The Subjective Cost of Young Children: A European Comparison
Understanding child-related costs is crucial given their impact on fertility and labour supply decisions. Sonja Spitzer, Angela Greulich and Bernhard Hammer quantify and compare the cost of children in Europe by analysing the effect of child births on parents’ self-reported ability to make ends meet. Their study is based on EU-SILC longitudinal data for 30 European countries from 2004 to 2015, enabling comparisons between country groups of different welfare regimes. Results show that newborns decrease subjective economic wellbeing in all regions, yet with economies of scale in the number of children. Figure 4 displays the drop associated with first-order children. The decrease is mainly caused by increased expenses due to the birth of a child (direct costs), which are largest in high-income regions. Immediate labour income losses of mothers (indirect costs) are less important in explaining the decrease. These income losses are closely related to the employment patterns of mothers and are highest in regions where women take extensive parental leave. In the first years after the birth, indirect costs are mostly compensated for via public transfers or increased labour income of fathers, while direct costs of children are not compensated for.
If you want to read their paper, klick here.
Good things come in threes: multigenerational transmission of human capital
For the AWA project, Hector Moreno from the Paris School of Economics analysed the effect of grandparental education on their children’s and grandchildren’s educative outcomes. Find a summary of his work below – if you want to read his working paper, klick here.
“This paper examines the effect of grandparental education on their children’s and grandchildren’s educative outcomes. The endogeneity of parental schooling is addressed by the use of a two-fold instrumental variable approach. A natural experimental set up from a regional war that occurred in 1926 is exploited to instrument years of schooling of the “grand-parents” generation whereas labour market indicators serve as an instrument for the education of the “parents” generation. Using a nationally representative Mexican survey that gathers retrospective information on the three generations, the paper first shows that accounting for endogeneity unveils less mobility than ignoring it. This allows documenting more persistence of family background in the older pair of parent-child link than in the younger pair in the three generations at hand. Finally, results also suggest that the influence of the grandparents’ educative legacy, conditional on parental education, does not seem to reach the grandchildren’s generation.”
The Rush Hour of Life in Austria, Italy and Slovenia
Marina Zannella, Bernhard Hammer, Alexia Prskawetz, and Jože Sambt recently published work from the AWA project in the European Journal of Population. Find the abstract below – if you want to read the whole paper, klick here.
“This article builds on time use data to explore cross-country differences between Austria, Italy and Slovenia in unpaid labour and its implications in terms of gender distribution of total work. A contribution of this paper is to measure the ‘rush hour of life’ (RHOL) based on age spans in which individuals’ working time (including paid and unpaid work) exceeds their free time. In total, men and women work similar hours in Austria, whereas Italy and Slovenia show a gender gap with women working an average of approximately 50 min more per day during prime working ages. The different compositions and loads of total work are reflected in cross-country variations of the length and intensity of the RHOL, with Slovenian women reporting, on average, the larger squeeze of time. However, breadwinner arrangements differ considerably among the three countries, which can affect the amounts of work and free time available for men and even more so for women. Therefore, we further extend our analysis by developing a regression model to quantitatively assess the association between couples’ working arrangements and levels of the RHOL indicator for men and women. Results indicate a dual burden for women in dual-earner couples, squeezing out their free time. By contrast, women in male-breadwinner arrangements report the lowest amounts of total work. Breadwinner models show no significant relation to male levels of work and free time, with the main exception of Italy where men face higher RHOL in full-time employed couples.”
Figure 2 shows the rush hour of life for all three countries analysed.
Why are we the least happy in middle age?
The relationship between wellbeing and age often appears U-shaped when analysed with cross-sectional data. The typical findings suggest that we are the least happy around ages 35 to 45. One interpretation of this U-shape in age is that it reflects the different events that occur to individuals over the life cycle, such as income, marriage, the birth of a child and retirement. In other words, the U-shape reflects the events that are associated with aging. Alternatively, the U-shape can reflect a cohort effect – individuals who were born in certain years and socialised in a certain way have certain levels of wellbeing (that they will keep throughout their lives). The analysis of well-being with cross-sectional data cannot distinguish the age and cohort effects.
Andrew Clark from the Paris School of Economics has considered whether the cohort-effect hypothesis holds when applied to British longitudinal data from 1991 to 2008. He finds that the British are also the least happy in their late 30s and early 40s. This finding can be derived from pooled regressions with and without demographic controls (see Figure 1). However, the U-shape remains even after estimating panel regressions that include individual fixed effects, which will hold constant any potential cohort effects. Based on these findings, as well as a number of additional tests, he concludes that the cohort effect does not fully explain why wellbeing is U-shaped in age. Instead, wellbeing seems to be driven by a combination of age effects and cohort effects.
If you want to read the whole paper, klick here: AWA Clark 2018