AEJapp

<p>The data is taken from Chong et al. (2016), who study the effect of iron deficiency anemia
  (i.e., anemia caused by a lack of iron) on school-age children’s educational attainment and cognitive ability in Peru.</p>

datasets

Estimate average treatment effects (ATEs) in stratified randomized experiments. `sreg` supports a wide range of stratification designs, including matched pairs, n-tuple designs, and larger strata with many units — possibly of unequal size across strata. 'sreg' is designed to accommodate scenarios with multiple treatments and cluster-level treatment assignments, and accommodates optimal linear covariate adjustment based on baseline observable characteristics. 'sreg' computes estimators and standard errors based on Bugni, Canay, Shaikh (2018) <doi:10.1080/01621459.2017.1375934>; Bugni, Canay, Shaikh, Tabord-Meehan (2024+) <doi:10.48550/arXiv.2204.08356>; Jiang, Linton, Tang, Zhang (2023+) <doi:10.48550/arXiv.2201.13004>; Bai, Jiang, Romano, Shaikh, and Zhang (2024) <doi:10.1016/j.jeconom.2024.105740>; Liu (2024+) <doi:10.48550/arXiv.2301.09016>; and Cytrynbaum (2024) <doi:10.3982/QE2475>.

Juri Trifonov

sreg

Stratified Randomized Experiments

Yuehao Bai

Azeem Shaikh

Max Tabord-Meehan

AEJapp function

<p>A data frame with 215 observations on the 62 variables.</p>

Format

Replication data for: Iron Deficiency and Schooling Attainment in Peru (Chong et al, 2016) — AEJapp

Replication data for: Iron Deficiency and Schooling Attainment in Peru (Chong et al, 2016)

AEJapp: Replication data for: Iron Deficiency and Schooling Attainment in Peru (Chong et al, 2016)

Description

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References

Examples