Increasing childlessness in Europe: the contribution of changes in structure and propensity

Annalisa Donno, Università di Padova
Maria-Letizia Tanturri, Università di Padova
Pietro Mozzi, Università di Padova

Substantial changes in female access to education and family formation patterns in most European countries have been accompanied by a gradual increase in permanent childlessness. Even if to variable extents, childlessness has always existed; what seems to have changed, together with the proportion of childless people, is the composition of the childless groups, in terms of both motivations and social characteristics. Childlessness is a cohort phenomenon. Having no children is the result of decisions and behaviors that cumulate over the reproductive life course. Members of a birth cohort make decisions about childbearing and family formation in response to shared social and economic conditions. Changes in the social environment faced by cohorts over the twentieth century, as well as changes in the composition of cohorts, have the potential to explain long-term trends in childlessness. The goal of this study is not to understand the individual-level determinants of childlessness, but to identify which of the large-scale demographic changes in the second half of the twentieth century in Europe are most closely associated with changing levels of childlessness. By specifically focusing on education and marital status, we want answer the following questions: which of the demographic changes occurred in Europe in the last decades are most closely associated with changing levels of childlessness, and which is the role propensity toward childlessness plays in this mechanism? Does it fuels or counterbalances compositional changes, and how does it change over birth cohorts? We try to disentangle between increases of childlessness caused by the structural changes in population (specifically the growth of permanent celibacy or the increase of women education) and by variations in the propensity towards childbearing by using Fairlie’s decomposition technique, in a logistic regression framework.