But the amount of time we each control does vary: for the single mother trying to hold down a full-time job while caring for her two kids and her own mother, time is a precious commodity. And money and time can substitute for each other – if you need more money you can work more hours; if you need more time you can pay someone to do some of your chores.
Inequality of time is potentially important then – and people who have little time and money are likely to be particularly disadvantaged. But is it really possible to measure time poverty in a way that has real, practical implications?
The easiest way to look at time inequality is to look at how much time people commit to paid and unpaid work. Brendan did a lovely post on this last week – it was the trigger for writing this post. To turn this into a measure of time poverty, though, we need to have some sort of cut-off for where people become time-poor.
Several researchers including Tania Burchardt – one of my thesis supervisors, and a generally fabulous person/researcher – have measured time poverty as less than 60% of the median level of disposable time in the whole population. This is analagous to the way that we arbitrarily define relative poverty as 60% of median income. It’s hard to defend the exact number, except to say that we have to choose some cut-off, and this one seems to work OK.
The time-income trade-off
If we measure time poverty in this way, then we see a pattern like the graph below – poorer people have more disposable time, because they do less paid work. (They actually do slightly more unpaid work, but this doesn’t outweigh the paid work effect).
But this isn’t quite right – as I said at the start, time and income can substitute for each other. Poorer people may have more time, but because they have less income, they may be more likely to feel restricted by their total amount of time+money.
Tania’s study therefore extends the initial measure of time poverty in two ways. Firstly, she looks at people who are both time-poor and income-poor, reasoning that these people are likely to feel seriously strained. Only 1.6% of adults are both time-poor and income-poor, but because these adults are more likely to have children, 6.7% of all children are in household where one adult is both time- and income-poor.
People who cannot avoid poverty
The second method is the really creative one however. The trouble with measures of people’s situations is that they depend on the choices they make, not the choices available to them. As Tania puts it,
While for one interviewee ‘struggling to make ends meet’ meant not being able to afford her daughter’s cello lessons, for another it meant missing a friend’s birthday party, because she had ‘no clothes that weren’t half worn out’
The crucial question, then, is whether a person has any allocation of time that allows them to avoid both time poverty and income poverty
To estimate this, we not only need to know how people spend their time, and how much money they have – but also how much time/money they would have had if they had made different choices. In particular, Tania looks at the choice to work different numbers of hours, and to buy-in another person to do some of the unpaid work (particularly care work). I’m not going to even try and describe the details of this in a short post – see ch3 of the full report for this – but it’s safe to say this isn’t the kind of calculation that you can do in a hurry…
When we look at the results, the overall poverty rate for those who cannot avoid being either time-poor or income-poor is 2.4%. More importantly, the people who are particularly likely to be in this situation are young adult women, with no partner, low qualifications, and more/younger children. (There are a range of further results in the report itself, but these are the headline figures).
Time poverty and policy
Estimating the rate of unavoidable time+income poverty is a fiendish task then – but does it have any payoff in policy terms? Tania makes two points here. Firstly, the more highly-skilled people are, the more they can earn, and the easier the trade-offs become. I’m not convinced about this, however, given the relative nature of poverty definitions – you either need to earn relatively more than the people whose time you’re buying, or your earnings need to be relatively higher compared to the median. So if everyone becomes more highly-skilled, then there may still be problems.
The second point is harder to argue with: we need to consider parental time as well as parental income when thinking about child poverty. The summary from Tania’s research noted how Government efforts to reduce income-poverty could increase time-poverty if they are badly designed. The exciting if even-more-complicated comparative work on discretionary time by Rice et al leads to similar policy suggestions about the welfare state’s role in providing particularly parents and other carers with more time.
But do we really need to have these complicated estimates of time poverty to know that these policies are a good idea? The think-tank Demos launched a well-publicised report in early 2011 that made the link between time and child outcomes, without (as far as I know) making any use of the time poverty figures.
That said, we can never underestimate the power of the single number. When a measure such as GDP becomes the headline measure of policy success, then this becomes a problem even if everyone knows there are many things GDP doesn’t capture. Similarly, to the extent that policymakers have been concerned with child poverty figures over any other measure of disadvantage – as they were under the previous Government – then there is a definite need to add a new, more inclusive measure of success.
To achieve this, though, we’re going to have to find a better way than I found in this article of communicating roughly what the time+income poverty figures mean…