Feature Where Low-Income Jobs Are Being Lost to COVID-19
Subtitle
The places and industries most at risk
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The neighborhoods hardest hit by COVID-19 job losses are home to workers in industries like tourism and transportation, which are bearing the brunt of the economic shutdown. To identify which neighborhoods are most at risk, we estimate how many low-income jobs have been lost by workers living in each census tract or are at risk when stay-at-home orders are in place. We hope that this tool can help nonprofits, foundations, and government target support where it’s most needed.

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ABOUT THE DATA

Correction: On December 7, 2020, we discovered an error in our code that affected the September, October, and November data releases. In almost all cases, the error led to an underestimate of job losses. The largest discrepancy occurred in the October release, in which job loss was underestimated by about 11 percent. The largest impacts occurred in areas with relatively large numbers of accommodation and food services and retail trade jobs. However, because of the relatively small size of the error and the relatively uniform geographic distribution of accommodation and food services and retail trade jobs, errors in relative comparisons of job loss between tracts were minimal. Therefore, use of the data for local targeting should be largely unaffected.

The table below shows corrected national estimates of the low-income jobs lost in September, October, and November. For estimates of specific counties or metropolitan areas, please email datacatalog@urban.org.

Total estimated low-income jobs lost in the US (millions)

Month and year

Published

Corrected

Difference

September 2020

6.9

7.3

5.8%

October 2020

6.1

6.8

10.8%

November 2020

5.6

6.2

10.1%

 

Methodology: The data track losses of low-income jobs by the neighborhoods and counties where workers live (not where the jobs are located). Low-income jobs are defined here as jobs with annual earnings below $40,000, and exclude some workers, such as independent contractors and those working in the gig economy. This tool does not attempt to estimate the number of low-income jobs with pay cuts.

We base our estimates on the US Bureau of Labor Statistics (BLS) Current Employment Statistics data for monthly employment by industry and on 2014–18 five-year American Community Survey (ACS) IPUMS USA microdata.

To estimate the number of jobs lost by neighborhood nationwide, we apply the BLS data on employment change at all income levels by industry, adjusted to align to state-level BLS data on employment change and to ACS microdata at the Public Use Microdata Area level to estimate low-income job loss by industry locally. We then apply these estimates to census tract–level data detailing the number of low-income jobs by industry.

This feature was first published on April 16, 2020. We update our estimates every month with BLS national and state employment numbers. We will also continue to refine our methodology and may add new data sources to improve our estimates.

These numbers are estimates and may not be comparable to actual job loss in each neighborhood. We highly recommend interpreting these results as relative job loss levels, which can be used to inform investments that alleviate some of the economic burden in hard-hit neighborhoods.

For more information, please see our technical appendix.

PROJECT CREDITS

This project was funded by internal Urban Institute funds. The views expressed are those of the authors and should not be attributed to the Urban Institute, its trustees, or its funders. Funders do not determine research findings or the insights and recommendations of Urban experts.

 We thank our Urban Institute colleagues Shena AshleySolomon Greene, and Kathryn L.S. Pettit for their valuable input and feedback.

RESEARCH Graham MacDonaldChristopher DavisAjjit NarayananVivian Sihan Zheng, and Yipeng Su

DESIGN Christina Baird

DEVELOPMENT Ben Chartoff and Jerry Ta

EDITING Fiona Blackshaw

WRITING Serena Lei

View this web application and the associated code to generate the data on GitHub

View the data on Urban’s Data Catalog