In the end, I won the best poster prize. An updated of that poster (with a slightly modified title) is available here.

In the meantime, the research this poster was based on was published in the Journal of Marriage of Family, as:

Rense Nieuwenhuis, Ariana Need, & Henk van der Kolk (2012). Institutional and Demographic Explanations of Women’s Employment in 18 OECD Countries, 1975-1999 Journal of Marriage and Family, 74 (June), 614-630

Today, with this study, I was honored to receive the best research of 2012 award, given by the School of Management and Governance of the University of Twente. I’m very excited about the recognition our study receives, and the amount of attention it attracts.

So, please bear with me as I run you through some of our findings.

In our study we set out to describe the degree to which mothers in industrialized societies between 1975 to 1999 were less likely to be employed than women without children. We found that in no country mothers were more likely to be employed than women without children. Over time, women were increasingly likely to combine motherhood and employment in many, but not all, countries. Both mothers and women without children were more likely to be employed in societies with a large service sector and low unemployment. As you might expect, but has not been tested often, the employment of women without children was generally unaffected by family policies. Mothers were more likely to be employed in societies with extensive reconciliation policies and limited family allowances.

Interesting, right? If you want to know more (or don’t have access to the publication linked above), please don’t hesitate to contact me.

PS: this is the award!

In the application of multilevel models in country-comparative studies, however, it has long been overlooked that on the country-level only a limited number of observations are available. As a result, measurements on single countries can easily overly influence the regression outcomes.

Diagnostic tools for detecting influential data in multilevel regression are becoming available (including our own influence.ME), but what are your experiences with influential cases in country-comparative (multilevel) studies? How do you deal with influential cases if you encounter them?

The premise of my project was something that is commonly believed amongst sociologists – and something I took for granted for many years. I even remember when I first learned about it during a first-year course on inequality. Simply put: because spouses’ earnings are positively correlated, the earnings inequality between households is thought to be bigger than the earnings inequality between individuals. Intuitively, this makes sense: it is easy to imagine how the earnings gap between households with two top-earners and households with two low-income spouses is quite big. Recently, a new book by Esping-Andersen (The incomplete revolution) is getting quite some attention, and is reporting on how increased women’s earnings have contributed to income inequalities between households in several countries.

I thought it would be very interesting to delve into this issue more, and try to analyze differences between countries in the degree to which women’s earnings affects the inequality between households in these countries. Interestingly, though, once I started working seriously on the project, it quickly became clear that women’s earnings attenuate – rather than increase – inequalities between households. Virtually all authors analyzing the contribution of women’s earnings to household inequalities report this attenuating affect, and do so for various countries and for different points in time. So, the common conception that women’s earnings generally increase inequalities, turned out to be a misconception. No need to be concerned of this unintended consequence of women’s increased labour force participation.

So, I had to revise my plan. I turned my attention to a systematic review of the literature with a special focus on how it is possible that spouses’ earnings are positively correlated (which, indeed, they are), but that when women’s earnings are high the inequalities between households tend to be reduced. I won’t spoil the anticipation by disclosing my results here (but please, do contact me if you want to be informed when these results become available), but I figured it out. Also, I am still working on a systematic analysis comparing countries with very interesting preliminary results (again, no spoilers).

This concludes the most technical part of this series of blogs on my New York experiences. Tomorrow some notes on how PhD students are trained in New York.