Abstract
This paper contributes to the relatively limited literature on the correlation of labor market outcomes of parents and their children. This literature is relevant to the larger literature on intergenerational income mobility since correlation in intergenerational labor market outcomes is one of the potential factors contributing to the intergenerational correlation of permanent incomes. In this paper, we consider the time spent in unemployment by both sons and daughters, while accounting for the potential endogeneity of education. Using the Household, Income and Labour Dynamics in Australia data, we find evidence of a positive correlation of labor market outcomes between fathers and sons and, to a lesser extent, between mothers and daughters. In addition, the results reveal a significant relationship between parents’ and children’s education levels, indicating that there is an indirect association of parental education with their children’s labor market outcomes through education.
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Notes
The two measures are roughly equal provided the variances of the log earnings are similar for parents and their children.
See for example, O’Neill and Sweetman (1998) or Farré and Vella (2013) for labor market outcomes; Pacheco and Maloney (2003), Maloney et al. (2003) or Beaulieu et al. (2005) for welfare participation; Österberg (2000), Corak (2006), Ermisch et al. (2006), Blanden et al. (2007), Mocetti (2007), Nicoletti and Ermisch (2007), Raaum et al. (2007) or Justman and Krush (2010) for earnings and income; Carneiro et al. (2013), Casey and Dustman (2007) or Heineck and Riphahn (2009) for education; and Björklund et al. (2007) or Currie and Moretti (2007) for economic status.
The literature on the intergenerational correlation of welfare participation is much larger (see footnote 2). Although the two issues are related, they are not the same. Welfare recipients can be working, while being out of work does not necessarily imply being a welfare recipient. Other papers, such as Couch and Dunn (1997), are interested in the intergenerational correlation in hours of work rather than employment or labor force participation.
See Blanden (2013) for a discussion of these measurement issues and for a recent review of the literature on intergenerational mobility.
Mocetti (2007) mentions the downward bias of the intergenerational correlation arising from estimation based on homogenous samples.
In this regard, this paper extends the New Zealand analysis of Maloney et al. (2003), which focuses on welfare participation, by explicitly allowing for correlation between the unobserved factors in the education and labor market outcome equations.
Mocetti (2007) also recognizes the importance of education in earnings outcomes and explores the educational mobility separately in his paper. He finds a strong correlation between the father's and son's education.
When deducting the time since completing full-time education from the current age of the respondent, an age of around 18 was frequently computed as the age of completing full-time education. This was the case even for those with a bachelor’s degree or more (which would typically be completed at a minimum age of 20 or 21).
A crosscheck carried out on a subsample of young respondents, for whom more comprehensive parental information is available, indicated that, overall, parental employment status when the respondent was aged 14 is a reasonable proxy to distinguish between parents with poor and good labor market histories. That is, although some detail is lost, the variation in the parent’s labor market outcome measure is meaningful in terms of identifying differences between parents in outcomes. Of course, this is just an indication, since childhood was a relatively recent event for this subsample.
The coefficients on parental education in the respondent’s labor market outcome equation are not jointly significant. That is, we cannot reject the null hypothesis of these coefficients being equal to zero in this equation. Tests carried out on male and female models result in P-values that are all above 0.45. Similar tests for parental occupation lead to P-values all above 0.38. In contrast, Appendix Table 4 shows that these variables are significant in the education equation.
In an alternative specification based on the six discrete education categories, we used the ordered Probit approach instead of OLS. This was found to leave the estimated coefficients of interest in the labor market outcome equation essentially unchanged.
Unemployment rates by gender are provided by the International Labour Office (ILO 2008) for the period from 1969 to 2005 and by the Australian Bureau of Statistics (Series: 6204055001TS0001) for the period from 1966 to 1968 (not available by gender for this period). No information was available for 1965; instead we use the unemployment rate as it was in August 1966, which is the earliest data available from the ABS. This is a reasonable approximation since unemployment was very low and stable in those years. More detailed information by region is not useful since no information is available on where respondents lived in childhood, or when they were aged between 18 and 22.
Controls for unemployment rates later in life are not included due to collinearity issues (in particular with the age dummies).
The complete set of coefficient estimates is available from the authors upon request.
Leigh (2007) examined the intergenerational correlation of earnings for Australia using multivariate analysis and estimated an intergenerational earnings elasticity of about 0.2 to 0.3 using HILDA data with imputed earnings for parents.
More information would be needed to disentangle the cohort and unemployment rate effects.
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Acknowledgments
This research was commissioned by the Australian Government Department of Education, Employment and Workplace Relations (DEEWR) under the Social Policy Research Services Agreement (2005–2009) with the Melbourne Institute of Applied Economic and Social Research. The paper uses the confidentialized unit record file from the Department of Families, Housing, Community Services and Indigenous Affairs (FaHCSIA’s) Household, Income and Labour Dynamics in Australia Survey, which is managed by the Melbourne Institute of Applied Economic and Social Research. The views expressed in this paper are those of the authors alone and do not represent the views of the Minister for Families, Housing, Community Services and Indigenous Affairs, FaHCSIA, DEEWR or the Commonwealth Government. Additional funding to support this research was provided by the Faculty of Economics and Commerce, The University of Melbourne. We are grateful to Deborah Cobb-Clark, John Creedy, Dean Hyslop, Moshe Justman, Matthias Sinning, two anonymous referees and participants at the Intergenerational Workshop at the Australian National University for helpful comments and discussions.
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Hérault, N., Kalb, G. Intergenerational correlation of labor market outcomes. Rev Econ Household 14, 231–249 (2016). https://doi.org/10.1007/s11150-013-9218-5
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DOI: https://doi.org/10.1007/s11150-013-9218-5