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Migrants’ Fertility in Italy: A Comparison Between Origin and Destination

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Abstract

Previous research has analyzed the effect of migration on fertility, and a number of hypotheses have been developed: namely adaptation, socialization, selection, disruption and interrelation of events. Comparison among stayers in the origin countries, migrants and non-migrants in the destination country is essential to gain better understanding of the effects of migration on fertility. However, this joint comparison has been rarely conducted. We sought to fill this gap and analyze migrants’ fertility in Italy. By merging different data sources for the first time, we were able to compare our target group of migrant women, respectively, born in Albania, Morocco and Ukraine with both Italian non-migrants and stayers in the country of origin. Considering the first three orders of births, multi-process hazard models were estimated in order to provide a more exhaustive and diversified scenario and to test the existing hypotheses. The results show that there is no single model of fertility for migrants in Italy. In addition, some hypotheses provide a better explanation of the fertility behavior than others do. Among women from Morocco, the socialization hypothesis tends to prevail, whereas Albanians’ fertility is mostly explained in terms of adaptation. Disruption emerged as the main mechanism able to explain the fertility of migrants from Ukraine, and a clear interrelation between fertility and migration is apparent for women from Albania and Morocco, but only for the first birth.

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Fig. 1

Source: Our elaboration on FSS (for non-migrants in Italy), SCIF (for migrants) and DHS (for stayers in the country of departure) data

Fig. 2

Source: Our elaboration on FSS (for non-migrants in Italy), SCIF (for migrants) and DHS (for stayers in the country of departure) data

Fig. 3

Source: Our elaboration on FSS (for non-migrants in Italy) and SCIF (for migrants) data

Fig. 4

Source: Our elaboration on SCIF data

Fig. 5

Source: Our elaboration on SCIF data

Fig. 6

Source: SCIF (for migrants) and DHS (for stayers in the country of departure) data

Notes

  1. 1.

    All the dates were computed on a monthly scale in century month code (CMC), i.e. number of months since January 1900. In the SCIF dataset, the dates at birth of women and their children are not available and they were estimated using the information on the woman’s age at interview, the woman’s age at childbirth, and the date of the interview. This caused an inaccuracy in the episode duration. In other words, there is no single moment, but a “window” within which the event occurred. In our analysis, the uncertainty in the event date is accounted for by the two duration variables: the lower and the upper bounds of event windows (see, Lillard and Panis 2003).

  2. 2.

    Through a piecewise linear spline specification, the parameter estimates for the baseline log-hazard are slopes for linear splines over user-defined periods. With sufficient nodes (bend points), piecewise linear-specification can efficiently capture any pattern in the data (Lillard and Panis 2003).

  3. 3.

    As an additional robustness check, we relaxed the normality assumption in favor of a finite mixture distribution. The results (available on request) largely confirmed those reported in this article.

  4. 4.

    Education was considered as the number of years of attendance to achieve the highest level of education at the time of the interview. Given the marked heterogeneity among countries, we considered the years of education standardized according to the country of origin. Thus, considering four main groups, i.e. non-migrants in Italy, women born in Albania (living both in Italy and Albania), women born in Morocco, and women born in Ukraine, the standardized level of education was computed as follows: (number of years of schooling—mean of the group)/standard deviation of the same groups. In order to relax the assumption of a linear relationship between education and the likelihood of having a j-th childbirth, a quadratic term was also included in the models. By introducing this variable in the models, we assumed that those who achieve higher levels of education are, from a very early age, oriented toward accomplishing them (see e.g. Bratti and Tatsiramos 2011; Kravdal 2000). However, in this case the estimates may have been confounded by reverse causality, given that childbearing may have affected a woman’s interest in, and opportunities for, further education, thus entailing underestimation of the true causal effect (Kravdal 2004, 2007; Hoem and Kreyenfeld 2006). For example, the original education goals may be hindered by an unplanned childbirth and revised upwards in the case of unexpected childlessness (Kravdal 2001). Taking cognizance of this factor, we successfully checked the robustness of our results even when this variable was dropped from the models. However, we preferred to include in our models the education at the interview being one of the few variables at our disposal in order to (partially) take into account selection bias among migrants.

  5. 5.

    The questionnaire included the following question “What were the main reasons that led you to leave your origin country?” Among all the possible answers, we selected those related to family reasons (migrated for marriage/cohabitation/family reunification) and those related to employment and living conditions (lack of/difficulty in finding a job in the origin country; to earn higher wages; improve the quality of life). All the other possible answers (study, persecutions, war/conflicts, seeking new experiences, other reasons) were recoded as “Other.” The three, not mutually exclusive, resulting categories (work, family, other) were treated as three separate dummy variables.

  6. 6.

    The hazard was computed for all the three migrant groups without distinguishing by the country of origin. However, estimations not shown here demonstrated that the effect of the reason for migration is similar within each group.

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Acknowledgements

The authors would like to thank the Italian National Institute of Statistics (Istat) and the Demographic and Health Surveys Program (DHS) for having granted access to the microdata used in the making of this paper. The results and any errors are entirely the responsibility of the authors alone.

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This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

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Correspondence to Roberto Impicciatore.

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Impicciatore, R., Gabrielli, G. & Paterno, A. Migrants’ Fertility in Italy: A Comparison Between Origin and Destination. Eur J Population (2020). https://doi.org/10.1007/s10680-019-09553-w

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Keywords

  • Migration
  • Fertility
  • Longitudinal data
  • Hazard regression
  • Birth order
  • Italy
  • Country of origin