# **Data and Code for "Difference-in-Differences Designs: A Practitioner’s Guide", Journal of Economic Literature**
## **Authors**
Andrew Baker, Brantly Callaway, Scott Cunningham,
Andrew Goodman-Bacon, and Pedro H. C. Sant’Anna
## **Abstract**
**Abstract from the paper:** Difference-in-differences
(DiD) is arguably the most popular quasi-experimental research design.
Its canonical form, with two groups and two periods, is well-understood.
However, empirical practices can be ad hoc when researchers go beyond
that simple case. This article provides an organizing framework for
discussing different types of DiD designs and their associated DiD
estimators. It discusses covariates, weights, handling multiple periods,
and staggered treatments. The organizational framework, however, applies
to other extensions of DiD methods as well.
**Replication Instructions**
**Overview:** To replicate the analysis and results,
users have two options: (1) use R, or (2) use Stata. Both approaches
will ultimately produce the same figures and tables. The simplest
approach is to use one environment throughout, but advanced users could
mix (for example, use R to rebuild data and Stata for analysis). We
outline both methods below. In all cases, start by unpacking the
replication archive and setting the working directory to the top-level
folder containing this README.
**Option 1: Replication using R**
1. Install R (version 4.x). It
will also be convenient to use
RStudio.
2. Open the provided
DiD_JEL.Rproj
RStudio project, which will set your working directory to the
replication package folder. Alternatively, set your R working
directory manually to the folder containing the
scripts/ and
data/
directories.
3. Open
scripts/R/00_master_did_jel.R
in R (or RStudio) and run it. This master
script will:
- Load required libraries.
We use renv to create a fully reproducible package. In the master
code it will call renv::restore() which will download and install
the packages from the renv.lock file into the local
environment.
- Execute the code
0_make_data.R,
which will read in the raw data and produce the combined dataset
county_mortality_data.csv.
(This step will also attempt to fetch data from the Census API for
poverty and income – an internet connection is required for that
step. If offline, the script will use the included final data from
the archive, which already contains those variables. You can do
this by commenting out the code from the master file (line 16 of
scripts/R/00_master_did_jel.R)
- Execute the analysis
scripts
1_Adoption_Table.R
through 4_GxT.R in
sequence. These will perform all calculations and generate
intermediate results, figures, and tables. As they run, you will
see output in the R console and figures may briefly
display.
- Execute
5_honestdid.R to
run the sensitivity analysis and produce any additional outputs
(e.g., adjusted estimates or plots for the appendix).
4. Once
00_master_did_jel.R
completes, check the
figures/ and
tables/ directories.
They should now contain the replication results. You can open the
PDF figures to compare with the paper’s figures, and open any table
files (or the console output in R) to compare with the published
tables. All numbers and graphs should match the article and its
appendix.
*Note:* The R code is written to use relative paths via
the here package, so as
long as the working directory is correctly set (by opening the RProj or
using setwd to the
project root), no manual path edits are required. The entire R pipeline
should run automatically. Total runtime in R is expected to be around 10
minutes (depending on your system).
**Option 2: Replication using Stata**
1. Install Stata (version 15
or higher recommended; the do-files were tested on Stata 17). Ensure
you have an internet connection for the first run so that Stata can
install any needed user-written commands.
2. Start Stata and change the
working directory to the replication package folder. For example, in
Stata’s command window, type:
cd
"C:\path\to\Difference-in-Differences-replication"
3. Open the file
scripts/Stata/00_stata_master_did_jel.do
in Stata’s do-file editor. Edit the line near the top that defines
the global root
directory: global rootdir "your pathname
here"
Replace "your
pathname here" with the full path to the
replication folder on your system (the folder containing the
data,
scripts, etc.). Save
the changes.
4. In Stata, run the master
do-file. You can do this by clicking “Do” in the editor or by typing
do
scripts/Stata/00_stata_master_did_jel.do in
the Stata command prompt. The master script will then:
- Install any missing Stata
packages from SSC (you will see Stata connecting to net sites to
install commands like
csdid,
drdid, etc.). This
happens only once; subsequent runs will skip installations if the
packages are already present.
- Execute
0_stata_Make_data.do,
which will load the provided
data/county_mortality_data.csv
and set up the analysis dataset in memory.
- Execute the analysis
scripts 1 through 5 in order. You will see output in the Stata
Results window: for example, the adoption table being printed,
regression results, and graph generation. The do-files will save
graphs to figures/
(as PDF files) and tables to
tables/ as they
go.
5. When the master script
finishes, verify the outputs against the paper’s tables and figures
for confirmation.
6. Check the
figures/ folder for
newly created figure PDFs. Open them to ensure they correspond to
the figures in the paper (titles, axes, and plotted values should
match). For example, you should find figures illustrating the
mortality trends for expansion vs. non-expansion states, event-study
plots for multiple years, etc., as described in the paper.
7. Check the
tables/ folder for
any output tables. If LaTeX tables were saved (e.g.,
.tex files), you can
compile them or open them in a text editor to verify the numbers
align with the published tables.
*Troubleshooting:* If Stata stops with an error, read
the error message and refer to the corresponding do-file. Common issues
might include not having write permission in the directory (ensure that
Stata can create files in
figures/ and
tables/) or package
installation problems (if a network issue prevented downloading a
package, try installing it manually via ssc
install \[pkg\] and re-run). Make sure the
\$rootdir path was set
correctly; if it’s incorrect, Stata will not find the data or scripts.
If replication still fails, one can fall back to using R, as the R code
might be more self-contained for data assembly.
**Results Verification:** Both approaches should yield
the same substantive results. Differences will be due either to the use
of bootstrap standard errors or cosmetic details of software graphing
functionality (ie. histograms), but these should be small. The figures
and tables generated by the R code can be directly compared to the
figures and tables in the published article’s main text and
appendix.
## **Data Availability**
This replication package includes all data used in the
study. All datasets are from public sources and are provided in the
data/ directory of the
archive. The authors certify that they have legitimate access to these
data and permission to use them, and that they have the rights to
redistribute the included data for replication purposes. Details on each
data source are provided below. (All data are publicly available, so no
special access or confidentiality restrictions apply.)
### **Data Sources and Provenance**
- **CDC Mortality and
Population Data:** County-level mortality counts and population
figures for various subgroups were obtained from publicly available
CDC sources. Files in
data/cdc/ include
mortality.csv (total
deaths of adults 20-64 by county-year) and population files broken
down by demographic groups
(female_pop.csv,
white_pop.csv,
hispanic_pop.csv,
etc.). These data were originally sourced from the CDC WONDER database
and are in the public domain.
Source =
https://wonder.cdc.gov/deaths-by-underlying-cause.html.
- **BLS Unemployment Data:**
County-level unemployment rates and labor force data for years
2009–2019 were obtained from the U.S. Bureau of Labor Statistics Local
Area Unemployment Statistics (LAU). The files
laucnty09.xlsx through
laucnty19.xlsx
(located in data/bls/)
contain annual county unemployment statistics for each year 2009–2019.
These Excel files are directly from BLS (publicly available government
data). Source = https://www.bls.gov/lau/tables.htm.
- **Medicaid Expansion
Status:** The file
expansion_status.csv
in data/kff/ was
compiled from the Kaiser Family Foundation’s data on state Medicaid
expansion decisions under the Affordable Care Act. It lists each U.S.
state’s expansion status and the year of expansion (if applicable).
This information is publicly available through KFF and included here
for convenience. Source =
https://www.kff.org/status-of-state-medicaid-expansion-decisions/.
- **Socioeconomic Covariates:**
County-level poverty rates and median household incomes for the
relevant years were retrieved from the U.S. Census Bureau (American
Community Survey). These variables are incorporated into the final
dataset. The replication code uses the Census API to obtain these
measures; however, the final compiled dataset (described below)
already contains these variables for each county-year. Source =
https://www2.census.gov/programs-surveys/saipe/datasets/
### **Compiled Analysis Dataset**
The above sources were merged to create a single
analysis dataset of county-level observations used in the paper’s
empirical analysis. This compiled dataset is provided in two formats for
convenience:
- data/county_mortality_data.csv
– a CSV file containing the merged panel dataset of 3,143 U.S.
counties over multiple years (2010–2019). It includes variables such
as county identifiers, year, adult mortality counts, population (ages
20–64), mortality rates, demographic population shares, unemployment
rate, poverty rate, median income, and an indicator of Medicaid
expansion status with the year of expansion.
- data/did_jel_aca_replication_data.dta
– the same merged dataset in Stata
.dta format. This is
provided for users replicating with Stata, to avoid any import issues.
It contains the identical information as the CSV file above. (Note: In
the scripts, the CSV version is used as input for the data
construction step, and this Stata file is provided as a ready-to-use
copy of the final data.)
*Rights and Licenses:* All data in this package are
publicly available and either in the public domain or provided under
terms that allow redistribution for research purposes. The authors have
verified that including these data in the replication archive does not
violate any usage agreements. If users reuse the data, please cite the
original data sources (e.g., CDC, BLS, KFF) as appropriate. No sensitive
or restricted data are used in this study.
## **Software and Computational Requirements**
**Supported Platforms:** The replication materials can
be run on any modern Windows, macOS, or Linux system. The code and data
are not platform-dependent. The analysis was originally run on a 64-bit
machine with 64 GB RAM and an Intel Xeon 1.8GHz processor, but such high
specifications are not strictly required. A typical desktop or laptop
(e.g., 8–16 GB RAM, multi-core CPU) should be sufficient to reproduce
the results, though runtime may vary.
**Software Required:** Two software environments are
provided for replication:
- **Stata:** The Stata
replication code was tested on Stata 16 and 17. It should run on Stata
15 or higher, as it uses standard commands and user-written packages
compatible with these versions. Stata is required if using the
.do files for
replication.
- **R:** The analysis can also
be replicated in R (version 4.1 or higher recommended). An R Project
file (DiD_JEL.Rproj)
is included to facilitate using RStudio. The R scripts rely on several
R packages (listed below).
**R Packages Needed:** To replicate in R, the following
R packages (with the versions used in the original replication) are
required:
- tidyverse
(data manipulation and plotting; v2.0.0)
- readxl
(reading Excel files; v1.4.5)
- here
(for file paths, v1.0.2)
- kableExtra
and modelsummary (for
formatting tables; v1.4.0 and v2.5.0)
- did
(Difference-in-Differences estimation package by Callaway & Sant’Anna,
v2.3.0)
- HonestDiD
(for robustness checks, v0.2.6)
- fixest
(for fixed effects regressions, v0.13.2)
- ggthemes
and patchwork (for
plotting themes and combining plots, v5.2.0 and v.1.3.2)
All these packages are available on CRAN or
GitHub.
**Stata User-Written Packages:** The Stata do-files
will automatically attempt to install required user-contributed commands
(from SSC) if they are not already present. Specifically, the master
do-file uses:
- csdid
(Callaway and Sant’Anna’s staggered DID estimation command,
v.1.81)
- drdid
(Doubly-Robust DID commands by Sant’Anna et al., v1.91)
- honestdid
(Honest DiD sensitivity analysis, by Rambachan & Roth, wrapped for
Stata)
- regsave
and estout (for saving
regression results and outputting tables)
- coefplot
and grc1leg2 (for
plotting coefficients and combining graphs)
An active internet connection in Stata is required to
install these on the fly. If internet access is not available, the user
should install these packages beforehand or uncomment any provided
alternative steps. Once installed, the do-files will run offline. No
other proprietary tools are needed beyond Stata itself.
**Memory and Runtime:** The full replication (all
analyses and generating all figures and tables) completes in
approximately 10–15 minutes on a modern computer. Peak memory usage is
moderate; a machine with at least 4 GB of free RAM is recommended. Most
of the runtime is spent running estimations. There is no significant
randomness in the code (any random number generation for, e.g.,
bootstrap confidence intervals uses set seeds), so results should be
exactly reproducible across runs and platforms.
## **Structure of the Archive**
The replication package is organized into the following
files and folders:
README.html
– *(this document)* A detailed guide to the replication package,
including how to run the code and information on data and
software.
LICENSE
– License file for the code (MIT License). This covers the authors’ code
in the repository. (Data sources are public domain or similarly open;
see Data Availability above.)
DiD_JEL.Rproj
– R Project file for easy use with RStudio. Opening this project will
set the working directory to the replication package root, which helps
the R scripts find the data and output folders automatically.
**Data Directory –**
**data/**: Contains all
datasets used or produced (poverty and median income read directly from
census.gov in /scripts/R/0_data_processing.R)
data/bls/
– raw unemployment data from BLS. Files
laucnty09.xlsx ...
laucnty19.xlsx are
yearly spreadsheets of county unemployment and labor force data (for
years 2009 through 2019 respectively).
data/cdc/
– raw mortality and population data from CDC sources. This includes
mortality.csv
(county-level deaths of age 20–64, by year), and population breakdowns
by demographic group:
female_pop.csv,
white_pop.csv,
hispanic_pop.csv,
total_pop.csv (total
population aged 20–64). These were used to compute mortality rates and
demographic shares.
data/kff/
– contains
expansion_status.csv,
which lists each state’s Medicaid expansion status and year. Used to
assign treatment (expansion) timing to each state’s counties.
data/county_mortality_data.csv
– the merged analysis dataset (in CSV format) combining all of the above
sources plus additional covariates (poverty rate, median income). Each
row is a county-year observation. This is the main input data for
analysis scripts.
data/did_jel_aca_replication_data.dta
– the same merged dataset in Stata format. Provided for convenience to
Stata users (so they do not need to manually import the CSV). The Stata
code uses the CSV, but this file can be opened directly in Stata to
inspect the data.
The following files are
included in the archive under `data/`.
| File name | Directory | Provided in archive? | Source | Notes |
|----|----|----|----|----|
| laucnty09.xlsx | data/bls | Yes | BLS LAU county unemployment (laucnty##.xlsx) | Original download; public data |
| laucnty10.xlsx | data/bls | Yes | BLS LAU county unemployment (laucnty##.xlsx) | Original download; public data |
| laucnty11.xlsx | data/bls | Yes | BLS LAU county unemployment (laucnty##.xlsx) | Original download; public data |
| laucnty12.xlsx | data/bls | Yes | BLS LAU county unemployment (laucnty##.xlsx) | Original download; public data |
| laucnty13.xlsx | data/bls | Yes | BLS LAU county unemployment (laucnty##.xlsx) | Original download; public data |
| laucnty14.xlsx | data/bls | Yes | BLS LAU county unemployment (laucnty##.xlsx) | Original download; public data |
| laucnty15.xlsx | data/bls | Yes | BLS LAU county unemployment (laucnty##.xlsx) | Original download; public data |
| laucnty16.xlsx | data/bls | Yes | BLS LAU county unemployment (laucnty##.xlsx) | Original download; public data |
| laucnty17.xlsx | data/bls | Yes | BLS LAU county unemployment (laucnty##.xlsx) | Original download; public data |
| laucnty18.xlsx | data/bls | Yes | BLS LAU county unemployment (laucnty##.xlsx) | Original download; public data |
| laucnty19.xlsx | data/bls | Yes | BLS LAU county unemployment (laucnty##.xlsx) | Original download; public data |
| female_pop.csv | data/cdc | Yes | CDC WONDER / Bridged-race population | Original download; public data |
| hispanic_pop.csv | data/cdc | Yes | CDC WONDER / Bridged-race population | Original download; public data |
| white_pop.csv | data/cdc | Yes | CDC WONDER / Bridged-race population | Original download; public data |
| total_pop.csv | data/cdc | Yes | CDC WONDER / Population totals | Original download; public data |
| mortality.csv | data/cdc | Yes | CDC WONDER / Mortality | Original download; public data |
| poverty and median income | N/A | No | Census/ SAIPE | read from census.gov in 0_data_processing.R |
| expansion_status.csv | data/kff | Yes | KFF Medicaid Expansion Status | Original download; public data |
| county_mortality_data.csv | data | Yes | Constructed dataset | Built by scripts from CDC+BLS+KFF |
| did_jel_aca_replication_data.dta | data | Yes | Constructed dataset (Stata) | Analysis-ready panel |
**Scripts Directory –**
**scripts/**: Contains
all code to reproduce the results. Two subfolders are provided for the
two software options:
- scripts/R/
– R scripts for replication. These are numbered in the order they
should be run:
- 0_make_data.R
– Reads the raw data (BLS, CDC, KFF) and constructs the merged
dataset. It also retrieves the Census poverty and income data via
API and adds those. The final combined dataset is written to
data/county_mortality_data.csv.
- 1_Adoption_Table\>R
– Produces a summary table of Medicaid expansion adoption timing
(e.g., how many states or counties expanded in 2014, 2015, etc.,
versus not at all). This corresponds to a descriptive table in the
paper.
- 2_2x2.R
– Performs the basic 2x2 Difference-in-Differences analysis (two
groups, two periods) using the example of Medicaid expansion vs.
non-expansion pre- vs. post-2014. It generates results for the
simplest DiD scenario.
- 3_2XT.R
– Extends the analysis to a 2 x T setting (two groups, multiple
periods). This script examines multiple periods of data (pre- and
post-treatment) for treated vs. control groups, producing dynamic
event-study style figures.
- 4_GxT.R
– Generalizes to a G x T setting (multiple groups with staggered
treatment timing across T periods). This script uses methods
appropriate for staggered adoption (multiple treatment groups
entering at different times) and produces the corresponding results
(aggregate treatment effect estimates and/or figures). It leverages
the “building block” approach described in the paper to compute
aggregate effects from 2x2 comparisons.
- 5_honestdid.R
– Conducts robustness and sensitivity checks, specifically the
“Honest DiD” bias-adjustment and sensitivity analysis (following the
approach of Rambachan and Roth). Computes how sensitive the results
are to deviations from the parallel trends assumption and may output
results or plots for an appendix.
- 00_master_did_jel.R
– A master R script that sequentially runs all the above R scripts
in the correct order. Running this one file will execute the entire
analysis pipeline from data construction to final output. (Note:
Before running, ensure the required R packages are installed as
described above.)
- scripts/Stata/
– Stata do-files for replication. The key files are:
- 00_stata_master_did_jel.do
– Master do-file that sets up the environment and runs all analysis
do-files. \*\*Important:\*\* This file contains a line to define the
global path
\$rootdir. The user
should edit this line to point to the folder where the replication
files are located on their system (or alternatively, launch Stata in
that directory and define global
rootdir accordingly). Once the path is set,
running this master script will call all other scripts in order. It
also installs any needed Stata packages (via SSC) at the beginning.
The master script estimates that the full run takes about 11 minutes
on a high-end machine (as noted in comments).
- 0_stata_Make_data.do
– Constructs the merged dataset for analysis (similar to the R
script 0_data_processing.R). It reads in the
county_mortality_data.csv
provided in the data folder (which contains all needed variables)
and performs any additional cleaning or variable transformations in
Stata. The output is kept in memory for use by subsequent do-files
(and could be saved as a Stata data file if needed).
- 1_stata_adoption_table.do
– Replicates the generation of the adoption summary table using
Stata. It produces counts of states/counties by expansion status and
year, matching the results from the R script for Table 1 (Medicaid
expansion timing breakdown).
- 2_stata_2x2.do
– Runs the 2x2 DiD analysis in Stata, analogous to the R script.
Computes the difference-in-differences estimate for the two-period,
two-group case and stores or displays the results.
- 3_stata_2xT.do
– Runs the multiple-period (event study) analysis in Stata,
computing period-by-period treatment effects or dynamic effects for
the treated vs. control comparison, and aggregates them if needed.
Results should match those from the R version.
- 4_stata_GxT.do
– Runs the staggered adoption (multiple group, multiple period)
analysis in Stata. This uses the provided user-written commands
(like csdid or
drdid) to estimate
the proper aggregations of 2x2 comparisons across groups and time,
reproducing the main empirical results in the paper.
- 5_stata_honestdid.do
– Performs the Honest DiD sensitivity analysis in Stata, paralleling
the R script. Uses the
honestdid package to
calculate how robust the findings are to potential violations of the
parallel trends assumption, and outputs the results (e.g.,
bias-adjusted estimates or confidence intervals).
**Output Directories –**
**figures/** **and**
**tables/**: These
folders are placeholders for the results generated by the code.
- figures/
– After running the replication scripts, this will contain the figures
(graphs) produced by the analysis, in PDF or PNG format. Each figure
corresponds to one in the paper or appendix (e.g., event-study plots
of mortality rates, etc.). The R scripts use
ggsave() to save
figures here, and the Stata scripts also output graphs (using
graph export) to this
folder with similar names.
- tables/
– This will contain output tables (as LaTeX
.tex files, CSVs, or
text) generated by the code, such as regression result tables or
summary statistics tables. For example, the adoption summary table and
regression tables are saved here. These files can be opened or
compiled (if LaTeX) to verify that they match the tables in the
published paper.
*Note:* Initially,
figures/ and
tables/ are empty. They
will be populated when the code is run. The names and formats of outputs
are chosen to correspond with those referenced in the paper (for
instance, adoptions.tex
or Figure3.pdf,
etc.).
**List of Programs and their
Outputs **
Each exhibit below lists the output file and the
script (with the line where it is written/exported).
**R outputs**
| Type | Output file (relative path) | Generated by script | Line no. |
|--------|-----------------------------|------------------------------|----------|
| Table | tables/table1_R.tex | scripts/R/1_Adoption_Table.R | 86 |
| Table | tables/table2_R.tex | scripts/R/2_2x2.R | 173 |
| Table | tables/table3_R.tex | scripts/R/2_2x2.R | 208 |
| Table | tables/table4_R.tex | scripts/R/2_2x2.R | 426 |
| Table | tables/table5_R.tex | scripts/R/2_2x2.R | 466 |
| Table | tables/table6_R.tex | scripts/R/2_2x2.R | 514 |
| Table | tables/table7_R.tex | scripts/R/2_2x2.R | 638 |
| Figure | figures/figure1_R.pdf | scripts/R/2_2x2.R | 673 |
| Figure | figures/figure2_R.pdf | scripts/R/3_2XT.R | 105 |
| Figure | figures/figure3_R.pdf | scripts/R/3_2XT.R | 199 |
| Figure | figures/figure4_R.pdf | scripts/R/3_2XT.R | 259 |
| Figure | figures/figure5_R.pdf | scripts/R/4_GxT.R | 100 |
| Figure | figures/figure6_R.pdf | scripts/R/4_GxT.R | 148 |
| Figure | figures/figure7_R.pdf | scripts/R/4_GxT.R | 173 |
| Figure | figures/figure8_R.pdf | scripts/R/4_GxT.R | 218 |
| Figure | figures/figure9_R.pdf | scripts/R/4_GxT.R | 280 |
**Stata outputs**
| Type | Output file (relative path) | Generated by script | Line no. |
|----|----|----|----|
| Table | tables/table1_stata.tex | scripts/Stata/1_stata_adoption_table.do | 146 |
| Table | tables/table2_stata.tex | scripts/Stata/2_stata_2x2.do | 93 |
| Table | tables/table3_stata.tex | scripts/Stata/2_stata_2x2.do | 212 |
| Table | tables/table4_stata.tex | scripts/Stata/2_stata_2x2.do | 477 |
| Table | tables/table5_stata.tex | scripts/Stata/2_stata_2x2.do | 614 |
| Table | tables/table6_stata.tex | scripts/Stata/2_stata_2x2.do | 688 |
| Table | tables/table7_stata.tex | scripts/Stata/2_stata_2x2.do | 805 |
| Figure | figures/figure1_stata.pdf | scripts/Stata/2_stata_2x2.do | 930 |
| Figure | figures/figure2_stata.pdf | scripts/Stata/3_stata_2xT.do | 57 |
| Figure | figures/figure3_stata.pdf | scripts/Stata/3_stata_2xT.do | 152 |
| Figure | figures/figure4_stata.pdf | scripts/Stata/3_stata_2xT.do | 277 |
| Figure | figures/figure5_stata.pdf | scripts/Stata/4_stata_GxT.do | 63 |
| Figure | figures/figure6_stata.pdf | scripts/Stata/4_stata_GxT.do | 194 |
| Figure | figures/figure7_stata.pdf | scripts/Stata/4_stata_GxT.do | 223 |
| Figure | figures/figure8_stata.pdf | scripts/Stata/4_stata_GxT.do | 320 |
| Figure | figures/figure9_stata.pdf | scripts/Stata/4_stata_GxT.do | 423 |
**Markdown Documentation –**
**markdown/**: This
folder contains supplementary documentation in Quarto/Markdown format
(and rendered HTML) for the code appendix:
- markdown/Code_Appendix.qmd
and Code_Appendix.html
– An appendix documenting the R and Stata code.
These markdown files serve as a
readable “code appendix” and do not need to be executed by
replicators.
.gitignore
and .DS_Store – Standard
housekeeping files (the former for git version control, the latter are
incidental Mac system files). They have no bearing on replication and
can be ignored.
**Licenses and Permissions**
**Code License:** The authors have released the
code in this replication package under the MIT License (see the
LICENSE file for full
terms). This means that users are free to use, modify, and distribute
the code, provided proper attribution is given. In short, the code is
open-source. If you reuse substantial portions of the code, please cite
the paper and give credit to the authors.
**Data Usage:** All data included are from public sources as noted.
Government datasets (CDC, BLS, Census) are in the public domain and free
to use. The Kaiser Family Foundation data on expansion status is
publicly reported information. By including these files, we are
redistributing them for replication purposes. To the best of our
knowledge, this redistribution is allowed. Users should also cite the
original data sources in any new work. No proprietary or confidential
data are included, so no additional data licenses are required.
**Authorizations:** We reaffirm that we have permission to share all
materials in this archive. If there are any questions about data
permissions or if any data provider has concerns, please contact the
authors (see the email above) or the AEA Data Editor’s office.
**Acknowledgments**
We thank the American Economic Association Data
Editor and staff for their guidance on preparing this replication
package. We also thank the providers of the public data used in this
study – specifically the CDC, BLS, Census Bureau, and Kaiser Family
Foundation – for making their data readily accessible. Their efforts in
data collection and dissemination made this research possible.
The authors are grateful to colleagues and seminar participants who
provided feedback on earlier versions of the paper and its companion
code. Additionally, we acknowledge the contributors of the open-source
software and packages utilized (including R and Stata communities, and
the developers of the
did,
csdid, and related
packages). Their tools greatly facilitated our analysis.
Any errors in the replication code or documentation are the sole
responsibility of the authors. Please report any issues or questions
about the replication package to the contact email provided, and we will
be happy to assist.