IPUMS USA

Posts related to IPUMS USA

2020 Public Use Microdata Area (PUMA) Updates in the 2022 American Community Survey

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By Natalie Mac Arthur, Senior Research Associate, SHADAC

Thank you to our collaborators at the State Health Access Data Assistance Center (SHADAC) for contributing this blog post; view the original blog on the SHADAC website.

A Public Use Microdata Area (PUMA) is a type of geographic unit created for statistical purposes. PUMAs represent geographic areas with a population size of 100,000–200,000 within a state (PUMAs cannot cross state lines). PUMAs are the smallest level of geography available in American Community Survey (ACS) microdata. They are designed to protect respondent confidentiality while simultaneously allowing analysts to produce estimates for small geographic areas.

Every ten years, the decennial census results are used to redefine ACS PUMA boundaries to account for shifts in population and continue to maintain respondent confidentiality. This process is intended to yield geographic definitions that are meaningful to many stakeholders.

Most recently, new PUMAs were created based on the 2020 Census; these 2020 PUMAs were implemented in the ACS starting in the 2022 data year. Although Public Use Microdata Area components remain consistent to the extent possible, they are updated based on census results and revised criteria. Therefore, they are not directly comparable with PUMAs from any previous ACS data years. For example, the 2020 PUMAs used in the 2022 data year are distinct from the 2010 PUMAs, which were used in the 2012–2021 ACS data years.

The 2020 PUMAs were created based on definitions that include two substantive changes relative to the 2010 PUMAs:

1) An increase in the minimum population threshold for the minimum size of partial counties from 2,400 to 10,000. Increasing the population minimum for a PUMA-county part aims to further protect the confidentiality of respondents. However, exceptions are allowed on a case-by-case basis in order to maintain the stability of PUMA definitions (that were based on the previous minimum of 2,400) and due to unique geography.

2) Allowing noncontiguous geographic areas. Allowing PUMAs to include noncontiguous geographic areas aims to avoid unnecessarily splitting up demographic groups in order to provide more meaningful data. This change is not intended to create highly fragmented PUMAs.

Other than the two changes listed above, PUMA criteria remained consistent, such as treating 100,000 as a strict minimum population size for PUMAs. The maximum population size for PUMAs can exceed a population of 200,000 in certain instances due to expected population declines or geographic constraints.

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New to IPUMS USA: The Adjust Monetary Values Feature

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By Danika Brockman and the Adjust Monetary Values Team

Introducing the Adjust Monetary Values feature

The team at IPUMS is excited to introduce a brand-new extract feature, Adjust Monetary Values, which gives you the option to adjust monetary variables to constant units in the IPUMS data extract system. We know firsthand how tedious it can be to compare things like income and rent over time when you have to manually adjust for inflation. This feature allows you to request pre-adjusted monetary variables (e.g., INCWAGE) as part of your extract request! The feature is first being released on IPUMS USA, where you will be able to adjust monetary variables to 2010 dollars.

What does the Adjust Monetary Values feature do?

This feature gives you the option to adjust the monetary variables you have added to your data cart into constant dollars, so that all samples in your data cart are comparable across time for your selected monetary variables. IPUMS USA variables are adjusted to 2010 dollars using the Consumer Price Index for All Urban Consumers (CPI-U). For more information about why the CPI-U was chosen as the pricing index for this feature, see the Monetary Adjustment Feature page.

When you add an inflation-adjusted version of a variable to your data cart, the IPUMS data extract system applies the appropriate CPI-U adjustment factor for each sample year to the variable(s) you’ve selected. Your extract will include both the original monetary variable and the inflation-adjusted monetary variable. Special codes (e.g., NIU, missing) will not be affected by the inflation adjustment. Inflation-adjusted versions of variables will assign all specialty (i.e., non-monetary) codes to a code comprised exclusively of “9’s” with a width two digits greater than the largest value in the original variable (e.g., a variable where the maximum monetary value is “8500,” would assign all specialty codes to “999999” and apply a label of “Non-monetary.”) For details on the original specialty codes and their labels, consult the documentation for the original variable on the IPUMS USA website or cross-tab the adjusted and original variables in your statistical program (note that you may want to include a qualifying if statement so you see only the non-monetary codes).

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

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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 Announces 2020 Research Award Recipients

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IPUMS research awardsIPUMS is excited to announce the winners of its annual IPUMS Research Awards. These awards honor the best-published research and nominated graduate student papers from 2020 that used IPUMS data to advance or deepen our understanding of social and demographic processes.

IPUMS, developed by and housed at the University of Minnesota, is the world’s largest individual-level population database, providing harmonized data on people in the U.S. and around the world to researchers at no cost.

There are six award categories, and each is tied to the following IPUMS projects:

  • IPUMS USA, providing data from the U.S. decennial censuses, the American Community Survey, and IPUMS CPS from 1850 to the present.
  • IPUMS International, providing harmonized data contributed by more than 100 international statistical office partners; it currently includes information on 500 million people in more than 200 censuses from around the world, from 1960 forward.
  • IPUMS Health Surveys, which makes available the U.S. National Health Interview Survey (NHIS) and the Medical Expenditure Panel Survey (MEPS).
  • IPUMS Spatial, covering IPUMS NHGIS and IPUMS Terra. NHGIS includes GIS boundary files from 1790 to the present; Terra provides data on population and the environment from 1960 to the present.
  • IPUMS Global Health: providing harmonized data from the Demographic and Health Surveys and the Performance Monitoring and Accountability surveys, for low and middle-income countries from the 1980s to the present.
  • IPUMS Time Use, providing time diary data from the U.S. and around the world from 1965 to the present.

Over 2,500 publications based on IPUMS data appeared in journals, magazines, and newspapers worldwide last year. From these publications and from nominated graduate student papers, the award committees selected the 2020 honorees.

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What’s new with IPUMS USA? Updates for Industry and Occupation Variables

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By Megan Schouweiler (Senior Data Analyst, IPUMS USA) and Sophia Foster (Data Analyst, IPUMS USA)

The Census Bureau drops ACS 1-year PUMS files tomorrow (October 15, 2020)! Don’t worry, the IPUMS USA team will get right to work to get you some data as soon as possible. In the meantime, let’s talk a little about what’s new with occupation and industry variables on IPUMS USA.

New OCCSOC and INDNAICS Crosswalks Available

You may be familiar with our harmonized occupation (OCC1950, OCC1990, OCC2010) and industry variables (IND1950, IND1990). These variables harmonize occupation/industry codes based on Census Bureau classification systems to a base year, making comparisons across time much easier. Researchers are also interested in using the Standard Occupational Classification (SOC) system and North American Industry Classification System (NAICS) codes that are available in the public use data; IPUMS has not created nifty harmonized variables for these codes. We hope to harmonize these codes someday– until then, we will settle for providing great documentation about how these codes have changed over time. And we’ve recently made the documentation even better!

OCCSOC reports the primary occupation based on the SOC system, and INDNAICS reports the type of establishment of the primary occupation based on the NAICS system. Both of these coding systems are periodically updated. In the past two decades, the OCCSOC codes have been updated six times and the INDNAICS codes have been updated five times, creating a challenge for those utilizing the codes to conduct research across time. Beyond navigating the changes to the coding schemes, there are separate crosswalks for each update. We recently updated each of our crosswalks to include all iterations of the underlying coding systems from 2000 onward in a single table for OCCSOC and INDNAICS, respectively. Instead of a bunch of links to crosswalks that just compare adjacent schemes, we’ve combined all years into one table.

In total, we created four crosswalks: OCC to OCCSOC; IND to INDNAICS; OCCSOC only; and INDNAICS only. These crosswalks include detailed descriptions of how OCCSOC and INDNAICS codes have changed over time from the 2000 Census to present. Examples of changes include one occupation/industry splitting into multiple new categories, multiple categories collapsing into one occupation/industry, and updates to codes and titles. Because these types of changes occur with each new iteration of the coding scheme, it can be difficult to understand how the codes relate to one another across time. We hope that these new crosswalks provide a more comprehensive mapping of the OCCSOC and INDNAICS codes over time and will aid researchers in using these variables. These crosswalks are available to view on the IPUMS USA website and for download in both Excel and CSV format. Trust us, you’ll want to download these crosswalks to make your programming a lot easier.

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IPUMS USA: County Variable Name Changes

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“What’s in a name? That which we call a COUNTYFIPS by any other name would still be accurate” ~ Steve Shakespeare (the lesser known, quantitative Shakespeare).

Couldn’t have said it better myself. Though, when you are sharing your variable names with tens of thousands of researchers who really just want their analysis to work you may want to be cautious about changing variable names. This is exactly the reason our (now re-named) COUNTY variable held onto that moniker for so long. And when you do change variable names you better try your hardest to let everyone know, so let’s start with that part.

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