A Big Data Implementation of Difference-in-Differences Estimation with Staggered Treatment.

DiD for Big Data in R

This R package provides a big-data-friendly and memory-efficient difference-in-differences (DiD) estimator for staggered (and non-staggered) treatment contexts. It supports controlling for time-varying covariates, heteroskedasticity-robust standard errors, and (single and multi-way) clustered standard errors. It addresses 4 issues that arise in the context of large administrative datasets:

1. Speed: In less than 1 minute, DiDforBigData will provide estimation and inference for staggered DiD with millions of observations on a personal laptop. It is orders of magnitude faster than other available software if the sample size is large; see the demonstration here.
2. Memory: Administrative data is often stored on crowded servers with limited memory available for researchers to use. DiDforBigData helps by using much less memory than other software; see the demonstration here.
3. Dependencies: Administrative servers often do not have outside internet access, making it difficult to install dependencies. This package has only two dependencies, data.table for big data management and sandwich for robust standard error estimation, which are already installed with most R distributions. Optionally, it will use the fixest package to speed up the estimation if it is installed. If the progress package is installed, it will also provide a progress bar so you know how much longer the estimation will take.
4. Parallelization: Administrative servers often have a large number of available processors, but each processor may be slow, so it is important to parallelize. DiDforBigData makes parallelization easy as long as the parallel package is installed.

Installation

To install the package from CRAN:

install.packages("DiDforBigData")

To install the package from Github:

devtools::install_github("setzler/DiDforBigData")

To use the package after it is installed:

library(DiDforBigData)

It is recommended to also make sure these optional packages have been installed:

library("progress")
library("fixest")
library("parallel")

Basic Use

There are only 3 functions in this package:

1. SimDiD(): This function simulates data.
2. DiDge(): This function estimates DiD for a single cohort and a single event time.
3. DiD(): This function estimates DiD for all available cohorts and event times.

Details for each function are available from the Function Documentation.

Before estimation, set up a variable list with the names of your variables:

varnames = list()
varnames$time_name = "year"
varnames$outcome_name = "Y"
varnames$cohort_name = "cohort"
varnames$id_name = "id"

To estimate DiD for a single cohort and event time, use the DiDge command. For example:

DiDge(inputdata = yourdata, varnames = varnames,
             cohort_time = 2010, event_postperiod = 3)

A detailed manual explaining the various features available in DiDge is available here or by running this command in R:

?DiDge

To estimate DiD for many cohorts and event times, use the DiD command. For example:

DiD(inputdata = yourdata, varnames = varnames,
    min_event = -3, max_event = 5)

A detailed manual explaining the various features available in DiD is available here or by running this command in R:

?DiD

Further Information

For more information, read the following articles:

• Get Started
• Background and Demonstration
• Theory and Methods
• Function Documentation
• Detailed Examples

Acknowledgements: Thanks to Mert Demirer and Kirill Borusyak for helpful comments.