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Instrumental variables based on twin births are by definition not valid

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  • Öberg, Stefan

    (Department of Economic History, School of Business, Economics and Law, Göteborg University)

Abstract

Instrumental variables based on twin births are a well-known and widespread method to find exogenous variation in the number of children when studying the effect on siblings or parents. This paper argues that there are serious problems with all versions of these instruments. Many of these problems have arisen because insufficient care has been given to defining the estimated causal effect. This paper discusses this definition and then applies the potential outcomes framework to reveal that instrumental variables based on twin birth violate the exclusion restriction, the independence assumption and one part of the stable unit treatment value assumption. These violations as well as the characteristics of the populations studied have contributed to hiding any true effect of the number of children. It is time to stop using these instrumental variables and to return to these important questions using other methods.

Suggested Citation

  • Öberg, Stefan, 2018. "Instrumental variables based on twin births are by definition not valid," Göteborg Papers in Economic History 23, University of Gothenburg, Unit for Economic History.
    • Handle: RePEc:hhs:gunhis:0023
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    File URL: http://hdl.handle.net/2077/56132
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    References listed on IDEAS

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    1. Öberg, Stefan, 2017. "An introduction to using twin births as instrumental variables for sibship size," Göteborg Papers in Economic History 22, University of Gothenburg, Unit for Economic History.
    2. Helena Holmlund & Helmut Rainer & Thomas Siedler, 2013. "Meet the Parents? Family Size and the Geographic Proximity Between Adult Children and Older Mothers in Sweden," Demography, Springer;Population Association of America (PAA), vol. 50(3), pages 903-931, June.
    3. Holmlund, Helena & Rainer, Helmut & Siedler, Thomas, 2009. "Meet the Parents? The Causal Effect of Family Size on the Geographic Distance between Adult Children and Older Parents," IZA Discussion Papers 4398, Institute of Labor Economics (IZA).
    4. Guido W. Imbens, 2010. "Better LATE Than Nothing: Some Comments on Deaton (2009) and Heckman and Urzua (2009)," Journal of Economic Literature, American Economic Association, vol. 48(2), pages 399-423, June.
    5. Ponczek, Vladimir Pinheiro & Souza, André Portela Fernandes de, 2007. "The causal effect of family size on child labor and education," Textos para discussão 162, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
    6. Joshua D. Angrist & Jörn-Steffen Pischke, 2015. "The path from cause to effect: mastering 'metrics," CentrePiece - The magazine for economic performance 442, Centre for Economic Performance, LSE.
    7. Lembke B., 1918. "√ a. p," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 111(1), pages 709-712, February.
    8. Karlsson, Tobias, 2015. "Pushed into Unemployment, Pulled into Retirement: Facing Old Age in Gothenburg, 1923-1943," Göteborg Papers in Economic History 19, University of Gothenburg, Unit for Economic History.
    9. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881.
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    Cited by:

    1. Öberg, Stefan, 2019. "Too LATE for Natural Experiments: A Critique of Local Average Treatment Effects Using the Example of Angrist and Evans (1998)," SocArXiv acdv4, Center for Open Science.
    2. Öberg, Stefan, 2021. "Treatment for natural experiments: How to improve causal estimates using conceptual definitions and substantive interpretations," SocArXiv pkyue, Center for Open Science.

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    More about this item

    Keywords

    causal inference; natural experiments; local average treatment effect; complier average causal effect; Rubin’s causal model; quantity–quality trade-off; family size;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation
    • J13 - Labor and Demographic Economics - - Demographic Economics - - - Fertility; Family Planning; Child Care; Children; Youth

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