- There is a significant gap in the recoveries experienced by small businesses in the urban cores of large metropolitan areas and those in suburbs and smaller cities: employment in these urban cores has recovered at a 48% lower rate than in suburbs and smaller cities.
- This gap is most apparent in regions hit hardest by the pandemic – the Middle Atlantic and Pacific. Core urban areas in the Mid-Atlantic remain 4.8% below March headcount while suburban counties and smaller cities are 9.8% ahead—that’s a 312% difference. In the Pacific region, small businesses in city centers have recovered at a rate 135% less than those in suburban areas.
- The differential effect of this recession on urban areas is a large reason that disadvantaged populations have borne the brunt of the economic costs: they are much more likely to live and work in these hard-hit areas. The differential effect of this pandemic on cities is responsible for 15% of the gap in unemployment rates between White and BIPOC workers and 10% of the gap between White and Latinx workers.
- This gap will have real costs: Slower growth in central cities will lead to 400,000 fewer jobs per year in these urban cores – 175,000 of these jobs have shifted out into the higher-growth areas and 225,000 which are permanently lost. Permanently losing out on 225,000 jobs per year in these urban cores would represent $16.3 billion in lost economic growth per year.
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After decades of declining population, urban centers have seen a resurgence in growth in recent years. From the middle of the twentieth century through 2010, growth across suburbs and smaller cities far outstripped growth experienced in the cores of America’s largest urban centers. That trend began to reverse in 2010, and through the middle of the past decade urban centers grew faster than their suburban counterparts.
Densely populated areas offer several economic advantages: the density of residents leads to increased interactions that enhances workers’ productivity, increases innovation, and supports small businesses that offer a more diverse range of goods and services. This growth within the largest cities, however, began to stall in 2019, and the current coronavirus pandemic threatens to further upend years of progress. Density has very suddenly become a drawback as workers spend more time at home and families value space above the advantages of urban living.
Data from Gusto—the people platform offering full-service payroll, benefits, compliance, and expert HR services for 100,000+ small businesses nationwide—shows that small businesses in these high-density urban cores have borne the brunt of this economic downturn, and have recovered at rates half as large as those businesses in suburbs and smaller cities. This gap by geography can account for a significant portion of the recovery gap among BIPOC and Latinx workers—and permanently lower growth rates and remove $22 billion annually from the US economy.
Whether this pandemic will permanently change the economic landscape is not at all certain, but without a concerted effort to direct aid to these hard-hit areas, the scars from this swift and dramatic shift over the past year will be felt for years to come.
The Recovery Gap in Cities Centers
To examine the differences in small businesses recoveries between city centers and other areas, we first sort companies using Gusto’s platform into one of five groups, based on the size of the county in which they are located. These classes include those businesses located in the central counties of America’s largest metropolitan areas; suburban counties; small- and medium-sized metropolitan areas; and micropolitan areas, or small towns.
Figure 1 plots small business recovery rates — measured as the cumulative change in total employment between the week of March 2, 2020 through the end of 2020 — among all businesses (black line) and then across these geographic classes. Overall, small businesses using Gusto’s platform have recovered to employment levels 7.1% above the pre-pandemic level at the beginning of March 2020. This statistic, however, masks the differential recovery experienced by business located in urban cores, relative to their counterparts in suburbs and smaller cities. There is a wide gap in the recovery rates between firms located in the largest urban centers and those in both the suburbs and smaller towns. Indeed, while small businesses in suburbs and smaller cities have reached headcount levels on average 9.1% above pre-pandemic levels, the downtowns of the largest metropolitan areas have recovered at a 48% lower rate – reaching a level just 4.7% above headcount in March.
Figure 1: Gap in Small Businesses Recovery Across Geographic Classes
There are several explanations for this unequal recovery between businesses in center cities and other areas. One important driver may be the different types of industries between these two areas. Today, America’s largest cities have a greater concentration of service-sector jobs, such as retail shops, restaurants, and hotels, which have been hard-hit by the pandemic due to social distancing precautions and government stay-at-home orders. To gauge the extent to which this different mix of industries is driving this unequal recovery, I examine the small business recovery in these two areas only including “hard-hit” industries: “Tourism”, “Accommodations”, “Food & Beverage”, “Arts & Entertainment”, “Sports & Recreation”, “Salon & Spa”, and “Retail” businesses.
If this different recovery is only due to the fact that urban centers have a higher concentration of hard-hit businesses (in service sectors), we would expect the recoveries to be similar among these industries in the suburbs and the urban centers. Interestingly, this gap in recovery exists – and is in fact wider – looking just at these service sector businesses. While these hard-hit small businesses outside urban cores have recovered just barely to their pre-pandemic employment levels (on average 1% higher), service-sector small businesses in urban centers are still 8.6% below pre-pandemic levels. This data suggests that this trend is not simply caused by the types of business located in urban centers, but is driven by a larger force threatening the economic health of small businesses in American cities.
Figure 2: Gap in Small Businesses Recovery Across Geographic Classes, Hard-Hit Industries Only
The main reason that small businesses have suffered more in urban cores is that these areas have experienced a greater overall decline in economic activity. Consumers in urban downtowns have, to a much greater degree than their suburban counterparts, restricted their overall mobility. This contraction has hurt small businesses in city centers, which often depend on revenue from foot traffic and in-store consumption. Figure 3 plots the change in time spent outside the home among individuals in urban centers, suburban areas, and smaller cities using Google Mobility data from the Opportunity Insights Economic Tracker. At the lowest point of mobility, people in urban centers reduced time spent outside the home by 32% more than those in the non-urban areas (-25% decline vs. -19%). Since that time, a roughly 5% constant gap in mobility rates has emerged. This permanently lower level of movement in cities has hampered small businesses’ ability to maintain staff relative to pre-pandemic levels.
Figure 3: Gap in Mobility Across Geographic Classes
This threat to the health of small business in urban centers is important because small businesses (defined as those with fewer than 100 employees) account for 92.5% of all businesses in city centers, and almost one-third (30.9%) of all employment within these areas, as shown in Table 1. Across the country, young, high-growth firms drive job creation, and unless relief is delivered to hard-hit areas, the scars left by this recession in urban areas will be felt through lower jobs growth well into the future.
Table 1. Share of Firms and Employment Among Small Firms, by Geographic Classification
Geographic Dimensions of the Recovery Gap
The above analysis pools data on small businesses across the U.S. to examine the urban-suburban divide in the economic recovery. However, different regions of the country have endured more or less severe public health and have reacted with different public policies – both in terms of instituting lockdown orders and delivering relief to struggling businesses.
Table 2 examines how this urban/non-urban recovery gap differs across US regions. There is wide variation in the extent to which suburban areas and smaller cities have recovered faster than urban centers. The regions initially hit hardest by the pandemic, the Pacific region (California, Oregon, and Washington) and Mid-Atlantic region (New York, New Jersey, Pennsylvania) have experienced the widest gaps in the urban-suburban recovery gap. Core urban areas in the Mid-Atlantic have recovered at a rate 312% lower than the suburban areas in the same region. In the Pacific region, small businesses in city centers have recovered at a rate 135% less than those in suburban areas.
Table 2. Gap between Urban/Non-Urban Small Business Recovery by U.S. Region
Looking within the largest metropolitan areas, those with populations over 1 million people, wide divides exist between the recoveries small businesses have achieved in suburban counties and the challenges facing the central urban cores of those metro areas. Table 3 presents March-December headcount changes for these metropolitan areas, and then breaks this down into growth in the Urban Core and growth in the suburban counties. While companies in the Portland Metro Area have on average reached employment 2.41% above March levels, this average covers up the fact that the urban core of this metro area still has not reached March levels (down 0.45%), while suburban counties in the same metro area are at +6.45%.
Table 3: Top Ten Metropolitan Areas with Widest Urban/Non-Urban Recovery Gap
Figure 4: Top Ten Metropolitan Areas with Widest Urban/Non-Urban Recovery Gap
How this Gap Exacerbates Racial Economic Disparities
The urban-suburban recovery gap is an important feature of the pandemic recession not only because of its potentially long-lasting impact on the U.S economic landscape, but also because this pattern is partially responsible for the disproportionate impact felt by BIPOC and Latinx communities over the course of this recession.
Relative to White, Non-Hispanic workers, Latinx and BIPOC workers are significantly more likely to live in these urban city centers hardest hit by the economic downturn and slowest to recover. Using data from the Bureau of Labor Statistics’ Current Population Survey, we calculate the portion of workers living in central cities versus outside these areas for White (non-Hispanic), BIPOC, and Latinx workers. As presented in Table 3, Only 24% of White workers live in central cities, whereas 76% live in the suburbs or smaller cities. On the other hand, among both Latinx and BIPOC workers, much greater shares of workers live in central cities.
Table 4: Share of Labor Force In Each Geographic Area, by Race
To get a sense of how much this differential exposure to the shock is responsible for the different experiences of these populations, we conduct an exercise in which we calculate unemployment rates under a scenario where the portions of BIPOC and Latinx workers living in central cities matched those of White non-Hispanic workers as shown above.
Table 4 presents both the monthly unemployment rates by race/ethnicity as well as these composition-adjusted calculations for three months of 2020. First, significant gaps in the unemployment rate existed before the pandemic began, with an unemployment rate of 3% for White workers, but 5.4% and 5% for BIPOC and Latinx workers in February. Second, in the depths of the recession and through November, this pandemic has resulted in a widening in the unemployment gap as BIPOC and Latinx workers continue to feel the economic harm of this recession.
The rightmost two columns of Table 4 present estimates of unemployment rates for BIPOC and Latinx workers under this counterfactual scenario. By December, the unemployment rates for BIPOC workers would be 8.0% (rather than the actual 8.5%) if their urban-suburban labor force composition matched those of White non-Hispanic workers. For Latinx workers the unemployment rate would be 8.5% instead of 8.9%. Thus, the differential effect of this pandemic on cities is responsible for 15% of the gap in unemployment rates between White and BIPOC workers and 10% of the gap between White and Latinx workers.
Table 5: Hypothetical Unemployment Rates, if Worker Urban/Non-Urban Distribution Matched White Non-Hispanic Population
The cost of this recovery gap between suburban areas and central cities can be thought of in terms of lost potential growth in the coming years – which is particularly clear when we compare 2020 employment growth to 2019 rates, as shown in Figure 4. By the end of 2019, small businesses in suburban areas and smaller cities (shown on the left) experienced headcount growth of 10.6%. In 2020, although employment did fall at the early stages of the pandemic, by the end of the year, growth in these geographies nearly matched last year. In urban cores (right), however, growth in 2020 has been 64% less than that of 2020 (13.8% vs. 4.7%) and shows no signs of catching up.
Figure 5: 2019-2020 Growth Rates in Suburbs (Left) and Central Cities (Right)
These lower growth rates within urban areas will have significant costs in the years to come. Using data from the Quarterly Census of Employment and Wags (QCEW), total employment in these urban areas grew by roughly 630,000 jobs between 2019Q1 and 2020Q2. A growth rate 64% smaller in 2020, as the above analysis suggests, would lead to 400,000 “missing” jobs per year in these areas – jobs that failed to materialize each year due to the permanently lower growth rate in these urban areas.
These lost jobs are caused by both permanently lost employment and activity that has shifted from urban cores out to the suburbs and smaller cities. We get a sense for the size of each component by comparing how these growth rates across areas compare to prior recessions, such the Great Recession from 2007-2009. Using QCEW data spanning 2007-2009, we find that in the Great Recession, urban and non-urban areas were hit equally hard by the economic downturn (both experiencing employment declines of 7%). If the impact of this downturn had affected urban and non-urban areas in the same way as the Great Recession (holding the total impact of this recession the same), we would have seen growth fall by 225,000 jobs per year in urban areas and 175,000 in suburban counties and smaller cities. Thus, of these 400,000 missing jobs in the urban cores, 175,000 have shifted out to higher-growth non-urban areas and 225,000 have been permanently lost.
To get a sense of what type jobs are shifting out from urban cores, we identify five industries that are simultaneously experiencing contractions in urban cores (relative to growth in 2019) and growing in suburbs and smaller cities. These industries are presented in Table 6, along with the corresponding growth or contraction rates. For instance, in non-urban areas, employment in Finance grew by a 34.5% greater rate in 2020 than in 2019, while was 38% lower in urban cores. This concentration of professional services in Table 6 bolsters the conclusion that the 175,000 jobs shifting from urban cores to less dense areas are desk-based, professional jobs.
Table 6: Industries Experiencing Year-Over-Year Expansion in Non-Urban Areas and Contraction in Urban Areas
The Cost of Missing Growth
Both the permanently missing growth and shift in employment to suburbs and smaller cities comes with a steep price tag, to the cities that will feel the loss and the national economy. According to QCEW data in 2020, jobs in urban cores had average annual earnings of $72,592 in urban cores. The cost to the US economy comes in the form of this permanently lower growth. Using the above earnings data, losing out on 225,000 jobs per year would represent $16.3 billion in lost economic growth annually. Additionally, the shift of 175,000 jobs per year from large urban cores to suburbs and smaller cities, at average earning levels, amounts to a shift in $12.7 billion dollars economic activity from urban cores elsewhere.
Policies to Revive Hard-Hit Areas
Most immediately, Congress should continue to direct relief to small businesses in these hardest hit regions. In the relief package recently passed, Congress set aside $15 billion in new Paycheck Protection Program Funds for Community Development Financial Institutions, which serve distressed communities. Additionally, Congress created the Neighborhood Capital Investment Program, a $12 billion fund aimed at increasing access to capital among small business owners in underserved communities.
This targeted aid should serve as a model for a long-term revitalization program aimed at providing small businesses in these hard-hit areas with the resources to recover faster and emerge stronger. Such a plan could take the form of targeted block grants that Congress provides to distressed localities, who are then able to spend the funds on small businesses development, job training, and support programs to improve job retention. Congress could make the Neighborhood Capital Investment program permanent, with $12 billion in annual appropriation. Given the estimated $16.3 billion in annual lost economic activity estimated above, this spending would pass a cost-benefit analysis. Indeed similar targeted development policies in response to COVID have been proposed elsewhere, and have gained traction because research on existing programs has found these policies to be quite effective at improving small business formation and job growth in these neighborhoods.
There are a number of solutions policymakers can enact to help small businesses in these hardest-hit areas. The important point is that, rather than wait and see if small businesses can survive, state and federal policymakers must act now to ensure that the wounds inflicted by the pandemic do not turn into permanent scars.
National Center for Health Statistics, Center for Disease Control and Prevention. 2013.
Urban-Rural Classification Scheme for Counties. Accessed 11 January 2021. https://www.cdc.gov/nchs/data_access/urban_rural.htm#Data_Files_and_Documentation
Sarah Flood, Miriam King, Renae Rodgers, Steven Ruggles and J. Robert Warren. Integrated
Public Use Microdata Series, Current Population Survey: Version 8.0 [dataset]. Minneapolis, MN: IPUMS, 2020. https://doi.org/10.18128/D030.V8.0
US Census Bureau. 2020. Statistics of U.S. Businesses (SUSB). https://www.census.gov/data/tables/2016/econ/susb/2016-susb-annual.html. Accessed 12 January 2021.
A. Urban Classification Methodology
We classify the counties in which Gusto’s businesses are located according to the National Center for Health Statistics Urban-Rural Classification Scheme for Counties (NCHS). This scheme one of six groups:
- Inner City: Large Metro Center (“Inner City”)
- Suburbs (Fringe of Large Metro)
- Medium Metro
- Small Metro
- Micropolitan Areas
In this analysis, central cities include Group 1 and Suburbs/Smaller Cities include groups 2-5. We exclude rural areas (group 6) from this analysis because rural areas are likely fundamentally different from Metropolitan or Micropolitan areas in groups 2-6, and because data limitations prevent precise estimates of small business trends in rural areas alone.