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Intro to sreg

Here, we provide the empirical application example using the data from (Chong et al., 2016), who studied the effect of iron deficiency anemia on school-age children’s educational attainment and cognitive ability in Peru. The example replicates the empirical illustration from (Bugni et al., 2019). For replication purposes, the data is included in the package and can be accessed by running data("AEJapp").

library(sreg)

## Package 'sreg' version 1.0.0
## Type 'citation("sreg")' for citing this R package in publications.

library(haven)
library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

intro-to-sreg.R We can upload the AEJapp dataset to the R session via data():

data(AEJapp)
data <- AEJapp

intro-to-sreg.R It is pretty straightforward to prepare the data to fit the package syntax using dplyr:

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
head(data.clean)

##      Y D S
## 1 11.2 1 1
## 2 12.4 0 3
## 3 11.9 0 5
## 4 13.1 0 1
## 5 13.4 2 2
## 6 10.7 0 1

intro-to-sreg.R We can take a look at the frequency table of D and S:

table(D = data.clean$D, S = data.clean$S)

##    S
## D    1  2  3  4  5
##   0 15 19 16 12 10
##   1 16 19 15 10 10
##   2 17 20 15 11 10

intro-to-sreg.R Now, it is straightforward to replicate the results from (Bugni et al, 2019) using sreg():

result <- sreg::sreg(Y = Y, S = S, D = D)
print(result)

## Saturated Model Estimation Results under CAR
## Observations: 215 
## Number of treatments: 2 
## Number of strata: 5 
## Covariates used in linear adjustments: 
## ---
## Coefficients:
##        Tau   As.se   T-stat P-value CI.left(95%) CI.right(95%) Significance
## 1 -0.05113 0.20645 -0.24766 0.80440     -0.45577       0.35351             
## 2  0.40903 0.20651  1.98065 0.04763      0.00427       0.81379            *
## ---
## Signif. codes:  0 `***` 0.001 `**`0.01 `*` 0.05 `.` 0.1 ` ` 1

intro-to-sreg.R Besides that, sreg allows adding linear adjustments (covariates) to the estimation procedure:

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::sreg(Y, S, D, G.id = NULL, X = X)
print(result)

## Saturated Model Estimation Results under CAR
## Observations: 215 
## Number of treatments: 2 
## Number of strata: 5 
## Covariates used in linear adjustments: pills, age
## ---
## Coefficients:
##        Tau   As.se   T-stat P-value CI.left(95%) CI.right(95%) Significance
## 1 -0.02862 0.17964 -0.15929 0.87344     -0.38071       0.32348             
## 2  0.34609 0.18362  1.88477 0.05946     -0.01381       0.70598            .
## ---
## Signif. codes:  0 `***` 0.001 `**`0.01 `*` 0.05 `.` 0.1 ` ` 1

intro-to-sreg.R

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They may not be fully stable and should be used with caution. We make no claims about them.
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