By Kari Williams
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:
By Julia A. Rivera Drew and Natalie Del Ponte
The Medical Expenditure Panel Survey-Household Component, or MEPS-HC, data are an invaluable resource for studying short-term trajectories in health, including adult mental health. An integrated series of the MEPS-HC data is available at IPUMS MEPS. Collected on the Self-Administered Questionnaire and, starting in 2019, the Preventive Self-Administered Questionnaire, the MEPS-HC includes two validated adult mental health scales. The Kessler Psychological Distress Scale (K6) and the two-item Patient Health Questionnaire depression screener (PHQ-2) are asked twice per panel, during interview rounds 2 and 4 (see Table 1). There are also two validated scales measuring health-related quality of life (HRQOL) that capture several interrelated health domains, including mental health. These include the Short Form-12 (SF-12) in 2000-2016 and the Veterans RAND-12 (VR-12) starting in 2017 (see Table 2 for VR-12 measures). For more information on the SF-12, see the section on SF-12 scoring on MCS and for more information on the VR-12, see ADDAYA.
By Renae Rodgers
Did you know that the Current Population Survey (CPS) – an important source of information on unemployment, poverty, and many other topics – has a panel component? If you didn’t, you’re not alone. The CPS rotation pattern is complex and can be difficult to work with. In fact, IPUMS CPS has held multi-day workshops intended to introduce researchers to the CPS panel component, help them understand the rotation pattern, and show some convenient IPUMS CPS features that make working with CPS panel data a little easier. If you’re completely new to the CPS panel, check out the materials from our latest workshop!
Maybe you did know about the CPS panel component, but looked at the complex rotation pattern, the Census Bureau guidelines and linking keys, and decided that this was for the birds. If this sounds like you, then our newest IPUMS CPS feature may be right for you! IPUMS CPS users can now download CPS-ASEC panels that contain two observations per person across a one-year period as longitudinal extracts. The rest of this blog post will explain what you are getting when you make a CPS-ASEC longitudinal extract and will walk you through how to create one for yourself.
By Chris M. Boyd
The IPUMS International Historical Geographic Information System (IHGIS) provides subnational data from agricultural and population and housing censuses from around the world. The agricultural census data cover a wide range of information on agricultural inputs, labor, production, and more, which can be used to explore a variety of research questions. IHGIS data can help understand, for instance, which factors contribute to better crop productivity, including the role of fertilizer use. Researchers have used agricultural census data at the subnational level to analyze the negative relationship between farm size and fertilizer overuse in China1; the relationship between maize yield, farm size and fertilizer and irrigation use in Mexico2; the use of chemical fertilizers in direct market farms in the U.S.3; and the environmental sustainability of using fertilizers, insecticides and pesticides in Pakistan4.
To date, IHGIS has released Agricultural Census tables for ten countries (see https://ihgis.ipums.org/dataset-descriptions), including seven developing countries in Africa and the Pacific Islands. These seven datasets include information about fertilizer use, though each measures it in a different way (see Table 1). Despite the differences, these data can reveal broad patterns in the use of fertilizer by farmers among these countries.
By Solvejg Wastvedt and Yash Singh
A major challenge for researchers studying racism is measuring the consequences of this macro-level phenomenon in people’s everyday lives. For the purposes of this post we define structural racism as a system of racialized advantage and disadvantage underlying and structuring multiple domains of life, including housing, interactions with government, education, the criminal legal system, and more. 5 6 7 8 9 Structural racism does not refer to individual prejudices or discriminatory beliefs. Rather, it is a macro-level phenomenon: a principle upon which structures in our society are built. When researchers study racism as a structural force, its impacts in any one area affect and reinforce impacts in every other area.
The consequences of structural racism for individuals are well-documented; for example, research shows structural racism produces poorer health outcomes for racialized groups. 10 However, research often focuses on a single aspect of structural racism, such as police brutality, and struggles to capture the full impact of this multidimensional phenomenon. Measures used to assess impacts of structural racism typically differ by domain, and while robust research documents these impacts, it can also be difficult to capture the mechanisms through which they occur.
Time — specifically time use — is a type of data that can unite studies of structural racism across domains and offer a glimpse into how this macro-level force acts on individuals.
Time is a form of capital: we only have so much of it, and time spent in one place is not spent elsewhere. For example, an individual living in a segregated neighborhood because of residential discrimination may be required to drive long distances for work or health care. This reduces time available for all other activities, including exercise. The resulting lack of time may lead, in turn, to negative health outcomes. While simply an illustration of one possible pathway linking the macro and micro levels, this example shows how time use data can capture impacts across multiple domains and, ultimately, on daily life.
IPUMS Time Use provides data from the American Time Use Survey (ATUS), a nationally representative U.S. time diary survey. In this post, we highlight measures from the ATUS corresponding to two aspects of structural racism: residential segregation and discrimination in government services. Researchers interested in other aspects can create and select variables from IPUMS ATUS data to match their areas of interest.
By Solvejg Wastvedt and Yash Singh
Disability is dynamic: it evolves over time and interacts with environmental and societal factors. Due to the complex nature of disability, researchers conceptualize and measure disability differently depending on their research question and available data. For instance, an identical condition might evolve differently for a child facing food insecurity compared to one that has stable access to food. Similarly, a physical limitation for a worker in New York City may have a vastly different impact compared to a similar worker in rural Iowa. Disability can be viewed as the relational concept between individuals with physical or mental impairment and the environment.11 12 This complexity makes measuring disability a challenging task. The following post aims to help researchers understand and use disability measurements available in IPUMS data collections.
By Kelsey J. Drotning
Curious about how COVID-19 has affected time in leisure, sleep, work, and family activities in the United States? The 2020 ATUS will provide some clues. Data from the 2020 American Time Use Survey (ATUS) were released by the Bureau of Labor Statistics on July 22, 2021. The data have been harmonized with previous ATUS surveys by the IPUMS team, released on July 30, 2021. The 2020 ATUS provides a window into how daily lives shifted in response to COVID-19 pandemic conditions and the corresponding economic recession via 24-hour time diaries. Potential research topics include, but are not limited to:
• What, where, and with whom people spent their time post-onset compared to pre-onset of the pandemic
• Differences in daily activities by gender, race, ethnicity, nativity, age, parental and marital status, household composition, employment status, education.
• How daily activities, such as work, time with children, school, commuting, socializing and sleep changed as social distancing restrictions were enacted, lifted or maintained across U.S. states