Abstract
We use time-diary data from the 2003 and 2004 American Time Use Surveys and the 2000 United Kingdom Time Use Study to estimate the effect of family structure on the time mothers and fathers spend on primary and passive child care and on market work, using a system of correlated Tobit equations. Our results indicate that estimates are sensitive to the inclusion of a common household factor that controls for selection into family type. Estimates from the selection-controlled models indicate that single parents in both countries spend more time in child care than married or cohabiting parents, perhaps in part to compensate for the missing parent, but that there is no difference in the time allocation of married and cohabiting parents. There are substantial cross-country differences, however, as single parents in the U.S. work more than other parents and single parents in the U.K. work less.
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Notes
About 11% of children lived with cohabiting parents and 24% lived with single parents in the U.K. in 2004 (www.statistics.gov.uk/cci/nugget.asp?id=1163). In 2000 less than 6% of children in the U.S. lived with cohabiting parents and somewhat less than 28% lived with single parents (www.census.gov/prod/2004pubs/censr-14.pdf).
In 2005 about 75% of employed mothers in the U.S. were working full-time (www.bls.gov/news.release/famee.t05.htm), as compared with about half of all women in the U.K. (www.statistics.gov.uk/cci/nugget.asp?id=1654).
Policy reforms have been implemented in both countries since these data were collected. The 2006 Deficit Reduction Act, which reauthorized TANF, further increased the work orientation in the U.S. system. Meanwhile, the British government is aiming for 70% employment by lone parents by 2010.
An advantage of The UKTUS over the ATUS is that all intrahousehold relationships are identified. Thus, it is possible to ‘fix’ misreported relations using other information in the sample and to accurately identify all children of cohabiting partners. In this respect, the UKTUS sample will be ‘cleaner’ than the ATUS sample. However, a comparison of the UKTUS sample used here with another constructed using the same restrictions imposed upon the ATUS reveals only minor differences. Most notably, less than 40 households with unrelated children and only 1 adult (who might reasonably be considered the legal guardian) are excluded from our UKTUS sample but would be included under ATUS sample rules.
Diaries containing fewer than five different activity codes and those missing more than one hour of information are excluded from both the ATUS and the UKTUS diary samples.
An age cutoff of 14 is introduced here because the UKTUS does not provide sufficient detail when recording who else is present during an activity to identify children aged 15–17.
Data availability poses another problem. Wage information is only available for a subset of employed persons and household income is but imperfectly measured.
The system of equations was estimated using the aML software. Estimation of the time use equations actually proceeded using ordered probit models with known thresholds (60 min intervals) as aML was unable to estimate the Tobit specification with family structure equations using a discretely distributed unobserved factor.
As an example of how factor-analytic covariance restrictions can identify a model, consider a simple specification with a single outcome variable, y, and two endogenous explanatory variables, x 1 and x 2. Assume that x 1 and x 2 each depend on a common unobserved random component (factor), μ, and independent random components, ɛ1 and ɛ2, such that x 1 = μ + ɛ1 and x 2 = μ + ɛ2. The outcome variable, y, depends on the two observed explanatory variables, the common random component, and its own independent random component, η, such that y = β1 x 1 + β2 x 2 + μ + η where β1 and β2 are coefficients. For simplicity, assume that all of the random components are continuously distributed with zero means, constant variances, and no mutual correlations so that \(\hbox{E}(\varepsilon_{1}) = \hbox{E}(\varepsilon_{2}) = \hbox{E}(\eta) = \hbox{E}(\mu) = 0\), \(\hbox{E}(\varepsilon_{1}^{2})=\sigma_{1}^{2}\), \(\hbox{E}(\varepsilon_{2}^{2})=\sigma_{2}^{2}\), \(\hbox{E}(\eta^{2})=\sigma_{\eta}^{2}\), \(\hbox{E}(\mu^{2})= \sigma_{\mu}^{2}\), and \(\hbox{E}(\varepsilon_{1} \varepsilon_{2}) = \hbox{E}(\varepsilon_{1}\eta) = \hbox{E}(\varepsilon_{2}\eta) = \hbox{E}(\varepsilon_{1}\mu) = \hbox{E}(\varepsilon_{2}\mu) = \hbox{E}(\eta\mu) = 0\). There are six parameters in this system, and the covariance matrix for y, x 1 and x 2 has six elements. Let s 2 y , s 21 and s 22 denote the sample variances, and let s y1, s y2 and s 12 denote the sample covariances. Method of Moments estimators for β1 and β2 are \(\hat{{\beta}}_1 =\frac{\left({s_{y1} -s_{12} } \right)s_2^2 -\left({s_{y2} -s_{12} } \right)s_{12} }{s_1^2 s_2^2 -s_{12} }\) and \(\hat{{\beta}}_2 =\frac{\left({s_{y2} -s_{12} } \right)s_1^2 -\left({s_{y1} -s_{12} } \right)s_{12} }{s_1^2 s_2^2 -s_{12} },\) which are identified even though there are no variable exclusion restrictions.
Note, however, that we do find significant differences between married and cohabiting parents in the U.S. when the common factor is not included. This finding underscores the importance of controlling for selection into family type.
Ideally we would distinguish between own, step, and foster children. Such a distinction is, however, not possible with the U.S. data and likely not feasible in either case due to small populations. Only 4.5% of all children in the U.K. sample are not ‘own’.
U.S. statistics come from http://www.bls.gov/news.release/famee.t05.htm; U.K. statistics come from http://www.statistics.gov.uk/cci/nugget.asp?id=1655.
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Appendices
Appendix A: Sample statistics
Appendix B: Remaining coefficient estimates from correlated Tobit models of time-use
Appendix C: Family structure equations
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Kalenkoski, C.M., Ribar, D.C. & Stratton, L.S. The effect of family structure on parents’ child care time in the United States and the United Kingdom. Rev Econ Household 5, 353–384 (2007). https://doi.org/10.1007/s11150-007-9017-y
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DOI: https://doi.org/10.1007/s11150-007-9017-y