Instructions

How to complete this assignment.

  • Attempt each exercise in order.

  • In each code chunk, if you see “# INSERT CODE HERE”, then you are expected to add some code to create the intended output (Make sure to erase “# INSERT CODE HERE” and place your code in its place).

  • If my instructions say to “Run the code below…” then you do not need to add any code to the chunk.

  • Many exercises may require you to type some text below the code chunk, interpreting the output and answering the questions.

  • Please follow the rules from the course syllabus regarding seeking help with this assignment.

How to submit this assignment.

  • When you are finished, click the “Knit” button at the top of this panel. If there are no errors, an HTML file should pop up after a few seconds.

  • Take a look at the resulting HTML file that pops up. Make sure everything looks correct, your name is listed at the top, and that there is no ‘junk’ code or output.

  • Save the HTML file (to your local computer, and/or to a cloud location) as: Lab 1 “Insert Your Name”.

  • Upload the lab HTML to HuskyCT. Do not upload the original .Rmd version.

  • This assignment is due Thursday, February 8, 2023, no later than 11:00 am Eastern. Points will be deducted for late submissions.

  • TIP: Start early so that you can troubleshoot any issues with knitting to HTML.

Grading Rubric

There are 5 possible points on this assignment.

Baseline (C level work)

  • Your .Rmd file knits to HTML without errors.
  • You answer questions correctly but do not use complete sentences.
  • There are typos and ‘junk code’ throughout the document.
  • You do not put much thought or effort into the Reflection answers.

Average (B level work)

  • You use complete sentences to answer questions.
  • You attempt every exercise/question.

Advanced (A level work)

  • Your code is simple and concise.
  • Unnecessary messages from R are hidden from being displayed in the HTML.
  • Your document is typo-free.
  • At the discretion of the instructor, you give exceptionally thoughtful or insightful responses.

Do Women Promote Different Policies than Men?

(Based on Raghabendra Chattopadhyay and Esther Duflo. 2004. “Women as Policy Makers: Evidence from a Randomized Policy Experiment in India.” Econometrica, 72 (5): 1409–43.)

In a few problem sets, we will estimate the average causal effect of having a female politician on two different 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”. Table 1 shows the names and descriptions of the variables in this dataset, where the unit of observation is villages.

Exercise 1. Using the correct R code, (a) set your working directory, (b) load the tidyverse package, (c) load the data and assign the name “india” to it (0.625 points)

Exercise 2. Load the tidyverse() package. (0.625 points)

library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.4     ✔ readr     2.1.4
## ✔ forcats   1.0.0     ✔ stringr   1.5.1
## ✔ ggplot2   3.4.4     ✔ tibble    3.2.1
## ✔ lubridate 1.9.3     ✔ tidyr     1.3.0
## ✔ purrr     1.0.2     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

Exercise 3. Load the data and assign the name “india” to it. (0.625 points)

library(readr)
india <- read_csv("india.csv")
## Rows: 322 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): village
## dbl (3): female, water, irrigation
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
View(india)


Exercise 4. Utilizing either head() or glimpse() view the first few rows of the dataset. Substantively describe what these functions do. (0.625 points)

head(india)
## # A tibble: 6 × 4
##   village      female water irrigation
##   <chr>         <dbl> <dbl>      <dbl>
## 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          0

ANSWER: head() is primarily used for viewing the first few rows of a dataset, glimpse() offers a more comprehensive overview by providing information about the structure and types of variables within the dataset.


Exercise 5. What does each observation in this dataset represent?. (0.625 points)

ANSWER: Village: Nominal (unordered categorical) Female: Nominal (binary categorical) Water: Discrete numerical Irrigation: Discrete numerical


Exercise 6. Please substantively interpret the first observation in the dataset. (0.625 points)

ANSWER:

the first observation is GP1_village2 so that means the value in gram panchayat 1 and in village number 2


Exercise 7. For each variable in the dataset, please identify the type of variable (character vs. numeric binary vs. numeric non-binary) (0.625 points)

ANSWER: Village: Character (identifier for each village) Female: Numeric binary (indicating presence or absence of a female politician) Water: Numeric non-binary (number of drinking water facilities) Irrigation: Numeric non-binary (number of irrigation facilities)


Exercise 8. How many observations are in the dataset? In other words, how many villages were part of this experiment? (Hint: the function dim() might be helpful here.) Additionally, provide a substantive answer. (0.625 points)

dim(india)
## [1] 322   4

ANSWER: there are 322 villages