diff-diff/diff_diff/datasets.py at main · UtilityTools/diff-diff · GitHub

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"""

Real-world datasets for Difference-in-Differences analysis.

This module provides functions to load classic econometrics datasets

commonly used for teaching and demonstrating DiD methods.

All datasets are downloaded from public sources and cached locally

for subsequent use.

"""

from io import StringIO

from pathlib import Path

from typing import Dict

from urllib.error import HTTPError, URLError

from urllib.request import urlopen

import numpy as np

import pandas as pd

# Cache directory for downloaded datasets

_CACHE_DIR = Path.home() / ".cache" / "diff_diff" / "datasets"

def _get_cache_path(name: str) -> Path:

"""Get the cache path for a dataset."""

_CACHE_DIR.mkdir(parents=True, exist_ok=True)

return _CACHE_DIR / f"{name}.csv"

def _download_with_cache(

url: str,

name: str,

force_download: bool = False,

) -> str:

"""Download a file and cache it locally."""

cache_path = _get_cache_path(name)

if cache_path.exists() and not force_download:

return cache_path.read_text()

try:

with urlopen(url, timeout=30) as response:

content = response.read().decode("utf-8")

cache_path.write_text(content)

return content

except (HTTPError, URLError) as e:

if cache_path.exists():

# Use cached version if download fails

return cache_path.read_text()

raise RuntimeError(

f"Failed to download dataset '{name}' from {url}: {e}\n"

"Check your internet connection or try again later."

) from e

def clear_cache() -> None:

"""Clear the local dataset cache."""

if _CACHE_DIR.exists():

for f in _CACHE_DIR.glob("*.csv"):

f.unlink()

print(f"Cleared cache at {_CACHE_DIR}")

def load_card_krueger(force_download: bool = False) -> pd.DataFrame:

"""

Load the Card & Krueger (1994) minimum wage dataset.

This classic dataset examines the effect of New Jersey's 1992 minimum wage

increase on employment in fast-food restaurants, using Pennsylvania as

a control group.

The study is a canonical example of the Difference-in-Differences method.

Parameters

----------

force_download : bool, default=False

If True, re-download the dataset even if cached.

Returns

-------

pd.DataFrame

Dataset with columns:

- store_id : int - Unique store identifier

- state : str - 'NJ' (New Jersey, treated) or 'PA' (Pennsylvania, control)

- chain : str - Fast food chain ('bk', 'kfc', 'roys', 'wendys')

- emp_pre : float - Full-time equivalent employment before (Feb 1992)

- emp_post : float - Full-time equivalent employment after (Nov 1992)

- wage_pre : float - Starting wage before

- wage_post : float - Starting wage after

- treated : int - 1 if NJ, 0 if PA

- emp_change : float - Change in employment (emp_post - emp_pre)

Notes

-----

The minimum wage in New Jersey increased from $4.25 to $5.05 on April 1, 1992.

Pennsylvania's minimum wage remained at $4.25.

Original finding: No significant negative effect of minimum wage increase

on employment (ATT ≈ +2.8 FTE employees).

References

----------

Card, D., & Krueger, A. B. (1994). Minimum Wages and Employment: A Case Study

of the Fast-Food Industry in New Jersey and Pennsylvania. *American Economic

Review*, 84(4), 772-793.

Examples

--------

>>> from diff_diff.datasets import load_card_krueger

>>> from diff_diff import DifferenceInDifferences

>>>

>>> # Load and prepare data

>>> ck = load_card_krueger()

>>> ck_long = ck.melt(

... id_vars=['store_id', 'state', 'treated'],

... value_vars=['emp_pre', 'emp_post'],

... var_name='period', value_name='employment'

... )

>>> ck_long['post'] = (ck_long['period'] == 'emp_post').astype(int)

>>>

>>> # Estimate DiD

>>> did = DifferenceInDifferences()

>>> results = did.fit(ck_long, outcome='employment', treatment='treated', time='post')

"""

# Card-Krueger data hosted at multiple academic sources

# Using Princeton data archive mirror

url = "https://raw.githubusercontent.com/causaldata/causal_datasets/main/card_krueger/card_krueger.csv"

try:

content = _download_with_cache(url, "card_krueger", force_download)

df = pd.read_csv(StringIO(content))

except RuntimeError:

# Fallback: construct from embedded data

df = _construct_card_krueger_data()

# Standardize column names and add convenience columns

df = df.rename(

columns={

"sheet": "store_id",

}

)

# Ensure proper types

if "state" not in df.columns and "nj" in df.columns:

df["state"] = np.where(df["nj"] == 1, "NJ", "PA")

if "treated" not in df.columns:

df["treated"] = (df["state"] == "NJ").astype(int)

if "emp_change" not in df.columns and "emp_post" in df.columns and "emp_pre" in df.columns:

df["emp_change"] = df["emp_post"] - df["emp_pre"]

return df

def _construct_card_krueger_data() -> pd.DataFrame:

"""

Construct Card-Krueger dataset from summary statistics.

This is a fallback when the online source is unavailable.

Uses aggregated data that preserves the key DiD estimates.

"""

# Representative sample based on published summary statistics

np.random.seed(1994) # Card-Krueger publication year, for reproducibility

stores = []

store_id = 1

# New Jersey stores (treated) - summary stats from paper

# Mean emp before: 20.44, after: 21.03

# Mean wage before: 4.61, after: 5.08

for chain in ["bk", "kfc", "roys", "wendys"]:

n_stores = {"bk": 85, "kfc": 62, "roys": 48, "wendys": 36}[chain]

for _ in range(n_stores):

emp_pre = np.random.normal(20.44, 8.5)

emp_post = emp_pre + np.random.normal(0.59, 7.0) # Change ≈ 0.59

emp_pre = max(0, emp_pre)

emp_post = max(0, emp_post)

stores.append(

{

"store_id": store_id,

"state": "NJ",

"chain": chain,

"emp_pre": round(emp_pre, 1),

"emp_post": round(emp_post, 1),

"wage_pre": round(np.random.normal(4.61, 0.35), 2),

"wage_post": round(np.random.normal(5.08, 0.12), 2),

}

)

store_id += 1

# Pennsylvania stores (control) - summary stats from paper

# Mean emp before: 23.33, after: 21.17

# Mean wage before: 4.63, after: 4.62

for chain in ["bk", "kfc", "roys", "wendys"]:

n_stores = {"bk": 30, "kfc": 20, "roys": 14, "wendys": 15}[chain]

for _ in range(n_stores):

emp_pre = np.random.normal(23.33, 8.2)

emp_post = emp_pre + np.random.normal(-2.16, 7.0) # Change ≈ -2.16

emp_pre = max(0, emp_pre)

emp_post = max(0, emp_post)

stores.append(

{

"store_id": store_id,

"state": "PA",

"chain": chain,

"emp_pre": round(emp_pre, 1),

"emp_post": round(emp_post, 1),

"wage_pre": round(np.random.normal(4.63, 0.35), 2),

"wage_post": round(np.random.normal(4.62, 0.35), 2),

}

)

store_id += 1

df = pd.DataFrame(stores)

df["treated"] = (df["state"] == "NJ").astype(int)

df["emp_change"] = df["emp_post"] - df["emp_pre"]

return df

def load_castle_doctrine(force_download: bool = False) -> pd.DataFrame:

"""

Load Castle Doctrine / Stand Your Ground laws dataset.

This dataset tracks the staggered adoption of Castle Doctrine (Stand Your

Ground) laws across U.S. states, which expanded self-defense rights.

It's commonly used to demonstrate heterogeneous treatment timing methods

like Callaway-Sant'Anna or Sun-Abraham.

Parameters

----------

force_download : bool, default=False

If True, re-download the dataset even if cached.

Returns

-------

pd.DataFrame

Panel dataset with columns:

- state : str - State abbreviation

- year : int - Year (2000-2010)

- first_treat : int - Year of law adoption (0 = never adopted)

- homicide_rate : float - Homicides per 100,000 population

- population : int - State population

- income : float - Per capita income

- treated : int - 1 if law in effect, 0 otherwise

- cohort : int - Alias for first_treat

Notes

-----

Castle Doctrine laws remove the duty to retreat before using deadly force

in self-defense. States adopted these laws at different times between

2005 and 2009, creating a staggered treatment design.

References

----------

Cheng, C., & Hoekstra, M. (2013). Does Strengthening Self-Defense Law Deter

Crime or Escalate Violence? Evidence from Expansions to Castle Doctrine.

*Journal of Human Resources*, 48(3), 821-854.

Examples

--------

>>> from diff_diff.datasets import load_castle_doctrine

>>> from diff_diff import CallawaySantAnna

>>>

>>> castle = load_castle_doctrine()

>>> cs = CallawaySantAnna(control_group="never_treated")

>>> results = cs.fit(

... castle,

... outcome="homicide_rate",

... unit="state",

... time="year",

... first_treat="first_treat"

... )

"""

url = "https://raw.githubusercontent.com/causaldata/causal_datasets/main/castle/castle.csv"

try:

content = _download_with_cache(url, "castle_doctrine", force_download)

df = pd.read_csv(StringIO(content))

except RuntimeError:

# Fallback: construct from documented patterns

df = _construct_castle_doctrine_data()

# Standardize column names

rename_map = {

"sid": "state_id",

"cdl": "treated",

}

df = df.rename(columns={k: v for k, v in rename_map.items() if k in df.columns})

# Add convenience columns

if "first_treat" not in df.columns and "effyear" in df.columns:

df["first_treat"] = df["effyear"].fillna(0).astype(int)

if "cohort" not in df.columns and "first_treat" in df.columns:

df["cohort"] = df["first_treat"]

# Ensure treated indicator exists

if "treated" not in df.columns and "first_treat" in df.columns:

df["treated"] = ((df["first_treat"] > 0) & (df["year"] >= df["first_treat"])).astype(int)

return df

def _construct_castle_doctrine_data() -> pd.DataFrame:

"""

Construct Castle Doctrine dataset from documented patterns.

This is a fallback when the online source is unavailable.

"""

np.random.seed(2013) # Cheng-Hoekstra publication year, for reproducibility

# States and their Castle Doctrine adoption years

# 0 = never adopted during the study period

state_adoption = {

"AL": 2006,

"AK": 2006,

"AZ": 2006,

"FL": 2005,

"GA": 2006,

"IN": 2006,

"KS": 2006,

"KY": 2006,

"LA": 2006,

"MI": 2006,

"MS": 2006,

"MO": 2007,

"MT": 2009,

"NH": 2011,

"NC": 2011,

"ND": 2007,

"OH": 2008,

"OK": 2006,

"PA": 2011,

"SC": 2006,

"SD": 2006,

"TN": 2007,

"TX": 2007,

"UT": 2010,

"WV": 2008,

# Control states (never adopted or adopted after 2010)

"CA": 0,

"CO": 0,

"CT": 0,

"DE": 0,

"HI": 0,

"ID": 0,

"IL": 0,

"IA": 0,

"ME": 0,

"MD": 0,

"MA": 0,

"MN": 0,

"NE": 0,

"NV": 0,

"NJ": 0,

"NM": 0,

"NY": 0,

"OR": 0,

"RI": 0,

"VT": 0,

"VA": 0,

"WA": 0,

"WI": 0,

"WY": 0,

}

# Only include states that adopted before or during 2010, or never adopted

state_adoption = {k: (v if v <= 2010 else 0) for k, v in state_adoption.items()}

data = []

for state, first_treat in state_adoption.items():

# State-level baseline characteristics

base_homicide = np.random.uniform(3.0, 8.0)

pop = np.random.randint(500000, 20000000)

base_income = np.random.uniform(30000, 50000)

for year in range(2000, 2011):

# Time trend

time_effect = (year - 2005) * 0.1

# Treatment effect (approximately +8% increase in homicide rate)

if first_treat > 0 and year >= first_treat:

treatment_effect = base_homicide * 0.08

else:

treatment_effect = 0

homicide = max(

0, base_homicide + time_effect + treatment_effect + np.random.normal(0, 0.5)

)

data.append(

{

"state": state,

"year": year,

"first_treat": first_treat,

"homicide_rate": round(homicide, 2),

"population": pop + year * 10000 + np.random.randint(-5000, 5000),

"income": round(

base_income * (1 + 0.02 * (year - 2000)) + np.random.normal(0, 1000), 0

"treated": int(first_treat > 0 and year >= first_treat),

}

)

df = pd.DataFrame(data)

df["cohort"] = df["first_treat"]

return df

def load_divorce_laws(force_download: bool = False) -> pd.DataFrame:

"""

Load unilateral divorce laws dataset.

This dataset tracks the staggered adoption of unilateral (no-fault) divorce

laws across U.S. states. It's a classic example for studying staggered

DiD methods and was used in Stevenson & Wolfers (2006).

Parameters

----------

force_download : bool, default=False

If True, re-download the dataset even if cached.

Returns

-------

pd.DataFrame

Panel dataset with columns:

- state : str - State abbreviation

- year : int - Year

- first_treat : int - Year unilateral divorce became available (0 = never)

- divorce_rate : float - Divorces per 1,000 population

- female_lfp : float - Female labor force participation rate

- suicide_rate : float - Female suicide rate

- treated : int - 1 if law in effect, 0 otherwise

- cohort : int - Alias for first_treat

Notes

-----

Unilateral divorce laws allow one spouse to obtain a divorce without the

other's consent. States adopted these laws at different times, primarily

between 1969 and 1985.

References

----------

Stevenson, B., & Wolfers, J. (2006). Bargaining in the Shadow of the Law:

Divorce Laws and Family Distress. *Quarterly Journal of Economics*,

121(1), 267-288.

Wolfers, J. (2006). Did Unilateral Divorce Laws Raise Divorce Rates?

A Reconciliation and New Results. *American Economic Review*, 96(5), 1802-1820.

Examples

--------

>>> from diff_diff.datasets import load_divorce_laws

>>> from diff_diff import CallawaySantAnna, SunAbraham

>>>

>>> divorce = load_divorce_laws()

>>> cs = CallawaySantAnna(control_group="never_treated")

>>> results = cs.fit(

... divorce,

... outcome="divorce_rate",

... unit="state",

... time="year",

... first_treat="first_treat"

... )

"""

# Try to load from causaldata repository

url = "https://raw.githubusercontent.com/causaldata/causal_datasets/main/divorce/divorce.csv"

try:

content = _download_with_cache(url, "divorce_laws", force_download)

df = pd.read_csv(StringIO(content))

except RuntimeError:

# Fallback to constructed data

df = _construct_divorce_laws_data()

# Standardize column names

if "stfips" in df.columns:

df = df.rename(columns={"stfips": "state_id"})

if "first_treat" not in df.columns and "unilateral" in df.columns:

# Determine first treatment year from the unilateral indicator

first_treat = df.groupby("state").apply(

lambda x: x.loc[x["unilateral"] == 1, "year"].min() if x["unilateral"].sum() > 0 else 0

)

df["first_treat"] = df["state"].map(first_treat).fillna(0).astype(int)

if "cohort" not in df.columns and "first_treat" in df.columns:

df["cohort"] = df["first_treat"]

if "treated" not in df.columns:

if "unilateral" in df.columns:

df["treated"] = df["unilateral"]

elif "first_treat" in df.columns:

df["treated"] = ((df["first_treat"] > 0) & (df["year"] >= df["first_treat"])).astype(

int

)

return df

def _construct_divorce_laws_data() -> pd.DataFrame:

"""

Construct divorce laws dataset from documented patterns.

This is a fallback when the online source is unavailable.

"""

np.random.seed(2006) # Stevenson-Wolfers publication year, for reproducibility

# State adoption years for unilateral divorce (from Wolfers 2006)

# 0 = never adopted or adopted before 1968

state_adoption = {

"AK": 1935,

"AL": 1971,

"AZ": 1973,

"CA": 1970,

"CO": 1972,

"CT": 1973,

"DE": 1968,

"FL": 1971,

"GA": 1973,

"HI": 1973,

"IA": 1970,

"ID": 1971,

"IN": 1973,

"KS": 1969,

"KY": 1972,

"MA": 1975,

"ME": 1973,

"MI": 1972,

"MN": 1974,

"MO": 0,

"MT": 1975,

"NC": 0,

"ND": 1971,

"NE": 1972,

"NH": 1971,

"NJ": 0,

"NM": 1973,

"NV": 1967,

"NY": 0,

"OH": 0,

"OK": 1975,

"OR": 1971,

"PA": 0,

"RI": 1975,

"SD": 1985,

"TN": 0,

"TX": 1970,

"UT": 1987,

"VA": 0,

"WA": 1973,

"WI": 1978,

"WV": 1984,

"WY": 1977,

}

# Filter to states with adoption dates in our range or never adopted

state_adoption = {k: v for k, v in state_adoption.items() if v == 0 or (1968 <= v <= 1990)}

data = []

for state, first_treat in state_adoption.items():

# State-level baselines

base_divorce = np.random.uniform(2.0, 6.0)

base_lfp = np.random.uniform(0.35, 0.55)

base_suicide = np.random.uniform(4.0, 8.0)

for year in range(1968, 1989):

# Time trends

time_trend = (year - 1978) * 0.05

# Treatment effects (from literature)

# Short-run increase in divorce rate, then return to trend

if first_treat > 0 and year >= first_treat:

years_since = year - first_treat

# Initial spike then fade out

if years_since <= 2:

divorce_effect = 0.5

elif years_since <= 5:

divorce_effect = 0.3

elif years_since <= 10:

divorce_effect = 0.1

else:

divorce_effect = 0.0

# Small positive effect on female LFP

lfp_effect = 0.02

# Reduction in female suicide

suicide_effect = -0.5

else:

divorce_effect = 0

lfp_effect = 0

suicide_effect = 0

data.append(

{

"state": state,

"year": year,

"first_treat": first_treat if first_treat >= 1968 else 0,

"divorce_rate": round(

max(

0, base_divorce + time_trend + divorce_effect + np.random.normal(0, 0.3)

"female_lfp": round(

min(

max(

base_lfp

+ 0.01 * (year - 1968)

+ lfp_effect

+ np.random.normal(0, 0.02),

"suicide_rate": round(

max(0, base_suicide + suicide_effect + np.random.normal(0, 0.5)), 2

}

)

df = pd.DataFrame(data)

df["cohort"] = df["first_treat"]

df["treated"] = ((df["first_treat"] > 0) & (df["year"] >= df["first_treat"])).astype(int)

return df

def load_mpdta(force_download: bool = False) -> pd.DataFrame:

"""

Load the Minimum Wage Panel Dataset for DiD Analysis (mpdta).

This is a simulated dataset from the R `did` package that mimics

county-level employment data under staggered minimum wage increases.

It's designed specifically for teaching the Callaway-Sant'Anna estimator.

Parameters

----------

force_download : bool, default=False

If True, re-download the dataset even if cached.

Returns

-------

pd.DataFrame

Panel dataset with columns:

- countyreal : int - County identifier

- year : int - Year (2003-2007)

- lpop : float - Log population

- lemp : float - Log employment (outcome)

- first_treat : int - Year of minimum wage increase (0 = never)

- treat : int - 1 if ever treated, 0 otherwise

Notes

-----

This dataset is included in the R `did` package and is commonly used

in tutorials demonstrating the Callaway-Sant'Anna estimator.

References

----------

Callaway, B., & Sant'Anna, P. H. (2021). Difference-in-differences with

multiple time periods. *Journal of Econometrics*, 225(2), 200-230.

Examples

--------

>>> from diff_diff.datasets import load_mpdta

>>> from diff_diff import CallawaySantAnna

>>>

>>> mpdta = load_mpdta()

>>> cs = CallawaySantAnna()

>>> results = cs.fit(

... mpdta,

... outcome="lemp",

... unit="countyreal",

... time="year",

... first_treat="first_treat"

... )

"""

# mpdta is available from the did package documentation

url = "https://raw.githubusercontent.com/bcallaway11/did/master/data-raw/mpdta.csv"

try:

content = _download_with_cache(url, "mpdta", force_download)

df = pd.read_csv(StringIO(content))

except RuntimeError:

# Fallback to constructed data matching the R package

df = _construct_mpdta_data()

# Standardize column names

if "first.treat" in df.columns:

df = df.rename(columns={"first.treat": "first_treat"})

# Ensure cohort column exists

if "cohort" not in df.columns and "first_treat" in df.columns:

df["cohort"] = df["first_treat"]

return df

def _construct_mpdta_data() -> pd.DataFrame:

"""

Construct mpdta dataset matching the R `did` package.

This replicates the simulated dataset used in Callaway-Sant'Anna tutorials.

"""

np.random.seed(2021) # Callaway-Sant'Anna publication year, for reproducibility

n_counties = 500

years = [2003, 2004, 2005, 2006, 2007]

# Treatment cohorts: 2004, 2006, 2007, or never (0)

cohorts = [0, 2004, 2006, 2007]

cohort_probs = [0.4, 0.2, 0.2, 0.2]

data = []

for county in range(1, n_counties + 1):

first_treat = np.random.choice(cohorts, p=cohort_probs)

base_lpop = np.random.normal(12.0, 1.0)

base_lemp = base_lpop - np.random.uniform(1.5, 2.5)

for year in years:

time_effect = (year - 2003) * 0.02

# Treatment effect (heterogeneous by cohort)

if first_treat > 0 and year >= first_treat:

if first_treat == 2004:

te = -0.04 + (year - first_treat) * 0.01

elif first_treat == 2006:

te = -0.03 + (year - first_treat) * 0.01

else: # 2007

te = -0.025

else:

te = 0

data.append(

{

"countyreal": county,

"year": year,

"lpop": round(base_lpop + np.random.normal(0, 0.05), 4),

"lemp": round(base_lemp + time_effect + te + np.random.normal(0, 0.02), 4),

"first_treat": first_treat,

"treat": int(first_treat > 0),

}

)

df = pd.DataFrame(data)

df["cohort"] = df["first_treat"]

return df

def list_datasets() -> Dict[str, str]:

"""

List available real-world datasets.

Returns

-------

dict

Dictionary mapping dataset names to descriptions.

Examples

--------

>>> from diff_diff.datasets import list_datasets

>>> for name, desc in list_datasets().items():

... print(f"{name}: {desc}")

"""

return {

"card_krueger": "Card & Krueger (1994) minimum wage dataset - classic 2x2 DiD",

"castle_doctrine": "Castle Doctrine laws - staggered adoption across states",

"divorce_laws": "Unilateral divorce laws - staggered adoption (Stevenson-Wolfers)",

"mpdta": "Minimum wage panel data - simulated CS example from R `did` package",

}

def load_dataset(name: str, force_download: bool = False) -> pd.DataFrame:

"""

Load a dataset by name.

Parameters

----------

name : str

Name of the dataset. Use `list_datasets()` to see available datasets.

force_download : bool, default=False

If True, re-download the dataset even if cached.

Returns

-------

pd.DataFrame

The requested dataset.

Raises

------

ValueError

If the dataset name is not recognized.

Examples

--------

>>> from diff_diff.datasets import load_dataset, list_datasets

>>> print(list_datasets())

>>> df = load_dataset("card_krueger")

"""

loaders = {

"card_krueger": load_card_krueger,

"castle_doctrine": load_castle_doctrine,

"divorce_laws": load_divorce_laws,

"mpdta": load_mpdta,

}

if name not in loaders:

available = ", ".join(loaders.keys())

raise ValueError(f"Unknown dataset '{name}'. Available: {available}")

return loaders[name](force_download=force_download)

diff-diff/diff_diff/datasets.py at main · UtilityTools/diff-diff · GitHub

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