load_bolivia
data.load_bolivia()Load the Bolivia province (ADM2, n=112) PWT-anchored GDP panel.
Subnational GDP for 2012–2022 derived from the 0.25-degree gridded estimates of Rossi-Hansberg & Zhang (2026) under their most aggressive low-population-density censoring (0_05), proportionally rescaled so Bolivian national totals equal Penn World Table 11.0 (rgdpo and pop), and aggregated to GADM 4.10 provinces. GDP and population are therefore in interpretable 2021 PPP US$ units and the relative spatial pattern of the underlying model is preserved exactly.
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Returns
| Name | Type | Description |
|---|---|---|
| gdf | geopandas.GeoDataFrame | 112 province geometries with columns ["gid", "geometry"], CRS EPSG:4326. Five provinces (BOL.2.1_2, BOL.2.8_2, BOL.2.11_2, BOL.2.13_2, BOL.5.16_2) have no panel rows: all of their grid cells are censored at the 0_05 threshold. geometrics’ alignment warns about them, which is expected. |
| df | pandas.DataFrame | Balanced panel of 1177 rows (107 provinces x 11 years, 2012–2022). Key variables: gdp_pwt (millions of 2021 PPP US\(), ``pop_pwt`` (millions of persons), ``gdppc`` (2021 PPP US\) per person) and ln_gdppc, plus provenance/scaling columns documented in the dictionary. Sorted by gid then year. |
| df_dict | pandas.DataFrame | Data dictionary with one row per df column, in df column order (gid is the entity, name the entity name, year the time id). |
Raises
| Name | Type | Description |
|---|---|---|
| GeometricsDataError | If a source file cannot be downloaded or fails hash verification. |
See Also
load_bolivia_departments : Department-level (ADM1, n=9) version. load_bolivia_grid : The underlying 0.25-degree grid cells (n=1603). load_bolivia_raw : Untouched files of any level, including ADM0.
Examples
>>> from geometrics.data import load_bolivia
>>> gdf, df, df_dict = load_bolivia()
>>> df.shape
(1177, 21)