In the Archive: “25 Years of IPUMS Data”

“25 Years of IPUMS Data,” the current IPUMS/MPC archive exhibit, highlights a dynamic quarter center history of data innovation at the University of Minnesota. In the late 1980s, the Social History Research Laboratory at the University of Minnesota’s History Department proposed “the creation of a single integrated microdata series composed of public use samples for every year … with the exception of the 1890 census, which was destroyed by fire.”  The primary aim was to make the U.S. census microdata “as compatible over time as possible while losing little, if any, of the detail in the original datasets” (Integrated Public Use Microdata Series: A Prospectus).

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New Course using IPUMS PMA and IPUMS DHS Data

Professor Kathryn Grace (geography) is providing a unique opportunity for students to conduct independent research this semester through a brand-new course, “Applied Quantitative Methods Using Survey Data.” In the course, which is open to both graduates and undergraduates, students develop a research question related to global health and, using IPUMS PMA or IPUMS DHS data, learn the steps for answering it.

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IPUMS USA: County Variable Name Changes

“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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IPUMS DHS at Work | “Child Stunting: National Figures Conceal Subnational Heterogeneity”

While summary national-level statistics from sources such as the World Bank are a useful tool, these national-level figures may conceal great heterogeneity across subnational units such as provinces and large urban areas.  Such differences are displayed in the figures below, with data on the percentage of stunted children under age 5, nationally and by region within countries. For example, while 40 to 50 percent of Tanzanian children overall are stunted, the figures range from under 20 percent to 50 percent or more across Tanzanian regions.

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