Testing for Rank Invariance or Similarity in Program Evaluation

• 成果类型: Article
• 署名作者: Dong, Yingying; Shen, Shu
• 署名单位: University of California System; University of California Irvine; University of California System; University of California Davis
• 刊物名称: REVIEW OF ECONOMICS AND STATISTICS
• ISSN/ISSBN: 0034-6535
• DOI: 10.1162/REST_a_00686
• 发表日期: 2018-03
• 页码: 78-85
• 关键词: instrumental variable estimation; treatment effect models; quantile regression; inference; impacts; identification

摘要

This paper discusses testable implications of rank invariance or rank similarity, assumptions that are common in program evaluation and in the quantile treatment effect (QTE) literature. We nonparametrically identify, estimate, and test the counterfactual distribution of potential ranks, or features of the distribution. The proposed tests allow treatment to be endogenous, with exogenous treatment following as a special case. The tests essentially do not require any additional assumptions other than those to identify and estimate QTEs. We apply the proposed tests to investigate whether the Job Training Partnership Act training causes trainees to systematically change their ranks in the earnings distribution.