diff_diff.load_divorce_laws#

diff_diff.load_divorce_laws(force_download=False)[source]

Load the synthetic-only 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) – Retained for API compatibility. No verified source currently satisfies the loader’s composite schema.
Returns:: 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
Return type:: pd.DataFrame

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.

No verified source currently reproduces all documented columns without deriving new variables or changing pre-panel treatment semantics. This loader therefore emits one UserWarning containing SYNTHETIC and returns df.attrs["source"] == "synthetic_fallback".

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"
... )