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Estimating the causal effect of fertility on economic wellbeing: data requirements, identifying assumptions and estimation methods

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  • Bruno Arpino
  • Arnstein Aassve

Abstract

This article aims to answer to what extent fertility has a causal effect on households’ economic wellbeing—an issue that has received considerable interest in development studies and policy analysis. However, only recently has this literature begun to give importance to adequate modelling for estimation of causal effects. We discuss several strategies for causal inference, stressing that their validity must be judged on the assumptions we can plausibly formulate in a given application, which in turn depends on the richness of available data. We contrast methods relying on the unconfoundedness assumption, which include regressions and propensity score matching, with instrumental variable methods. This discussion has a general importance, representing a set of guidelines that are useful for choosing an appropriate strategy of analysis. The discussion is valid for both cross-sectional or panel data. Copyright Springer-Verlag 2013

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  • Bruno Arpino & Arnstein Aassve, 2013. "Estimating the causal effect of fertility on economic wellbeing: data requirements, identifying assumptions and estimation methods," Empirical Economics, Springer, vol. 44(1), pages 355-385, February.
  • Handle: RePEc:spr:empeco:v:44:y:2013:i:1:p:355-385
    DOI: 10.1007/s00181-010-0356-9
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    4. Anna Baranowska-Rataj & Xavier de Luna & Anneli Ivarsson, 2016. "Does the number of siblings affect health in midlife? Evidence from the Swedish Prescribed Drug Register," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 35(43), pages 1259-1302.
    5. Muluken Gezahegn Wordofa & Maria Sassi, 2017. "Impact of Farmers’ Training Centres on Household Income: Evidence from Propensity Score Matching in Eastern Ethiopia," Social Sciences, MDPI, vol. 7(1), pages 1-12, December.
    6. Bruno Arpino & Arnstein Aassve, 2014. "The role of villages in households’ poverty exit: evidence from a multilevel model for rural Vietnam," Quality & Quantity: International Journal of Methodology, Springer, vol. 48(4), pages 2175-2189, July.
    7. Olukorede Abiona, 2017. "The Impact of Unanticipated Economic Shocks on the Demand for Contraceptives: Evidence from Uganda," Health Economics, John Wiley & Sons, Ltd., vol. 26(12), pages 1696-1709, December.
    8. Bernd Fitzenberger & Michael Lechner & Jeffrey Smith, 2013. "Estimation of treatment effects: recent developments and applications," Empirical Economics, Springer, vol. 44(1), pages 1-11, February.
    9. Baris Ucar & Gianni Betti, 2016. "The effect of a newborn on household poverty: a multi-indicator analysis," Department of Economics University of Siena 742, Department of Economics, University of Siena.
    10. Anna Baranowska, 2013. "The family size effects on female employment. Evidence from the “natural experiments” related to human reproduction," Working Papers 57, Institute of Statistics and Demography, Warsaw School of Economics.
    11. Nicoletta Balbo & Bruno Arpino, 2016. "The Role of Family Orientations in Shaping the Effect of Fertility on Subjective Well-being: A Propensity Score Matching Approach," Demography, Springer;Population Association of America (PAA), vol. 53(4), pages 955-978, August.
    12. Márta K. Radó, 2020. "Tracking the Effects of Parenthood on Subjective Well-Being: Evidence from Hungary," Journal of Happiness Studies, Springer, vol. 21(6), pages 2069-2094, August.

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    More about this item

    Keywords

    Fertility; Poverty; Causal inference; Unconfoundedness; Instrumental variables; VLSMS; D19; I32; J13;
    All these keywords.

    JEL classification:

    • D19 - Microeconomics - - Household Behavior - - - Other
    • I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty
    • J13 - Labor and Demographic Economics - - Demographic Economics - - - Fertility; Family Planning; Child Care; Children; Youth

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