The Advantages of Time Use Data
There are several advantages of time diary data over other types of data that ask how much time people devote to specific types of activities. First, time diary data capture many activities of daily life. Rather than asking people to report how much time they spend in a specific activity (e.g., paid work), in a time diary data collection, respondents simply report what they did over the course of the day. In the ATUS, this detail is captured and coded into a 400+ category activity coding scheme. Second, time diary data contain excellent detail on time use contexts. Nearly all time diary data collections include some detail on where the activity took place and who else was involved. This enables researchers to investigate how much time people spend away from home or with friends, for example. Additional dimensions of context are timing and sequencing - with time diary data, researchers can examine when activities occur during the day and the patterning of activities across the day. Third, because respondents report all the activities they do in a 24-hour period, researchers can look at time tradeoffs, demands, and constraints. Fourth, because time diaries don’t ask respondents about specific activities, respondent reports tend to be less influenced by social desirability bias. For example, it is widely understood that reading to children is good for their development. When parents are asked about how much time they spend reading to their children, they tend to overestimate the time they read to their children because it makes them look like a better parent.
The time diary data available via IPUMS tend to be nationally representative and to include most population subgroups. For example, the ATUS includes individuals ages 15 and older from across the U.S. Age is available for all respondents, enabling researchers to focus on the age group of interest. Much of the classic research using time diary data has focused on parents’ time with children and the gender division of household labor. However, research using these data is broadening and now includes topics such as caregiving for adults, volunteering, sleep, exercise, time with others, and time away from home. There are many topics, especially relevant to older adults, that can be studied using these data. In fact, they’re an excellent source of information on how people spend time as they exit paid work, which most older adults eventually do! The ATUS covers all of the activities of daily life, enabling researchers to paint a picture of daily life after paid work or daily life with a spouse who has health problems.
Analytical Considerations
One of the biggest challenges for researchers who are new to time diary data is deciding what kinds of activities they want to study. For example, there is no single measure of time spent in adult care. The user must decide what activities (out of the more than 400 detailed activity codes) constitute adult care. Does adult care include both caring for older household adults (ACTIVITY 0304xx) and/or helping older household adults (ACTIVITY 0305xx)? What about caring for and helping non-household adults (ACTIVITY codes 0404xx and 0405xx)? Once these decisions are made, creating time use variables and conducting analyses are pretty straightforward.
Researchers should also keep in mind that despite how wonderful time diary data are, they tend to miss some activities that aren’t done daily and those that don’t take too much time. For example, some people might have a Monday, Wednesday, Friday exercise routine but report about their Tuesday activities. On average, this isn’t a problem. However, it means that researchers should be careful with their language. For example, estimates of the share of people who participate in exercise on average per day should be stated as such rather than as the share of people who participate in exercise. Just because we don’t observe someone doing an activity on the day they report their activities doesn’t mean they never do it. Activities that don’t take long may also be missed in a time diary. For example, a respondent who stops working to quickly put wet laundry into the dryer while on one’s way to make lunch may not report the laundry activity. Some respondents will report it, but others won’t, and we can’t know who did that same activity but didn’t report it. The takeaway here is to look carefully at how often an activity is reported and how much time is devoted to it before making strong claims about it.
In short, navigating the initial learning curve of time diary data by using IPUMS Time Use opens the door to powerful insights about daily human behavior. With time diary data, researchers can provide rich descriptions of the contexts in which people live, work, and interact and how this changes across the life course. IPUMS simplifies access to these rich, complex data, lowering barriers to access for understanding the evolving rhythms of daily life.