Identifying Program Benefits When Participation Is Misreported

Arbeitspapier

In cases of non-compliance with a prescribed treatment, estimates of causal effects typically rely on instrumental variables. However, when participation is also misreported, this approach can be severely biased. We provide an instrumental variable method that researchers can use to identify the true heterogeneous treatment effects in data that include both non-compliance and misclassification of treatment status. Our method can be used regardless of whether the treatment is misclassified because it is missing at random, missing not at random, or was generally mismeasured. We conclude with the use of a dedicated Stata command, ivreg2m, to assess the return on education in the United Kingdom.

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Series: IZA Discussion Papers ; No. 15427

Klassifikation und Themen

• Klassifikation: Wirtschaft ; Semiparametric and Nonparametric Methods: General ; Single Equation Models; Single Variables: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions ; Single Equation Models: Single Variables: Instrumental Variables (IV) Estimation ; Multiple or Simultaneous Equation Models: Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions ; Model Construction and Estimation
• Thema: treatment effect ; causality ; non-differential misclassification ; weighted average of LATEs ; endogeneity ; program evaluation

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Tommasi, Denni ; Zhang, Lina
• Ereignis: Veröffentlichung
• (wer): Institute of Labor Economics (IZA)
• (wo): Bonn
• (wann): 2022

Weitere Informationen

• Handle: http://hdl.handle.net/10419/263643
• Letzte Aktualisierung: 02.03.2025, 11:41 MEZ