IPUMS FAQ: Alternative Measures of Unemployment

By Matthew Bombyk

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 can I use IPUMS CPS to calculate the Alternative Measures of Unemployment published by the BLS?

Every month the Bureau of Labor Statistics (BLS) publishes a set of Alternative Measures of Labor Underutilization as part of its well-known Employment Situation News Release. A common question we are asked at IPUMS is how to calculate these rates using IPUMS CPS data. The “headline” unemployment figure is known as U-3 and is a straightforward calculation using only the main employment status variable, EMPSTAT. However, the other measures are not quite as simple. Nonetheless, these can be calculated using IPUMS CPS! Using the table below, you can calculate these rates using the public use microdata.

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Malaria Transmission in Context: Linking Health, Census, and Ecological Data

by Yara Ghazal, Ilyana Hohenkirk, Tracy Kugler, and Kelly Searle

Malaria, like many vector-borne diseases, impacts health, economic growth, and society. The burden of malaria incidence and death is concentrated in Sub-Saharan Africa; in 2020, 95% of all malaria cases and 96% of all deaths occurred in Sub-Saharan Africa (WHO, 2022). Malaria impacts not only population health but also the economic growth of these 32 countries. It is estimated that up to 1.3% of economic growth in this region of Africa is slowed each year due to malaria (CCP-JHU, 2015). Understanding malaria transmission is essential to ending its spread and creating a healthier and more prosperous future for developing nations.

The literature on malaria transmission patterns has shown that several environmental factors impact mosquito and parasite vital rates, and thus affect the transmission intensity, seasonality, and geographical distribution of malaria (Castro, 2017). Temperature and precipitation are the primary climate-based factors that influence malaria transmission patterns. Temperature creates geographical constraints for vector and parasite development. Increasing temperatures have been found to shorten mosquito maturation time and increase feeding frequency. However, areas of extremely high temperatures usually yield smaller, less fecund mosquitoes. In parallel, because mosquitoes often breed in pools formed by rainfall and flooding, the frequency, duration, and intensity of precipitation have a significant influence on mosquito populations.

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Guidance for Pooling Multiple Years of NHIS Data

By Julia A. Rivera Drew


Depending on their research question, analysts will commonly pool multiple years of the National Health Interview Survey (NHIS) data together in order to increase sample sizes of particular subpopulations of interest, such as bisexual adults, immigrants, or pregnant women. The complex design of the NHIS, however, requires analysts to take additional steps to correctly construct and analyze pooled NHIS datasets. Moreover, planned changes to the NHIS design implemented in 2019, as well as changes made in response to the COVID-19 pandemic, require additional special handling to correctly analyze datasets combining multiple years of NHIS data. The objectives of this blog post are to: (1) share tips to correctly construct and analyze pooled NHIS datasets and (2) identify resources for more information.

Tips to Correctly Construct and Analyze Pooled NHIS Datasets

1. Create a pooled sampling weight to use with your pooled dataset.

In general, when pooling multiple years of NHIS data together, you will need to create a new sampling weight to use with the pooled sample. To create this new sampling weight, divide the appropriate sampling weight by the number of years within each distinct sample design period. For example, if one wished to estimate the number of children living in families with low or very low food security (FSSTAT) using pooled 2020-2021 NHIS data (e.g., similar to this report), one would need to create a new sampling weight by dividing the sampling weight identified under the “weights” tab for FSSTAT, SAMPWEIGHT, by the number of years pooled together from the same sampling design period (in this case, two). The sum of the pooled weights would then represent the average annual population size for the pooled time period, rather than the total cumulative population size for the pooled time period. For any given combination of variables, refer to information under the “weights” tab for the variables included in your analysis to help select the appropriate sampling weight. The distinct NHIS sample design periods are 1963-1974, 1975-1984, 1985-1994, 1995-2005, 2006-2015, 2016-2018, and 2019-present.

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