Last updated July 30, 2020
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The COVID-19 pandemic has claimed many thousands of lives and disrupted millions of jobs, but neither burden has been shouldered equally across American society. Black and brown communities in the US have been hit hardest, as is often the case in crises, worsening existing health and economic inequities.
Moving forward, policymakers and practitioners can work toward an equal recovery for all Americans by pursuing solutions that account for the pandemic’s unequal effects on communities of color. To design these race-conscious policies, policymakers need data to gauge how the pandemic may be affecting people’s health, housing, and livelihoods. This tool uses the near-real-time Household Pulse Survey data to track a set of measures for US households as the pandemic and recovery unfold.
ABOUT THE DATA
This feature uses data from the federal Household Pulse Survey public use files to measure how the COVID-19 pandemic has affected US adults and their households. The Household Pulse Survey has been collecting weekly data from US adults since April 23, 2020; it reports data for all 50 states and the District of Columbia, along with data for the 15 largest metropolitan statistical areas.
The public use files report data on race and ethnicity in two separate variables: rhispanic, which is 1 if a respondent is not of Hispanic, Latino, or Spanish origin and 2 if they are of Hispanic, Latino, or Spanish origin; and rrace, which has four options: white alone, Black alone, Asian alone, and any other race alone or races in combination. We use these two variables to create the race/ethnicity categories shown in the feature, mirroring the categories in the Pulse Survey data tables.
We calculate the indicators shown in the feature for each two-week interval, geography, and racial or ethnic group. We use two-week rolling averages to obtain more precise estimates at each point in time, owing to the considerable standard errors in some cases for the estimates when disaggregated by state/metropolitan statistical area and race or ethnicity. We then use the 80 replicate weights provided with the public use files to calculate the standard error for each estimate. We use these standard errors to produce the 95 percent confidence intervals shown in the feature.
We also use the replicate weights to calculate the significance of the difference between the given subgroup estimate and the total population estimate shown on each chart. While it is true that two statistics with non-overlapping confidence intervals are necessarily significantly different, the converse is not true, and two estimates with overlapping confidence intervals may be significantly different.
These numbers are estimates and may not equal the actual totals in each geography. We highly recommend interpreting these results as relative impacts of COVID-19 that can be used to inform race-conscious solutions that account for the pandemic's disparate impacts by race and ethnicity.
This feature will be updated weekly throughout the summer as new data are released. Seven variables were added during the July 30, 2020, update. The Household Pulse Survey started asking about federal stimulus payments distributed through the CARES Act and household spending on June 11; as a result, those variables lack data from April 23 to June 9.
For more information, please see our technical appendix.
This feature was funded by the Urban Institute. 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.
View the data on Urban’s data catalog
DESIGN Allison Feldman
EDITING Fiona Blackshaw
WRITING Wesley Jenkins