A BEJEAP Advances article.

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.

Submitted: February 12, 2003 · Accepted: August 16, 2005 · Published: December 31, 2005

Originally published in Advances in Economic Analysis & Policy.

Recommended Citation

Mullin, Charles H. (2005) "Bounding Treatment Effects with Contaminated and Censored Data: Assessing the Impact of Early Childbearing on Children," Advances in Economic Analysis & Policy: Vol. 5 : Iss. 1, Article 8.
Available at: http://www.bepress.com/bejeap/advances/vol5/iss1/art8