plays a role as well. As sociologist Saskia Sassen, the leading global expert on global cities, points out,

“The deep economic history of a place matters for the type of knowledge economy a city or a city-region winds up developing. This goes against the common view that globalization homogenizes economies. How much this deep economic history matters varies, partly depending on the particulars of a city’s or a region’s economy. But it matters more than is commonly assumed, and it matters in ways that are not generally recognized.”

As she observes, if you want to trade steel internationally you go to Chicago not New York, because Chicago was the centre of the industrial economy of which steel was a part, giving it deeply specialized knowledge and expertise in steel and similar commodities.

Read the rest of this piece at Comment.

Aaron M. Renn is a writer and researcher on urban policy and culture. He focuses on the cities of the American Midwest. A former Senior Fellow at the Manhattan Institute and partner with the consulting firm Accenture, he is regularly featured and cited in the global media. He and his family currently reside in Indianapolis.

Demographia World Urban Areas, 2020: Tokyo Lead Diminishing

Wendell Cox — Fri, 26 Jun 2020 20:29:01 -0400

For the first time in more than six decades the world’s second ranked built-up urban area has reached within 10% of leader Tokyo. The 2020 edition of Demographia World Urban Areas reports that Jakarta has reached a population of 34.5 million, behind Tokyo-Yokohama’s 38.0 million (Figure 1). The report can be downloaded here (Note 1). Yet Tokyo’s growth has slowed, a reflection of Japan’s overall demographic implosion, although it is still adding people while the country is losing population.

Background

Built-up urban areas are continuously built-up development that excludes rural lands (Note 2) are formally defined by a number of nations, using relatively similar criteria. Demographia develops urban area definitions (urban perimeters) based on satellite images for all known built-up urban areas for which there is no formal national census authority definition. Demographia combines extensions of continuously built up areas (as designated by census authorities), where they are a part of a larger labor market (such as New York, Bridgeport-Stamford and New Haven, Los Angeles, Riverside-San Bernardino and Mission Viejo and Toronto, Hamilton and Oshawa).

Demographia World Urban Areas provides pre-COVID-19 population estimates for all of the 1,055 identified built-up urban areas with 500,000 or greater population. The list includes a total population equal to 51.7% of the estimated world urban population (Figure 2).

Population estimates are based on national statistical authority estimates for built-up urban areas consistent with international practice are available. Beginning with this year, most other larger built-up urban area populations are estimated based on small area grids that provide population estimates that are tailored for the urban perimeters (Note 3).

Demographia World Urban Areas contains five tables that provide summary and ranking information:

Table 1: World Summary: Built-Up Urban Areas Over 500,000
Table 2: Largest Built-Up Urban Areas in the World
Table 3: Built-Up Urban Areas Ranked by Land Area (Urban Footprint)
Table 4: Built-Up Urban Areas Ranked by Urban Population Density
Table 5: Alphabetical List of Built-Up Urban Areas

The Principal Challenger: Jakarta

Jakarta’s urban area now spreads from the Special Capital Region, to Tangerang and South Tangerang to the West, the city of Bogor to the south and to Karawang Regency to the East. Even with this massive land area of 3,540 square kilometers (1,367 square miles), Jakarta covers less than one-half the territory of Tokyo-Yokohama. But Jakarta’s population density is 9,800 per square kilometer (25,300 per square mile), more than double that of Tokyo-Yokohama’s 4,600 per square kilometer (12,000 per square mile).

However, storm clouds threaten Jakarta’s future growth. The present city site has extreme environmental challenges, such as sinking due to ground water extraction, as well as some of the world’s worst traffic congestion. The government has announced plans to move the capital by 2024 (video) to the sparsely populated province of East Kalimantan on the island of Borneo (about 1,300 kilometers or 800 miles from Jakarta).

A 2024 move date is very aggressive and even if achieved may not slow Jakarta’s pursuit of the number one spot (assuming continuation of present projected growth rates). Previous national capital moves do not appear to have materially reduced the growth in the urban areas the capitals have deserted. For example, Brasilia is perhaps the most successful post-World War II new national capital, measured in terms of its population growth. In the two decades following its 1960 opening, Brasilia struggled to reach 1.3 million residents, while Rio de Janeiro added 4.3 million.

India’s Largest Megacities

However, if in the unlikely event that Jakarta should falter, fast growing Delhi, the world's third largest built-up urban area should pass Tokyo-Yokohama by 2035. Delhi has a population of 29.6 million residents and a density of 13,300 per square kilometer (34,400 per square mile). Delhi should continue to widen the gap over Mumbai that currently stands at 6.5 million. Mumbai used to be India’s largest built-up urban area.

Even so, Mumbai ranks as the fourth largest built-up urban area in the world, with a population density of 24,800 per square kilometer (64,200 per square mile), making it the second densest megacity (built-up urban area over 10 million) in the world, trailing only Dhaka. It is also slightly less dense than Hong Kong, which is the densest high-income world built-up urban area (25,300 per square kilometer 65,600 per square mile).

Other Population Leaders

Manila is now the fifth largest built-up urban area, with 23.1 million residents. Manila has a population density of 12,300 per square kilometer (31,900 per square mile). The top ten built-up urban areas are rounded out by Shanghai, Sao Paulo, Seoul-Incheon, Mexico City and Guangzhou-Foshan.

Largest Urban Footprint (Urban Land Area)

New York has the largest urban footprint of any built-up urban area, covering 12,100 square kilometers (4,700 square kilometers). New York covers about 50 percent more land area than much larger Tokyo-Yokohama and 90 percent more land area than Los Angeles.

Highest Urban Density

Dhaka, ranked 19^th with a population of 15.4 million, continues to have the highest urban population density, at 33,900 per square kilometer (87,700 per square mile). This is 19 times as dense as the New York built-up urban area. Kinshasa is the second densest, at 28,500 per square kilometer (73,900 per square mile).

Decline of the West

For the first time, the high-income West is not represented in the 10 largest urban areas (Figure 3). In 1950, four such cities — New York, London, Paris and Chicago — were on the United Nations list. New York, which had been the largest built-up urban area from the 1920s to 1950, fell to 11^th place, while the others had long ago fallen out. Chicago has suffered the largest drop, from 8^th to 41^st. That is despite a near doubling of its population from 1950 to 2020. The Los Angeles built-up urban area, which ranked 12^th in 1950, nearly quadrupled its population, but fell to 20^th, after having reached 6^th largest from 1965 to 1975.

Note 1: Demographia World Urban Areas was the first to identify the under-estimation of population in some of the largest urban areas, by other sources. For example, Demographia’s early population estimates for the Jakarta, Delhi, Manila, Seoul-Incheon and Kuala Lumpur built-up urban areas were far higher than reported by others at the time. Other sources have revised their estimates upward. The earlier, lower estimates of others were, in actuality, municipal estimates that did not sufficiently take into consideration the spread of urbanization beyond city or other geographical limits. Demographia’s larger population estimates were the result of examining actual satellite maps to determine the extent of individual built-up urban areas.

Note 2: Demographia World Urban Areas is in its 16th year of publication. It was established for the purpose of bringing some consistency to the subject of urban density, in hopes of replacing often grossly invalid anecdotal comparisons between cities. The built-up urban area is the only level at which there is sufficiency consistency and sufficient data to estimate the densities of the urban organism at anything approximating international standards.

There continues to be considerable confusion about the measurement of urban densities. The key is in comprehending the differences between urban areas and metropolitan areas. Built-up urban areas are continuously built-up development that excludes rural lands. This is illustrated by the Paris built up urban area and the Paris metropolitan area in Figure 4. Built-up urban areas are the city in its physical form, as opposed to metropolitan areas, which are the economic or functional cities (the labor and housing markets). These terms are defined by Cheshire, et al. of the London School of Economics (see: “People rather than places, ends rather than means: LSE economists on urban containment”).

Demographia World Urban Areas uses base population figures, derived from official census and estimates data, to develop basic year population estimates within the confines of built-up urban areas. These figures are then adjusted to account for population change forecasts, principally from the United Nations or the various national statistics bureaus for a 2018 estimate.

Note 3: Demographia World Urban Areas is a continuing project providing “state of the art” data. Revisions are made as more accurate satellite photographs and population estimating resources become available. As a result, Demographia World Urban Areas is not intended for trend analysis.

Photograph: Cover, 16^th Annual Demographia World Urban Areas (Guangzhou: Zhujiang New Town).

The Urban Project: Urbanization, Urbanisms, and the Virus – A Historical Take

Fanis Grammenos — Wed, 17 Jun 2020 20:29:01 -0400

Observing and writing 20-some years before the oil embargo (1974) and 30 years before the stern Brundtland report (1987), Jane Jacobs (1961) resolved that density comes in “good” and “bad” varieties. That temporal distance from two of the tectonic events of the 20th century was inescapably reflected in Jacobs’s assessment: “Good” density was unrelated to fuel consumption, or “climate change”; “good” was a social and an urban life-quality yardstick spanning the range between “liveliness, vitality” and the “Great Blight of Dullness” as well as between “concentration” and “overcrowding.” Jacobs shed light on ways that concentration (aka density) could generate liveliness, conviviality, diversity, safety, and economic vitality—not on its potential for reducing CO₂ emissions. The recent painful event, a massive viral infection that has drained all liveliness from cities, implicates concentration and, as a result, the “density debate rages alongside the pandemic.”

Why the rage? This piece explores probable causes and also views on the issues that surround the debate’s principal trigger—density.

The apparent general cause is the search for and attribution of culpability for death—a loaded task. In the virus case, the link to planning is arguably indirect and this circuity raises doubts, which heighten the debate’s tenor. Its roots, however, lie in a takeover.

Figure 1. Urbanization in plain sight: New York City’s population grew 25-fold during the first 100 years and more than doubled in the next 100. The growth rate has generally been decreasing. Between 2010 and 2019 population growth declined further and entered population loss territory in 2016. This growth path is common to most imperial, national, or regional capitals since Rome—all are people magnets. It is unclear from this chart at what points New York’s “urbanism” began, peaked, or declined. (Data: Wikipedia- Demographics of NY)

A Friendly Takeover

City planners, architects, and transportation experts embraced Jacobs’s unequivocal and hopeful “urban” message and, as the climate-change issue overtook the city planning field with fierce urgency, combined the emissions reduction issue with her influential exposition, now tagged “urbanism.” This newborn conceptual amalgam “urbanism plus environmentalism” expectedly acquired undeniable moral authority; after all, humanity’s survival was at stake. Driven by such compelling duty, researchers looked for, and found, evidence that “bad” density contributes inadvertently to the planet’s demise and to a decline in the wellbeing of its inhabitants. Onwards from that unsettling assertion, research outcomes are slotted as: “urbanist” or “anti-urbanist” and, invariably, “right” and “wrong” or, alternatively, “objective” and “biased.” The “urbanism” movement casually subsumed the environmental imperative and hence took an adjudication role.

With the merger complete, the outcome is a sustained rage. Rage between -isms of all stripes and from all corners: suburbanism, liberalism, big-brotherism, urbanism, modernism, anti-gigantism, back-to-earth-ism etc. etc. with their mutually suspect moral or intellectual shortcomings: A weighty mantle adorns all discussants, replete with intolerance and covert patronizing.

Figure 2. Plateauing, homeostasis or optionality. From the 1920s on, three boroughs in New York City gained no population or lost some (a combined million). This slow growth/loss coincides with the proliferation of new transport means (subways, buses, and cars), unavailable in the 1800s. (Data: Wiki historical data)

Urbanization and Urbanism

“Urbanization” can be easily grasped: People move to cities, and cities grow (Fig. 1). The forces propelling this move and the resulting, often chaotic, settlements have largely been understood. As Jacobs intuited in 1969 (simplified by C. Abrams): ”Import and export or die.” And, invariably, “produce!” (or work), the strongest appeal of cities. Economic activity sustains their growth and survival. It also boosts collective and personal wealth and expands choice; all irresistible tendencies.

“Urbanism” by contrast is elusive; impossible to trace its historical progression using actual city examples. It evokes a desirable end state (as in “egalitarianism”) but lacks the active or descriptive forms (as in “urbanize” and “urbanized”) that capture process and final outcome. Due to this vagueness, it has spawned numerous personas: “60 urbanisms” each encapsulating either a process (i.e. “Tactical Urbanism” and “Guerrilla Urbanism”) or outcomes (i.e. “Agricultural Urbanism,” “Neotraditional”). However, the one recurring, stable component of all “urbanisms” that can be traced historically and measured accurately is density, hence the persistent focus of current debates on the subject. S. Handy (2017) made a forceful case for its centrality in achieving environmental goals: “Compact development cannot reduce driving very much on its own, but we cannot reduce driving very much without it.”

Is There a ‘Bad Density’?

The urban project, with density as its focus, advances in earnest. Not the project of urbanization, which proceeds organically with mathematical inevitability (Fig.1), but the project of urbanism, which needs to be brought about with concerted, conscious effort. Urbanization is circumstantial, collateral, bottom up, and messy, while urbanism would be largely intentional, deterministic, orderly and rule-bound, and, consequently, programmatic.

The urban project would inject purpose into urbanization: First and foremost, reduce its impact on climate change, but also reduce inequality, enhance opportunity, and improve health outcomes. It would create the good town, the convivial city, the exciting metropolis, and, perhaps, even a just society. Simple, profoundly inspiring, emotionally engaging, and entirely ahistorical (see UN’s 17 Sustainable Development Goals).

To achieve such goals, Jacobs mused about density: "I should guess, roughly, that it is apt to hover at about 200 dwellings to the net acre"(page 217, 1961). A prime example of the good city was Greenwich Village of the 1950s, that she praised profusely. It had a density of 381 p/ha (154 per acre) (Fig. 3).*

Figure 3. From five Boroughs to five Neighbourhoods: densities in Manhattan with a focus on Greenwich Village (Column). Like the Boroughs’ populations, the density of these neighbourhoods fluctuated widely through time and stabilizing after 1980 in a band between 244 and 505 p/ha (99 and 204 per acre). The band of all 22 neighbourhoods stretches between 138 and 505 p/ha, a three-fold plus range. (Data: The rise and Fall of Manhattan’s Densities, 1800 -2010 by Shlomo Angel and Patrick Lamson-Hall PDF)

Evidently, Greenwich Village (Fig.3) experienced wide swings in density: tripling from 1900 to 1910, then falling stepwise to less than half of the 1910 mark and almost doubling again in the next ten years. Neighboring West Village had at the time (1950), and now, only two thirds of its neighbor’s density (246 vs 381 p/ha). All five neighbourhoods, most remarkably the East Village and the Upper West Side, experienced fluctuations. The latter had almost twice the density of Greenwich in the ‘50s (706 vs 381 p/ha). Fluctuations, variability and impermanence permeate these five; these are typical attributes of all 22 neighbourhoods.

Two main questions about density arise from this limited historical tableau: Are all these densities from the extreme high (1323 p/ha) of East Village and lows of Greenwich (201 p/ha) “good” or only a select number of them and, if the latter, which? Are there any upper or lower bands of density that are “bad” and, again, which?

These questions remain open. But what can be distilled from the fluctuation and wide range is an insight that Manhattan (and cities in general) can be viewed as large ecosystems that contain many microsystems, each adapted to a range of local conditions (a port, a park, a square, a thoroughfare and so on) that shape its density and other characteristics. The city then appears more like an organism than a malleable artifact, just as Jacobs implied (page 433, 1961).

The second question, central to this discussion, is whether density can be deliberately commandeered to upper or lower levels (an order of magnitude apart!) or whether densities are a collateral outcome of urbanization acting in tandem with other conditioning variables. Is there a known top-down process or system ultimately capable of inducing specific density levels district by district? This quick historic recount suggests that density ranges represent bottom-up choices not top-down interventions.

Figure 4. Admired Parisian-like “urbanism” in Manhattan—among skyscrapers that Paris prohibits

If “bad” population density is hard to frame, an unambiguous “good” density with a clear upper threshold does exist, yet it rarely enters the discussion of the good city—job density per employable population. It gives a city its livelihood and liveliness but remains invisible—just a number—hence it receives only perfunctory mention as a planning topic. A jobless city is a ghost city, just like a city under viral quarantine.

Intermission: Manhattan and Paris

Research in a variety of complex systems clarifies that “[…] a complex system is a system that exhibits all of the conditions for complexity and at least one of the products emerging from the conditions.” (link in caption for Figure 5) An example of self-organization and robustness of order in cities has been hiding in plain sight.

A Curious, Surprising Variant

Comparing paragons of density and “urbanism” sheds light to the question of programmatic density planning: Manhattan and especially Paris are generally acknowledged as supreme examples of urbanism. Ideas like “the more strangers the merrier” (Jacobs page 40,1961) and “bumping into each other” generally serve as a casual placeholder definition of “urbanism” (not Jacobs’s term.)

Residents may have a different perception. Significantly, Manhattan, the densest borough of New York City, shed nearly 700,000 people since 1910. So did Paris (663,000) in roughly the same period. Both figures surpass the entire population of many U.S. cities (e.g., Portland), Lyon of France, and imperial Rome itself. Put simply, Manhattan and Paris each shed an entire city from their respective densest districts. The causes of this outflow are partially understood and a matter of speculation. What’s relevant here, is that these outflows were spontaneous, not induced or regulated. This points once more to the city as a self-organizing complex-system. Figure 5 shows that Paris’ uncontested “urbanism” registers densities at the district scale that are consistently lower than those of Manhattan and, arguably, sufficient to achieve its status.

Figure 5. Densities of local neighbourhoods (or districts) of NY City, Central Paris in descending order of density. (Data: NY- Shlomo Angel, Paris - APUR -La Metropole du Grand Paris)

The Crux

We witnessed the unstoppable process of urbanization and the resulting city growth. We also saw that, in this process, districts within cities gain or loose populations, gain or loose density and, similarly, change land uses; as a result, their density and diversity fluctuate. It appears that internal factors, such as social tensions, income disparities, and property values, among others, trigger these changes. External influences also play a part. These emerge unpredictably: new modes of transport and communication, novel manufacturing and building technologies, and demand for labour—from manual to managerial.

None of these changes and the forces that induce them are commandeered—they are dispersed, uncoordinated initiatives attributable to thousands of individuals seeking, finding, and making personal life choices. One universal choice is the amount of space that each individual consumes: from cramped to abundant. Taken together, these choices shape the city and its densities. Densities have and will always fluctuate temporally and geographically.

At any population density, however, people need to and do make direct contacts. These can be either utilitarian (a transaction), altruistic (helping out), or pleasurable (amusements), some obligatory others discretional. Discretional contacts can be reduced and replaced in an epidemic setting. The one direct contact that cannot be reduced without risking a city’s very existence is reporting for work, the fundamental reason why people gravitate to cities in the first place.

And therein lies the crux. To survive and flourish, a city must work: produce, import, and export. Work invariably necessitates direct contact, so providing a livelihood in a viral environment becomes a threat to life. Families that have at least one provider, no matter where they live or at what density, have, consequently, a potential vector in both directions. To sidestep work and focus on population density as a cause of or defense against virus spread is, at best, pointless chatter. If a city is at work, so is the virus.

Planners have learned this lesson before. A fierce debate erupted after 9/11 about the need for and benefits of tall buildings, only to subside in acquiescence to be followed by an oversupply of such buildings. The same will happen after Covid-19 regarding population density; it will follow its organic evolution, whatever the talk.

* Densities in the article are given in people per hectare (and acre) to be consistent with the data sources.

Photo credit: Athens urbanization at 3,170k (by author).

Fanis Grammenos is the director Urban Pattern Associates in Ottawa, Ontario and the author of Remaking the City Street Grid: A Model for Urban and Suburban Development. (i.e. The fused grid.) Reach him by email with questions or comments.

From tragedy to opportunity: We could live better when today's mayhem ends

Joel Kotkin — Mon, 08 Jun 2020 20:29:01 -0400

For most people in this locked-down, riot-scarred world, the future beckons unpleasantly. There is a growing sense that, economically, the 2020s may look more like the 1930s than some halcyon post-industrial future. “Dark days ahead,” suggests The Week. “This is what the end of the end of history looks like."

Yet, beyond the depressing statistics, the deserted malls, the looted or abandoned Main Streets, lies the potential to use the pandemic to create the impetus for better, more sustainable and family-centric communities. This is not just some return — imagined from the security of the high punditry — to a “plainer,” more noble past but actual, meaningful improvements in our daily lives, made largely possible by technology.

Pestilence has long reshaped economies and communities. The plague took as many as one in three Europeans but also generated higher wages for the remaining workforce and provided new opportunities for enterprising peasants, who soon would morph into a nascent middle class. “In an age where social conditions were considered fixed,” suggested historian Barbara Tuchman, the new adjustments seemed “revolutionary.” Similarly, the disease-ridden depredations of the industrial city eventually led to new sanitation systems, the growth of public health systems, as well as a century-long exodus to less crowded, more family-friendly suburban communities.

Changing how we work and live — for the better

The growth of telecommuting and its surprising productivity gains have been turning corporate heads during the pandemic. Many companies, including banks and leading tech firms such as Facebook, Salesforce and Twitter, now expect a large proportion of their workforce to work remotely, permanently. A University of Chicago study suggests this could grow to as much as one-third of the workforce. In Silicon Valley, it notes, the number reaches near 50 percent.

The shift to dispersed work — likely to be further accelerated by the ongoing riots and protests — opens up unique opportunities for parts of the country that have not enjoyed the benefits of tech growth. With two out of three tech workers now willing to leave San Francisco, Big Tech can get bigger while spreading talent and wealth more widely. Rather than steering high-wage employment places where earnings tend to disappear through inflated living costs and taxes, much of the workforce now will be able to live closer to where they can afford to live comfortably, raise a family and lift up local economies.

Some urban planners, notably in California, will try to stifle these developments, seeking to push large apartments and transit over suburban growth. But no degree of urban boosterism will erase the searing memories of the pandemic and the riots which have devastated the hearts of our largest cities, and which will impact location decisions for a generation.

Read the rest of this piece at The Hill

Joel Kotkin is the author of the just-released book The Coming of Neo-Feudalism: A Warning to the Global Middle Class. He is the Presidential Fellow in Urban Futures at Chapman University and Executive Director for Urban Reform Institute — formerly the Center for Opportunity Urbanism. Learn more at joelkotkin.com and follow him on Twitter @joelkotkin

Economy Loses Jobs Equal to Metro New York, Los Angeles, Chicago, and DFW

Wendell Cox — Sun, 07 Jun 2020 20:29:01 -0400

The economic distress resulting from the COVID-19 pandemic and strategies to limit its spread have been substantial. The most intense effects have been inflicted on the estimated 26 million workers who have lost their jobs, either temporarily or permanently. Many businesses may not be able to recover, while others may never be restored to their previous employment, as customer revenues take years to recover.

The gross impact on society and the economy has been catastrophic. The non-partisan Congressional Budget Office (CBO) assessed the situation in its May Interim Economic Projections for 2020 and 2021.

The increase in unemployment is particularly stark. According to CBO, “The pandemic and the social distancing measures taken to contain it have widely disrupted economic activity, causing a wave of job losses and ending the longest expansion since World War II.”

This article summarizes data from the CBO May report (above) as a letter from CBO Director Phillip L. Swagel to Senate Democratic Leader Charles Schumer. Senator Schumer had requested a comparison of the May report projections with the last previous pandemic CBO projections, issued in January.

The Unemployment Shock

Just before the pandemic, US workers enjoyed the lowest unemployment rates in half a century. According to the CBO’s May report supplemental data, the annual unemployment rate in 2019 was 3.7%., the best annual unemployment rate since 1969.

Unemployment was trending more favorably in the fourth quarter of 2019, at 3.5% (Figure 1 [CBO Report Figure 3]. The unemployment rate had edged up to 3.8% in the first quarter, as social distancing and lockdown strategies began to be implemented toward the end of the period.This ended a streak of eight consecutive quarters of below 4.0% unemployment, dating from the second quarter of 2018. Out of the previous 153 quarters for which CBO provided data, stretching back to 1980, the unemployment rate had been below 4.0% only twice before, both in 2000.

The pandemic brought on a previously inconceivable shock to the nation’s labor market. CBO estimates that the unemployment rate in the 2020 second quarter will be 15.1%. This unemployment rate is more than four times the ratesof both the 2019 fourth quarter (3.5%) and the 2020 first quarter (3.8%).

CBO projects that in the second quarter of 2020 (ending June 30), the number of people employed will be 26 million lower than in the last quarter of 2019. This is the equivalent of losing all the employment in the nation’s four largest metropolitan areas (New York, Los Angeles, Chicago and Dallas-Fort Worth).

CBO expects things to get worse, projecting a 15.8% unemployment rate in the third quarter. This would be nearly 50% greater than the highest quarterly unemployment rate since 1980 (10.7% in the fourth quarter of 1982) and higher than any annual unemployment rate since 1939 (17.2%).CBO then projects improvements, to 10.2% in the fourth quarter, with a gradual improvement to 8.6% in the fourth quarter of 2021.

The comparison with CBO’s pre-pandemic projections is stark. Overall, CBO expects an unemployment rate of 11.5% in 2020 and 9.3% in 2021. Before the pandemic, CBO had projected that the unemployment rate for both 2020 and 2021 would be 3.5%. This drop rise from the actual 3.7% 2019 unemployment rate to the forecast 11.5% rate in 2020 is greater than the first year of the Great Depression. The projected 2019 to 2020 unemployment rate in increase is 210%, which compares to the 172% increase From 3.2% in 1929 to 8.7% in 1930 (Figure 2).

Job Losses by Sector

The job losses vary widely by industry (Figure 3 [May Report Figure 1]). In the Leisure and Hospitality industry (sectors such as airlines, hotels, amusement parks, resorts), 48.3% of employees lost their jobs in March and April alone. This was by far the largest loss, both in percentage and actual number of jobs (8.2 million). The next largest losses were in Professional and Business Services, Retail Trade and Health Care and Social Services, each at 2.2 million.

Employment has held up the best in the Federal Government, which experienced a 0.6% gain and in Utilities, which had a 0.5% loss.

The Overall Economy

Not surprisingly, with 26 million more people out of work, the gross domestic product (GDP) is plummeting. CBO expects a cumulative quarterly real GDP loss of $1.76 trillion from the fourth quarter of 2019 to the fourth quarter of 2020. This is the equivalent of the New York state annual GDP. The combined 2020 and 2021 quarterly cumulative the loss would be $3.06 trillion, the equivalent of California’s annual GDP. These are staggering numbers.

Uncertainty and Distress

CBO expects the economy to recover to its previously projected level by the fourth quarter of 2029 (Figure 4 [Senator Schumer letter Figure 3]). CBO has also projected that by 2030, the unemployment rate should drop to 4.5%, nearly equaling the previous projection of 4.4%. But that is a very long time. Already millions of people have been thrown out of work and into distress, and there is great uncertainty.

CBO cautions that: “An unusually high degree of uncertainty surrounds these economic projections, particularly because of uncertainty about how the pandemic will unfold this year and next year, how the pandemic and social distancing will affect the economy, how recent policy actions will affect the economy, and how economic data will ultimately be recorded for a period when extreme changes have disrupted standard estimation methods and data sources.”

Life needs to return to normal as soon as possible. Governments around the world have used a variety of strategies, seeking to control the spread of COVID-19, without destroying their economies, societies and even their democracies. There are likely important lessons to be learned about how to better deal with any future pandemics, but for now it’s the economic sickness that may hit the hardest.

Photo: US Lockdown Map (coding explained at image source: Wikipedia)

UPDATE: Just as we were going to press, the new jobs report came out, indicating an increase in employment of 2.5 million jobs in May. This is the largest month-to-month employment increase in history, and far exceeds the 8.3 million job loss that some economists were predicting. The Leisure and Hospitality Industry account for about half the restored jobs. According to CNBC: “As it turned out, May’s numbers showed the U.S. may well be on the road to recovery after its fastest plunge in history.”

Why This New Yorker Returned to the Midwest

Aaron M. Renn — Thu, 28 May 2020 20:29:01 -0400

New Urbanism Editor Lewis McCrary's Note: Before the pandemic changed the urban landscape of American life, the last two decades have seen a familiar dynamic: the coastal cities have recorded dramatic increases of wealth as highly-educated workers concentrate in a few major metro areas, including New York, San Francisco, and Washington. At the same time, much of Heartland America has seen comparatively slower growth, but some places are economically resilient and affordable, such as Columbus, Des Moines, Kansas City, and Indianapolis. So are some people ready to leave a prosperous coastal city and seek a better deal in the Midwest?

Aaron Renn, a longtime analyst of urban trends, did just that—he recently left New York City for the Hoosier state. Renn made the move to Indianapolis a few months ago, only a short time before the coronavirus storm. This week New Urbs editor Lewis McCrary asked Renn why he made the move, what portends for the Midwestern economy these days, and what he is working on in Indianapolis. A contributing editor at City Journal and author of The Urban State of Mind, Renn also recently launched Heartland Intelligence, a newsletter that aims to understand the regional economy.

Why did you leave New York City and move to Indianapolis?

It was a difficult decision because I love both cities and so does my wife. They are both great though in different ways obviously. When a fellowship ended in NYC I had to make a decision about what to do next. When I had moved to New York I was single but by this point I was married with a two-year-old. My wife and I are both from Indiana and our parents are here. As with many others who’ve made the same kind of move at the same time, it was a good time to be closer to family and where we can have more space.

Of course in light of the coronavirus, moving to Indy looks like a smart move at present. It’s much better staying at home when that home is a single-family house with a front porch and a backyard.

If I had unlimited funds, I’d probably keep a place in both cities. So yes, more money would have allowed us to stay in New York in a sense. But as importantly even with more money I’d still want us to be in Indianapolis as well. Indy is better today than at any time in it’s past and only continuing to go up. It’s great to be a part of not just what it is but what it is becoming.

Read the rest of this piece at The American Conservative.

Aaron M. Renn is a contributing editor at City Journal, and an economic development columnist for Governing magazine. He focuses on ways to help America’s cities thrive in an ever more complex, competitive, globalized, and diverse twenty-first century. During Renn’s 15-year career in management and technology consulting, he was a partner at Accenture and held several technology strategy roles and directed multimillion-dollar global technology implementations.

Photo credit: Miyin2 via Wikimedia under CC 4.0 License.

Dispersion in US Metros Increases Even Before COVID-19: New Census Estimates

Wendell Cox — Tue, 05 May 2020 20:29:01 -0400

The latest US Census Bureau metropolitan area population estimates (for 2019) were largely lost in the coverage of the COVID-19 pandemic. Yet the new results, released a few weeks ago, indicate that people are moving to where social distancing is less challenging — the suburbs and exurbs, with their lower density and perhaps from a pandemic point of view, their lower exposure density — with less intense human interaction and hence lower infection risk associated with ground-oriented housing (detached and attached houses and townhouses), travel by car and generally less crowded conditions, such as in stores.

Moving to Lower Densities

The new data indicates that within the major metropolitan areas, domestic migration away from the core counties was 2.23 million from 2010 to 2019. In contrast, the suburban and exurban counties gained 1.94 million. The suburban and exurban counties attracted 4.2 million more moving residents than the core counties (Figure 1). The rate has been accelerating. In the first two years of the decade, the suburbs and exurbs had about a 175,000 domestic migration advantage over the core counties. In the last three years, the suburban advantage has widened to over 600,000 (Figure 2).

This is just another manifestation of the trends that have been underway since World War II. Most recently, since 2010, 92% of major metropolitan area growth was outside the functional urban cores (Figure 3). Employment dispersion continues, with more than 90% of new jobs being created outside the downtowns (central business districts) of the major metropolitan areas since 2010 (Figure 4).

Generally Declining Growth

Population growth has fallen off strongly among the major metropolitan areas. Eight of the largest 10 had slower growth from 2015 to 2019 than the first four years of the decade. Only Atlanta and Phoenix had larger growth rates in the later period (Figure 5).

Declining Megacities

The nation’s two megacities continued their decline. New York has lost nearly 120,000 residents since 2016. Domestic migration accounted for a loss of 196,000 New York metropolitan area residents in just the last year, 1.02% of its 2018 population. Second largest Los Angeles has lost more than 60,000 residents since 2017, while its net domestic migration loss was 122,000 in the last year. This is a loss of 0.92% of its 2018 population.

Wannabe megacity Chicago continues to slip away from the 10 million status, with a population stuck at less than 9.5 million. The metropolitan area has lost about 90,000 residents since 2014 and has slightly fewer residents than in 2010. If Chicago had continued to grow at its tepid 2000s rate, the 10 million figure might have been achieved by the mid 2020s. Now, that may never occur. But as grim as things may seem, Chicago’s net domestic migration was the lowest in five years, and less as a percent of its population (0.79%) than in New York or Los Angeles.

All three largest metropolitan areas lost overall population at a similar rate last year, with New York dropping 0.31%, Los Angeles 0.27% and Chicago 0.26%.

The “Five Million” Metros

Dallas-Fort Worth retained its fourth position, three other over-5 million metropolitan areas, Houston, Washington and Miami each moved up a place, as Philadelphia fell from 5th to 8th. Philadelphia had been ranked fourth from 1950 to 2000. There was a time when Philadelphia was the nation’s largest urban center, with the city and adjacent suburbs (Southwark and Northern Liberties) having a population greater than New York City in 1790 and 1800.

Phoenix: The Next 5 Million Metropolitan Area

The “star” of this year’s population estimates was Phoenix (photo above), which reached 4,948,000 and has probably already passed five million. Phoenix became the 10th largest metropolitan area, having displaced long time top 10 incumbent Boston. It also moved past San Francisco earlier in the decade. Phoenix grew 2.0% over the past year, a feat exceeded only by Austin (2.8%) and Raleigh (2.1%) among the nations 53 metropolitan areas with more than 1,000,000 population.

Phoenix added 399,000 net domestic migrants between 2010 and 2019, trailing only Dallas-Fort Worth (449,000). In the last year (2018-2019), Phoenix led the nation, with 71,000 net domestic migrants, easily outdistancing Dallas-Fort Worth (46,600).

Moving Away from the Largest Metropolitan Areas

Overall, domestic migration patterns have shifted away from the largest metropolitan areas. The metropolitan areas with more than 1,000,000 population had net domestic migration of a minus 328,000 from 2010 to 2019. Metropolitan areas with from 500,000 to 1,000,000 gained 583,000. Metropolitan areas with 100,000 to 500,000 population gained 460,000. The balance of the nation, which includes smaller metropolitan areas as well as all areas outside metropolitan areas lost 716,000 (Figure 6).

After COVID-19?

The big question is how things will change in light of the COVID-19 epidemic and the lockdowns.

The metropolitan areas with the greatest pockets of urban density have a substantial challenge in controlling an epidemic that requires social distancing. The problem, as we have discussed before is not so much the population density at any macro level, but rather the personal level (See: Exposure Density and the Pandemic). Exposure density is intensified for individuals by crowded conditions (such as crowded subways, residences, elevators, shopping, events, etc.) far more than any theoretical area-wide population density. Combined with all this is the evidence that low-income citizens are more likely to fall victim to the epidemic than the rest of the population (see: Data Suggests Many New York City Neighborhoods Hardest Hit by COVID-19 Are Also Low-Income Areas).

The ability of commerce and public policy — companies, governments and people — to respond to the necessity of social distancing through internet meetings has been a revelation. Business meetings, not all, but most, can be conducted without any concern about social distancing. There could be substantial benefits to the extent that technology can virtualize the work place for millions of people.

Foremost could be the environmental gains as millions more eliminate the work trip (working at home, mostly telecommuting), along with shorter commutes made possible by lessened traffic congestion. Even before the epidemic, working at home had raced ahead of transit as a commute option in the United States. In 2018, working at home led transit in 44 of the 53 metropolitan areas with more than 1,000,000 population.

Not being tied to a physical commute every day could make it possible for households to move to places they would prefer more. This is not just the continuing movement of people away from the crowded urban cores to the suburbs and exurbs, but even beyond. The key, obviously, is that they be able to carve out an affluent standard of living out of the post-COVID economy.

Little of this seems to portend any sort of greater centralization. It seems likely that the dispersion that has been going on for decades in the United States (and around the world) will continue.

Photograph: The Central Avenue corridor (downtown) in Phoenix (by author).