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Tracking COVID-19’s Effects by Race and Ethnicity: Questionnaire Two

Updates on People’s Health, Housing, and Livelihoods between August 19 and March 1, 2021

Last updated February 16, 2021

See full series here

After a summer of lockdowns and unprecedented job loss due to the COVID-19 pandemic in the US, the economy has begun to recover, and some people have returned to work. But that recovery has not been felt equally; communities of color—Black and Latinx communities especially—continue to bear the brunt of the pandemic’s effects. With rising COVID-19 cases nationwide, the halting progress of economic recovery could recede as Americans’ health remains in jeopardy.

Soon after the pandemic hit, we created a tool that used data from the first round of the federal Household Pulse Survey to track its effects by race and ethnicity on people’s health, housing, and livelihoods. This tool continues to monitor these effects using the second and third rounds of Household Pulse Survey data. As the nation’s uneven recovery continues, policymakers and practitioners can use these data to design race-conscious solutions that address widening racial disparities and unequal recovery efforts.

ABOUT THE DATA

This feature uses data from phase 2 and phase 3 of the federal Household Pulse Survey public use files to measure how the COVID-19 pandemic has affected US adults and their households. The third phase of the Household Pulse Survey began on October 28, 2020, and will collect data in two-week intervals through March 1, 2021. The second phase of the Household Pulse Survey ran from August 19,  to October 26, 2020. The first phase of the Household Pulse Survey ran from April 23 to July 21, 2020. The survey 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: Asian alone, Black alone, white 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 survey period, geography, and racial or ethnic group. 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 biweekly as new data are released. Phase 2 of the Household Pulse Survey introduced significant changes to the questionnaire and moved to a two-week survey window, creating differences in unit and item nonresponse between the two phases that make direct comparison with phase 1 estimates difficult. We therefore chose to produce a separate feature for phase 2. Phase 3 continued with the questionnaire and methodology from phase 2, so we incorporated phase 3 data into the phase 2 feature. The two collection periods are demarcated by a dashed line in the charts above.

For more information, please see our technical appendix.

Project Credits

This feature was funded by the Urban Institute through the Racial Equity Analytics Lab. 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 this web application and the associated code to generate the data on GitHub

View the data on Urban’s data catalog

RESEARCH

Shena Ashley,  Alena Stern, Steven Brown, Yipeng Su, Ajjit Narayanan, Megan Randall, Tomas Monarrez, and Margery Austin Turner

Design

Allison Feldman

Development

Alice Feng and Jerry Ta

Editing

Fiona Blackshaw

Writing

Wesley Jenkins

Technical Review

Rob Santos and Doug Wissoker

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