IPUMS USA – Use It for Good

Multigenerational Households Across Multiple Data Collections

December 2, 2025 by mpcblog

By Etienne Breton

We recently updated a key IPUMS-constructed variable for understanding multigenerational households: MULTGEN, which identifies the number of generations in a household. This variable is needed to answer important questions in our era of rapid population aging. For example, do multigenerational households become more numerous during economic recessions, and if so for whom exactly? Can they buffer against physical and cognitive decline for older adults? Do young people living with their grandparents have distinct educational, professional or even health trajectories? All of these questions – and many more – can be investigated creatively and rigorously using MULTGEN.

MULTGEN has long been available for most IPUMS USA samples. We recently adapted our methodology to add this variable to IPUMS CPS for all samples from January 1994 to the present. This means that users can now research multigenerational households with another IPUMS data collection, tackling key research questions with added precision and contextual richness, in addition to analysis of topics in the CPS that are not covered in the ACS (e.g., tobacco use, volunteering, voting and registration).

The construction of MULTGEN in IPUMS CPS (as in IPUMS USA) relies on IPUMS family interrelationship variables (see this classic paper, or this more recent paper, or our user guide, for how these variables are constructed) and information from the variable RELATE (insufficient information in the RELATE variable before 1994 explains why MULTGEN is not available for older samples). At present, MULTGEN in IPUMS CPS only provides general codes about the number of generations per household, whereas MULTGEN in IPUMS USA also provides detailed codes identifying subtypes of 2-generations and 3+ generations households.

Measuring Food Security with U.S. Federal Data

October 30, 2025October 10, 2025 by mpcblog

By Kari Williams & Isabel Pastoor

The U.S. Department of Agriculture (USDA) defines a household as being food secure when all household members at all times have access to “enough food for an active, healthy life;” it sets a minimum threshold for food security of “ready availability of nutritionally adequate and safe foods” and the “assured ability to acquire acceptable foods in socially acceptable ways” (USDA Economic Research Service, 2025). The USDA provides survey modules for assessing food security in the U.S. (see Table 1), which are used in a number of federal surveys.

Following the recent announcement by the USDA that they plan to cease data collection for the Food Security supplement fielded as part of the December Current Population Survey, we are highlighting data sources for studying food security in the U.S. Table 2 provides an overview of a number of federal data sources that can be used to study aspects of food security in the U.S. This list of data sources is not exhaustive; we have prioritized data available through IPUMS and other long-running and large-scale population surveys. Additional sources covering shorter time periods or more specific focal populations can be found from the USDA’s Food Security in the United States Documentation page and the Food Access Research Atlas.

IPUMS MLP: Revolutionizing Linked Data

August 28, 2025July 17, 2025 by mpcblog

By Etienne Breton

As researchers, we often ask questions we cannot answer due to lack of data. More intriguingly, however, there are questions we only think of asking once we encounter data that may answer them. Good data address existing problems; great data inspire new questions. The latest iteration of the IPUMS Multigenerational Longitudinal Panel (MLP) project, which links together records from the full count US census data, fits this description. Visit our data browser and project description for inspiration on research questions you did not know could be asked – and answered.

Full count census data offer unprecedented opportunities for social scientific research. Once harmonized, these data enable precise measurement of key demographic, economic, and social patterns across time and space. Researchers can observe entire populations over long periods and produce estimates virtually free of sampling error. Estimates can also be produced down to the smallest geographical units, allowing researchers to define and observe communities with an outstanding level of detail.

Perhaps even more powerfully, full count data have opened the possibility of automated record linkages across census years to construct millions of individual life histories and trace millions of families over multiple generations. These linked data speak compellingly to core research questions in the social sciences, including intergenerational mobility and the intergenerational transmission of socioeconomic characteristics; exhaustive descriptions of individual and family trajectories; internal migration patterns within small geographic units; long-term outcomes of early-life conditions; and many more.

IPUMS disseminates full count census enumerations for ten census years from 1850 to 1950. These data, covering over 800 million individual records, are the fruit of collaboration between IPUMS and the world’s two largest genealogical organizations — Ancestry.com and FamilySearch — to leverage genealogical data for scientific purposes. IPUMS MLP now offers longitudinal links between individuals and households enumerated in those ten censuses. As shown in the figure below, we offer 645 million links between census pairs in MLP’s current iteration. This amounts to more than 175 million people linked over two or more censuses.

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

June 15, 2025March 25, 2024 by mpcblog

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:

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.
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.

New to IPUMS USA: The Adjust Monetary Values Feature

July 2, 2025September 22, 2022 by mpcblog

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).

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

April 1, 2022March 31, 2022 by mpcblog

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.

IPUMS Announces 2020 Research Award Recipients

June 10, 2025May 17, 2021 by mpcblog

IPUMS 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.