Misclassified Treatment Status and Treatment Effects: An Application to Returns to Education in the United Kingdom
Misclassified Treatment Status and Treatment Effects: An Application to Returns to Education in the United Kingdom
复制标题
错误分类的治疗状况和治疗效果：英国重返教育的申请
DOI：
10.1162/rest_a_00175
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发表时间：
2011
影响因子：
8
通讯作者：
B. Sianesi
中科院分区：
文献类型：
--
作者：
Erich Battistin;B. Sianesi
摘要：我们研究了误报治疗状态对因果治疗效果估计的影响，重点研究了没有额外信息或重复测量可用的应用。我们首先描述了在条件独立假设下，在二元和多重治疗设置下，对被治疗（ATT）的平均治疗效果的错误分类所引入的偏差。我们发现从错误分类数据中计算出的匹配型估计量的偏差一般不能被签名。随后，我们提供了易于实现的方法来半参数地约束感兴趣的ATT，特别是考虑到非常一般的影响异质性形式和无治疗结果方程，以及对个体特征的误报概率的一些依赖。激发我们论文的经验问题是对英国许多教育资格的工资回报的估计，允许在成就方面的误报。我们研究了原始估计对错误分类存在的敏感性，并探讨了对错误分类的性质和程度的合理限制的识别能力。我们表明，所得的边界有时很宽，但通常指向受教育者平均上学回报的正值的合理范围。对于所考虑的教育资格范围，我们进一步表明，有时提出的测量误差偏差大致抵消选择偏差的说法是不支持的。更一般地说，我们的结果表明，在相对温和的限制下，我们可以得到关于我们感兴趣的问题的强有力的结论。
Abstract We study the impact of misreported treatment status on the estimation of causal treatment effects, focusing on applications where no additional information or repeated measurements are available. We first characterize the bias introduced by misclassification on the average treatment effect on the treated (ATT) under a conditional independence assumption, in both a binary and a multiple-treatment setting. We find that the bias of matching-type estimators computed from misclassified data cannot in general be signed. We subsequently provide easily implementable methods to bound the ATT of interest semiparametrically, in particular allowing for very general forms of impact heterogeneity and of the no-treatment outcome equations, as well as for some dependence of the misreporting probabilities on individual characteristics. The empirical problem that motivates our paper is the estimation of the wage returns to a number of educational qualifications in the United Kingdom, allowing for misreporting in attainment. We investigate the sensitivity of the raw estimates to the presence of misclassification and explore the identification power of plausible restrictions on the nature and extent of misclassification. We show that the resulting bounds are sometimes wide but generally point to reasonable ranges of positive values for average returns to schooling among the schooled. For the range of educational qualifications considered, we further show that the claim sometimes made that measurement error bias roughly cancels out selection bias is not supported. More generally, our results show that under relatively mild restrictions, we can obtain strong conclusions regarding our questions of interest.