Readme.md

sythdid: Synthetic Difference in Difference Estimation

This package implements the synthetic difference-in-differences estimation procedure, along with a range of inference and graphing procedures, following the work of the author. The package draws on R and Julia code for optimization and Stata code for implementation in contexts with staggered adoption over multiple treatment periods (as well as in a single adoption period as in the original code). The package extends the functionality of the original code, allowing for estimation in a wider range of contexts. Overall, this package provides a comprehensive toolkit for researchers interested in using the synthetic difference-in-differences estimator in their work.

Instalation

pip install synthdid

Usage

Input class Synthdid

• outcome: Outcome variable (numeric)
• unit: Unit variable (numeric or string)
• time: Time variable (numeric)
• quota: Dummy of treatement, equal to 1 if units are treated, and otherwise 0 (numeric)

Methods:

• .fit(cov_method: Literal["optimized", "projected"] | None): Estimates the ATE, as well as the time and unit weights. Can use covariates and fit them with the method specified in cov_method
• .vcov(method: Literal["placebo", "bootstrap", "jackknife"], n_reps:int = 50): Estimates the standard error of the ATE estimator

Examples Based on Empirical Application

from synthdid.get_data import california_prop99, quota
from synthdid.synthdid import Synthdid
from matplotlib import pyplot as plt
import numpy as np

Block Design

df = california_prop99()
california_sdid = Synthdid(df, "State", "Year", "treated", "PacksPerCapita").fit().vcov().summary()
california_sdid.summary2

	ATT	Std. Err.	t	P > |t|
0	-15.60383	10.789924	-1.446148	0.148136

plt.show(california_sdid.plot_outcomes())
plt.show(california_sdid.plot_weights())

california_did = Synthdid(df, "State", "Year", "treated", "PacksPerCapita").fit(synth = True)
plt.show(california_sc.plot_outcomes())
plt.show(california_sc.plot_weights())

california_did = Synthdid(df, "State", "Year", "treated", "PacksPerCapita").fit(did = True)
plt.show(california_did.plot_outcomes())
plt.show(california_did.plot_weights())

Staggered Adoptions Design

df = quota()
fit_model = Synthdid(df, "country", "year", "quota", "womparl").fit().vcov().summary()
fit_model.summary2

	ATT	Std. Err.	t	P > |t|
0	8.0341	1.684382	4.769762	0.000002

fit_model.att_info

	time	att_time	att_wt	N0	T0	N1	T1
0	2000.0	8.388868	0.170213	110	10	1	16
1	2002.0	6.967746	0.297872	110	12	2	14
2	2003.0	13.952256	0.276596	110	13	2	13
3	2005.0	-3.450543	0.117021	110	15	1	11
4	2010.0	2.749035	0.063830	110	20	1	6
5	2012.0	21.762715	0.042553	110	22	1	4
6	2013.0	-0.820324	0.031915	110	23	1	3

fit_covar_model = Synthdid(df[~df.lngdp.isnull()], "country", "year", "quota", "womparl", covariates = ["lngdp"]).fit().vcov(method = "bootstrap").summary()
fit_covar_model.summary2

	ATT	Std. Err.	t	P > |t|
0	8.04901	3.395295	2.370636	0.017757

fit_covar_model_projected = Synthdid(df[~df.lngdp.isnull()], "country", "year", "quota", "womparl", covariates = ["lngdp"]).fit(cov_method = "projected").vcov(method = "bootstrap").summary()
fit_covar_model_projected.summary2

	ATT	Std. Err.	t	P > |t|
0	8.05903	3.428897	2.350327	0.018757

Inference Options

countries_for_excluding = ["Algeria", "Kenya", "Samoa", "Swaziland", "Tanzania"]
se_examples = Synthdid(
        df[~df.country.isin(countries_for_excluding)],
        "country",
        "year",
        "quota",
        "womparl"
).fit().vcov(method = "bootstrap").summary()
se_examples.summary2

	ATT	Std. Err.	t	P > |t|
0	10.33066	5.404923	1.911343	0.055961

se_examples.vcov(method = "placebo").summary()
se_examples.summary2

	ATT	Std. Err.	t	P > |t|
0	10.33066	2.244618	4.602413	0.000004

se_examples.vcov(method = "jackknife").summary()
se_examples.summary2

	ATT	Std. Err.	t	P > |t|
0	10.33066	6.04213	1.709771	0.087308