Data Projects

Accessing International Census Data Tables in R

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by Tsu Zhu and Tracy Kugler

ipumsr now supports IHGIS!

IPUMS spatial data users now have programmatic access to international census data tables in IHGIS. The recent release of ipumsr 0.9.0 enables users to explore metadata; build, submit, and download extracts; and read IHGIS tables directly in R. Many of the ipumsr functions that had been NHGIS-specific have now been generalized to accommodate both IHGIS and NHGIS. More information on the new functions can be found in the ipumsr changelong.

About IPUMS IHGIS

The International Historical Geographic Information System (IHGIS) provides data tables from population, housing, and agricultural censuses from around the world. The data are derived from tables originally published by national statistical offices. The format and structure of the published tables varies widely between countries and across time, even within the same country. IHGIS extracts the tables and standardizes them into a machine-readable structure along with consistently formatted metadata and corresponding GIS boundary files. As of this writing, the IHGIS collection consists of 40 datasets from population and housing censuses, 14 from agricultural censuses, and an additional 305 datasets tabulated from IPUMS International microdata samples.

Working with ipumsr for IHGIS

The following sections walk through how you would use new ipumsr functionality to explore metadata and submit an extract request for data tables from Ireland censuses from 1966 through 1991. This example highlights changes to ipumsr functions that have been generalized to support both IHGIS and NHGIS. For more details see the Aggregate Data API Requests article on the ipumsr webpage.

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Demographic and Health Survey (DHS) Data Again Available to New Users

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By Miriam King

After funding for USAID and the Demographic and Health Surveys (DHS) was eliminated in February 2025, new researchers could no longer apply online for access to DHS data, and existing DHS users could not gain access to additional countries’ data. This restriction affected would-be users of both the original DHS public use files and the integrated version of DHS data through IPUMS DHS. Fortunately, The DHS Program just announced, “We are now open for new registrations.”

The DHS Program logoAccording to The DHS Program website, a three-year grant from the Gates Foundation is supporting the dhsprogram.com website and data archive, where researchers apply for access and can download the original public use files. Once a researcher is approved for DHS data access, they can log in to the IPUMS DHS website, create a customized dataset with the samples and harmonized variables they need, and download that file for analysis on their computer. Anyone can use the IPUMS DHS website to learn about the data, including documentation about the consistently coded variables and the availability of variables by sample, to plan a research project; they need to log in only if they would like to create and download a customized data file.

The grant funding will also support other useful elements of The DHS Program website: StatCompiler (for summary statistics by sample), the DHS Program API, and the Spatial Data Repository (for maps and shapefiles).

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IPUMS MLP: Revolutionizing Linked Data

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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.

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Working with Subnational Geographies in IPUMS Global Health

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By Divya Pandey and Anna Bolgrien

In a research project combining data from IPUMS MICS and IPUMS DHS, IPUMS Global Health staff examined trends in the relationship between open defecation and high infant mortality rates (IMR) in the Eastern Indo-Gangetic Plains. The project focused on selected bordering regions in Nepal, Bangladesh, and India. By analyzing these environmentally and agro-climatically comparable regions, the study aimed to isolate the impact of national and local policies on open defecation and infant mortality rates.

Figure 1: Regions included in the study

A map of the border and surrounding regions of Nepal, Bangladesh, and India that highlights the sub-national regions included in the study.

The study pooled data from IPUMS MICS and IPUMS DHS to look at trends over almost two decades. IPUMS DHS includes data for all three countries, and IPUMS MICS provides additional years of data for Nepal and Bangladesh. Since the study focused on selected bordering geographies, the authors worked with data from lower administrative levels—divisions in Bangladesh, states in India, and regions in Nepal. Leveraging the geography resources provided by IPUMS, the team used both spatially harmonized and sample-specific geography variables (learn more about IPUMS DHS geography variables and IPUMS MICS geography variables). Spatially harmonized geography variables identify geographic regions using a consistent spatial footprint to allow for the comparison of the same physical space over time. Sample-specific geography variables are not harmonized across time; as their name suggests, they use the geographic boundaries that are sample-specific or contemporaneous to the survey-year in a country.

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What is going on with the weighted counts in the January 2025 CPS?

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By Kari Williams & Sarah Flood

The signature activity of IPUMS is data harmonization, or making variables interoperable across time, to facilitate pooling of multiple months or years of data, as well as comparative and trend analyses. It’s easy to get carried away in the magic of not needing to perform routine data cleaning and having documentation organized at the variable level, and perhaps miss some bigger picture considerations. The Current Population Survey (CPS) annual population controls adjustment is an excellent example.

Each January, the Census Bureau revises the CPS weights to incorporate new population controls, based on the Census Bureau’s updated population estimates. However, the Census Bureau doesn’t re-release previous weights for the CPS based on the new population controls. If you look at trendlines of weighted count estimates using CPS monthly data, you might notice a discontinuity between each December and January – these are the annual population control adjustments at work. In January 2025, the shift is particularly abrupt; this is because the 2024 vintage population estimates (i.e., the population controls for the 2025 CPS) reflect an improvement in the Census Bureau’s methodology for measuring net international migration.

Line chart showing a general upward trend from 2020-2025 with disruptions each January

Figure from Jed Kolko’s Population adjustments will cause the next jobs report to be misinterpreted and misconstrued.

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IPUMS CPS Checks on Basic Monthly Data

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By Sarah Flood, Renae Rodgers, and Kari Williams

Federal data are critical for understanding much about the US population from its size and composition to its health and employment. The Current Population Survey (CPS) is our nation’s official source of information about the labor force. At the beginning of each month, we eagerly await the first Friday when the Employment Situation Summary (aka the monthly jobs report) will be released (it isn’t just us, right??). The monthly snapshot of the US labor force serves as a bellwether for how our economy is faring.

The Wednesday after the jobs report is released, we at IPUMS clear the decks in preparation for the release of the CPS Basic Monthly Survey (BMS) by the Census Bureau. The CPS BMS is the individual-level data from which the jobs report is generated. Our goal is always to process these data as soon as they’re released by the Census Bureau so that we can deliver them to IPUMS CPS users as quickly as possible. Those who rely on CPS BMS data each month might be familiar with coping strategies while waiting for the data–obsessive page refreshing, some nervous pacing, maybe wondering why they haven’t yet been released (iykyk).

While quickly processing CPS Basic Monthly data is a priority, so, too, is ensuring data quality. Each month, we carefully inspect CPS BMS data at several points in our process. First, we review all of the variables for codes that are undocumented or have suspicious frequencies. Second, we rely on a suite of tools during our integration process that alert us to any codes in the data that we haven’t accounted for in our variable-level harmonizations. After harmonization, we compare univariate statistics from the newest month data to the previous month of data. Generally we expect very little change across months and we have built tools that are designed to flag variable-level differences above a certain threshold as well as new codes on either end of the distribution.

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Unlocking Spatial and Social Data with R: Introducing the R Spatial Notebook Series

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By Kate Vavra-Musser

Introduction: What is the R Spatial Notebooks Project?

The R Spatial Notebooks Project is a series of R code notebooks, structured like a textbook, designed to guide users through the intricacies of data extraction, integration, cleaning, analysis, and visualization using R. The notebooks are specifically tailored for social science research and applications using spatial data. The modular textbook-style structure is designed for comprehensive skill development by working through sequences of notebooks. The project was developed through a partnership between the Institute for Social Research and Data Innovation (ISDRI), which houses IPUMS, and the Institute for Geospatial Understanding through an Integrated Discovery Environment (I-GUIDE). IPUMS provides census and survey data from around the world integrated across time and space. I-GUIDE is cyberinfrastructure that combines distributed geospatial data with computing for researchers, students, and policymakers.

The initial R Spatial Notebooks release includes roughly 20 freely-available notebooks on topics including IPUMS data extraction via API, accessing open-source data, data cleaning, foundational spatial data principles, exploratory data analysis, and mapping.

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