The long answer is the length of an academic paper, which I recently published together with Ariana Need and Henk van der Kolk . Of course, concerns have been raised for a longer time that long periods of (childcare) leave might be detrimental for women’s attachment to the labour force, and long leave has even been described as a ‘mechanism of exclusion’ of women from the labour market (Pettit and Hook, 2009).

Comparative research on the effects of long periods of leave, however, has been taking a variety of strategies – not all of them optimal. So, based on a literature overview and our own empirical research, we formulated four recommendations for studying the impact of long childcare leave on women’s employment:

• The relationship between duration of leave and employment of women is curvilinear: whereas long leave may reduce women’s employment, we should not overlook that short period can be beneficial (and vice versa).
• Childcare leave is expected to affect only mothers, not women without children.
• Testing the long-leave hypothesis requires the use of country-comparative data in which countries are observed repeatedly over time. Among other benefits, this reduces the sensitivity of the analyses to influential cases.
• The long-leave hypothesis is best tested against person-level data.

We conclude that our findings suggest that longer periods of leave can be detrimental to maternal employment. While short periods of leave can be useful, or even necessary, to maintain women’s attachment to the labour market after becoming a mother, very long interruptions of employment indeed seem to be a “mechanism of exclusion” (Pettit and Hook, 2009). There are, of course, alternative to long periods of leave, that include stimulating the availability of affordable and high-quality childcare, and stimulating the availability and uptake of paternity leave (Eydal et al., 2015)

References

Eydal, G.B., Gíslason, I., Rostgaard, T., Brandth, B., Duvander, A.-Z. and Johanna, L.-T. (2015), Trends in parental leave in the Nordic countries: has the forward march of gender equality halted?, Community, Work & Family, Vol. 18 No. 2, pp. 167-181, doi: 10.1080/13668803.2014.1002754.

Nieuwenhuis, R., Need, A., & Van Der Kolk, H. (2017). Is there such a thing as too long childcare leave? International Journal of Sociology and Social Policy, 37(1/2), 2–15. http://doi.org/10.1108/IJSSP-07-2015-0074

Pettit, B. and Hook, J.L. (2009), Gendered Tradeoffs. Family, Social Policy, and Economic Inequality in Twenty-One Countries, Russel Sage Foundation, New York, NY.

This is why I love the open source community so much. Not only can users extend the use of influence.ME, and all other R packages, to do things that the package authors/maintainers did not implement. Or to check procedures. Or fix mistakes. Moreover, in line with the positive attitude towards sharing in the open access community, the improved code was shared back so that other users can benefit.

So, thanks to the help of the community, I am happy to announce an update to influence.ME, with two improvements:

• influence.ME now better handles binomial models
• influence.ME now supports functions inside the model call;for instance:
  model.a <- lmer(math ~ structure + scale(SES) + (1 | school.ID), data=school23)

influence.ME is an extension package for the R statistical software. It provides tools for detecting influential data in multilevel regression models (also known as mixed effects models). It was introduced in the R Journal (Nieuwenhuis, Te Grotenhuis & Pelzer, 2012). influence.ME can be downloaded from with the R software.

Nieuwenhuis, R., Grotenhuis, te, H. F., & Pelzer, B. J. (2012). Influence. ME: tools for detecting influential data in mixed effects models. R Journal, 4(2), 38–47.