diff_diff.load_divorce_laws#

diff_diff.load_divorce_laws(force_download=False)[source]

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:: 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.

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"
... )