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Bounding Treatment Effects with Contaminated and Censored Data: Assessing the Impact of Early Childbearing on Children

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  • Mullin Charles H

    (Bates White, LLC)

Abstract

Empirical researchers commonly invoke instrumental variable (IV) assumptions to identify treatment effects. This paper considers what can be learned under two specific violations of those assumptions: contaminated and corrupted data. Either of these violations prevents point identification, but sharp bounds of the treatment effect remain feasible. In an applied example, random miscarriages are an IV for women’s age at first birth. However, the inability to separate random miscarriages from behaviorally induced miscarriages (those caused by smoking and drinking) results in a contaminated sample. Furthermore, censored child outcomes produce a corrupted sample. Despite these limitations, the bounds demonstrate that delaying the age at first birth for the current population of non-black teenage mothers reduces their first-born child’s well-being.

Suggested Citation

  • Mullin Charles H, 2005. "Bounding Treatment Effects with Contaminated and Censored Data: Assessing the Impact of Early Childbearing on Children," The B.E. Journal of Economic Analysis & Policy, De Gruyter, vol. 5(1), pages 1-31, December.
    • Handle: RePEc:bpj:bejeap:v:advances.5:y:2005:i:1:n:8
    DOI: 10.1515/1538-0637.1119
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    Cited by:

    1. Devon Gorry, 2023. "Consequences of Teenage Childbearing on Child Outcomes in the United States," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 42(1), pages 225-254, January.
    2. Melissa S. Kearney & Phillip B. Levine, 2012. "Why Is the Teen Birth Rate in the United States So High and Why Does It Matter?," Journal of Economic Perspectives, American Economic Association, vol. 26(2), pages 141-163, Spring.
    3. Kasey S. Buckles & Elizabeth L. Munnich, 2012. "Birth Spacing and Sibling Outcomes," Journal of Human Resources, University of Wisconsin Press, vol. 47(3), pages 613-642.

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