The Relation Between Maternal Work Hours and the Cognitive Development of Young School-Aged Children

Abstract

This paper analyses the relation between maternal work hours and the cognitive development of young school-going children. We find that children’s language and sorting test scores are higher when their mothers have a large part-time job or even a full-time job. We find no evidence that this can be explained by a richer home environment in terms of the number of parent–child activities provided to the child.

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Notes

  1. 1.

    In our empirical analyses, differences in school inputs are accounted for by the inclusion of school fixed effects, making it more likely that test scores reflect initial abilities and parental inputs.

  2. 2.

    See Goldberg et al. (2008) for an extensive overview and meta-analyses of this stream of literature.

  3. 3.

    It is important to distinguish between children from one- and two-parent families, since children from single mothers might benefit from maternal employment and the related increased income and children in two-parent families might experience adverse maternal employment effects if the mother is absent from the home (Lucas-Thompson et al. 2010). On the other hand, the mother’s absence due to employment might have greater consequences for parent–child activities in one-parent families than in two-parent families.

  4. 4.

    The construction of these and the other variables included in model (3) is explained in detail in Sect. 4.

  5. 5.

    In a robustness check, we perform our analyses for a subsample of children of full-time working fathers.

  6. 6.

    Whereas we have two observations for test scores, we have only one observation for the input variables. Since we have school information on children attending the first and second years of compulsory kindergarten, we cannot use sibling fixed effects either.

  7. 7.

    Our focus is on the richness of a child’s environment in terms of the quantity of joint activities provided. Of course, the quality of the home environment provided is important as well.

  8. 8.

    Unfortunately, our data are cross-sectional, with the exception that test scores are measured in two successive years. Therefore, our estimations do not warrant a causal interpretation. However, since the children in our data were very young and had not yet started learning how to read, write, or do arithmetic at school, it is likely that both parental work hours and the number of joint parent–child activities are exogenous with respect to child outcomes, because at preschool ages the child endowments we analyse are not revealed to parents (Ermisch and Francesconi 2000; Rosenzweig and Wolpin 1995). Due to the unique control variables included in the estimations, such as non-cognitive skills, parental views, and parenting goals, unobserved heterogeneity and the associated likelihood of spurious effects are not likely to be a serious issue. There might be another source of endogeneity as well: Parents who are less productive in raising their children could also be less productive in the labour market. This could lead to an upward bias in our findings. However, since we include parental education in the analyses to control for this potential source of unobserved heterogeneity, the bias is likely to be small. Nevertheless, our findings should not be interpreted causally.

  9. 9.

    By law, schools must provide at least 880 h and at most 940 h of education per year. Most children have Wednesday afternoon off, but schools are free to institute their own schedules (see http://www.rijksoverheid.nl/onderwerpen/schooltijden-en-onderwijstijd).

  10. 10.

    Although the industrial structure of the southern part of Limburg differs in some aspects from the rest of the Netherlands (e.g. a larger share of the employed work in the chemical sector and a lower share works in the food industry), the share of women in the labour force is similar (45 %), as is the number of working hours (34, on average, per week).

  11. 11.

    There are some exceptions: 2 % of the children attended the first year of primary school twice. For these children, we use their first test scores to make sure the test scores are comparable to those children who did not attend the first year of school twice. The age of children at the time they perform the tests is included in the analyses to take into account their age when entering school. If we exclude those attending the first year of primary school twice, our results remain the same.

  12. 12.

    One questionnaire was sent per child, with the child’s name, address, date of birth, and gender at the top.

  13. 13.

    This could also include step-parents and parent’s partners.

  14. 14.

    This more restricted sample of children is representative of the larger sample in terms of both cognitive test scores, maternal employment, and control variables.

  15. 15.

    The sample of children included seems to be slightly positively selected. The children in our sample scored, on average, one point higher per test compared to children whose parents did not complete the survey. For the distribution of test scores of the sample and the population, see Fig. 4 in Appendix 1.

  16. 16.

    Group mean comparison tests show that girls performed significantly better in both tests than boys. This is a common finding in the literature.

  17. 17.

    The factor loadings for this factor analysis and the upcoming ones are shown in Tables 68 in Appendix 1.

  18. 18.

    For a full list of questions related to the home environment and the distribution of these variables (differentiated by maternal work status), see Appendix 2. It turns out that there is a positive significant correlation between maternal work hours and joint planned activities.

  19. 19.

    Bertrand and Pan (2013) show the importance of such situations for child behaviour.

  20. 20.

    In terms of the International Standard Classification of Education (ISCED): low (ISCED 0–2), intermediate (ISCED 3), and high (ISCED 5–7).

  21. 21.

    We have no data on parental income. We do, however, have information on the number of children’s books are available in the household. This is often used as a proxy for parental resources (e.g. Brunello and Checchi 2007). In our data set, this variable is not related to the human capital development of children and its inclusion therefore does not affect our results. Although we have no information on family income, the variables age, work hours, and education level together proxy for differences in family income due to work.

  22. 22.

    In an alternative specification, we estimate ordinary least squares (OLS) models including all children, which yields similar results. This is most probably due to the fact that the more restricted sample of children is representative of the larger sample.

  23. 23.

    Source: CBS Statline, Regional information: Limburg: Years 2006, 2007, 2008.

  24. 24.

    For the mean of the test scores by maternal work status, see Table 1.

  25. 25.

    Since Figs. 2 and 3, show that the distribution of the test scores is truncated (most children perform well on the test), we performed Tobit fixed effects analyses to estimate the relation between maternal work status and the cognitive development of children. Since these Tobit estimates are similar to the OLS estimates (both with school fixed effects), we report the latter in this paper. Full results of the OLS school-fixed effects models are reported in Table 10 in Appendix 1.

  26. 26.

    In Appendix 1, Table 10 reports all coefficients.

  27. 27.

    The positive relation between joint planned activities and children’s language test scores is driven by middle- and highly educated mothers. If we estimate the regressions separately for middle-/highly educated and low-educated mothers, the positive significant relation between joint planned activities and children’s language test scores is only found for children from middle-/highly educated parents.

  28. 28.

    The observation that the coefficient for small part-time jobs is not significant might be caused by the small number of mothers with such a job.

  29. 29.

    Even though the coefficient for mothers in large part-time jobs is only weakly significant in specification (1), it is not statistically different from that of mothers with a full-time job or from the coefficient for large part-time jobs in specification (2).

  30. 30.

    We have found no heterogeneous effects of maternal employment with respect to either children’s test scores, nor with respect to maternal education.

  31. 31.

    We find no heterogeneous effects of our measures of home-environment with respect to maternal employment.

  32. 32.

    The correlation between maternal education and language test scores disappears when we instrument the lagged language test score using lagged ordering scores. This is not surprising since the correlation between maternal education and the sorting test score in year two is fully captured by lagged ordering scores (see columns (1) and (3) in Table 5).

  33. 33.

    Our results are similar when we leave out items for which the direction of the parent-child interaction is less obvious such as ‘Watching children’s programs on TV with your child’ and ‘Watching TV/video (other programs) with your child’.

  34. 34.

    This suggests that not all activities parents undertake with their children are mentioned in the questionnaire. However, since the distribution is similar for boys and girls, this is not a problem in our analyses.

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Correspondence to Annemarie Künn-Nelen.

Additional information

We thank Lex Borghans, Thomas Dohmen, Trudie Schils, Maria Zumbühl, Thomas Zwick, two anonymous reviewers and the participants of the 2010 Verein für Socialpolitik and the Maastricht University DUHR seminar for useful comments and suggestions on earlier versions of the paper. Moreover, we thank the Research Centre on Opportunities in Education (Kaans) related to Maastricht University for providing the Moelejaan data.

Appendices

Appendix 1

See Fig. 4 and Tables 610.

Fig. 4
figure4

Distribution of test scores for sample and population. a Language test b Sorting test

Table 6 Factor loadings on children’s non-cognitive skills
Table 7 Factor loadings on parental views on traditional gender divides
Table 8 Factor loadings on parenting goals related to their child’s independence
Table 9 Summary statistics of control variables
Table 10 Full table with main results

Appendix 2: Home-Environment

Questions related to joint daily activities (\(DA_h\)) are asked in the following way: In their joint daily activities, children do lots of things together with their parents. What applies to your child? The activities referred to are the following:

  • Playing with toys inside with your child

  • Playing outside with your child

  • Playing on a computer with your child

  • Drawing/painting with your child

  • Making up stories with your child

  • Going to the sports club or swimming pool with your child

  • Watching children’s programs on TV with your child

  • Watching TV/video (other programs) with your child

  • Reading stories to your child

  • Reading stories focused on development to your child

  • Talking about school with your child

We know when at least one of the parents undertook the abovementioned activities with their child, but we do not know which parent. We construct a variable measuring the number of joint activities parents undertook with their child.Footnote 33

Questions dealing with planned activities (\(\textit{PA}_h\)) are asked in the following way: When was the last time you (or your partner) went on a trip together with your child? The trips referred to are the following:

  • Visiting a museum with your child

  • Going to a swimming pool with your child

  • Going to a sports club with your child

  • Going to a zoo with your child

  • Going to a library with your child

  • Going to a park or forest with your child

  • Going to an amusement park with your child

Possible answers are ‘today’, ‘in the last week’, ‘some weeks ago’, ‘some months ago’, ‘more than half a year ago’, and ‘I rarely do’. ‘I rarely do’ was coded zero and all other answers were coded one. We then calculated an index for the number of joined planned activities as the sum of all activities parents undertake with their child.

Figure 5 plots the distribution of the number of joint daily and planned activities: Panel (a) shows that only a few parents undertake less than five joint daily activities with their child. About 10 % of the parents undertake 7 out of the 11 activities with their child. Undertaking 9, 10, or even 11 joint activities is most common in the sample. Panel (b) shows that most parents undertake at least five out of seven planned activities with their child. Both distributions seem to be truncated.Footnote 34

Fig. 5
figure5

Distribution of joint parent-child activities. a Joint daily activities b Joint planned activities

In Table 11, the average joint daily and planned activities are reported. These averages are reported for the full sample, and differentiated across maternal work status. From the table we do not see a clear relation between maternal work status on the one hand, and the average number of joint parent-child activities on the other. However, correlations show a significant relation between work hours (as measured by the four categories) and the number of planned activities.

Table 11 Joint daily activities per maternal employment status

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Künn-Nelen, A., de Grip, A. & Fouarge, D. The Relation Between Maternal Work Hours and the Cognitive Development of Young School-Aged Children. De Economist 163, 203–232 (2015). https://doi.org/10.1007/s10645-014-9247-3

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Keywords

  • Intergenerational human capital investments
  • (Non) cognitive skills
  • Maternal labour supply
  • Home environment

JEL Classification

  • D10
  • J13
  • J22
  • J24