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Municipality Characteristics and the Fertility of Refugees in Norway

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Abstract

The study of the fertility of immigrants has received much attention in recent years, particularly in societies with fertility rates below replacement levels. However, fertility in refugee populations remains understudied. Using rich register data on all female refugees of childbearing age (15–45 years) who arrived and settled in Norway between 2002 and 2015 (N = 23,527), we utilize the Norwegian settlement policy for refugees—which assigns all refugees coming to Norway to a municipality where they start their integration process—to study how fertility behavior in the years following settlement is related to the characteristics of the municipality to which refugee women are assigned. Importantly, we are able to control for individual-level characteristics used by the government agency at assignment, thus limiting the problem of selection on (un)observables. As explanatory variables, we focus on municipality unemployment rates, the share of non-Western immigrants already living in the municipality, and the total fertility rate in the municipality, and also control for the municipality’s age structure and childcare coverage. The study is thus of an exploratory nature. We measure these municipality characteristics the year before refugees settle and estimate their respective correlations with fertility (measured as the likelihood of having had at least one child in Norway) at the individual level for up to 8 years after settlement. We also explore heterogeneity by education and parity at settlement. We find no systematic associations between the share of non-Western immigrants in the municipality and refugees’ fertility; however, the municipality’s fertility rate is positively correlated with the likelihood of giving birth to a child in Norway, especially for women who are childless at arrival. The links between local unemployment rates and fertility are heterogeneous across education groups and parity.

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Notes

  1. Due to data regulations and privacy concerns, we do not have access to data on sexual orientation or severe physical illnesses suffered by the women in our sample.

  2. See, for instance, Saarela and Skirbekk (2019) on the fertility of the Karelian population forced to move to other regions of Finland in the 1940s.

  3. Intention to treat (ITT) is a concept borrowed from experimental methods which refers to estimates that are based on the initial treatment assignment (i.e., the assigned municipality) and not on the treatment eventually received (i.e., the municipality where an individual actually lives after a certain number of years in Norway) (Angrist & Pischke, 2008). We report ITT estimates first and foremost because we are interested in the municipality of assignment, and because these are not biased by systematic selection in later internal migration. Since most refugees stay in the assigned municipality (Ordemann, 2017), the difference between ITT and other estimates such as ATE (average treatment effect) is minimized.

  4. Note that Balbo et al. (2013) show that some macro-level studies also look at the role of contraceptive technologies or differences in welfare regimes in fertility dynamics which in our study should be homogeneous across Norwegian municipalities.

  5. Spouses of persons who have been granted protection in Norway may also stay in Norway as long as they fulfil certain requirements (Norwegian Directorate of Immigration (UDI) 2020). Note that this group is not included in our analyses.

  6. As other papers in the settlement literature mention, an additional concern is that unemployment rates of non-Western migrants may underestimate true unemployment if underemployment, self-employment, and discouragement are proportionally higher among migrants than among natives (Godøy, 2017). Thus, we can consider those rates as lower bound levels.

  7. More precisely, 2G (two times the National Insurance scheme basic amount), which in 2019, equalled almost NOK 200,000.

  8. This highlights that the assignment process is not completely random, and underscores the importance of using IMDi’s data on individual characteristics in our estimations.

  9. Note that we have also estimated models with a count variable for the number of children a woman has had in Norway, and that the results show the same overall patterns.

  10. If we were to follow them for more than eight years, our sample would be too small.

  11. The only municipality variable in this study that is not available in StatBank is the TFR at municipality level, which is calculated by the authors based on population and birth data in Statistics Norway’s registers.

  12. Discrepancies between the two data sets are discussed in Tønnessen and Andersen (2019).

  13. Note, however, that for younger cohorts of native Norwegians, patterns of educational differences in (cohort) fertility have almost completely vanished (Jalovaara et al., 2019).

  14. Furthermore, in separate estimations, we have added instead the share of votes for the Progress Party in the municipality at the national elections to capture anti-immigrant sentiment. The variable is statistically insignificant.

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Authors and Affiliations

  1. Statistics Norway and University of Oslo, Oslo, Norway

    Synøve Andersen

  2. Princeton University and IZA, A29 JRR Building, Princeton University, Princeton, NJ, 08544, USA

    Alícia Adserà

  3. Norwegian Institute for Urban and Regional Research (NIBR), Oslo Metropolitan University, Oslo, Norway

    Marianne Tønnessen

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  1. Synøve Andersen
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  2. Alícia Adserà
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  3. Marianne Tønnessen
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Correspondence to Alícia Adserà.

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Appendices

Appendix A: Descriptive statistics

Table 7 Correlation matrix for main municipality variables
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Table 8 Descriptive statistics, individuals, by duration of follow-up
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Table 9 Missing education in the sample, and share of those with information on education who have lower secondary education or less. Origin countries with more than 1% of all missing in the sample
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Fig. 4
figure 4

Total fertility rates distributed across Norwegian municipalities, by year 2003–2014

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Fig. 5
figure 5

Share of population, non-Western immigrants, across Norwegian municipalities, by year 2003–2014

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Appendix B: Regression models

Table 10 Intention-to-treat estimates, full sample, total unemployment. The outcome variable is the probability that a woman has had at least one child in Norway 1–8 years after settlement, with covariates
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Table 11 Intention-to-treat estimates, by the highest completed educational level at settlement, total unemployment. The outcome variable is the probability that a woman has had at least one child in Norway 1, 3, and 5 years after settlement, with covariates
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Table 12 Intention-to-treat estimates, by parity at settlement, total unemployment. The outcome variable is the probability that a woman has had at least one child in Norway 1, 3, and 5 years after settlement, with covariates
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Table 13 Intention-to-treat estimates, stepwise models for robustness, full sample. The outcome variable is the probability that a woman has had at least one child in Norway 5 years after settlement, with covariates
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Andersen, S., Adserà, A. & Tønnessen, M. Municipality Characteristics and the Fertility of Refugees in Norway. Int. Migration & Integration 24 (Suppl 1), 165–208 (2023). https://doi.org/10.1007/s12134-021-00840-2

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