Fertility Analysis with EU-SILC: A Quantification of Measurement Bias

The European Union Statistics on Income and Living Condition (EU-SILC) database is increasingly used in demographic analysis, due to its large country coverage, the availability of harmonized socioeconomic measures and the possibility to merge partners. However, so far there exists no comprehensive analysis of the representativeness of fertility behavior reported by EU-SILC. This paper quantifies the quality of fertility measures in EU-SILC. We compare several fertility measures obtained with EU-SILC to unbiased measures from the Human Fertility Database (HFD) for several European countries, by applying a longitudinal as well as a cross-sectional perspective. We show that EU-SILC underestimates completed fertility mainly because the questionnaire does not ask about the number of children ever born to a woman/man, and we identify significant socioeconomic differentials in this measurement bias. Measures of periodic fertility behavior are biased downward mainly due to attrition, while births of order one for ages 20-29 are particularly underreported. However, we find no evidence for socio-economic differentials in attrition. Our results suggest that for the majority of European countries, Eu-SILC can be used for demographic analysis when respecting the measures of precaution mentioned in this article. These contain for example applying a retrospective approach and differentiating by rotation groups when calculating aggregate measures of periodic fertility differentiated by socio-economic groups.

[1]  J. Bavel,et al.  Partners’ Educational Pairings and Fertility Across Europe , 2018, Demography.

[2]  Emilie Filmer-Wilson,et al.  The United Nations Population Fund , 2018 .

[3]  P. Gobbi,et al.  Having a Second Child and Access to Childcare: Evidence from European Countries , 2017 .

[4]  Bruno Arpino,et al.  The Effect of Gender Policies on Fertility: The Moderating Role of Education and Normative Context , 2016, European Journal of Population.

[5]  M. Rendall,et al.  Multiple imputation for demographic hazard models with left-censored predictor variables: Application to employment duration and fertility in the EU-SILC. , 2014, Demographic research.

[6]  Hippolyte d’Albis,et al.  Avoir un enfant plus tard: Enjeux sociodémographiques du report des naissances , 2015 .

[7]  M. Klesment,et al.  Varying association between education and second births in Europe: Comparative analysis based on the EU-SILC data , 2014 .

[8]  Gustavo De Santis,et al.  Un indice synthétique de fécondité enrichi à partir des données de panel , 2014 .

[9]  Jean-Paul Lorgnet,et al.  L’attrition dans l’enquête SRCV: déterminants et effets sur la mesure des variables monétaires , 2014 .

[10]  Daniele Vignoli,et al.  Diverse Effects of Women’s Employment on Fertility: Insights From Italy and Poland , 2013, European journal of population = Revue europeenne de demographie.

[11]  Daniele Vignoli,et al.  Whose job instability affects the likelihood of becoming a parent in Italy? A tale of two partners , 2012 .

[12]  M. N. Bhrolcháin,et al.  Sources of error in reported childlessness in a continuous British household survey , 2011, Population studies.

[13]  L. Toulemon,et al.  Multi-residence in France and Australia: Why count them? What is at stake? Double counting and actual family situations , 2010 .

[14]  K. Kiernan.  Cohabitation in Western Europe: Trends, issues, and implications. , 2002 .

[15]  G. Desplanques.  Mesurer les disparités de fécondité à l'aide du seul recensement. , 1993 .