The bundled datasets

Provenance and dictionaries for the two case studies

Both of the paper’s empirical applications ship inside the wheel as plain CSVs (about 150 kB total), copied verbatim from the replication package’s nonproprietary export. Each loader returns a SpillPanel — the long panel with a 0/1 treated column, the raw spatial weights aligned by donor label, and a config_kwargs() helper that feeds SCSPILLConfig directly. The weights are stored unnormalized; the estimator row-normalizes spatial_W and scales spatial_w to sum to one, exactly as the R package does.

California Proposition 99

from scspill.data import load_california

ca = load_california()
ca.df.head()

	state	state_id	year	cigsale	retprice
0	Alabama	1	1970	89.800003	39.599998
1	Alabama	1	1971	95.400002	42.700001
2	Alabama	1	1972	101.099998	42.299999
3	Alabama	1	1973	102.900002	42.099998
4	Alabama	1	1974	108.199997	43.099998

Field	Value
Units	39 U.S. states (38 donors + California)
Periods	1970–2000 (annual); treatment from 1988
Outcome	`cigsale` — per-capita cigarette sales (packs)
Covariate	`retprice` — retail cigarette price
`spatial_w`	California’s rook-contiguity row (only Nevada = 1)
`spatial_W`	binary rook adjacency among the donors

Provenance: the Abadie–Diamond–Hainmueller (2010) tobacco panel as distributed with Cunningham’s Causal Inference: The Mixtape; contiguity derived from the 2024 TIGER/Line state shapefile with spdep::poly2nb(queen = FALSE).

ca.spatial_W.sum(axis=1).sort_values(ascending=False).head(5)  # most-connected donors

state
Tennessee    8.0
Missouri     8.0
Kentucky     7.0
Nebraska     6.0
Arkansas     6.0
dtype: float64

The 2011 Sudan secession

from scspill.data import load_sudan

sd = load_sudan()
sd.df.head(3)

	country_id	year	country	gdp_pc	exports_gdp_share	merchandise_trade_gdp_share	trade_gdp_share	clean_fuels_access	inflation	net_migration
0	1	2000	Algeria	3553.324205	0.420697	0.569479	0.628583	0.964	0.003392	-16454
1	1	2001	Algeria	3610.006056	0.341212	0.489334	0.545296	0.970	0.042260	-26539
2	1	2002	Algeria	3754.660815	0.331372	0.500162	0.565896	0.974	0.014183	-36255

Field	Value
Units	34 African countries (33 donors + “Sudan”)
Periods	2000–2015 (annual); treatment from 2011
Outcome	`gdp_pc` — GDP per capita, constant 2015 US$
Covariates	`exports_gdp_share`, `merchandise_trade_gdp_share`, `trade_gdp_share`, `clean_fuels_access`, `inflation`, `net_migration`
`spatial_w`	average 2000–2010 bilateral trade with Sudan (raw US$) \| \| `spatial_W` \| average bilateral trade among donors (raw US$)

Provenance: World Development Indicators (percentage series stored as proportions), with the treated unit aggregating Sudan and South Sudan after the split; trade weights from the IMF Direction of Trade Statistics, symmetrized and averaged over the pre-period. The loader renames the R export’s mangled WDI headers to snake_case; pass raw_names=True for the original headers:

sd.column_map

{'GDP.per.capita..constant.2015.US..': 'gdp_pc',
 'Exports.of.goods.and.services....of.GDP.': 'exports_gdp_share',
 'Merchandise.trade....of.GDP.': 'merchandise_trade_gdp_share',
 'Trade....of.GDP.': 'trade_gdp_share',
 'Access.to.clean.fuels.and.technologies.for.cooking....of.population.': 'clean_fuels_access',
 'Inflation..consumer.prices..annual...': 'inflation',
 'Net.migration': 'net_migration'}

Using your own data

Any long panel works. You need:

a DataFrame with unit, time, outcome, and 0/1 treatment-indicator columns (1 for the treated unit in post-treatment periods);
spatial_w: a Series/dict keyed by donor label (or an array in sorted donor order) measuring each donor’s exposure to the treated unit;
spatial_W: a labelled DataFrame (or array) of donor-to-donor weights;
optionally, covariate columns.

Alignment is by label wherever labels are given, so the order of your weights never has to match the panel’s — mismatched or missing donor labels raise immediately.