Papers in Evolutionary Economic Geography # 20

[Pages:29]Papers in Evolutionary Economic Geography # 20.41

Cities in a Post-COVID World Richard Florida, Andr?s Rodr?guez-Pose & Michael Storper



Cities in a Post-COVID World

Richard Florida* Andr?s Rodr?guez-Pose** Michael Storper***

*University of Toronto ** London School of Economics *** London School of Economics; UCLA

Abstract: This paper examines the effect of the COVID-19 pandemic and its related economic, fiscal, social and political fallout on cities and metropolitan regions. We assess the effect of the pandemic on urban economic geography at the intra- and inter-regional geographic scales in the context of four main forces: the social scarring instilled by the pandemic; the lockdown as a forced experiment; the need to secure the urban built environment against future risks; and changes in the urban form and system. At the macro-geographic scale, we argue the pandemic is unlikely to significantly alter the winner-take-all economic geography and spatial inequality of the global city system. At the micro-geographic scale, however, we suggest that it may bring about a series of short-term and some longer-running social changes in the structure and morphology of cities, suburbs, and metropolitan regions. The durability and extent of these changes will depend on the timeline and length of the pandemic. Keywords: Cities, COVID-19, Pandemic, Urban Structure, Remote Work.

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Introduction

COVID-19 is not the first virus to strike our cities, nor will it be the last. Over the course of history, cities have often been hotbeds of contagion. The Black Plagues of the fourteenth century killed a third of Europe and the Middle East. The Cholera outbreaks of the 19th century decimated London, Paris, Moscow, Hamburg, New York, Chicago, and Washington DC, among other large cities. The Great Flu ? also known as the Spanish Flu ? took the life of as many as 50 million people worldwide between 1918 and 1920. In the United States it killed more people than the two World Wars and the Korean and Vietnam wars combined and especially ravaged cities like Pittsburgh, Philadelphia, Louisville, and Nashville (Correia et al., 2020). Yet, the current pandemic is also leaving profound scars in many of our cities. In New York City, COVID-19 has provoked 35% more excess deaths than the 1918 Great Flu and resulted in more than 3.3 times the increase mortality of the 9/11 attacks on the city (McCann et al., 2020). In Bergamo, Italy, excess deaths in March 2020 were 6.67 times higher than in a normal March (McCann et al., 2020).

Past pandemics wreaked havoc in the moment, and influenced substantial cultural, political, and urban design changes, but none of them succeeded in denting the role large cities have in society. No pandemic, natural disaster, or war has ever managed to stifle their growth and pre-eminence over the long term. Claims abound that "this time is different," because this is the first pandemic in history to occur when there is a widely available alternative to face-to-face work, and an alternative to doing one's own shopping. But there is a long history of failed forecasts that "this time, distance is dead"; indeed, with every major improvement in transport and telecommunications capacity in the past two centuries, there has been an increase in urbanization (Leamer and Storper, 2001). It is therefore highly unlikely that COVID-19, despite its high levels of devastation in certain cities, will derail the long-standing process of urbanization and the economic role of cities. Innovation, creativity, and economic growth require the clustering of talent and economic assets, face-to-face interaction, buzz, diversity, and the critical mass that only cities can provide (Storper and Venables, 2004). Perhaps, paradoxically, the more efficient transportation and telecommunication technologies become in spreading out certain kinds of routine interactions, the more we invent creative new cutting-edge interactions that demand face-to-face interaction. This is fundamentally why, throughout history, large cities have rebounded from the devastation of epidemics and many other types of crises and catastrophes.

Nonetheless, even if cities will not shrink or die from the COVID pandemic, they will certainly change--in the short term, and even after a permanent solution to the pandemic, like a vaccine, is found. The current pandemic is producing four main forces that have the potential to lead to a relatively long-lasting transformation of cities and regions as we currently know them. These four forces are:

? Social scarring: The fear instilled by the pandemic may pull citizens apart and cause people to avoid crowded spaces for a certain period. This will influence residence choice, travel and commute patterns, and the economic viability of certain kinds of businesses and social gathering spaces.

? The forced experiment for employment, shopping, workplace and residence choice, commuting of the lockdown: The lengthy confinements triggered by the health 2

emergency have provided the conditions for a forced ? or, as some will say, a `natural'? experiment. Workplaces and classrooms have transitioned to remote, shopping to delivery, and social life has become played out largely over social media. These changes ? many of which were more seamless than expected ? will leave a legacy on how we interact, work, shop, and, consequently, live. The lockdown is showing that there are radically different ways of living made possible by digital tools. However, the extent to which these alternatives will be complements or substitutes for traditional ways of interaction, once the immediate threat has passed, remains to be seen. There are strong signs that, for many types of work, socialization and leisure, distanced interaction is not a full substitute and that there is hunger to return to face-to-face.

? The need to secure the urban built environment against this and future health and climate risks: Public infrastructure, public-facing businesses, and all manner of spaces where lots of people gather will need to make immediate changes to facilitate social distancing and adequate hygiene standards. In the long-run, this crisis will prompt architects, designers, and planners to more seriously consider ? as was the case in previous pandemics, such as the 19th century cholera outbreak ? permanent interventions that respond to the threat of future pandemics and climate risks.

? Changes to urban built form, real estate, design, and streetscapes: Social distancing creates the immediate need for different configurations of indoor and outdoor spaces. At least some of these changes will likely be preserved after the immediate threat passes, whether for their public health benefits, or because people simply enjoy them. The outcome of the forced experiment could also lead to more permanent changes in how and where people live and work.

Ultimately, the extent of the pandemic's effect on cities will depend on how long it lasts, considering potential new waves; how deadly it is; and how fast life returns to something resembling normal. A short pandemic could lead to a quicker return to business as usual, without extreme changes to cities. A lengthier pandemic with several peaks is more likely to bring about more durable and lasting changes in our cities, suburbs, and metro areas

This essay examines the potential immediate and long-term effects of the pandemic on cities and regions across two geographic scales. The first is concerned with the large-scale arrangement of humans and economic activity across the landscape, at the inter-regional scale or what we will call the "macrogeographic" scale. It seeks to understand the sorting of population and employment and activity into cities of different sizes and their different economic attributes. The second focuses on the arrangement of people and activity (and hence land use) within urban regions, between central and suburban areas, and at the finer granularity of neighborhoods and even streets and smaller districts. We examine these issues through the lens of urban and economic geography, by which we seek to contextualize limited evidence from short-term trends in a longer-term dynamic perspective.

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Pandemic: geography what we know so far

SARS-CoV-2 is a novel coronavirus thought to have originated in bats in China. COVID-19 is the disease caused by the virus (Yong, 2020). It is poised to be the deadliest viral pandemic since the emergence of HIV in 1981 and the worst airborne virus since the Great Flu of 1918-1920. It is the first time an airborne pandemic has gone global in the age of widespread commercial air travel (Rosenwald, 2020).

Big cities, with a very high degree of air connectivity, lots of international travelers, and lots of people in close proximity, were the places hardest hit during the first wave of the pandemic in the Western world, in March and April 2020. At the time of writing New York City remains ? with 27,200 excess deaths relative to the previous five years (FT, 2020) ? the largest epicenter in the world in absolute terms, although cities like Lima and Mexico City are closing in on this grim statistic. Many other developed world global cities, such as Madrid, Milan, Paris, London, Barcelona, Chicago, or Stockholm have also experienced severe outbreaks. And the pandemic is also affecting a number of large cities in the emerging world, starting from Wuhan ? widely considered to be as the source of the pandemic ? but has reached far-flung cities, such as Guayaquil, Lima, Mexico City, Manaus, or Jakarta (FT, 2020). In the USA, so far the largest world center of COVID in absolute terms, the original regional focus of the virus in the Northeast, was replaced by severe outbreaks in Texas, Florida and Arizona by June, and a resurgence in California, which had a mild initial outbreak and strong public health measures, by July. We still do not have a definitive geography of the pandemic.

Most importantly, there is a great difference between the geography of the first-hit places, and the ultimate geography of infections. For the first-hit places, the severity of the outbreak appears less due to density and more a result of their greater connectivity to the world and initial interactions in highly interactive creative local economies. Early analyses tended to argue that the density of the first-hit places was the cause of infection there. Nathan (2020) contends, correctly in our view, that this is an ecological fallacy. There is a weakening relationship to density over time. Many small cities and rural regions have subsequently been hard-hit in per capita terms. Iowa, a notoriously lowdensity state with a population of about 3 million, recently surpassed South Korea, an extremely dense nation with a population of about 50 million, in number of coronavirus cases. In the first national COVID-19 study developed in Spain, Soria (with a population density of 9.2 hab./km2) was the hardest hit province, with the share of its population having developed antibodies after exposure to the virus standing at almost three times the Spanish average (Ministerio de Sanidad, 2020). Exposure to the illness in the region of Madrid (population density 830 inhab./km2 and widely regarded as the epicenter of the pandemic in Spain) was, by contrast, 30% lower. Similarly, in the UK the highest case rates can be found outside London, with the Newcastle city-region becoming one of the main hotspots (Nathan, 2020). East Asian cities in general, including Hong Kong, Singapore, and Tokyo, stand as a testament to the fact that density does not equal destiny during this pandemic (Patino, 2020). San Francisco, the second densest city in the U.S. (Kahn and Marinucci, 2020), and dense cities throughout Germany and Northern Europe, prove the same point.

Aside from a swift, decisive response from local and national leaders and strong adherence to public health policies, a few specific factors stand out as potential determinants of which cities ? and which communities within cities ? experience the most severe outbreaks.

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The first is connectivity. Cities that became early epicenters are global hubs for tourism and business, with some of the world's busiest airports. Many smaller communities that have experienced severe outbreaks are in fact highly cosmopolitan, like ski resorts in the Alps and Rockies that played host to "super spreader" events (Hoffower, 2020). Places that bring together people from all around the world have the potential to spread the virus both globally and locally. Thus, these places were hit before preventive strategies and better treatment became available, i.e. in the February-early March 2020 period. Less dense places generally got hit later, when control measures (of different levels) were already in place.

Subsequently, it has emerged that the type of density may matter more than the level. First is the issue of work density versus residential density. Work density appears to be a greater transmitter than residential density. And cities with high work density tend to combine both high creative density in office environments and high public-facing density in theatres, clubs, bars, restaurants, hotels, sports arenas, and other highly interactive and crowded venues. Research increasingly indicates that the virus is much more communicable indoors (Lewis, 2020). Initially, it was believed that public transit was a major factor in transmitting the virus, but it turns out that it depends on the type of transport and the length of trips. Most people are not enclosed it in long enough to get the viral load that creates infection, although long commutes in local, rather than express, train or underground lines represent a serious risk (Harris, 2020). By contrast, interactions at work are of longer duration in more enclosed spaces. In short, the hardest-hit initial cities were mostly rather dense, but more importantly, the combination of connectivity and type of work, at a time when distancing measures were not in place, made them ideal targets for severe outbreaks (Glaeser, 2020; Hamidi et. Al, 2020).

The second-stage geography of the outbreak ? for example to cities such as Houston and Miami in the United States ? has many causes, among which were weak initial control strategies, due to a combination of politically influenced choices and low initial infection rates. But a paradox also emerged: as office-based workers could stay home, for the most part, the public-facing workers remained most exposed to risk at work. Little was done to limit interactions in risky high-contact spaces of contact between these workers and the public, or among the workers. And while many of these spaces bring people of different social classes into contact, the impact of the virus diverges according to geography and social class, with the least privileged people and places normally seeing the worst effects. One is more likely to catch the virus working all day in a restaurant than while going there briefly as a customer, though there is risk for all. White collar knowledge workers are much more likely to be able to work from home, to have access to a personal car for transportation, and to live in pleasant, uncrowded homes. The ability of so many workers to work from home could be a major reason why San Francisco and the rest of the Silicon Valley region have had such low overall infection rates. However, the differential impact of the spread of the virus is also conditional on availability and access to health services in different parts of a city and by different income groups.

Thus, from the beginning, in many of the hardest-hit places, the urban geography of infection is highly stratified by social group and neighborhood. In New York, wealthy Manhattan had infection and death rates far below of the Bronx or Brooklyn the peak period of April-May 2020. This is in part, because high-income people are generally at lesser risk of contagion due to their home, work, and commute environments. Many of them are able to leave cities altogether. About 5 percent of New

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York City's population left the city in March or April, with well-off Manhattan neighborhoods, like the Upper East Side and Soho, shedding as much as 40 percent of their population (Quealy, 2020). While some of these individuals were students instructed to go home, a clear majority were second homeowners fleeing to small towns elsewhere in the Tri-State area or to South Florida (Quealy, 2020). This pattern continues in the second wave of infections. Poorly-paid service workers are preyed upon by the virus on their often-lengthy public transit commutes and their essential, in-person jobs--stocking supermarkets, clerking pharmacies, cleaning, collecting rubbish, driving buses and trains, and, last but not least, performing all types of jobs in hospitals. Immigrants and women are disproportionately represented in these fields of work. The high-risk settings that low wage workers are routinely exposed to are compounded in many cases by high-risk home environments, with many people living in over-crowded conditions where multiple members of the household routinely leave the home for work. Initial research indicates that overcrowding, rather than density, is one of the best predictors of viral contagion (Furman Center, 2020). Pre-existing health conditions among low-income individuals, including higher rates of hypertension and obesity due to poor access to healthcare, healthy food, and recreational opportunities, further increase their susceptibility to the virus. The geography of the infection in some cities illustrates the significance of these class-based factors as opposed to density itself. In many expensive cities, gentrification has pushed many low wage workers out of the most dense, central neighborhoods, indicating that outer neighborhoods are more at risk. In New York City, for instance, the hardest-hit places are not the hyper-dense parts of Manhattan, but outer borough neighborhoods home to large proportions of essential service workers, and low-density parts of Staten Island with large numbers of nurses and first responders Furman Center, 2020) (Figure 1) . Similarly, the highest death rates are not to be found in central and southern Manhattan or in the highly gentrified Brooklyn Heights and Brooklyn Downtown, but in lower income areas, often with a large presence of migrants, in the outer fringes of the city. This is, for example, the case of places like Edgemere and Far Rockaway in Queens, with a large AfricanAmerican population and a high share of Ethiopian migrants, or of East Elmhurst or Corona, also in Queens, home to a large Bengali population. Similar levels of contagion are found in Edenwald and Eastchester, in the Bronx, two areas notorious for their share of population living below the poverty line and high crime rates (Figure 1).

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Figure 1. COVID-19 cases and death rates per 100,000 inhabitants by ZIP Code.

As of 13 June 2020.

Source: New York City Health Department.

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