How the COVID-19 Pandemic Impacted the 2020 ACS 1-year PUMS Data

By Danika Brockman and Megan Schouweiler

One of the highlights of the past IPUMS USA release was the 2020 ACS 1-year Public Use Microdata Sample (PUMS) file. Due to the effects of the COVID-19 pandemic on 2020 ACS data collection and data quality, the Census Bureau did not release the standard PUMS data. Instead, they released the 2020 ACS 1-year data with experimental weights designed to account for the impact of the COVID-19 pandemic on data quality. In this blog post, we discuss the impact of the COVID-19 pandemic on the 2020 ACS and the development of the experimental weights, and we provide some recommendations for using the 2020 ACS 1-year PUMS file.

Impact of the Covid-19 Pandemic on Data Collection and Data Quality and the Development of the Experimental Weights

The COVID-19 pandemic severely disrupted data collection for the 2020 ACS. All methods of data collection were either shut down or significantly reduced from March 2020 through the end of the year. Data collection for group quarters was particularly disrupted by the COVID-19 pandemic; in-person visits to group quarter facilities were suspended or greatly reduced from March 2020 through the end of the year, and telephone interviews were not conducted due to logistical constraints. Beyond the impacts to data collection methods, the 2020 ACS had significant variability across the 2020 data collection year in response rates for both housing units and group quarters, and had the lowest overall response rate in the history of the ACS1.

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IPUMS FAQs: How do the original occupation and industry codes map onto harmonized versions created by IPUMS?

By Kari Williams

As part of the IPUMS mission to democratize data, our user support team strives to answer your questions about the data. Over time, some questions are repeated. This blog post is an extension of an earlier series addressing frequently asked questions. Maybe you’ll learn something. Perhaps you’ll just find the information interesting. Regardless, we hope you enjoy it!

Here’s one of those questions:

How do the original occupation and industry codes map onto harmonized versions created by IPUMS?

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Conceptualizing Structural Racism with IPUMS Time Use Data

By Solvejg Wastvedt and Yash Singh

A major challenge for researchers studying racism is measuring the consequences of this macro-level phenomenon in people’s everyday lives. For the purposes of this post we define structural racism as a system of racialized advantage and disadvantage underlying and structuring multiple domains of life, including housing, interactions with government, education, the criminal legal system, and more. 1 2 3 4 5 Structural racism does not refer to individual prejudices or discriminatory beliefs. Rather, it is a macro-level phenomenon: a principle upon which structures in our society are built. When researchers study racism as a structural force, its impacts in any one area affect and reinforce impacts in every other area.

The consequences of structural racism for individuals are well-documented; for example, research shows structural racism produces poorer health outcomes for racialized groups. 6 However, research often focuses on a single aspect of structural racism, such as police brutality, and struggles to capture the full impact of this multidimensional phenomenon. Measures used to assess impacts of structural racism typically differ by domain, and while robust research documents these impacts, it can also be difficult to capture the mechanisms through which they occur.

Time — specifically time use — is a type of data that can unite studies of structural racism across domains and offer a glimpse into how this macro-level force acts on individuals.

Time is a form of capital: we only have so much of it, and time spent in one place is not spent elsewhere. For example, an individual living in a segregated neighborhood because of residential discrimination may be required to drive long distances for work or health care. This reduces time available for all other activities, including exercise. The resulting lack of time may lead, in turn, to negative health outcomes. While simply an illustration of one possible pathway linking the macro and micro levels, this example shows how time use data can capture impacts across multiple domains and, ultimately, on daily life.

IPUMS Time Use provides data from the American Time Use Survey (ATUS), a nationally representative U.S. time diary survey. In this post, we highlight measures from the ATUS corresponding to two aspects of structural racism: residential segregation and discrimination in government services. Researchers interested in other aspects can create and select variables from IPUMS ATUS data to match their areas of interest.

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