README.Rmd

---
output: github_document
---

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<!-- README.md is generated from README.Rmd. Please edit that file -->

```{r setup, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```
# bacondecomp

`bacondecomp` is a package with tools to perform the Goodman-Bacon decomposition for differences-in-differences with variation in treatment timing. The decomposition can be done with and without time-varying covariates.

## Installation

You can install `bacondecomp 0.1.2` from CRAN: 

``` r
install.packages("bacondecomp")
```

You can install the development version of `bacondecomp` from GitHub:

``` r
library(devtools)
install_github("evanjflack/bacondecomp")
```

## Functions
* `bacon()`: calculates all 2x2 differences-in-differences estimates and weights for the Bacon-Goodman decomposition.

## Data
* `math_refom`: Aggregated data from Goodman (2019, JOLE)
* `castle`: Data from Cheng and Hoekstra (2013, JHR)
* `divorce:` Data from Stevenson and Wolfers (2006, QJE)

## Example

This is a basic example which shows you how to use the bacon() function to decompose the two-way fixed effects estimate of the effect of an education reform on future earnings following Goodman (2019, JOLE).

```{r example, fig.height = 4, fig.width = 6}
library(bacondecomp)
df_bacon <- bacon(incearn_ln ~ reform_math,
                  data = bacondecomp::math_reform,
                  id_var = "state",
                  time_var = "class")

library(ggplot2)

ggplot(df_bacon) +
  aes(x = weight, y = estimate, shape = factor(type)) +
  geom_point() +
  geom_hline(yintercept = 0) +
  labs(x = "Weight", y = "Estimate", shape = "Type")
```

## References

Goodman-Bacon, Andrew. 2018. "Difference-in-Differences with Variation in Treatment Timing." National Bureau of Economic Research Working Paper Series No. 25018. doi: 10.3386/w25018.

[Paper Link](https://cdn.vanderbilt.edu/vu-my/wp-content/uploads/sites/2318/2019/07/29170757/ddtiming_7_29_2019.pdf)