(Based on DSS Materials and on Chattopadhyay and Esther Duflo. 2004. ``Women as Policy Makers: Evidence from a Randomized Policy Experiment in India.” Econometrica, 72 (5): 1409–43.)
We will estimate the average causal effect of having a female politician on two policy outcomes. For this purpose, we will analyze data from an experiment conducted in India, where villages were randomly assigned to have a female council head. The dataset we will use is in a file called “india.csv”. The Table below shows the names and descriptions of the variables in this dataset, where the unit of observation is villages.
| Variable | Description |
|---|---|
| village | village identifier (“Gram Panchayat number _ village number”) |
| female | whether village was assigned a female politician: 1=yes, 0=no |
| water | number of new (or repaired) drinking water facilities in the village
since random assignment |
| irrigation | number of new (or repaired) irrigation facilities in the
village since random assignment |
In this problem set, we will practice loading, making sense of data, and understanding the basics of causal inference. We will also learn how to use R Markdown.
read.csv() to read the CSV file
“india.csv”. You can find it on the GitHub page: https://raw.githubusercontent.com/umbertomig/POLI30Dpublic/main/data/india.csv.
Use the assignment operator <- to store the data in an
object called india. Provide the R code you used. (1
point)# Your coding answers here.
india<-read.csv("https://raw.githubusercontent.com/umbertomig/POLI30Dpublic/main/data/india.csv")head() to view the first few
observations of the dataset. Provide the R code you used. (1 point)# Your coding answers here.
head(india)## village female water irrigation
## 1 GP1_village2 1 10 0
## 2 GP1_village1 1 0 5
## 3 GP2_village2 1 2 2
## 4 GP2_village1 1 31 4
## 5 GP3_village2 0 0 0
## 6 GP3_village1 0 0 0Answer: Each observation in this dataset relates the relationship between whether or not a village has a female politician, the number of new or repaired water drinking facilities since the beginning of the experiment, and the number of new or repaired irrigation facilities since the beginning of the experiment. The first observation shows that when the politician in a village was a woman, 10 new or repaired drinking water facilities were created and 0 new or repaired irrigation facilities were created.
Answer: The ‘female’ variable is numeric binary, the ‘water’ variable is numeric non-binary, and the ‘irrigation’ variable is numeric non-binary.
dim() might be helpful here.) (1 point)# Your coding answers here.
dim(india)## [1] 322 4322 Villages
mean() to calculate the average of the
variable female. Please provide a full substantive
interpretation of what this average means. (1 point)# Your coding answers here.
mean(india$female)## [1] 0.3354037Answer: This average shows that 33.5% of villages that are involved in the experiment have a woman council head
mean() to calculate the average of the
variable water. Please provide a full substantive
interpretation of what this average means. Make sure to provide the unit
of measurement. (1 point)# Your coding answers here.
mean(india$water)## [1] 17.84161Answer: This average means that the average village that was examined has built or repaired an average of 17.8 water drinking villages.
Answer: The treatment variable is whether or not a woman politician heads a village. It’s variable ‘female’. The outcome variables are the number of drinking water facilities that are built or repaired. The variable is ‘water’.
Answer: The treatment variable is whether or not a woman politician heads a village. It’s variable ‘female’. The outcome variables are the number of irrigation facilities that are built or repaired. The variable is ‘irrigation’.
Answer: The treatment group is a village with a woman head council member. The control group is a village with a male council member.