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Family Policies in the Context of Low Fertility and Social Structure

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  • Thomas Fent
  • Belinda Aparicio Diaz
  • Alexia Prskawetz

Abstract

The aim of this paper is to analyse the effectivity of family policies in the context of different assumptions regarding the social structure of a society. We use an agent based simulation model to analyse the impact of family policies on individual fertility decisions and on cohort fertility, intended fertility, and the fertility gap on the aggregate level. The crucial features of our simulation model are the agents' heterogeneity with respect to age, income, parity, and intended fertility, the social network and social influence. Our results indicate that both fixed and income dependent child supports have a positive and significant impact on fertility. However, several network and social influence parameters have the ability not only to influence fertility itself but also the effectivity of family policies. Therefore, policymakers aiming to transfer a certain policy mix that has proved successful from one country to another one ignoring differences in the social structure may fail. Family policies can only be successful if they explicitly take into account the characteristics of the society they are assigned for.

Suggested Citation

  • Thomas Fent & Belinda Aparicio Diaz & Alexia Prskawetz, 2010. "Family Policies in the Context of Low Fertility and Social Structure," VID Working Papers 1102, Vienna Institute of Demography (VID) of the Austrian Academy of Sciences in Vienna.
  • Handle: RePEc:vid:wpaper:1102
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    References listed on IDEAS

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    Cited by:

    1. Francesca Modena & Concetta Rondinelli & Fabio Sabatini, 2014. "Economic Insecurity and Fertility Intentions: The Case of Italy," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 60(S1), pages 233-255, May.
    2. Theresa Grafeneder-Weissteiner & Ingrid Kubin & Klaus Prettner & Alexia Prskawetz & Stefan Wrzaczek, 2012. "Coping with Inefficiencies in a New Economic Geography Model," VID Working Papers 1204, Vienna Institute of Demography (VID) of the Austrian Academy of Sciences in Vienna.
    3. Tom Warnke & Oliver Reinhardt & Anna Klabunde & Frans Willekens & Adelinde M. Uhrmacher, 2017. "Modelling and simulating decision processes of linked lives: An approach based on concurrent processes and stochastic race," Population Studies, Taylor & Francis Journals, vol. 71(0), pages 69-83, October.
    4. Nasibeh Esmaeili & Mohammad Jalal Abbasi Shavazi, 2024. "Impact of family policies and economic situation on low fertility in Tehran, Iran: A multi-agent-based modeling," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 51(5), pages 107-154.
    5. Pau Baizan & Bruno Arpino & Carlos Eric Delclòs, 2016. "The Effect of Gender Policies on Fertility: The Moderating Role of Education and Normative Context," European Journal of Population, Springer;European Association for Population Studies, vol. 32(1), pages 1-30, February.
    6. Anna Klabunde & Frans Willekens, 2016. "Decision-Making in Agent-Based Models of Migration: State of the Art and Challenges," European Journal of Population, Springer;European Association for Population Studies, vol. 32(1), pages 73-97, February.

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    More about this item

    Keywords

    Family policies; low fertility; social influence; social networks; social structure.;
    All these keywords.

    JEL classification:

    • J1 - Labor and Demographic Economics - - Demographic Economics
    • Z0 - Other Special Topics - - General

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