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

Description

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.

Usage

data("AEJapp")

Format

A data frame with 215 observations on the 62 variables.

Source

Chong, A., Cohen, I., Field, E., Nakasone, E., and Torero, M. (2016). Replication data for: Iron Deficiency and Schooling Attainment in Peru. Nashville, TN: American Economic Association [publisher], 2016. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2019-10-12. doi:10.3886/E113624V1.

References

Chong, A., Cohen, I., Field, E., Nakasone, E., and Torero, M. (2016). Iron Deficiency and Schooling Attainment in Peru. American Economic Journal: Applied Economics, 8(4), 222–255. doi:10.1257/app.20140494.

Examples

data(AEJapp)

Print sreg Objects

Description

Print the summary table of estimation results for sreg objects.

Usage

## S3 method for class 'sreg'
print(x, ...)

Arguments

Value

No return value, called for side effects.

Examples

data <- sreg.rgen(n = 200, tau.vec = c(0.1), n.strata = 4, cluster = TRUE)
Y <- data$Y
S <- data$S
D <- data$D
X <- data.frame("x_1" = data$x_1, "x_2" = data$x_2)
result <- sreg(Y, S, D, G.id = NULL, Ng = NULL, X)
print(result)

Estimate Average Treatment Effects (ATEs) and Corresponding Standard Errors

Description

Estimate the ATE(s) and the corresponding standard error(s) for a (collection of) treatment(s) relative to a control.

Usage

sreg(Y, S = NULL, D, G.id = NULL, Ng = NULL, X = NULL, HC1 = TRUE)

Arguments

Value

An object of class sreg that is a list containing the following elements:

• tau.hat: a 1 \times |\mathcal A| vector of ATE estimates, where |\mathcal A| represents the number of treatments

• se.rob: a 1 \times |\mathcal A| vector of standard errors estimates, where |\mathcal A| represents the number of treatments

• t.stat: a 1 \times |\mathcal A| vector of t-statistics, where |\mathcal A| represents the number of treatments

• p.value: a 1 \times |\mathcal A| vector of corresponding p-values, where |\mathcal A| represents the number of treatments

• CI.left: a 1 \times |\mathcal A| vector of the left bounds of the 95% as. confidence interval

• CI.right: a 1 \times |\mathcal A| vector of the right bounds of the 95% as. confidence interval

• data: an original data of the form data.frame(Y, S, D, G.id, Ng, X)

• lin.adj: a data.frame representing the covariates that were used in implementing linear adjustments

Author(s)

Authors:

Juri Trifonov jutrifonov@uchicago.edu

Yuehao Bai yuehao.bai@usc.edu

Azeem Shaikh amshaikh@uchicago.edu

Max Tabord-Meehan maxtm@uchicago.edu

Maintainer:

Juri Trifonov jutrifonov@uchicago.edu

References

Bugni, F. A., Canay, I. A., and Shaikh, A. M. (2018). Inference Under Covariate-Adaptive Randomization. Journal of the American Statistical Association, 113(524), 1784–1796, doi:10.1080/01621459.2017.1375934.

Bugni, F., Canay, I., Shaikh, A., and Tabord-Meehan, M. (2024+). Inference for Cluster Randomized Experiments with Non-ignorable Cluster Sizes. Forthcoming in the Journal of Political Economy: Microeconomics, doi:10.48550/arXiv.2204.08356.

Jiang, L., Linton, O. B., Tang, H., and Zhang, Y. (2023+). Improving Estimation Efficiency via Regression-Adjustment in Covariate-Adaptive Randomizations with Imperfect Compliance. Forthcoming in Review of Economics and Statistics, doi:10.48550/arXiv.2204.08356.

Examples

library("sreg")
library("dplyr")
library("haven")
### Example 1. Simulated Data.
data <- sreg.rgen(n = 1000, tau.vec = c(0), n.strata = 4, cluster = FALSE)
Y <- data$Y
S <- data$S
D <- data$D
X <- data.frame("x_1" = data$x_1, "x_2" = data$x_2)
result <- sreg(Y, S, D, G.id = NULL, Ng = NULL, X)
print(result)
### Example 2. Empirical Data.
?AEJapp
data("AEJapp")
data <- AEJapp
head(data)
Y <- data$gradesq34
D <- data$treatment
S <- data$class_level
data.clean <- data.frame(Y, D, S)
data.clean <- data.clean %>%
  mutate(D = ifelse(D == 3, 0, D))
Y <- data.clean$Y
D <- data.clean$D
S <- data.clean$S
table(D = data.clean$D, S = data.clean$S)
result <- sreg(Y, S, D)
print(result)
pills <- data$pills_taken
age <- data$age_months
data.clean <- data.frame(Y, D, S, pills, age)
data.clean <- data.clean %>%
  mutate(D = ifelse(D == 3, 0, D))
Y <- data.clean$Y
D <- data.clean$D
S <- data.clean$S
X <- data.frame("pills" = data.clean$pills, "age" = data.clean$age)
result <- sreg(Y, S, D, G.id = NULL, X = X)
print(result)

Generate a Pseudo-Random Sample under the Stratified Block Randomization Design

Description

The function generates the observed outcomes, treatment assignments, strata indicators, cluster indicators, cluster sizes, and covariates for estimating the treatment effect within the context of a stratified block randomization design under the covariate-adaptive randomization (CAR).

Usage

sreg.rgen(
  n,
  Nmax = 50,
  n.strata,
  tau.vec = c(0),
  gamma.vec = c(0.4, 0.2, 1),
  cluster = TRUE,
  is.cov = TRUE
)

Arguments

Value

An object that is a 'data.frame' with n observations containing the generated values of the following variables:

• Y: a numeric n \times 1 vector of observed outcomes

• S: a numeric n \times 1 vector of strata indicators

• D: a numeric n \times 1 vector of treatments indexed by \{0, 1, 2, \ldots\}, where \code{D} = 0 denotes the control

• G.id: a numeric n \times 1 vector of cluster indicators

• X: a data.frame with columns representing the covariate values for every observation

Examples

data <- sreg.rgen(n = 1000, tau.vec = c(0), n.strata = 4, cluster = TRUE)