2022 ATUS Eating and Health Module Data: New Variables and Updates

By Annie Chen & Sarah Flood

The American Time Use Survey Eating and Health Module, funded by the Economic Research Service, asks a series of questions related to grocery shopping, food preparation, and nutrition. The most recent module was fielded in 2022 during the COVID-19 pandemic and was previously fielded in 2006 to 2008 and 2014 to 2016. The 2022 Eating and Health Module, set to be fielded again in 2023, asks new questions, asks similar questions in different ways than previously fielded modules, and contains additional variables of high interest to researchers.

New Variables in 2022

The 2022 ATUS Eating and Health Module asks a series of new questions related to exercise/physical activity, grocery shopping, meal preparation, and food quality. The food quality questions are especially interesting because they provide researchers with the opportunity to assess relationships between food quality and time use, which hasn’t been possible previously with these data. This is the first time that the ATUS has asked any information about respondents’ food intake on the ATUS diary day. The module is also responsive to changes in shopping behavior during the pandemic, specifically online grocery shopping and grocery delivery/pickup options. The shopping and meal preparation enjoyment questions might allow for comparisons to the ATUS Well-Being Module (fielded in 2010, 2012, 2013, and 2021).

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Preparing Time Diary Data to Create Tempograms and to Conduct Sequence Analysis

By Sarah Flood and Kamila Kolpashnikova

Time diary data: a unique opportunity

Time diary data offer researchers an opportunity to visualize daily life in a way that just isn’t possible with other data and demonstrating how people spend time. Respondents report every activity that they engage in (along with where and who they were with) over the course of the day, which means that time diaries can indicate how much time was spent in various activities as well as when activities occur during the day (e.g., timing) and the order in which they occur (i.e., sequencing) . This blog post will describe how to transform IPUMS ATUS data to perform these types of analyses, illustrate how to create a tempogram (including sample code), and link to additional resources for creating tempograms and performing sequence analysis.

While there are several ways to leverage the unique properties of time diary data, analysts are increasingly interested in creating tempograms and conducting sequence analyses, both of which capitalize on the temporal specificity of time diary data. These techniques allow researchers to explore the timing and order of activities over the course of a day. Both creating tempograms and conducting sequence analysis require time units that are consistent across respondents. Most time diary data are not natively in this format.

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Conceptualizing Structural Racism with IPUMS Time Use Data

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. 1 2 3 4 5 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. 6 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.

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