Economically disadvantaged children are more likely than other children to experience worse cognitive, health, and behavioral outcomes. The mechanisms for these associations are not fully understood, hindering policy initiatives aimed at closing the gaps. One hypothesis is that children experiencing financial hardship allocate their time differently. In this study, we use seven waves of time use diary data from a large sample of Australian children to explore how children’s time use changes when their family experiences financial hardship or deprivation. Focusing on four key child health and development time inputs––screen time, physical activity, sleep, and reading––we find that financial hardship is associated with significantly more screen time, particularly passive screen time, and screen time at excessive levels. We explore potential mechanisms for these associations.
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Recent evidence suggests that income poverty is an important but insufficient measure of financial hardship for children (Schenck-Fontaine and Panico 2019).
This literature typically finds that children of more highly educated parents or of higher household income engage in more physical activity, read more, study more, and watch TV less than children from lower socioeconomic backgrounds. Other studies (usually of small samples) indicate that children from more disadvantaged families tend to sleep less and have more sleep disturbances (Bøe et al. 2012; Buckhalt et al. 2007; Jarrin et al. 2014).
Other commonly used child TUDs include the Child Development Supplement from the Panel Study of Income Dynamics which follows children every 5 years for three waves, the American Time Use Survey which collects a TUD from children aged 15 and older, and the Millennium Cohort Study which includes a TUD in the age-14 sweep.
Proportions of families experiencing financial hardship were 17.91%, 8.60%, 4.18%, and 2.4% for one, two, three, and four plus periods, respectively.
These values are consistent with previous research that shows experiences of poverty (measured by household income below a poverty threshold) are largely transient (Stevens 1994).
Information on expenses (unforeseen or otherwise) is unavailable in the LSAC.
Although income is closely related to financial hardship, we find in our data that only 50% of families experiencing hardship also experience a reduction in household income. Less than 5% experience employment loss, separation, or a decline in physical or mental health.
Examples of the diaries are available in Nguyen et al. (2020) Appendix Figure C1 and C3.
This means that the total time engaged in all activities could amount to more than 24 h. While one could force the total time to be 24 h by making assumptions about which activity is considered the “primary activity,” this would likely result in a loss of information on our key activities for children under 10 years of age. Note that for the child-completed diaries, we include only primary and secondary activities.
Note that our total number of observations includes up to two TUDs per child in the first three waves of data collection.
The main parent is the child’s primary caregiver or the parent who knows the child best and who answers the LSAC general survey items. The main parent in most cases (over 90%) is the child’s biological mother. Note that due to the fixed-effect model, the main parent’s sex, background and education is likely to be constant across time and drop out of the model, however we have left these variables in the model to capture occasional changes in who is the main parent (for example, through separation).
See Table 10 in the Appendix for sample means for all control variables and Appendix 3 for detailed descriptions of how the neighborhood, health, employment, and income variables are constructed.
Exact age-specific cutoffs based on sample deciles for screen time and sleep are available upon request.
For example, zero physical activity per day over a prolonged period is likely to have a harmful impact on a child’s health and well-being.
The marginal effects of screen time, sleep time, physical activity time and reading time do not add to zero as these time use categories account for on average two-thirds of the child’s daily time-use.
While this is not large, if this one hour was instead spent on educational activities with parents, estimates from Fiorini and Keane (2014), Tables 9 and 10) suggest it could translate to improvements in the child’s cognitive skills (by about 0.018 standard deviations) and language skills (by about 0.022 standard deviations).
Social screen time is only captured by the time use diaries for children 10 years old or older (see Appendix 2 for details).
Parents were asked to complete the TUD for one weekday and one weekend day, while children were asked to complete one TUD for the day before their interview. This resulted in more weekday observations (24,396) than weekend observations (17,053).
We calculate the top decile separately based on weekend and weekday screen time (cut-offs available upon request). Cut offs for extreme screen time are higher on weekends compared to weekdays.
We utilize survey responses from the LSAC general survey for this section of analysis, and do not restrict the sample to only children with a time use diary. Our specification for these models follows Eq. 1 but does not include variables directly related to a time use diary (e.g., day of the week).
This item is asked of cohort K when children were 6–15 years and of cohort B when children were aged 2–13 years. We use survey responses instead of TUDs for this analysis as we are unable to accurately capture parent’s active time with the study child from the TUDs. In parent-completed diaries, parent-child time is recorded if they were in the same room or nearby the child if outside. Because young children are often monitored by a main parent, we find that a large amount of the child’s time is spent with a parent, even, for example, when the child is sleeping or watching TV.
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The authors would like to thank the anonymous referees for helpful comments and suggestions.
Availability of data and material
This paper uses unit record data from Growing Up in Australia, the Longitudinal Study of Australian Children (LSAC). DOI:https://doi.org/10.26193/F2YRL5.
Jessica Arnup is supported through an Australian Government Research Training Program (RTP) Scholarship. Nicole Black is supported by an Australian Research Council fellowship (DE180100438).
Conflict of interest
The authors declare no competing interests.
This paper uses unit record data from Growing Up in Australia: the Longitudinal Study of Australian Children (LSAC). The LSAC is conducted by the Australian Government Department of Social Services(DSS). The findings and views reported in this paper, however, are those of the authors and should not be attributed to the Australian Government, DSS, or any of DSS’ contractors or partners.
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Items contributing to time use categories
|Parent completed diary||Child completed diary|
|Physical Activity||Crawl, climb, swing arms or legs||x||-||-||-||-||-||-||-||-||-||-|
|Active free play (e.g running, climbing, ball game)||-||x||x||-||x||x||-||-||-||-||-|
|Walking (for travel or fun)||-||x||x||x||x||x||-||-||-||-||-|
|Ride bicycle, trike etc. (for travel or fun)||-||x||x||x||x||x||-||-||-||-||-|
|Other exercise - swim / dance/ run about||-||-||-||x||-||-||-||-||-||-||-|
|Organised sport/physical activity (e.g., swim, dance, Auskick)||-||-||-||-||x||x||-||-||-||-||-|
|Organised team sports and training||-||-||-||-||-||-||x||x||x||x||x|
|Organised individual sport i.e. swimming||-||-||-||-||-||-||x||x||x||x||x|
|Ball games, riding a bike, scooter, skateboard||-||-||-||-||-||-||-||x||-||-||-|
|Taking pet for a walk||-||-||-||-||-||-||-||x||-||-||-|
|Walking pets / playing with pets||-||-||-||-||-||-||x||-||x||x||x|
|Unstructured active play||-||-||-||-||-||-||x||-||x||x||x|
|Active club activities||-||-||-||-||-||-||x||-||x||x||x|
|(Travel) By foot||-||-||-||-||-||-||x||x||x||x||x|
|(Travel) By bike, scooter, skateboard etc.||-||-||-||-||-||-||x||x||x||x||x|
|Napping (not night-time sleep)||-||-||-||-||-||-||x||x||x||x||x|
|Difference between reported bed time and wake up time||-||-||-||-||-||-||-||x||-||-||-|
|Difference between reported sleep time and wake up time||-||-||-||-||-||-||x||-||x||x||x|
|Reading||Read a story, talked/sung to, sing/talk||x||x||x||x||x||-||-||-||-||-||-|
|Being read or told a story||-||-||-||-||-||x||-||-||-||-||-|
|Reading or looking at book by self||-||-||-||-||x||x||-||-||-||-||-|
|Reading or being read to for leisure||-||-||-||-||-||-||x||x||x||x||x|
|Screen Time (not including homework||Watching TV, a video, or a DVD (P)||x||x||x||x||x||x||x||x||x||x||x|
|Using computer/computer game (A)||-||x||x||x||x||x||-||-||-||-||-|
|Electronic media, games, computer use (A)||-||-||-||-||-||-||-||x||-||-||-|
|Playing Games (electronic device) (A)||-||-||-||-||-||-||x||-||x||x||x|
|Computer games - internet (A)||-||-||-||-||-||-||-||x||-||-||-|
|Computer game - not internet (A)||-||-||-||-||-||-||-||x||-||-||-|
|Xbox, PlayStation, Nintendo, Wii (A)||-||-||-||-||-||-||-||x||-||-||-|
|Downloading/posting media (S)||-||-||-||-||-||-||x||-||x||x||x|
|Internet shopping (S)||-||-||-||-||-||-||x||-||x||x||x|
|General Internet Browsing (S)||-||-||-||-||-||-||x||-||x||x||x|
|Creating/maintaining websites (S)||-||-||-||-||-||-||x||-||x||x||x|
|General application use (e.g., Microsoft Office) (A)||-||-||-||-||-||-||x||-||x||x||x|
|Spending time on social networking sites (S)||-||-||-||-||-||-||x||-||x||x||x|
|Texting, email, social networking such as Facebook or Twitter (S)||-||-||-||-||-||-||-||x||-||-||-|
|Skype or Webcam (S)||-||-||-||-||-||-||x||x||x||x||x|
|Online chatting/instant messaging (S)||-||-||-||-||-||-||x||-||x||x||x|
|Other Internet/electronic device use (S)||-||-||-||-||-||-||x||x||x||x||x|
Description of regression covariates
Included in our health controls is parent’s mental and physical health. Mental health of both parents is measured using the Kessler-6 (K6) scale (Kessler et al. 2002), a six-item scale measuring psychological distress. This scale consists of six items related to distress including whether the parent felt nervous, hopeless, restless or fidgety, worthless, and so depressed that nothing could cheer them up and/or that everything was an effort, in the last 4 weeks. Total scores range from 6 to 30 with higher scores reflecting a greater level of psychological distress.
We create an indicator for poor physical health, if the parents respond to the item “In general, would you say your own health is…?” with poor or fair.
Using self-reported employment status, we include a dummy variable for whether the child’s mother and father are employed.
We create a household income variable by combining self-reported imputed gross weekly income of both parents (where applicable). Income was inflated to 2017–2018 Australian dollar values using consumer price index (CPI) data from the Australian Bureau of Statistics (ABS) and divided by 10,000.
Our measure of neighborhood disadvantage uses the Socio-Economic Index for Areas (SEIFA) Index of Relative Socio-Economic Disadvantage score of the child’s local area, provided by the Australian Bureau of Statistics Census of Population and Housing data 2006 and 2011. We standardize and reverse this scale for ease of interpretation, such that higher scores indicate more disadvantaged neighborhoods.
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Arnup, J.L., Black, N. & Johnston, D.W. Changes in children’s time use during periods of financial hardship. J Popul Econ (2021). https://doi.org/10.1007/s00148-021-00864-z
- Time use
- Screen time
- Financial hardship
- Material deprivation