---
title: "undidR"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{undidR}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
```
```{r setup}
library(undidR)
```
## Introduction
The **undidR** package implements difference-in-differences with unpoolable data (UNDID); a framework that enables the estimation of the average treatment effect on the treated (ATT) when the data from different silos is not poolable. UNDID allows for staggered or common adoption and the inclusion of covariates.
In addition, **undidR** also implements the randomization inference (RI) procedure for difference-in-differences described in [MacKinnon and Webb (2020)](https://doi.org/10.1016/j.jeconom.2020.04.024) to calculate RI p-values.
Below is an overview of the **undidR** framework:
```{r schematic, echo=FALSE, fig.align='center', out.width='90%', fig.cap="Schematic of the UNDID framework."}
knitr::include_graphics("figures/undidR_schematic.png")
```
The following sections detail some examples of implementing **undidR** at each of its three stages for both staggered and common adoption scenarios.
## 1. Stage One: Initialize
When calling `create_init_csv()` silo names must be specified along with their corresponding treatment times. Consequently, the `silos_names` vector must be the same length as the `treatment_times` vector.
All dates must be entered in the same date format. To see valid date formats within the **undidR** package call `undid_date_formats()`.
Covariates may be specified when calling either `create_init_csv()` or when calling `create_diff_df()` via the `covariates` parameter.
### 1.1 Common Treatment Time (Stage One)
```{r}
# First, an initializing CSV is created detailing the silos
# and their treatment times. Control silos (here, 73 and 46)
# should be labelled with "control".
init <- create_init_csv(silo_names = c("73", "46", "71", "58"),
start_times = "1989",
end_times = "2000",
treatment_times = c("control", "control",
"1991", "1991"))
init
# After the initializing CSV file is created, `create_diff_df()`
# can be called. This creates the empty differences data frame which
# will then be filled out at each individual silo for its respective portion.
init_filepath <- normalizePath(file.path(tempdir(), "init.csv"),
winslash = "/", mustWork = FALSE)
empty_diff_df <- create_diff_df(init_filepath, date_format = "yyyy",
freq = "yearly")
empty_diff_df
```
### 1.2 Staggered Adoption (Stage One)
```{r}
# The initializing CSV for staggered adoption is created in the same way.
# When `create_diff_df()` is run, it will automatically detect whether or not
# the initial setup is for a common adoption or staggered adoption scenario.
init <- create_init_csv(silo_names = c("73", "46", "54", "23", "86", "32",
"71", "58", "64", "59", "85", "57"),
start_times = "1989",
end_times = "2000",
treatment_times = c(rep("control", 6),
"1991", "1993", "1996", "1997",
"1997", "1998"))
init
# Creating the empty differences data frame and associated CSV file is
# the same for the case of staggered adoption as it is for common adoption.
init_filepath <- normalizePath(file.path(tempdir(), "init.csv"),
winslash = "/", mustWork = FALSE)
empty_diff_df <- create_diff_df(init_filepath, date_format = "yyyy",
freq = "yearly",
covariates = c("asian", "black", "male"))
head(empty_diff_df, 4)
```
## 2. Stage Two: Silos
The second stage function, `undid_stage_two()`, creates two CSV files. The first is the filled portion of the differences data frame for the respective silo. The second captures the mean (and the mean residualized by the specified covariates) of the outcome variable from the `start_time` to the `end_time` in intervals of `freq`.
These are returned from `undid_stage_two()` as a list of two data frames which can be accessed by the suffixes of `$diff_df` and `$trends_data`, respectively.
### 2.1 Common Treatment Time (Stage Two)
```{r}
# When calling `undid_stage_two()`, ensure that the `time_column` of
# the `silo_df` contains only character values, i.e. date strings.
silo_data <- silo71
silo_data$year <- as.character(silo_data$year)
empty_diff_filepath <- system.file("extdata/common", "empty_diff_df.csv",
package = "undidR")
stage2 <- undid_stage_two(empty_diff_filepath, silo_name = "71",
silo_df = silo_data, time_column = "year",
outcome_column = "coll", silo_date_format = "yyyy")
head(stage2$diff_df, 4)
head(stage2$trends_data, 4)
```
### 2.2 Staggered Adoption (Stage Two)
```{r}
# Here we can see that calling `undid_stage_two()` for staggered adoption
# is no different than calling `undid_stage_two()` for common adoption.
silo_data <- silo71
silo_data$year <- as.character(silo_data$year)
empty_diff_filepath <- system.file("extdata/staggered", "empty_diff_df.csv",
package = "undidR")
stage2 <- undid_stage_two(empty_diff_filepath, silo_name = "71",
silo_df = silo_data, time_column = "year",
outcome_column = "coll", silo_date_format = "yyyy")
head(stage2$diff_df, 4)
head(stage2$trends_data, 4)
```
## 3. Stage Three: Analysis
The third stage of **undidR** produces the aggregate ATT estimate, its standard errors, and its p-values, as well as group level ATT estimates for staggered adoption.
In the case of staggered adoption these group level ATTs can either be grouped by silo (`agg = "silo"`), by treatment time (`agg = "g"`), or by treatment time for every time period after treatment has started (`agg = "gt"`).
Plotting of parallel trends figures is conveniently fast tracked by the use of the `plot_parallel_trends()` function. Some of the most important parameters are highlighted in the examples below, but there are a plethora options. Call `?plot_parallel_trends()` to see all the parameters and their descriptions.
### 3.1 Common Treatment Time (Stage Three)
```{r, fig.width=6, fig.height=4, out.width = "70%", dpi=300, fig.align="center"}
# `undid_stage_three()`, given a `dir_path`, will search that folder
# for all CSV files that begin with "filled_diff_df_" and stitch
# them together in order to compute the group level ATTs, aggregate ATT
# and associated standard errors and p-values.
dir_path <- system.file("extdata/common", package = "undidR")
results <- undid_stage_three(dir_path, covariates = FALSE)
results
# We can also view a parallel trends plot of our silos by callling
# `plot_parallel_trends()`. This function will search a folder for all
# CSV files that start with "trends_data" and combine them. The combined
# data is returned by the function.
trends <- plot_parallel_trends(
dir_path,
simplify_legend = FALSE, # Setting to `FALSE` puts every silo in legend
lwd = 3, # Set line width
ylim = c(0, 1), # (min, max) of y-axis
xdates = as.Date(c("1989-01-01",
"1991-01-01",
"1993-01-01", # Explicity declare dates on x-axis
"1995-01-01",
"1997-01-01",
"1999-01-01")),
date_format = "%Y", # Specify x-axis date format
ylabels = c(0, 0.25, 0.5, 0.75, 1), # Values on y-axis
legend_location = "topleft", # Where should the legend go?
legend_on = TRUE, # Defaults `TRUE`, can be set to `FALSE` to omit legend
ylab = "coll", # y-axis title
treatment_colour = c("red", # Set colours of treatment silos
"coral"),
control_colour = c("#2fa7cf", # Colours can be entered as hexcodes
"skyblue")
)
head(trends, 4)
```
### 3.2 Staggered Adoption (Stage Three)
```{r, fig.width=6, fig.height=4, out.width = "70%", dpi=300, fig.align="center"}
# When calling `undid_stage_three()` for staggered adoption it is
# important to specify the aggregation method, `agg`.
dir_path <- system.file("extdata/staggered", package = "undidR")
results <- undid_stage_three(dir_path, agg = "silo", covariates = TRUE,
nperm = 501)
# The `nperm` parameter specifies how many permutations
# to consider for randomization inference.
results
# If we have many silos, but want to view a parallel trends plot
# we can set `combine = TRUE` when calling `plot_parallel_trends()`
# which will combine all treatment silos into a single line, and all
# control silos into a single line.
trends <- plot_parallel_trends(
dir_path,
combine = TRUE, # Plots one line for control and one line for treated
simplify_legend = TRUE, # Reduces legend to simply "Control" and "Treated"
lwd = 3, # Set line width
ylim = c(0.2, 0.6), # (min, max) of y-axis
xdates = as.Date(c("1989-01-01",
"1991-01-01",
"1993-01-01", # Explicity declare dates on x-axis
"1995-01-01",
"1997-01-01",
"1999-01-01")),
date_format = "%Y", # Specify x-axis date format
ylabels = c(0.2, 0.4, 0.6), # Values on y-axis
legend_location = "topright", # Where should the legend go?
treatment_indicator_lwd = 2, # line width for the treatment indicator lines
treatment_indicator_alpha = 0.2, # transparency for treatment indicator lines
treatment_indicator_col = "red" # colour for treatment indicator lines
)
head(trends, 4)
```
## References
Karim, S., Webb, M., Austin, N., and Strumpf, E. 2024. Difference-in-Differences with Unpoolable Data. [https://arxiv.org/abs/2403.15910](https://arxiv.org/abs/2403.15910)
MacKinnon, J. and Webb, M. 2020. Randomization inference for difference-in-differences with few treated clusters. Journal of Econometrics. [https://doi.org/10.1016/j.jeconom.2020.04.024](https://doi.org/10.1016/j.jeconom.2020.04.024)