Projecting future births with fertility differentials reflecting women’s educational and migrant characteristics

Abstract

Building on the well-established knowledge on fertility differentials by education and nativity/migration status, we employ microsimulation modelling to demonstrate the effect of accounting for such differences in population projections. We consider fertility differentials by educational attainment, enrolment in full-time education, region of birth, age at immigration, and duration of stay in the host country, which we introduce step-wise into the microsimulation model for the EU28. Results on projected TFRs and births by 2060 illustrate the importance of accounting for several sources of population heterogeneity. In the context of future educational expansion, modelling education differentials for students and for women with completed education is needed to capture the postponement effect of education on childbearing. Future migration assumptions that include migrant fertility differentials lead to widely varying projected numbers of future births. At fixed fertility differentials and a fixed composition of immigrant flows, the net effect of immigrant fertility on the overall TFR in the EU28 is projected to increase from the estimated 0.12 in 2015-2019 to 0.17 in 2055-59 in the scenario with baseline migration, and to 0.25 in 2055-59 in the scenario with doubled migration.

Projecting the fiscal impact of immigration in the European Union

“…(2019) provide an overview of the parameters driving all the modules that generate events. Marois, Sabourin and Bélanger (2019a) provide a detailed description of the labour force module and Marois, Sabourin and Bélanger (2019b) describe the education module, while Potančoková and Marois (2020) describe the fertility module.…”

Digital and computational demography

“…Many of the processes that lead families to form (e.g., marriage, unmarried cohabitation), grow (e.g., fertility), and dissolve (e.g., divorce, death of a partner) lend themselves well to MS and ABC modelling. For example, recognizing that there are sometimes large educational differentials in female fertility across countries, Potančoková and Marois (2020) used MS to explore how educational expansion, especially among women, may affect future fertility patterns in the EU. Yet, the study of families often requires us to consider the decisions of multiple actors in networked structures, such as the interdependent partnering decisions of prospective spouses.…”

“…Third, there is a long tradition of using MS to study the implications of demographic or social changes (e.g. educational expansion, diffusion of technologies like ultrasound) for population dynamics (Kashyap & Villavicencio, 2016;Potančoková & Marois, 2020) . MS has been used in the context of data scarcity, such as in historical demography (Murphy, 2011), in the context of mortality crises (Wachter et al, 2002;Zagheni, 2011).…”

“…The rapid increase in computational power has been matched by a renewed interest in computationally intensive approaches to calibration (Aparicio Diaz et al, 2011;Kashyap & Villavicencio, 2016;Potančoková & Marois, 2020). The Bayesian melding method (e.g., Poole & Raftery, 2000;Raftery et al, 1995), in particular, has proved useful to formalize the process of calibration and statistical inference.…”

Population aging, migration, and productivity in Europe

“…Alternative Scenarios for Migration and Labor-Force Participation Here we address the question of how to view aging through a multidimensional demographic approach in which the populations of all 28 EU member states are stratified, not only by the conventional age and gender, but also by labor-force participation, immigration status, and educational attainment which is also used as a proxy for productivity. Using a multidimensional population projection model by a microsimulation called CEPAM-Mic (6)(7)(8)(9), which also considers the duration of stay in the destination country and the age at immigration, we built different scenarios of changes in labor-force participation rates, the number of immigrants, their composition, and their integration into the labor market in an effort to measure the impact of different policies on projected dependency ratios for 2015-2060. Here, integration of immigrants into the labor market is modeled as the differential in labor-force participation rate by duration of residence compared to native's rates.…”