By Fozan Fareed, Patrick Lenain and Douglas Sutherland1
The COVID-19 pandemic has triggered severe recessions around the world. Beyond this short-term impact, long-lasting changes are also likely to happen. After past shocks, such as the global financial crisis, some industries have remained depressed for a long time, while others got back on their feet and returned to growth quickly, as shown by the evidence from the United States. Similarly, past shocks have hit large cities: some have been quick to recover, but others have struggled for many years, with severe social consequences. Drawing lessons from past shocks is useful as cities plan their own recovery from the pandemic — see also OECD (2020a).
Our research investigated why some cities have adapted to shocks, while others have struggled (Azzopardi et al., forthcoming). We built a dataset covering the 372 metropolitan areas and took advantage of the new Job-to-Job flow statistics compiled by the U.S. Census Bureau, which tracks all job moves. We used a machine-learning algorithm2 to classify the metropolitan areas in statistically distinct clusters. Preliminary results were included in the 2020 OECD Economic Survey of the United States.
Four categories of cities were identified: booming areas (67), prosperous mega metropolitan areas (99), resilient areas (149), and distressed metropolitan areas (57). This classification was obtained by focusing on indicators such as the job-to-job mobility rate, unemployment rate, income growth, population increase and GDP growth rate. The results show that prosperous cities are predominantly located in the West and the South of the United States (Figure 1). The main features of their success have revolved around embracing digital technologies, adopting local regulations friendly to job mobility and business creation, avoiding strict rules on land-use and housing market, and improving the wellbeing of the city’s population3. These results highlight that cities adopting well-targeted policies can accelerate the return to growth after a shock.
- Booming metropolitan areas: These 67 metropolitan areas, home to about 7% of urban population, have enjoyed very fast growth of GDP per capita. They have often found success thanks to fast-growing industries, notably technology clusters – Midland, Austin, and Colorado Springs are examples. Other cities have found prosperity by becoming retirement destinations – most obviously cities in Florida (The Villages, Pensacola area, Panama City). They have become magnets for people looking for good jobs, high quality of life and comparatively low cost of living. For example, in 2017, about 305,000 workers were attracted by cities in Texas, many having decided to leave California and Louisiana. About 260,000 workers left states such as Georgia and New York and moved to Florida.
- Prosperous mega metropolitan areas: This cluster is the largest one: it includes 99 metropolitan areas and about three quarters of the U.S. urban population resides here. These are very large cities, with an average population size of 2 million, which can take advantage of agglomeration externalities. This category includes some of the largest U.S. metropolitan areas such as New York, Los Angeles, Chicago, Dallas, Houston, Washington DC and Miami. They have stayed buoyant in the face of shocks and have benefited from low unemployment rates, average job mobility rate, and a high income per capita as compared to other clusters. However, rising inequality is a challenge here, and their future will depend on improving housing affordability and transportation.
- Resilient metropolitan areas: 149 metropolitan areas are part of this cluster and account for about 11% of the urban population. This cluster is mainly composed of relatively smaller areas such as Lewiston, ID-WA, Great Falls, MT, Columbus, IN and Kokomo, IN. Neither booming nor in distress, these areas are generally classified by relatively low job mobility. However, they have a comparatively higher income per capita growth rate, and a number of these areas seem to be on an upward trajectory. The average population size of this group is the lowest among all clusters.
- Distressed metropolitan areas: Home to 6% of the total urban population, these 57 metropolitan areas are characterized by a low job mobility rate, high unemployment rate, and low GDP and income per capita growth rates. This group includes many trailing cities and old industrial areas. They can be found in North Dakota (Bismarck), Illinois (Bloomington, Champaign-Urbana) and Southern California (El Centro). Metropolitan areas in central California are also in this cluster. Many of these distressed cities are located in states that are characterized by strict rules on occupational licensing, which has been found in recent OECD work as hindering labour mobility (Hermansen, 2019) and productivity growth (Bambalaite, Nicoletti and Rueden, 2020). In 2017, more than one-quarter million job-to-job moves went out of California to other states. The highest number of these jobs went to Texas (about 33,000) followed by Arizona (about 25,000) and Washington (about 24,000). Another major reason behind these moves seems to be the high cost of living and the high housing prices in some of these metropolitan areas.
Diverging trends between cities create social challenges because new jobs are being created in places far away from the places where old jobs are lost. Moving from job to job is essential for workers to avoid spells of joblessness, remain productive and benefit from higher earnings (Haltiwanger et al., 2018; Hermansen, 2019). However, the U.S. population has become less mobile: the share of the population moving each year has fallen from around 20% in the 1970s to under 10% more recently, with moves across state and metropolitan boundaries or moves to look for work also having been reduced.
Therefore, in order to address the economic and social challenges that the ongoing COVID-19 pandemic has brought to the fore, cities need to act now to avoid long periods of economic downturn. With drastic changes happening in the urban ecosystem, it has become more important than ever to focus on housing and land zoning rules, and other restrictions to mobility, notably occupational licensing. With a major reallocation coming up4, cities that address these regulatory barriers would be in a better position to benefit from new opportunities and attract businesses and talents looking for a new home.
Azzopardi, D., F. Fareed, P. Lenain, D. Sutherland (forthcoming), “Why are some U.S. cities successful, while others are not – New evidence from machine learning”, OECD Economics Department Working Paper.
Bambalaite, I., G. Nicoletti and C. von Rueden (2020), “Occupational entry regulations and their effects on productivity in services: Firm-level evidence”, OECD Economics Department Working Papers, No. 1605, OECD Publishing, Paris.
Barrero, J. M., Bloom, N., and Davis, S. J. (2020). “Covid-19 is also a reallocation shock”. National Bureau of Economic Research Working Paper, No. w27137.
Haltiwanger, J., Hyatt, H., and McEntarfer, E. (2018). “Who moves up the job ladder?”. Journal of Labor Economics, 36(S1), 301-336.
Hermansen, M. (2019). “Occupational licensing and job mobility in the United States”. OECD Economics Department Working Papers, No. 1585, OECD Publishing, Paris.
OCDE (2020a), “Cities Policy Responses”, COVID-19 notes on policy responses.
OECD (2020b). “OECD Economic Surveys: United States 2020
- Fozan Fareed is an OECD consultant and conducts research at Laboratoire Erudite (Université de Paris-Est), Patrick Lenain is an Assistant Director in the OECD Economics Department; Douglas Sutherland is a Senior Economist in the same Department.
- Hierarchical Ascending Clustering was used to classify these statistically distinct clusters.
- See Chapter 2 of the 2020 OECD Economic Survey of the United States for a detailed overview.
- Barrero et al. (2020) show that COVID-19 is also a reallocation shock and 2 out of 5 jobs lost during the pandemic in the United States may not come back.