diff --git a/CHANGELOG.md b/CHANGELOG.md index 0b2d2dd..3984990 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -9,12 +9,16 @@ Format: [Keep a Changelog](https://keepachangelog.com/en/1.1.0/). Versions: [Sem - `fair-shares fetch-data`: downloads every input dataset from source, verifies pinned checksums, records provenance in `data/PROVENANCE.md`. Missing files also fetch automatically on first use. - Python API reference (`docs/api/python-api.md`) for pip-only users. - Root `CONTRIBUTING.md`. This changelog. +- World Bank WDI Gini index (`SI.POV.GINI`) as a Gini source, with `analysis/gini_source_comparison.py` reporting the coverage and value differences against WIID. ### Changed - **Breaking:** `pip install fair-shares` installs only the allocation library. Notebook/pipeline tools moved to the `pipeline` extra, documentation tools to `docs`. - The package works when installed outside a clone. Data/output locations resolve: explicit argument → `FAIR_SHARES_DATA_DIR`/`FAIR_SHARES_OUTPUT_DIR` → existing per-user directory → repo root. - Config files moved into the package (`src/fair_shares/conf/`); repo-root `conf/` removed. +- **Breaking:** the default Gini source is now World Bank WDI (`wdi-2025`), which is CC-BY-4.0. WIID stays available as `active_gini_source=unu-wider-2025` but is opt-in, and outputs built on it cannot be redistributed under CC BY 4.0. Output directory names change, so existing WIID runs are not overwritten. The two sources give materially different capability-based allocations — WDI/PIP is consumption-based for most low- and middle-income countries. +- **Breaking:** analysis-country membership no longer depends on Gini availability. A country with complete emissions, GDP and population is now in the analysis even without a Gini value, and receives the analysis-country mean (`general.gini_missing_policy: fallback-mean`, or `strict` to refuse). The country set grows by 9 on the standard sources; `country_data_coverage_summary.csv` gains a `gini_imputed` column. +- Gini source config replaces the unused `world_key` and `gini_year` keys with `selection` and `year_window`, both of which the notebooks read. ### Deprecated @@ -28,3 +32,6 @@ Format: [Keep a Changelog](https://keepachangelog.com/en/1.1.0/). Versions: [Sem - Global Carbon Budget citation pointed at a DOI that does not resolve; now the correct paper DOI (10.5194/essd-17-965-2025) plus data-product DOI (10.18160/GCP-2024). - Licence statements corrected: WIID is CC BY-NC-SA 3.0 IGO, UN/OWID population is mixed-terms, CMIP7 is CC-BY-SA-4.0 (author-confirmed). +- The Python API's preprocessing path wrote `country_gini_stationary.csv` without a Rest-of-World row, unlike the notebook path. Both now use the same code. +- Notebook `100_data_preprocess_rcbs` passed the removed `project_root=` argument to `load_and_process_rcbs`, so the RCB pipeline could not run. +- The Gini-adjusted approaches ignored Gini entirely when `capability_reference_year` named a year before `allocation_year`: the capability snapshot was read from the unfiltered inputs without the adjustment, on both the budget and pathway side. Results on that parameterisation change — on the standard sources, China's share of a `per-capita-adjusted-gini-budget` allocation moves from 10.8% to 5.3% and India's from 23.2% to 28.4%. diff --git a/Makefile b/Makefile index dc5f67b..ddbc5a8 100644 --- a/Makefile +++ b/Makefile @@ -45,13 +45,13 @@ dev-pipeline-scenarios: ## [DEV] Create preprocessed data for AR6 scenarios (ma active_emissions_source=primap-202503 \ active_gdp_source=wdi-2025 \ active_population_source=un-owid-2025 \ - active_gini_source=unu-wider-2025 \ + active_gini_source=wdi-2025 \ active_lulucf_source=melo-2026 \ active_target_source=ar6 \ --cores 1 @echo "" @echo " AR6 scenarios pipeline created!" - @echo " Output: output/primap-202503_wdi-2025_un-owid-2025_unu-wider-2025_ar6/" + @echo " Output: output/primap-202503_wdi-2025_un-owid-2025_wdi-2025_ar6/" .PHONY: dev-pipeline-rcbs dev-pipeline-rcbs: ## [DEV] Create preprocessed data for RCBs (matches interactive defaults) @@ -64,13 +64,13 @@ dev-pipeline-rcbs: ## [DEV] Create preprocessed data for RCBs (matches interact active_emissions_source=primap-202503 \ active_gdp_source=wdi-2025 \ active_population_source=un-owid-2025 \ - active_gini_source=unu-wider-2025 \ + active_gini_source=wdi-2025 \ active_lulucf_source=melo-2026 \ active_target_source=rcbs \ --cores 1 @echo "" @echo " RCBs pipeline created!" - @echo " Output: output/primap-202503_wdi-2025_un-owid-2025_unu-wider-2025_rcbs/" + @echo " Output: output/primap-202503_wdi-2025_un-owid-2025_wdi-2025_rcbs/" .PHONY: dev-pipeline-rcb-pathways dev-pipeline-rcb-pathways: ## [DEV] Create preprocessed data for RCB-pathways (matches interactive defaults) @@ -83,13 +83,13 @@ dev-pipeline-rcb-pathways: ## [DEV] Create preprocessed data for RCB-pathways ( active_emissions_source=primap-202503 \ active_gdp_source=wdi-2025 \ active_population_source=un-owid-2025 \ - active_gini_source=unu-wider-2025 \ + active_gini_source=wdi-2025 \ active_lulucf_source=melo-2026 \ active_target_source=rcb-pathways \ --cores 1 @echo "" @echo " RCB-pathways pipeline created!" - @echo " Output: output/primap-202503_wdi-2025_un-owid-2025_unu-wider-2025_rcb-pathways_co2-ffi/" + @echo " Output: output/primap-202503_wdi-2025_un-owid-2025_wdi-2025_rcb-pathways_co2-ffi/" .PHONY: virtual-environment virtual-environment: ## update virtual environment with core dependencies only diff --git a/Snakefile b/Snakefile index bfabd20..23fd7d1 100644 --- a/Snakefile +++ b/Snakefile @@ -10,7 +10,7 @@ # active_emissions_source=primap-202503 \ # active_gdp_source=wdi-2025 \ # active_population_source=un-owid-2025 \ -# active_gini_source=unu-wider-2025 \ +# active_gini_source=wdi-2025 \ # active_target_source=rcbs \ # --cores 1 # @@ -55,7 +55,7 @@ if emission_category is None: "Required parameter: emission_category\n" "Example: snakemake --config emission_category=co2-ffi " "active_emissions_source=primap-202503 active_gdp_source=wdi-2025 " - "active_population_source=un-owid-2025 active_gini_source=unu-wider-2025 " + "active_population_source=un-owid-2025 active_gini_source=wdi-2025 " "active_target_source=pathway active_lulucf_source=melo-2026\n\n" "For custom allocations, use: notebooks/301_custom_fair_share_allocation.py" ) diff --git a/analysis/gini_source_comparison.py b/analysis/gini_source_comparison.py new file mode 100644 index 0000000..b36bde7 --- /dev/null +++ b/analysis/gini_source_comparison.py @@ -0,0 +1,261 @@ +"""Compare the two Gini sources, and the effect of decoupling, in one place. + +Run: + + uv run python analysis/gini_source_comparison.py + +Writes CSVs and a summary to ``output/_gini_source_comparison/``. Four questions: + +1. Which countries does each Gini source cover? +2. Which countries does the swap gain and lose, and how old is the WIID value + for the ones lost? +3. How far apart are the two sources where both have a value? +4. How many analysis countries end up with an imputed Gini? + +Reading WIID needs the file fetched first: +``fair-shares fetch-data --source unu-wider-2025``. +""" + +from __future__ import annotations + +import sys +from pathlib import Path + +import pandas as pd +from pyprojroot import here + +from fair_shares.library.utils import get_complete_iso3c_timeseries, last_year_column +from fair_shares.library.utils.dataframes import ensure_string_year_columns + +ROOT = here() +OUT = ROOT / "output" / "_gini_source_comparison" + +WDI_PATH = ROOT / "data/gini/wdi-2025/API_SI.POV.GINI_DS2_en_csv_v2.csv" +WIID_PATH = ROOT / "data/gini/unu-wider-2025/WIID-29APR2025.xlsx" +REGIONS_PATH = ROOT / "data/regions/iso3c_region_mapping_20240319.csv" +YEAR_WINDOW = (2015, 2023) + +# The two runs compared: same emissions, GDP and population, different Gini. +WIID_RUN = "primap-202503_wdi-2025_un-owid-2025_unu-wider-2025_rcbs_co2-ffi" +WDI_RUN = "primap-202503_wdi-2025_un-owid-2025_wdi-2025_rcbs_co2-ffi" + + +def wdi_selection() -> pd.DataFrame: + """Latest available Gini per country within the year window.""" + raw = pd.read_csv(WDI_PATH, skiprows=4) + years = [c for c in raw.columns if c.isdigit()] + long = raw.melt( + id_vars=["Country Name", "Country Code"], + value_vars=years, + var_name="year", + value_name="gini", + ).rename(columns={"Country Code": "iso3c"}) + long["year"] = long["year"].astype(int) + long = long.dropna(subset=["gini"]) + countries = set(pd.read_csv(REGIONS_PATH)["iso3c"]) + long = long[long["iso3c"].isin(countries)] + first, last = YEAR_WINDOW + long = long[(long["year"] >= first) & (long["year"] <= last)] + selected = long.sort_values(["iso3c", "year"]).groupby("iso3c").tail(1) + selected = selected[["iso3c", "year", "gini"]].copy() + selected["gini"] = selected["gini"] / 100.0 + return selected.set_index("iso3c") + + +def wiid_selection() -> pd.DataFrame: + """Latest high-quality Gini per country, falling back to latest of any quality. + + Mirrors notebook 105 exactly, including that the row is picked before + missing values are dropped: a country whose latest observation has no Gini + value is dropped rather than falling back to an earlier one. Checked + against the WIID run's own ``gini_stationary.csv`` below. + """ + raw = pd.read_excel(WIID_PATH)[["c3", "year", "gini", "quality"]] + raw = raw.rename(columns={"c3": "iso3c"}) + + def pick(group: pd.DataFrame) -> pd.Series: + high = group[group["quality"] == "High"] + pool = high if len(high) else group + return pool.loc[pool["year"].idxmax()] + + selected = raw.groupby("iso3c").apply(pick, include_groups=False) + selected = selected.reset_index()[["iso3c", "year", "gini", "quality"]].dropna() + selected["gini"] = selected["gini"] / 100.0 + selected = selected.set_index("iso3c") + + produced = ROOT / "output" / WIID_RUN / "intermediate/gini/gini_stationary.csv" + if produced.is_file(): + expected = set(pd.read_csv(produced)["iso3c"]) + if set(selected.index) != expected: + raise SystemExit( + "WIID selection here does not match the one the pipeline produced " + f"({len(selected)} vs {len(expected)} countries). The comparison " + "would be against a set nothing ever ran on." + ) + return selected + + +def analysis_countries(source_id: str, *, gate_on_gini: bool) -> set[str]: + """Recompute a run's analysis set from its own intermediates. + + Cheaper and more exact than rebuilding: the inputs are already on disk. + """ + base = ROOT / "output" / source_id / "intermediate" + + def load(path: Path, index: list[str]) -> pd.DataFrame: + return ensure_string_year_columns(pd.read_csv(path).set_index(index)) + + gdp = load(base / "gdp/gdp_timeseries.csv", ["iso3c", "unit"]) + pop = load(base / "population/population_timeseries.csv", ["iso3c", "unit"]) + sets = [ + get_complete_iso3c_timeseries( + frame, + expected_index_names=["iso3c", "unit"], + start=1990, + end=last_year_column(frame), + ) + for frame in (gdp, pop) + ] + + processed = sorted((base / "processed").glob("country_emissions_*_timeseries.csv")) + categories = [ + p.name.replace("country_emissions_", "").replace("_timeseries.csv", "") + for p in processed + ] + for category in categories: + frame = load( + base / "emissions" / f"emiss_{category}_timeseries.csv", + ["iso3c", "unit", "emission-category"], + ) + sets.append( + get_complete_iso3c_timeseries( + frame, + expected_index_names=["iso3c", "unit", "emission-category"], + start=1990, + end=last_year_column(frame), + ) + ) + + countries = sets[0].intersection(*sets[1:]) + if gate_on_gini: + gini = pd.read_csv(base / "gini/gini_stationary.csv") + countries &= set(gini["iso3c"]) + return countries + + +def main() -> int: + """Write the comparison and print its headline numbers.""" + OUT.mkdir(parents=True, exist_ok=True) + + wdi = wdi_selection() + wiid = wiid_selection() + + gained = sorted(set(wdi.index) - set(wiid.index)) + lost = sorted(set(wiid.index) - set(wdi.index)) + overlap = sorted(set(wdi.index) & set(wiid.index)) + + lost_table = wiid.loc[lost, ["year", "gini", "quality"]].copy() + lost_table = lost_table.rename( + columns={"year": "wiid_observation_year", "gini": "wiid_gini"} + ) + lost_table["vintage"] = pd.cut( + lost_table["wiid_observation_year"], + bins=[0, 2009, 2014, 9999], + labels=["pre-2010", "2010-2014", "2015 or later"], + ) + lost_table.sort_values("wiid_observation_year", ascending=False).to_csv( + OUT / "countries_lost.csv" + ) + + wdi.loc[gained].to_csv(OUT / "countries_gained.csv") + + diff = pd.DataFrame( + { + "wdi_gini": wdi.loc[overlap, "gini"], + "wdi_year": wdi.loc[overlap, "year"], + "wiid_gini": wiid.loc[overlap, "gini"], + "wiid_year": wiid.loc[overlap, "year"], + } + ) + diff["difference"] = diff["wdi_gini"] - diff["wiid_gini"] + diff.sort_values("difference").to_csv(OUT / "overlap_differences.csv") + + # Decoupling: same run, membership with and without the Gini gate. + gated = analysis_countries(WIID_RUN, gate_on_gini=True) + decoupled = analysis_countries(WIID_RUN, gate_on_gini=False) + + lines = [ + "# Gini source comparison", + "", + f"WDI window: {YEAR_WINDOW[0]}-{YEAR_WINDOW[1]}, latest available per country.", + "WIID: latest high-quality observation, any year.", + "", + "## Coverage", + "", + f"- WIID countries: {len(wiid)}", + f"- WDI countries: {len(wdi)}", + f"- Overlap: {len(overlap)}", + f"- Gained by the swap: {len(gained)} {gained}", + f"- Lost by the swap: {len(lost)}", + "", + "### Vintage of the countries lost", + "", + ] + counts = lost_table["vintage"].value_counts() + for label in ["2015 or later", "2010-2014", "pre-2010"]: + members = sorted(lost_table.index[lost_table["vintage"] == label]) + lines.append(f"- {label}: {counts.get(label, 0)} {members}") + + lines += [ + "", + "## Value differences on the overlap (WDI - WIID)", + "", + f"- median: {diff['difference'].median():.3f}", + f"- IQR: {diff['difference'].quantile(0.25):.3f} to " + f"{diff['difference'].quantile(0.75):.3f}", + f"- median absolute difference: {diff['difference'].abs().median():.3f}", + "", + "Ten largest downward movers (WDI lower than WIID):", + "", + ] + for iso3c, row in diff.nsmallest(10, "difference").iterrows(): + lines.append( + f"- {iso3c}: WDI {row['wdi_gini']:.3f} ({int(row['wdi_year'])}) vs " + f"WIID {row['wiid_gini']:.3f} ({int(row['wiid_year'])})" + ) + lines += ["", "Ten largest upward movers:", ""] + for iso3c, row in diff.nlargest(10, "difference").iterrows(): + lines.append( + f"- {iso3c}: WDI {row['wdi_gini']:.3f} ({int(row['wdi_year'])}) vs " + f"WIID {row['wiid_gini']:.3f} ({int(row['wiid_year'])})" + ) + + lines += [ + "", + "## Decoupling: analysis-country membership", + "", + f"Run compared: {WIID_RUN}", + f"- Gini-gated (before): {len(gated)}", + f"- Decoupled (after): {len(decoupled)}", + f"- Gained: {sorted(decoupled - gated)}", + f"- Lost: {sorted(gated - decoupled)} (must be empty)", + "", + "## Imputation under fallback-mean", + "", + ] + for label, gini_countries in (("WIID", set(wiid.index)), ("WDI", set(wdi.index))): + imputed = sorted(decoupled - gini_countries) + lines.append( + f"- {label}: {len(imputed)} of {len(decoupled)} analysis countries " + f"imputed {imputed}" + ) + + report = "\n".join(lines) + "\n" + (OUT / "summary.md").write_text(report, encoding="utf-8") + print(report) + print(f"Written to {OUT}") + return 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/data/gdp/wdi-2025/API_NY.GDP.MKTP.KD_DS2_en_csv_v2_213435.csv b/data/gdp/wdi-2025/API_NY.GDP.MKTP.KD_DS2_en_csv_v2_213435.csv index ee4e486..f6e004b 100644 --- a/data/gdp/wdi-2025/API_NY.GDP.MKTP.KD_DS2_en_csv_v2_213435.csv +++ b/data/gdp/wdi-2025/API_NY.GDP.MKTP.KD_DS2_en_csv_v2_213435.csv @@ -1,271 +1,270 @@ "Data Source","World Development Indicators", -"Last Updated Date","2026-02-24", +"Last Updated Date","2026-07-13", "Country Name","Country Code","Indicator Name","Indicator 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(2025). Gini index (`SI.POV.GINI`). World Bank Open Data. https://data.worldbank.org/ + +Files: +- `API_SI.POV.GINI_DS2_en_csv_v2.csv` — Gini index, WDI indicator `SI.POV.GINI` + +**Licence:** CC BY 4.0, the World Bank's default for its own open datasets +(https://datacatalog.worldbank.org/public-licenses). No DOI is issued for this +indicator; cite by name as above. + +**Provenance:** The World Bank sources this indicator from its own Poverty and +Inequality Platform (PIP). PIP reports consumption-based Gini for most low- and +middle-income countries and income-based Gini elsewhere, so values are not +directly comparable with income-based compilations such as WIID. + +**Filename:** the bulk export names the CSV with a World Bank export-job number +(`..._v2_499.csv`) that changes on every regeneration. It is stored here under +the stable name above; the contents are unchanged. diff --git a/docs/dev-guide/adding-data-sources.md b/docs/dev-guide/adding-data-sources.md index 91c95a4..a4ef8c7 100644 --- a/docs/dev-guide/adding-data-sources.md +++ b/docs/dev-guide/adding-data-sources.md @@ -21,7 +21,7 @@ Data sources are configured in `src/fair_shares/conf/data_sources/data_sources_u | `emissions` | Historical non-LULUCF emissions | PRIMAP-hist | | `gdp` | Economic capability | World Bank WDI | | `population` | Per capita calculations | UN/OWID | -| `gini` | Within-country inequality | UNU-WIDER | +| `gini` | Within-country inequality | World Bank WDI (default), UNU-WIDER WIID | | `lulucf` | NGHGI-consistent LULUCF emissions | Melo et al. (2026) | | `targets` | Global constraints | AR6 scenarios, RCBs | @@ -187,7 +187,7 @@ active_sources = { "emissions": "my-source-2026", # Use new source "gdp": "wdi-2025", "population": "un-owid-2025", - "gini": "unu-wider-2025", + "gini": "wdi-2025", "lulucf": "melo-2026", } ``` @@ -319,6 +319,7 @@ New data sources should: | `101_data_preprocess_emiss_primap-202503.py` | Emissions | NetCDF processing, category mapping | | `102_data_preprocess_gdp_wdi-2025.py` | GDP | CSV processing, country code mapping | | `103_data_preprocess_population_un-owid-2025.py` | Population | Combining historical and projected data | +| `105_data_preprocess_gini_wdi-2025.py` | Gini | Latest-available-in-window selection, aggregate filtering | | `105_data_preprocess_gini_unu-wider-2025.py` | Gini | Quality filtering, stationary output | | `107_derive_nghgi_categories_melo-2026.py` | LULUCF | NGHGI corrections, metadata export, world totals | diff --git a/docs/quickstart.md b/docs/quickstart.md index f6f36e8..cfdd4b3 100644 --- a/docs/quickstart.md +++ b/docs/quickstart.md @@ -44,8 +44,8 @@ uv run fair-shares fetch-data That fetches roughly 70 MB — everything a standard country-level run needs. Add `--all` for the large optional sources (the AR6 scenario ensemble, CMIP7 historical, and the WIID inequality data), another ~90 MB. You can skip `--all` -and let those arrive on demand: the WIID data, for instance, is downloaded the -first time you run a `-gini` approach that needs it. +and let those arrive on demand: the AR6 ensemble, for instance, is downloaded +the first time a run needs it. ```bash uv run fair-shares fetch-data --all # include the optional sources diff --git a/docs/science/parameter-effects.md b/docs/science/parameter-effects.md index f307aaa..af7e893 100644 --- a/docs/science/parameter-effects.md +++ b/docs/science/parameter-effects.md @@ -121,10 +121,11 @@ budget and pathway approaches with identical semantics. value in preserved mode. Values past the last observed GDP year are forward-filled. - **Set:** the snapshot year's metric is broadcast across the window. A year - before the allocation window is sourced from the unfiltered inputs (without - Gini adjustment); a year beyond the last observed GDP year falls back to - the last observed column with a warning; a year outside the data range - raises. + before the allocation window is sourced from the unfiltered inputs; a year + beyond the last observed GDP year falls back to the last observed column + with a warning; a year outside the data range raises. On the Gini-adjusted + approaches the Gini adjustment applies in all three cases — which input the + snapshot is read from does not change the capability definition. **When to use:** replicating studies that fix capability at a reference year (e.g. a single-year GDP-per-capita indicator), or holding the capability diff --git a/docs/user-guide/approach-catalog.md b/docs/user-guide/approach-catalog.md index 4e7de90..712ecb0 100644 --- a/docs/user-guide/approach-catalog.md +++ b/docs/user-guide/approach-catalog.md @@ -77,14 +77,14 @@ Gradual transition from current emissions to fair share target. See [Principle to Code](../science/principle-to-code.md) for detailed configuration. -!!! note "The `-gini` approaches need an extra dataset" - - Inequality adjustment uses the WIID data, which `fair-shares fetch-data` - leaves out by default because of its size. It is downloaded automatically - the first time a `-gini` approach asks for it, so nothing breaks — you just - get an unexpected ~10 MB download mid-run. Fetch it up front with - `uv run fair-shares fetch-data --all` if you would rather not wait. Note - also that WIID is licensed CC BY-NC-SA, unlike the other sources — see +!!! note "Which inequality dataset the `-gini` approaches use" + + The default Gini source is the World Bank's Gini index (`SI.POV.GINI`), + which `fair-shares fetch-data` brings down with everything else. WIID is + still available with `active_gini_source=unu-wider-2025`, but it is + licensed CC BY-NC-SA: outputs built on it cannot be redistributed under + this project's CC BY 4.0 terms. The two sources give materially different + capability-based allocations — see [Data Sources & Licensing](data-sources.md). !!! tip "Entry Points Framework" diff --git a/docs/user-guide/data-sources.md b/docs/user-guide/data-sources.md index fea3a6d..e7cdc47 100644 --- a/docs/user-guide/data-sources.md +++ b/docs/user-guide/data-sources.md @@ -20,7 +20,8 @@ Bundled sources carry different terms. Check the per-source entry below before r | LULUCF | Melo et al. 2026 v3.1 | **CC-BY-4.0** (Zenodo) | Yes | | Population | UN/OWID 2025 | Mixed — see below | Yes | | GDP | World Bank WDI 2025 | **CC-BY-4.0** | Yes | -| Gini | UNU-WIDER WIID 2025 | **CC BY-NC-SA 3.0 IGO** | Yes | +| Gini (default) | World Bank WDI 2025 (SI.POV.GINI) | **CC-BY-4.0** | Yes | +| Gini (opt-in) | UNU-WIDER WIID 2025 | **CC BY-NC-SA 3.0 IGO** | Yes | | Regions | regioniso3c (custom) | **MIT** | Optional | | Scenarios | IPCC AR6 (Gidden 2023) | **CC-BY-4.0** | Yes | | Carbon budgets | Lamboll et al. 2023 | Published values | Yes | @@ -97,15 +98,37 @@ The choice between PPP and MER GDP measures is not purely technical — it is a ## Inequality Data -### UNU-WIDER WIID +Two Gini sources are configured. `wdi-2025` is the default; `unu-wider-2025` +(WIID) still works and is selected with `active_gini_source=unu-wider-2025`. + +### World Bank WDI Gini index (default) + +**Source:** World Bank, World Development Indicators, Gini index (`SI.POV.GINI`), 2025 export. The World Bank sources this indicator from its own Poverty and Inequality Platform (PIP). No DOI is issued; cite by name. + +**License:** [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/), the World Bank's [default for its own open datasets](https://datacatalog.worldbank.org/public-licenses). + +**Location:** `data/gini/wdi-2025/` + +**What it provides:** Gini coefficients for 150 countries, taking each country's latest observation in 2015–2023 (`selection: latest-available`, `year_window: [2015, 2023]`). Survey-based Gini is sparse in any single year — a single-year rule would cover about 70 countries. + +!!! note "Income vs consumption Gini" +PIP reports consumption-based Gini for most low- and middle-income countries and income-based Gini elsewhere; WIID pools income-based series. Consumption Gini is systematically lower for the same country-year, and the gap is large for some countries (India 0.255 vs 0.515, Côte d'Ivoire 0.353 vs 0.607, Bangladesh 0.309 vs 0.499, South Africa 0.541 vs 0.670). Because higher Gini raises measured capability, the two sources give materially different capability-based allocations. This is a choice about which welfare concept the capability measure rests on, not a data-plumbing detail. + +!!! note "Countries without a Gini value" +Analysis-country membership depends on emissions, GDP and population — not on Gini. A country with no Gini value stays in the analysis and receives the analysis-country mean, the same value Rest-of-World gets, flagged as `gini_imputed` in `country_data_coverage_summary.csv`. Set `general.gini_missing_policy: strict` to stop the run instead. Under the default source, 35 of 176 analysis countries carry an imputed Gini, Saudi Arabia among them. + +### UNU-WIDER WIID (opt-in) **Source:** UNU-WIDER World Income Inequality Database (WIID), Version 29 April 2025. [doi:10.35188/UNU-WIDER/WIID-290425](https://doi.org/10.35188/UNU-WIDER/WIID-290425) **License:** [CC BY-NC-SA 3.0 IGO](https://creativecommons.org/licenses/by-nc-sa/3.0/igo/), per UNU-WIDER's [copyright terms](https://www.wider.unu.edu/about/copyright). The NonCommercial and ShareAlike clauses travel with derived Gini values, so they are incompatible with a plain CC BY compilation. -**Location:** `data/gini/unu-wider-2025/` +**Location:** `data/gini/unu-wider-2025/`. Opt-in: a plain `fair-shares fetch-data` does not download it. Fetch it with `fair-shares fetch-data --source unu-wider-2025` if it is not already present. + +**What it provides:** Gini coefficients for 194 countries, taking each country's latest high-quality observation and falling back to the latest of any quality (`selection: latest-high-quality`, no year window). Coverage is broader than WDI but older: 34 of the 194 values predate 2010, and four are states that no longer exist. -**What it provides:** Gini coefficients for income inequality. +!!! warning "Outputs built on WIID cannot be redistributed under CC BY 4.0" +The NonCommercial and ShareAlike terms travel with the derived Gini values, including a table of them. Selecting this source puts every Gini-derived file in the run outside this project's CC BY 4.0 data deposit. --- diff --git a/notebooks/100_data_preprocess_pathways.py b/notebooks/100_data_preprocess_pathways.py index 2614aa1..d450d52 100644 --- a/notebooks/100_data_preprocess_pathways.py +++ b/notebooks/100_data_preprocess_pathways.py @@ -39,6 +39,11 @@ DataLoadingError, DataProcessingError, ) +from fair_shares.library.preprocessing import ( + complete_gini, + emissions_path, + gini_missing_policy, +) from fair_shares.library.utils import ( add_row_timeseries, build_source_id, @@ -49,7 +54,6 @@ last_year_column, set_post_net_zero_emissions_to_nan, ) -from fair_shares.library.preprocessing import emissions_path from fair_shares.library.validation import ( validate_all_datasets_totals, validate_emissions_data, @@ -109,7 +113,7 @@ "emissions": "primap-202503", "gdp": "wdi-2025", "population": "un-owid-2025", - "gini": "unu-wider-2025", + "gini": "wdi-2025", "target": "pathway", } @@ -382,10 +386,9 @@ ) gini_analysis_countries = set(gini.index.get_level_values("iso3c").tolist()) -# Find intersection of all datasets (including alignment categories) -analysis_countries = ( - gdp_analysis_countries & population_analysis_countries & gini_analysis_countries -) +# Gini is not part of this intersection: a country without a Gini value stays +# in the analysis and is handled where Gini is used (complete_gini below). +analysis_countries = gdp_analysis_countries & population_analysis_countries for category in emiss_analysis_countries: analysis_countries = analysis_countries & emiss_analysis_countries[category] @@ -421,6 +424,11 @@ # Add final analysis indicator coverage_summary["in_analysis"] = coverage_summary["iso3c"].isin(analysis_countries) +# Record which analysis countries carry an imputed Gini rather than their own +coverage_summary["gini_imputed"] = ( + coverage_summary["in_analysis"] & ~coverage_summary["has_gini"] +) + # Add ROW indicator (countries that are in region mapping but not in final analysis) coverage_summary["in_row"] = coverage_summary["iso3c"].isin( all_region_countries @@ -518,22 +526,16 @@ expected_index_names=["iso3c", "unit"], ) -gini_analysis = gini[ - gini.index.get_level_values("iso3c").isin(analysis_countries) -].copy() - -if gini_analysis.empty: - raise DataProcessingError( - "No Gini coefficient data found for analysis countries. " - "Cannot calculate ROW average without data." - ) - -gini_analysis_average = gini_analysis["gini"].mean() -gini_row = pd.DataFrame( - {"gini": [gini_analysis_average]}, - index=pd.MultiIndex.from_tuples([("ROW", "unitless")], names=["iso3c", "unit"]), +# ROW, and any analysis country without a Gini value, get the mean of the +# analysis countries that have one. +gini_complete, gini_imputed_countries = complete_gini( + gini, analysis_countries, policy=gini_missing_policy(config) ) -gini_complete = pd.concat([gini_analysis, gini_row]) +if gini_imputed_countries: + print( + f"Gini imputed (analysis-country mean) for " + f"{len(gini_imputed_countries)}: {sorted(gini_imputed_countries)}" + ) # %% [markdown] # ## Validation of analysis datasets diff --git a/notebooks/100_data_preprocess_rcbs.py b/notebooks/100_data_preprocess_rcbs.py index 7ffc9cb..be556f2 100644 --- a/notebooks/100_data_preprocess_rcbs.py +++ b/notebooks/100_data_preprocess_rcbs.py @@ -43,6 +43,11 @@ DataLoadingError, DataProcessingError, ) +from fair_shares.library.preprocessing import ( + complete_gini, + emissions_path, + gini_missing_policy, +) from fair_shares.library.utils import ( add_row_timeseries, build_source_id, @@ -52,7 +57,6 @@ get_world_totals_timeseries, last_year_column, ) -from fair_shares.library.preprocessing import emissions_path from fair_shares.library.validation import ( validate_all_datasets_totals, validate_emissions_data, @@ -107,7 +111,7 @@ "emissions": "primap-202503", "gdp": "wdi-2025", "population": "un-owid-2025", - "gini": "unu-wider-2025", + "gini": "wdi-2025", "target": "rcbs", # RCB mode } @@ -334,10 +338,9 @@ ) gini_analysis_countries = set(gini.index.get_level_values("iso3c").tolist()) -# Find intersection of all datasets -analysis_countries = ( - gdp_analysis_countries & population_analysis_countries & gini_analysis_countries -) +# Gini is not part of this intersection: a country without a Gini value stays +# in the analysis and is handled where Gini is used (complete_gini below). +analysis_countries = gdp_analysis_countries & population_analysis_countries for category in final_categories: if category in emiss_analysis_countries: @@ -371,6 +374,11 @@ # Add final analysis indicator coverage_summary["in_analysis"] = coverage_summary["iso3c"].isin(analysis_countries) +# Record which analysis countries carry an imputed Gini rather than their own +coverage_summary["gini_imputed"] = ( + coverage_summary["in_analysis"] & ~coverage_summary["has_gini"] +) + # Add ROW indicator (countries that are in region mapping but not in final analysis) coverage_summary["in_row"] = coverage_summary["iso3c"].isin( all_region_countries @@ -468,22 +476,16 @@ expected_index_names=["iso3c", "unit"], ) -gini_analysis = gini[ - gini.index.get_level_values("iso3c").isin(analysis_countries) -].copy() - -if gini_analysis.empty: - raise DataProcessingError( - "No Gini coefficient data found for analysis countries. " - "Cannot calculate ROW average without data." - ) - -gini_analysis_average = gini_analysis["gini"].mean() -gini_row = pd.DataFrame( - {"gini": [gini_analysis_average]}, - index=pd.MultiIndex.from_tuples([("ROW", "unitless")], names=["iso3c", "unit"]), +# ROW, and any analysis country without a Gini value, get the mean of the +# analysis countries that have one. +gini_complete, gini_imputed_countries = complete_gini( + gini, analysis_countries, policy=gini_missing_policy(config) ) -gini_complete = pd.concat([gini_analysis, gini_row]) +if gini_imputed_countries: + print( + f"Gini imputed (analysis-country mean) for " + f"{len(gini_imputed_countries)}: {sorted(gini_imputed_countries)}" + ) # %% [markdown] # ## Validation of analysis datasets @@ -627,7 +629,8 @@ world_fossil_emissions=world_fossil_emissions, emission_category=emission_category, adjustments_config=adjustments_config, - project_root=project_root, + data_dir=project_root / "data", + output_dir=project_root / "output", source_id=source_id, actual_bm_lulucf_emissions=actual_bm_lulucf, verbose=True, diff --git a/notebooks/101_data_preprocess_emiss_primap-202503.py b/notebooks/101_data_preprocess_emiss_primap-202503.py index 1079510..96a32fd 100644 --- a/notebooks/101_data_preprocess_emiss_primap-202503.py +++ b/notebooks/101_data_preprocess_emiss_primap-202503.py @@ -94,7 +94,7 @@ "emissions": "primap-202503", "gdp": "wdi-2025", "population": "un-owid-2025", - "gini": "unu-wider-2025", + "gini": "wdi-2025", "target": "pathway", } diff --git a/notebooks/102_data_preprocess_gdp_wdi-2025.py b/notebooks/102_data_preprocess_gdp_wdi-2025.py index 43443d5..93efb83 100644 --- a/notebooks/102_data_preprocess_gdp_wdi-2025.py +++ b/notebooks/102_data_preprocess_gdp_wdi-2025.py @@ -76,7 +76,7 @@ "emissions": "primap-202503", "gdp": "wdi-2025", "population": "un-owid-2025", - "gini": "unu-wider-2025", + "gini": "wdi-2025", "target": "pathway", } diff --git a/notebooks/103_data_preprocess_population_un-owid-2025.py b/notebooks/103_data_preprocess_population_un-owid-2025.py index 847e8dc..6c24f41 100644 --- a/notebooks/103_data_preprocess_population_un-owid-2025.py +++ b/notebooks/103_data_preprocess_population_un-owid-2025.py @@ -77,7 +77,7 @@ "emissions": "primap-202503", "gdp": "wdi-2025", "population": "un-owid-2025", - "gini": "unu-wider-2025", + "gini": "wdi-2025", "target": "pathway", } diff --git a/notebooks/104_data_preprocess_scenarios_ar6.py b/notebooks/104_data_preprocess_scenarios_ar6.py index 17a2f6a..b407e69 100644 --- a/notebooks/104_data_preprocess_scenarios_ar6.py +++ b/notebooks/104_data_preprocess_scenarios_ar6.py @@ -109,7 +109,7 @@ "emissions": "primap-202503", "gdp": "wdi-2025", "population": "un-owid-2025", - "gini": "unu-wider-2025", + "gini": "wdi-2025", "target": "pathway", } diff --git a/notebooks/105_data_preprocess_gini_unu-wider-2025.py b/notebooks/105_data_preprocess_gini_unu-wider-2025.py index c9dc4c7..ff30730 100644 --- a/notebooks/105_data_preprocess_gini_unu-wider-2025.py +++ b/notebooks/105_data_preprocess_gini_unu-wider-2025.py @@ -29,6 +29,8 @@ import yaml from pyprojroot import here +from fair_shares.library.exceptions import ConfigurationError + # %% tags=["parameters"] emission_category = None active_target_source = None @@ -91,6 +93,15 @@ # Extract Gini paths and parameters gini_path = gini_config["path"] +gini_parameters = gini_config["data_parameters"] +selection = gini_parameters["selection"] +year_window = gini_parameters.get("year_window") + +if selection != "latest-high-quality": + raise ConfigurationError( + f"The WIID notebook implements selection 'latest-high-quality', " + f"got '{selection}'." + ) # Construct source-specific intermediate_dir intermediate_dir_str = f"output/{source_id}/intermediate/gini" @@ -132,6 +143,14 @@ print("\nMissing values:") print(gini_analysis.isnull().sum()) +# Restrict to the configured year window, if one is set +if year_window: + first_year, last_year = year_window + gini_analysis = gini_analysis[ + (gini_analysis["year"] >= first_year) & (gini_analysis["year"] <= last_year) + ] + print(f"\nObservations within {first_year}-{last_year}: {len(gini_analysis)}") + # Filter for the best quality data per country print("\nFiltering for best quality data per country...") diff --git a/notebooks/105_data_preprocess_gini_wdi-2025.py b/notebooks/105_data_preprocess_gini_wdi-2025.py new file mode 100644 index 0000000..c36eb72 --- /dev/null +++ b/notebooks/105_data_preprocess_gini_wdi-2025.py @@ -0,0 +1,220 @@ +# --- +# jupyter: +# jupytext: +# cell_metadata_filter: tags,-all +# formats: ipynb,py:percent +# text_representation: +# extension: .py +# format_name: percent +# format_version: '1.3' +# jupytext_version: 1.16.6 +# kernelspec: +# display_name: Python 3 (ipykernel) +# language: python +# name: python3 +# --- + +# %% [markdown] +# # Gini Data Preprocessing Script (World Bank WDI 2025 Source) +# +# Processes Gini coefficients from the World Bank WDI bulk CSV export of +# indicator SI.POV.GINI. The indicator republishes the World Bank's Poverty and +# Inequality Platform (PIP) estimates, which are consumption-based for most low- +# and middle-income countries and income-based elsewhere. WIID pools +# income-based series, so values are not directly comparable between the two +# sources — see docs/user-guide/data-sources.md. +# +# One observation per country: the latest available within the configured year +# window. Survey Gini is sparse in any single year, so a single-year rule would +# cover roughly 70 countries instead of 150. + +# %% [markdown] +# ## Set paths and library imports + +# %% +# Imports +import pandas as pd +import yaml +from pyprojroot import here + +from fair_shares.library.exceptions import ConfigurationError + +# %% tags=["parameters"] +emission_category = None +active_target_source = None +active_emissions_source = None +active_gdp_source = None +active_population_source = None +active_gini_source = None +active_lulucf_source = None +active_scenario_source = None +source_id = None + +# %% +if emission_category is not None: + # Running via Papermill — source_id is injected by the Snakefile + print("Running via Papermill") + + config_path = here() / f"output/{source_id}/config.yaml" + + print(f"Loading config from: {config_path}") + with open(config_path) as f: + config = yaml.safe_load(f) + +else: + # Running interactively + print("Running interactively - build desired config") + + # Interactive development configuration + emission_category = "all-ghg-ex-co2-lulucf" + active_sources = { + "emissions": "primap-202503", + "gdp": "wdi-2025", + "population": "un-owid-2025", + "gini": "wdi-2025", + "target": "pathway", + } + + # Build interactive development config using the same logic as the pipeline + from fair_shares.library.utils.data.config import build_data_config + + config, source_id = build_data_config(emission_category, active_sources) + # Convert Pydantic model to dict for consistency with pipeline + config = config.model_dump() + + # Set interactive development runtime parameters + active_target_source = active_sources["target"] + active_emissions_source = active_sources["emissions"] + active_gdp_source = active_sources["gdp"] + active_population_source = active_sources["population"] + active_gini_source = active_sources["gini"] + +# %% [markdown] +# ## Prepare parameters + +# %% +project_root = here() +print(f"Project root: {project_root}") + +# Get Gini configuration (active source already set via papermill or interactive mode) +gini_config = config["gini"][active_gini_source] + +# Extract Gini paths and parameters +gini_path = gini_config["path"] +gini_parameters = gini_config["data_parameters"] +selection = gini_parameters["selection"] +year_window = gini_parameters.get("year_window") + +if selection != "latest-available": + raise ConfigurationError( + f"The WDI Gini notebook implements selection 'latest-available', " + f"got '{selection}'. WDI publishes no quality flag, so a " + f"quality-preferring rule cannot be applied to it." + ) + +# Region mapping decides what counts as a country. The WDI bulk CSV mixes +# countries with regional and income-group aggregates (AFE, WLD, ...) that +# carry ISO3-shaped codes. +region_mapping_path = config["general"]["region_mapping"]["path"] + +# Construct source-specific intermediate_dir +intermediate_dir_str = f"output/{source_id}/intermediate/gini" +intermediate_dir = project_root / intermediate_dir_str +intermediate_dir.mkdir(parents=True, exist_ok=True) + +# Print out the parameters for debugging +print(f"Active Gini source: {active_gini_source}") +print(f"Gini path: {gini_path}") +print(f"Selection rule: {selection}") +print(f"Year window: {year_window}") +print(f"Intermediate directory: {intermediate_dir_str}") + +# %% [markdown] +# ## Load data + +# %% +# Read CSV, skip metadata rows (first 4 rows are headers/notes) +print("Loading Gini data...") +gini_data = pd.read_csv(project_root / gini_path, skiprows=4) +print(f"Gini data shape: {gini_data.shape}") + +region_mapping = pd.read_csv(project_root / region_mapping_path) +countries = set(region_mapping["iso3c"].unique()) +print(f"Countries in region mapping: {len(countries)}") + +# %% [markdown] +# ## Analysis + +# %% +# Melt the wide year columns to long format +id_cols = ["Country Name", "Country Code"] +year_cols = [col for col in gini_data.columns if col.isdigit()] +gini_long = gini_data[id_cols + year_cols].melt( + id_vars=id_cols, value_vars=year_cols, var_name="year", value_name="gini" +) +gini_long = gini_long.rename(columns={"Country Code": "iso3c"}) +gini_long["year"] = gini_long["year"].astype(int) +gini_long = gini_long.dropna(subset=["gini"]) + +print(f"Observations with a value: {len(gini_long)}") +print(f"Codes with any observation: {gini_long['iso3c'].nunique()}") + +# Drop aggregates (regional and income-group rows) +aggregates = sorted(set(gini_long["iso3c"]) - countries) +gini_long = gini_long[gini_long["iso3c"].isin(countries)] +print(f"Codes dropped as not countries: {aggregates}") + +# Restrict to the configured year window +if year_window: + first_year, last_year = year_window + gini_long = gini_long[ + (gini_long["year"] >= first_year) & (gini_long["year"] <= last_year) + ] + print(f"Observations within {first_year}-{last_year}: {len(gini_long)}") + +# One observation per country: the latest year available +gini_filtered = gini_long.sort_values(["iso3c", "year"]).groupby("iso3c").tail(1) + +print(f"Countries with a selected observation: {gini_filtered['iso3c'].nunique()}") +print("\nObservation years selected:") +print(gini_filtered["year"].value_counts().sort_index()) + +# Convert Gini coefficients from 0-100 range to 0-1 range +gini_processed = gini_filtered[["iso3c", "gini"]].copy() +gini_processed["gini"] = gini_processed["gini"] / 100.0 + +out_of_range = gini_processed[(gini_processed["gini"] < 0) | (gini_processed["gini"] > 1)] +if not out_of_range.empty: + raise ValueError(f"Gini values outside 0-1 after conversion:\n{out_of_range}") + +print("\nGini summary after conversion:") +print(gini_processed["gini"].describe()) + +# %% [markdown] +# ## Output + +# %% +# Convert to Stationary DataFrame format (for cross-sectional data) +print("Converting to Stationary DataFrame format...") + +gini_stationary_df = gini_processed.set_index(["iso3c"])[["gini"]] + +# Convert to MultiIndex with unit information +gini_stationary_df.index = pd.MultiIndex.from_tuples( + [(iso3c, "unitless") for iso3c in gini_stationary_df.index], names=["iso3c", "unit"] +) + +print(f"Stationary DataFrame shape: {gini_stationary_df.shape}") +print(f"Stationary DataFrame index levels: {list(gini_stationary_df.index.names)}") +print(f"Stationary DataFrame columns: {list(gini_stationary_df.columns)}") + +# Save Stationary DataFrame +stationary_output_path = intermediate_dir / "gini_stationary.csv" +gini_stationary_df.reset_index().to_csv(stationary_output_path, index=False) +print(f"Stationary DataFrame saved to: {stationary_output_path}") + +# Display sample +print("\nSample of stationary data:") +print(gini_stationary_df.head()) + +# %% diff --git a/notebooks/106_generate_pathways_from_rcbs.py b/notebooks/106_generate_pathways_from_rcbs.py index a30a1d9..37f2224 100644 --- a/notebooks/106_generate_pathways_from_rcbs.py +++ b/notebooks/106_generate_pathways_from_rcbs.py @@ -93,7 +93,7 @@ "emissions": "primap-202503", "gdp": "wdi-2025", "population": "un-owid-2025", - "gini": "unu-wider-2025", + "gini": "wdi-2025", "target": "rcb-pathways", # RCB pathways mode } diff --git a/notebooks/107_derive_nghgi_categories_melo-2026.py b/notebooks/107_derive_nghgi_categories_melo-2026.py index 689ee69..0d1ce98 100644 --- a/notebooks/107_derive_nghgi_categories_melo-2026.py +++ b/notebooks/107_derive_nghgi_categories_melo-2026.py @@ -109,7 +109,7 @@ "emissions": "primap-202503", "gdp": "wdi-2025", "population": "un-owid-2025", - "gini": "unu-wider-2025", + "gini": "wdi-2025", "lulucf": "melo-2026", "target": "rcbs", } diff --git a/notebooks/108_data_preprocess_bunkers_gcb-2024.py b/notebooks/108_data_preprocess_bunkers_gcb-2024.py index 60c0594..edbc4d5 100644 --- a/notebooks/108_data_preprocess_bunkers_gcb-2024.py +++ b/notebooks/108_data_preprocess_bunkers_gcb-2024.py @@ -62,7 +62,7 @@ ) else: emission_category = "co2-ffi" - source_id = "primap-202503_wdi-2025_un-owid-2025_unu-wider-2025_rcbs_co2-ffi" + source_id = "primap-202503_wdi-2025_un-owid-2025_wdi-2025_rcbs_co2-ffi" project_root = here() intermediate_dir = project_root / f"output/{source_id}/intermediate/emissions" diff --git a/notebooks/301_custom_fair_share_allocation.py b/notebooks/301_custom_fair_share_allocation.py index ddd45a0..47490e3 100644 --- a/notebooks/301_custom_fair_share_allocation.py +++ b/notebooks/301_custom_fair_share_allocation.py @@ -78,8 +78,9 @@ # - "un-owid-2025": UN Population Prospects + Our World in Data (2025) "population": "un-owid-2025", # Gini coefficient - For within-country inequality adjustments - # - "unu-wider-2025": UNU-WIDER World Income Inequality Database (2025) - "gini": "unu-wider-2025", + # - "wdi-2025": World Bank WDI Gini index (SI.POV.GINI), the default +# - "unu-wider-2025": UNU-WIDER WIID — opt-in, CC BY-NC-SA (see data-sources.md) + "gini": "wdi-2025", # LULUCF - Required for NGHGI-consistent RCB corrections # - "melo-2026": Melo et al. (2026) NGHGI v3.1 data "lulucf": "melo-2026", diff --git a/notebooks/302_example_templates_budget_allocations.py b/notebooks/302_example_templates_budget_allocations.py index a6e764c..6e2ea1f 100644 --- a/notebooks/302_example_templates_budget_allocations.py +++ b/notebooks/302_example_templates_budget_allocations.py @@ -61,7 +61,7 @@ "emissions": "primap-202503", "gdp": "wdi-2025", "population": "un-owid-2025", - "gini": "unu-wider-2025", + "gini": "wdi-2025", "lulucf": "melo-2026", } diff --git a/notebooks/303_example_templates_pathway_allocations.py b/notebooks/303_example_templates_pathway_allocations.py index 75fab0b..a9b11fb 100644 --- a/notebooks/303_example_templates_pathway_allocations.py +++ b/notebooks/303_example_templates_pathway_allocations.py @@ -86,7 +86,7 @@ # Population projections from UN (2025) "population": "un-owid-2025", # Income inequality (Gini coefficients) from UNU-WIDER (2025) - "gini": "unu-wider-2025", + "gini": "wdi-2025", # LULUCF data for NGHGI-consistent corrections "lulucf": "melo-2026", # Pathway generator for rcb-pathways (optional, defaults to "exponential-decay") diff --git a/notebooks/601_reproduce_esabcc_2023.py b/notebooks/601_reproduce_esabcc_2023.py index b4e691b..32dee9a 100644 --- a/notebooks/601_reproduce_esabcc_2023.py +++ b/notebooks/601_reproduce_esabcc_2023.py @@ -299,7 +299,7 @@ "emissions": "primap-202503", # Historical emissions (PRIMAP-hist v2.6.1) — allocation shares and § 5 netting actuals "gdp": "wdi-2025", # GDP per capita — capability-adjusted approaches "population": "un-owid-2025", # Population — per-capita and cumulative-per-capita shares, § 6 base-year weighting - "gini": "unu-wider-2025", # Gini — required by the data-source schema; no approach run here uses it + "gini": "wdi-2025", # Gini — required by the data-source schema; no approach run here uses it "lulucf": "melo-2026", # NGHGI LULUCF — used only for the all-ghg category } diff --git a/notebooks/602_reproduce_dekker_2025.py b/notebooks/602_reproduce_dekker_2025.py index 88de7ec..1b5b831 100644 --- a/notebooks/602_reproduce_dekker_2025.py +++ b/notebooks/602_reproduce_dekker_2025.py @@ -230,7 +230,7 @@ "emissions": "primap-202503", "gdp": "wdi-2025", "population": "un-owid-2025", - "gini": "unu-wider-2025", + "gini": "wdi-2025", "lulucf": "melo-2026", "rcb_generator": "exponential-decay", } @@ -467,7 +467,7 @@ # | Historical emissions | `primap-202503` | # | GDP | `wdi-2025` | # | Population | `un-owid-2025` | -# | Gini | `unu-wider-2025` | +# | Gini | `wdi-2025` | # | LULUCF | `melo-2026` | # | RCB pathway shape | `exponential-decay` | # diff --git a/notebooks/604_reproduce_vandenberg_2020.py b/notebooks/604_reproduce_vandenberg_2020.py index 24b1450..104e5d4 100644 --- a/notebooks/604_reproduce_vandenberg_2020.py +++ b/notebooks/604_reproduce_vandenberg_2020.py @@ -223,7 +223,7 @@ "emissions": "primap-202503", "gdp": "wdi-2025", "population": "un-owid-2025", - "gini": "unu-wider-2025", + "gini": "wdi-2025", # "lulucf": "melo-2026", # Only needed for "co2" (incl. LULUCF) categories "rcb_generator": "exponential-decay", } @@ -488,7 +488,7 @@ # - **Emissions:** `primap-202503` — historical country-level CO2-FFI emissions # - **GDP:** `wdi-2025` — World Bank GDP data (PPP) # - **Population:** `un-owid-2025` — UN population projections via Our World in Data -# - **Gini:** `unu-wider-2025` — income inequality data +# - **Gini:** `wdi-2025` — income inequality data # - **Target / global pathway:** `rcb-pathways` — global pathways derived from # remaining carbon budgets, with `exponential-decay` shape # diff --git a/notebooks/605_reproduce_matthews_2016.py b/notebooks/605_reproduce_matthews_2016.py index fd655df..236c42f 100644 --- a/notebooks/605_reproduce_matthews_2016.py +++ b/notebooks/605_reproduce_matthews_2016.py @@ -130,7 +130,7 @@ "emissions": "primap-202503", "gdp": "wdi-2025", "population": "un-owid-2025", - "gini": "unu-wider-2025", + "gini": "wdi-2025", "lulucf": "melo-2026", } @@ -296,7 +296,7 @@ # | Emissions | PRIMAP-hist v2.6.1 (`primap-202503`) | # | Population | UN WPP via Our World in Data (`un-owid-2025`) | # | GDP | World Development Indicators (`wdi-2025`) | -# | Gini | UNU-WIDER (`unu-wider-2025`) | +# | Gini | World Bank WDI (`wdi-2025`) | # | LULUCF | Melo et al. 2026 (`melo-2026`) | # | RCB anchor | Remaining Carbon Budgets (`rcbs`) | # diff --git a/notebooks/docs/parameter_effects_examples.py b/notebooks/docs/parameter_effects_examples.py index 5134c4d..007cb9a 100644 --- a/notebooks/docs/parameter_effects_examples.py +++ b/notebooks/docs/parameter_effects_examples.py @@ -59,7 +59,7 @@ "emissions": "primap-202503", "gdp": "wdi-2025", "population": "un-owid-2025", - "gini": "unu-wider-2025", + "gini": "wdi-2025", "lulucf": "melo-2026", } diff --git a/src/fair_shares/cli.py b/src/fair_shares/cli.py index 26a0676..b2c04da 100644 --- a/src/fair_shares/cli.py +++ b/src/fair_shares/cli.py @@ -196,7 +196,7 @@ def _cmd_fetch( "emissions": "primap-202503", "gdp": "wdi-2025", "population": "un-owid-2025", - "gini": "unu-wider-2025", + "gini": "wdi-2025", "lulucf": "melo-2026", } diff --git a/src/fair_shares/conf/data_registry.yaml b/src/fair_shares/conf/data_registry.yaml index c1ae1c0..b334af3 100644 --- a/src/fair_shares/conf/data_registry.yaml +++ b/src/fair_shares/conf/data_registry.yaml @@ -144,25 +144,43 @@ sources: redistributable: true citation: >- World Bank. World Development Indicators: GDP (constant 2015 US$, - NY.GDP.MKTP.KD) and GDP PPP (constant 2017 international $, - NY.GDP.MKTP.PP.KD), 2025 export. https://data.worldbank.org/ + NY.GDP.MKTP.KD), GDP PPP (constant 2017 international $, + NY.GDP.MKTP.PP.KD) and Gini index (SI.POV.GINI, sourced by the World Bank + from its Poverty and Inequality Platform), 2025 export. + https://data.worldbank.org/ # The bulk endpoint serves a zip whose CSV member carries a World Bank # export-job number (`..._v2_1004.csv`) that changes on every regeneration. # The member is extracted under the filename the configs expect; the job # number is upstream bookkeeping and carries no meaning for the data. + # + # All three references re-fetched and re-pinned 2026-08-03. The two GDP + # files had drifted from their previous pins — the World Bank regenerates + # these exports in place (this vintage says "Last Updated 2026-07-13"), + # which is what `unversioned` describes. The local copies are this vintage. downloads: - url: "https://api.worldbank.org/v2/en/indicator/NY.GDP.MKTP.PP.KD?downloadformat=csv" transform: unzip-member member_pattern: "API_NY.GDP.MKTP.PP.KD_DS2_en_csv_v2_*.csv" target: "gdp/wdi-2025/API_NY.GDP.MKTP.PP.KD_DS2_en_csv_v2_1004.csv" unversioned: true - reference_sha256: "5f25005d2d3748bfebf0d9f676800a8cd0e9a65cc7517f9219d5d2984a1a02d7" + reference_sha256: "00d482e29f4911826f6e91153606fd72539f311bbef1b0928e155dce11b512b5" - url: "https://api.worldbank.org/v2/en/indicator/NY.GDP.MKTP.KD?downloadformat=csv" transform: unzip-member member_pattern: "API_NY.GDP.MKTP.KD_DS2_en_csv_v2_*.csv" target: "gdp/wdi-2025/API_NY.GDP.MKTP.KD_DS2_en_csv_v2_213435.csv" unversioned: true - reference_sha256: "85c1fbdabaf67b8935ed409bc0599beaa97e0332868adeb9d02316e5e8c6b32b" + reference_sha256: "956908eafd226680383eb830f14392a722a2e7478e489da928bb143c35d4e846" + # Gini index, the default Gini source. Same provider, same licence and + # same bulk-export mechanics as the two GDP files above, so it sits in + # this entry rather than a separate one. The export-job number is dropped + # from the target filename: it changes on every regeneration and means + # nothing. Fetched live and pinned 2026-08-03. + - url: "https://api.worldbank.org/v2/en/indicator/SI.POV.GINI?downloadformat=csv" + transform: unzip-member + member_pattern: "API_SI.POV.GINI_DS2_en_csv_v2_*.csv" + target: "gini/wdi-2025/API_SI.POV.GINI_DS2_en_csv_v2.csv" + unversioned: true + reference_sha256: "ef26b11f4b4a46d08409c304606dfc274fc7a94ee7e808576e8ed924cd896d11" # --- Population ------------------------------------------------------------ @@ -196,6 +214,9 @@ sources: size_bytes: 990684 # --- Gini ------------------------------------------------------------------ + # + # The default Gini source is the World Bank's SI.POV.GINI, which is part of + # the `wdi-2025` entry above. WIID below is opt-in. unu-wider-2025: tier: optional diff --git a/src/fair_shares/conf/data_sources/data_sources_unified.yaml b/src/fair_shares/conf/data_sources/data_sources_unified.yaml index 7c3bc10..4628d0f 100644 --- a/src/fair_shares/conf/data_sources/data_sources_unified.yaml +++ b/src/fair_shares/conf/data_sources/data_sources_unified.yaml @@ -55,12 +55,25 @@ population: projected_world_key: "World" # --- Gini coefficients ------------------------------------------------------- +# `selection` says how one observation per country is picked; `year_window` +# bounds the years considered. Survey Gini is sparse in any single year, so a +# single-year rule would cut coverage by more than half. +# +# wdi-2025 is the default. unu-wider-2025 (WIID) is opt-in: it is licensed +# CC BY-NC-SA, so outputs built on it cannot be redistributed under this +# project's CC BY 4.0 terms. It is not shipped with the repository — fetch it +# with `fair-shares fetch-data --source unu-wider-2025`. gini: + wdi-2025: + path: "data/gini/wdi-2025/API_SI.POV.GINI_DS2_en_csv_v2.csv" + data_parameters: + selection: "latest-available" + year_window: [2015, 2023] + unu-wider-2025: path: "data/gini/unu-wider-2025/WIID-29APR2025.xlsx" data_parameters: - world_key: "WLD" - gini_year: 2020 + selection: "latest-high-quality" # --- Scenario source --------------------------------------------------------- # Where pathway data comes from. Currently AR6; add SCI here when available. @@ -129,3 +142,9 @@ harmonisation_year: 2020 general: region_mapping: path: "data/regions/iso3c_region_mapping_20240319.csv" + + # What happens to an analysis country with no Gini value. + # fallback-mean give it the analysis-country mean, and record it as + # imputed in country_data_coverage_summary.csv + # strict stop the run instead + gini_missing_policy: "fallback-mean" diff --git a/src/fair_shares/library/allocations/budgets/per_capita.py b/src/fair_shares/library/allocations/budgets/per_capita.py index 6ceaabe..c991345 100644 --- a/src/fair_shares/library/allocations/budgets/per_capita.py +++ b/src/fair_shares/library/allocations/budgets/per_capita.py @@ -414,18 +414,27 @@ def _per_capita_budget_core( pop_ref_label = [c for c in pop_full_numeric.columns if int(c) == ref_year][0] - if capability_per_capita: - snapshot = gdp_full_numeric[gdp_ref_label].divide( - pop_full_numeric[pop_ref_label] + gdp_at_ref = gdp_full_numeric[gdp_ref_label] + pop_at_ref = pop_full_numeric[pop_ref_label].reindex(gdp_at_ref.index) + + # Which input the snapshot is read from is a plumbing detail, + # so the Gini adjustment applies here as it does in the other + # two cases above. + if gini_s is not None: + gini_lookup = create_gini_lookup_dict(gini_s) + gdp_at_ref = apply_gini_adjustment( + gdp_data=gdp_at_ref, + population_data=pop_at_ref, + gini_lookup=gini_lookup, + income_floor=income_floor, + max_gini_adjustment=max_gini_adjustment, + group_level=group_level, ) - else: - snapshot = gdp_full_numeric[gdp_ref_label] - # Note: Gini adjustment is NOT applied in the pre-allocation-window - # branch. When ref_year < allocation_year, the snapshot is sourced - # from the raw (non-Gini-adjusted) inputs. If Gini correction at - # the reference year is needed, choose a ref_year within the - # allocation window (ref_year >= allocation_year). + if capability_per_capita: + snapshot = gdp_at_ref.divide(pop_at_ref) + else: + snapshot = gdp_at_ref # Broadcast the scalar-per-region series across every column in the # population window. The downstream adjustment and cumulative sum then diff --git a/src/fair_shares/library/allocations/pathways/per_capita.py b/src/fair_shares/library/allocations/pathways/per_capita.py index f1ee368..253729e 100644 --- a/src/fair_shares/library/allocations/pathways/per_capita.py +++ b/src/fair_shares/library/allocations/pathways/per_capita.py @@ -78,9 +78,9 @@ def _capability_snapshot( column, consistent with the year-by-year forward fill); before the allocation window (sourced from the unfiltered ``gdp_ts`` and ``population_ts``, raising when the year is outside the data range). - Gini adjustment applies to in-window and beyond-last snapshots; a - pre-window snapshot is taken without Gini adjustment, matching the - budget-side behaviour. + Gini adjustment applies in all three cases: which input the snapshot is + read from is a plumbing detail, not a reason for the capability measure to + ignore inequality. """ ref_year = int(reference_year) gdp_years = {int(c): c for c in gdp_numeric.columns} @@ -127,9 +127,6 @@ def _capability_snapshot( ) gdp_at_ref = gdp_full[gdp_all[ref_year]] pop_at_ref = pop_full[pop_all[ref_year]].reindex(gdp_at_ref.index) - if capability_per_capita: - return gdp_at_ref.divide(pop_at_ref) - return gdp_at_ref if gini_s is not None: gini_lookup = create_gini_lookup_dict(gini_s) diff --git a/src/fair_shares/library/config/models.py b/src/fair_shares/library/config/models.py index a8320f0..c258d42 100644 --- a/src/fair_shares/library/config/models.py +++ b/src/fair_shares/library/config/models.py @@ -85,20 +85,47 @@ def validate_path_exists(cls, v: str) -> str: class GiniDataParameters(BaseModel): - """Parameters for Gini data source.""" + """Parameters for Gini data source. - world_key: str = Field(..., description="Key identifier for world/global data") - gini_year: int = Field(..., description="Reference year for Gini coefficients") + Survey-based Gini is sparse in any single year, so a source is described by + how one observation per country is picked, not by a reference year. + """ + + selection: Literal["latest-high-quality", "latest-available"] = Field( + ..., + description=( + "How to pick one observation per country: 'latest-high-quality' " + "prefers the latest observation flagged high quality and falls back " + "to the latest of any quality; 'latest-available' takes the latest " + "observation" + ), + ) + year_window: list[int] | None = Field( + None, + description=( + "Optional [first, last] year bounds on the observations considered. " + "Omit to consider every year in the source." + ), + ) - @field_validator("gini_year") + @field_validator("year_window") @classmethod - def validate_gini_year_bounds(cls, v: int) -> int: - """Validate that gini_year is within reasonable historical bounds.""" - if not 1900 <= v <= 2100: + def validate_year_window(cls, v: list[int] | None) -> list[int] | None: + """Validate the year window is a plausible, ordered pair.""" + if v is None: + return v + if len(v) != 2: raise ConfigurationError( - f"gini_year must be between 1900 and 2100, got {v}. " + f"year_window must be [first_year, last_year], got {v}." + ) + first, last = v + if not 1900 <= first <= 2100 or not 1900 <= last <= 2100: + raise ConfigurationError( + f"year_window years must be between 1900 and 2100, got {v}. " "Historical inequality data is only meaningful within this range." ) + if first > last: + raise ConfigurationError(f"year_window is out of order: {v}.") return v @@ -281,6 +308,13 @@ class GeneralConfig(BaseModel): """General configuration data.""" region_mapping: RegionMappingConfig + gini_missing_policy: Literal["fallback-mean", "strict"] = Field( + "fallback-mean", + description=( + "What happens to an analysis country with no Gini value: " + "'fallback-mean' gives it the analysis-country mean, 'strict' raises" + ), + ) class NonCO2Overrides(BaseModel): diff --git a/src/fair_shares/library/pipeline/preprocessing.py b/src/fair_shares/library/pipeline/preprocessing.py index d1ee442..ba5869a 100644 --- a/src/fair_shares/library/pipeline/preprocessing.py +++ b/src/fair_shares/library/pipeline/preprocessing.py @@ -15,6 +15,7 @@ from ..paths import data_dir as resolve_data_dir from ..paths import output_dir as resolve_output_dir from ..paths import resolve_source_path +from ..preprocessing.gini import complete_gini, gini_missing_policy from ..preprocessing.loaders import ( load_emissions_data as _load_emissions, ) @@ -188,15 +189,19 @@ def determine_analysis_countries( emissions_data: dict[str, pd.DataFrame], gdp: pd.DataFrame, population: pd.DataFrame, - gini: pd.DataFrame, + gini: pd.DataFrame | None = None, ) -> set[str]: """Determine the set of analysis countries with complete data. + Membership depends on emissions, GDP and population only. Gini + availability is handled where Gini is used, not here — see + ``preprocessing.gini.complete_gini``. + Args: emissions_data: Dict of emission DataFrames by category gdp: GDP DataFrame population: Population DataFrame - gini: Gini DataFrame + gini: Accepted for backwards compatibility and ignored. Returns ------- @@ -234,12 +239,6 @@ def determine_analysis_countries( ) ) - # Gini is stationary (no year columns), so completeness is a presence - # check on iso3c. - complete_sets.append( - set(gini.index.get_level_values("iso3c").unique().tolist()) - ) - country_iso3c = complete_sets[0].intersection(*complete_sets[1:]) return country_iso3c @@ -414,8 +413,11 @@ def _run_common_preprocessing( expected_index_names=["iso3c", "unit"], ) - # Filter Gini - gini_complete = gini.loc[gini.index.get_level_values("iso3c").isin(country_iso3c)] + # Gini for every analysis country plus ROW, imputing where a country has + # none. Matches what the preprocessing notebooks write. + gini_complete, _gini_imputed = complete_gini( + gini, country_iso3c, policy=gini_missing_policy(config) + ) # Validate totals validate_all_datasets_totals( diff --git a/src/fair_shares/library/preprocessing/__init__.py b/src/fair_shares/library/preprocessing/__init__.py index 24531d8..8832c33 100644 --- a/src/fair_shares/library/preprocessing/__init__.py +++ b/src/fair_shares/library/preprocessing/__init__.py @@ -9,6 +9,12 @@ compute_analysis_countries, create_coverage_summary, ) +from fair_shares.library.preprocessing.gini import ( + DEFAULT_GINI_MISSING_POLICY, + GINI_MISSING_POLICIES, + complete_gini, + gini_missing_policy, +) from fair_shares.library.preprocessing.loaders import ( load_emissions_data, load_gdp_data, @@ -26,12 +32,16 @@ from fair_shares.library.preprocessing.scenarios import process_complete_scenarios __all__ = [ + "DEFAULT_GINI_MISSING_POLICY", + "GINI_MISSING_POLICIES", "NGHGI_CORRECTED_CATEGORIES", "add_row_to_datasets", + "complete_gini", "compute_analysis_countries", "create_coverage_summary", "emissions_filename", "emissions_path", + "gini_missing_policy", "load_and_process_rcbs", "load_emissions_data", "load_gdp_data", diff --git a/src/fair_shares/library/preprocessing/config.py b/src/fair_shares/library/preprocessing/config.py index d8107db..9097507 100644 --- a/src/fair_shares/library/preprocessing/config.py +++ b/src/fair_shares/library/preprocessing/config.py @@ -28,7 +28,7 @@ def load_preprocessing_config( active_emissions_source: Emissions source (e.g., "primap-202503") active_gdp_source: GDP source (e.g., "wdi-2025") active_population_source: Population source (e.g., "un-owid-2025") - active_gini_source: Gini source (e.g., "unu-wider-2025") + active_gini_source: Gini source (e.g., "wdi-2025") active_lulucf_source: LULUCF source (e.g., "melo-2026") output_dir: Directory holding pipeline products. Defaults to the resolved output directory (see :mod:`fair_shares.library.paths`). @@ -69,7 +69,7 @@ def load_preprocessing_config( "emissions": "primap-202503", "gdp": "wdi-2025", "population": "un-owid-2025", - "gini": "unu-wider-2025", + "gini": "wdi-2025", "target": "rcbs", } diff --git a/src/fair_shares/library/preprocessing/coverage.py b/src/fair_shares/library/preprocessing/coverage.py index a43d49d..bbf6186 100644 --- a/src/fair_shares/library/preprocessing/coverage.py +++ b/src/fair_shares/library/preprocessing/coverage.py @@ -14,15 +14,20 @@ def compute_analysis_countries( emissions_data: dict[str, pd.DataFrame], gdp: pd.DataFrame, population: pd.DataFrame, - gini: pd.DataFrame, + gini: pd.DataFrame | None = None, ) -> set[str]: - """Compute the set of countries with complete data across all datasets. + """Compute the set of countries with complete emissions, GDP and population. + + Gini is deliberately not part of this. A country without a Gini value used + to be dropped from every allocation, including approaches that never look + at inequality; it now stays in the analysis and its missing Gini is handled + where Gini is actually used (see ``preprocessing.gini.complete_gini``). Args: emissions_data: Dictionary of emission category DataFrames gdp: GDP DataFrame population: Population DataFrame - gini: Gini coefficient DataFrame + gini: Accepted for backwards compatibility and ignored. Returns ------- @@ -51,12 +56,7 @@ def compute_analysis_countries( start=1990, end=last_year_column(population), ) - gini_analysis_countries = set(gini.index.get_level_values("iso3c").tolist()) - - # Find intersection of all datasets - analysis_countries = ( - gdp_analysis_countries & population_analysis_countries & gini_analysis_countries - ) + analysis_countries = gdp_analysis_countries & population_analysis_countries for category_countries in emiss_analysis_countries.values(): analysis_countries = analysis_countries & category_countries @@ -137,6 +137,12 @@ def create_coverage_summary( # Add final analysis indicator coverage_summary["in_analysis"] = coverage_summary["iso3c"].isin(analysis_countries) + # An analysis country with no Gini value keeps its place and is given the + # analysis-country mean. This column is the record of where that happened. + coverage_summary["gini_imputed"] = ( + coverage_summary["in_analysis"] & ~coverage_summary["has_gini"] + ) + # Add ROW indicator coverage_summary["in_row"] = coverage_summary["iso3c"].isin( all_region_countries @@ -182,6 +188,14 @@ def create_coverage_summary( f"({countries_in_row / total_countries * 100:.1f}%)" ) + imputed_countries = coverage_summary[coverage_summary["gini_imputed"]][ + "iso3c" + ].tolist() + print( + f"Countries in analysis with an imputed Gini: {len(imputed_countries)} " + f"{sorted(imputed_countries)}" + ) + # Show countries in ROW row_countries = coverage_summary[coverage_summary["in_row"]]["iso3c"].tolist() print(f"\nCountries in ROW: {sorted(row_countries)}") diff --git a/src/fair_shares/library/preprocessing/gini.py b/src/fair_shares/library/preprocessing/gini.py new file mode 100644 index 0000000..de3620b --- /dev/null +++ b/src/fair_shares/library/preprocessing/gini.py @@ -0,0 +1,110 @@ +"""What happens to a country that has no Gini value. + +Analysis-country membership used to require a Gini value, so this case could +not arise: a country without one was dropped from every allocation, including +approaches that never look at inequality. Membership is now decided by +emissions, GDP and population alone, which means a country can reach the +allocation step with no Gini value, and something has to decide what it gets. + +``fallback-mean`` gives it the mean of the countries that do have one — the +value Rest-of-World has always received — and records that it was imputed. +``strict`` refuses instead, for work that must not impute silently. +""" + +from __future__ import annotations + +from typing import Any + +import pandas as pd + +from fair_shares.library.exceptions import ConfigurationError, DataProcessingError + +#: Policies for analysis countries with no Gini value. +GINI_MISSING_POLICIES = ("fallback-mean", "strict") + +#: Used when the caller does not say. Keeps every analysis country a named +#: entity rather than folding it into Rest-of-World. +DEFAULT_GINI_MISSING_POLICY = "fallback-mean" + + +def gini_missing_policy(config: dict[str, Any]) -> str: + """Read the missing-Gini policy from a run config. + + Configs written before this setting existed have no key for it, so the + default applies. + """ + general = config.get("general") or {} + return str(general.get("gini_missing_policy") or DEFAULT_GINI_MISSING_POLICY) + + +def complete_gini( + gini: pd.DataFrame, + analysis_countries: set[str], + *, + policy: str = DEFAULT_GINI_MISSING_POLICY, + row_key: str = "ROW", +) -> tuple[pd.DataFrame, set[str]]: + """Return Gini values for every analysis country, plus Rest-of-World. + + Parameters + ---------- + gini + Gini DataFrame indexed by ``['iso3c', 'unit']`` with a ``gini`` column. + analysis_countries + Countries in the analysis, decided without reference to Gini. + policy + ``fallback-mean`` (default) or ``strict``. + row_key + Index key for the Rest-of-World row. + + Returns + ------- + tuple + ``(gini_complete, imputed)`` — the Gini table covering every analysis + country and Rest-of-World, and the set of countries whose value was + imputed. Rest-of-World is not in ``imputed``: it has always been a mean. + + Raises + ------ + ConfigurationError + If ``policy`` is not a known policy. + DataProcessingError + If no analysis country has a Gini value, or ``policy`` is ``strict`` + and some analysis country lacks one. + """ + if policy not in GINI_MISSING_POLICIES: + raise ConfigurationError( + f"Unknown Gini missing-value policy {policy!r}. " + f"Valid options: {', '.join(GINI_MISSING_POLICIES)}." + ) + + observed = gini[ + gini.index.get_level_values("iso3c").isin(analysis_countries) + ].copy() + + if observed.empty: + raise DataProcessingError( + "No Gini coefficient data found for analysis countries. " + "Cannot calculate ROW average without data." + ) + + missing = sorted(analysis_countries - set(observed.index.get_level_values("iso3c"))) + + if missing and policy == "strict": + raise DataProcessingError( + f"{len(missing)} analysis countries have no Gini value under the " + f"'strict' policy: {missing}. Use 'fallback-mean' to impute the " + "analysis-country mean, or choose a Gini source covering them." + ) + + mean_gini = observed["gini"].mean() + + filler = pd.DataFrame( + {"gini": [mean_gini] * (len(missing) + 1)}, + index=pd.MultiIndex.from_tuples( + [(iso3c, "unitless") for iso3c in [*missing, row_key]], + names=["iso3c", "unit"], + ), + ) + + return pd.concat([observed, filler]), set(missing) diff --git a/src/fair_shares/library/preprocessing/row.py b/src/fair_shares/library/preprocessing/row.py index deb2a4c..5c6ad5b 100644 --- a/src/fair_shares/library/preprocessing/row.py +++ b/src/fair_shares/library/preprocessing/row.py @@ -2,7 +2,10 @@ import pandas as pd -from fair_shares.library.exceptions import DataProcessingError +from fair_shares.library.preprocessing.gini import ( + DEFAULT_GINI_MISSING_POLICY, + complete_gini, +) from fair_shares.library.utils import add_row_timeseries, get_world_totals_timeseries @@ -15,6 +18,7 @@ def add_row_to_datasets( emissions_world_key: str, gdp_world_key: str, population_world_key: str, + gini_missing_policy: str = DEFAULT_GINI_MISSING_POLICY, ) -> tuple[ dict[str, pd.DataFrame], pd.DataFrame, @@ -36,6 +40,8 @@ def add_row_to_datasets( emissions_world_key: Key for world totals in emissions data gdp_world_key: Key for world totals in GDP data population_world_key: Key for world totals in population data + gini_missing_policy: What to do with an analysis country that has no + Gini value — "fallback-mean" (default) or "strict" Returns ------- @@ -78,22 +84,10 @@ def add_row_to_datasets( expected_index_names=["iso3c", "unit"], ) - # Handle Gini separately - use average of analysis countries for ROW - gini_analysis = gini[ - gini.index.get_level_values("iso3c").isin(analysis_countries) - ].copy() - - if gini_analysis.empty: - raise DataProcessingError( - "No Gini coefficient data found for analysis countries. " - "Cannot calculate ROW average without data." - ) - - gini_analysis_average = gini_analysis["gini"].mean() - gini_row = pd.DataFrame( - {"gini": [gini_analysis_average]}, - index=pd.MultiIndex.from_tuples([("ROW", "unitless")], names=["iso3c", "unit"]), + # Gini is handled separately: ROW and any analysis country without a value + # of its own get the analysis-country mean. + gini_complete, _imputed = complete_gini( + gini, analysis_countries, policy=gini_missing_policy ) - gini_complete = pd.concat([gini_analysis, gini_row]) return emiss_complete, gdp_complete, population_complete, gini_complete, world_emiss diff --git a/tests/acceptance/clean_install.sh b/tests/acceptance/clean_install.sh index c8dc746..07542c4 100755 --- a/tests/acceptance/clean_install.sh +++ b/tests/acceptance/clean_install.sh @@ -56,7 +56,7 @@ SOURCES = dict( emissions_source="primap-202503", gdp_source="wdi-2025", population_source="un-owid-2025", - gini_source="unu-wider-2025", + gini_source="wdi-2025", lulucf_source="melo-2026", ) @@ -93,7 +93,7 @@ try: "emissions": "primap-202503", "gdp": "wdi-2025", "population": "un-owid-2025", - "gini": "unu-wider-2025", + "gini": "wdi-2025", "lulucf": "melo-2026", "target": "pathway", }, @@ -144,7 +144,7 @@ result = calculate_allocation_timeseries( emissions_source="primap-202503", gdp_source="wdi-2025", population_source="un-owid-2025", - gini_source="unu-wider-2025", + gini_source="wdi-2025", lulucf_source="melo-2026", data_dir=Path(os.environ["FS_DATA"]), output_dir=Path(os.environ["FS_OUTPUT"]), @@ -169,7 +169,7 @@ run = citations( "emissions": "primap-202503", "gdp": "wdi-2025", "population": "un-owid-2025", - "gini": "unu-wider-2025", + "gini": "wdi-2025", "lulucf": "melo-2026", }, emission_category="co2-ffi", diff --git a/tests/unit/test_citations.py b/tests/unit/test_citations.py index c3e4c68..7a21ae6 100644 --- a/tests/unit/test_citations.py +++ b/tests/unit/test_citations.py @@ -80,6 +80,29 @@ def test_wiid_appears_only_when_it_is_the_gini_source(self): ) assert "unu-wider-2025" not in swapped + def test_the_default_gini_source_is_credited_by_name_under_cc_by(self, tmp_path): + """The World Bank issues no DOI for SI.POV.GINI, so name-only is correct.""" + written = cit.write_citations_file( + tmp_path, {**COUNTRY_RUN, "gini": "wdi-2025"}, emission_category="co2-ffi" + ) + text = written.read_text(encoding="utf-8") + + assert "unu-wider-2025" not in text + assert "SI.POV.GINI" in text, "the Gini indicator must be named" + assert "World Bank" in text + assert "CC-BY-4.0" in text + assert "DOI: none issued; cite by name as written above" in text + assert not _dois_in( + text.split("### wdi-2025")[1].split("###")[0] + ), "no DOI may be invented for the World Bank entry" + + def test_a_run_on_the_default_gini_source_has_no_special_terms_to_flag(self): + """WIID's non-commercial terms are the reason for the swap.""" + run = cit.citations( + {**COUNTRY_RUN, "gini": "wdi-2025"}, emission_category="co2-ffi" + ) + assert "unu-wider-2025" not in dict(run.special_terms) + def test_an_unknown_source_is_an_error_not_a_silent_omission(self): with pytest.raises(ConfigurationError, match="not a known data source"): cit.resolve_source_names( diff --git a/tests/unit/test_data_registry.py b/tests/unit/test_data_registry.py index a8ec778..a3435bd 100644 --- a/tests/unit/test_data_registry.py +++ b/tests/unit/test_data_registry.py @@ -187,6 +187,23 @@ def test_only_wiid_sends_a_custom_user_agent(self, registry): } assert with_headers == {"unu-wider-2025"} + def test_the_default_gini_file_is_fetched_by_default(self, registry): + """A standard run must not need an opt-in source for its Gini values.""" + wdi = registry["wdi-2025"] + assert wdi.tier == "default" + assert wdi.redistributable + gini_targets = [ + t for t in wdi.targets if normalise_target(t).startswith("gini/") + ] + assert gini_targets == ["gini/wdi-2025/API_SI.POV.GINI_DS2_en_csv_v2.csv"] + + def test_wiid_stays_opt_in_and_unredistributable(self, registry): + """It still works, but it is not what a plain fetch brings down.""" + wiid = registry["unu-wider-2025"] + assert wiid.tier == "optional" + assert not wiid.redistributable + assert "NC" in wiid.license + def test_manual_sources_name_a_destination(self, registry): for name, source in registry.sources.items(): if source.tier != "manual": diff --git a/tests/unit/test_gini_capability_snapshot.py b/tests/unit/test_gini_capability_snapshot.py new file mode 100644 index 0000000..6b5ffe8 --- /dev/null +++ b/tests/unit/test_gini_capability_snapshot.py @@ -0,0 +1,127 @@ +"""The Gini adjustment applies wherever the capability snapshot comes from. + +``capability_reference_year`` can name a year before the allocation window, in +which case the snapshot is read from the unfiltered GDP and population inputs +rather than the window-filtered ones. That path used to skip the Gini +adjustment entirely, so a Gini-adjusted approach silently produced +Gini-independent results — including for the common +``allocation_year=2015, capability_reference_year=2014`` pairing. These tests +fail on that behaviour. +""" + +from __future__ import annotations + +import pandas as pd +import pytest +from conftest import STANDARD_EMISSION_CATEGORY + +from fair_shares.library.allocations.budgets import per_capita_adjusted_gini_budget +from fair_shares.library.allocations.pathways import per_capita_adjusted_gini + +# capability_reference_year 2019 is in the fixture's year set but before the +# allocation year, so it exercises the unfiltered-input branch. +PRE_WINDOW_YEAR = 2019 +ALLOCATION_YEAR = 2020 + +COMMON = dict( + emission_category=STANDARD_EMISSION_CATEGORY, + capability_weight=1.0, + capability_functional_form="power", + capability_exponent=1.0, + capability_per_capita=True, + capability_reference_year=PRE_WINDOW_YEAR, + income_floor=7500.0, + max_gini_adjustment=0.8, +) + + +def _flat_gini(gini_df: pd.DataFrame, value: float) -> pd.DataFrame: + """The same countries, every Gini set to one value.""" + flat = gini_df.copy() + flat["gini"] = value + return flat + + +def _budget_shares(test_data, gini_df): + result = per_capita_adjusted_gini_budget( + population_ts=test_data["population"], + gdp_ts=test_data["gdp"], + gini_s=gini_df, + allocation_year=ALLOCATION_YEAR, + preserve_allocation_year_shares=True, + **COMMON, + ) + return result.relative_shares_cumulative_emission[str(ALLOCATION_YEAR)].droplevel( + ["unit", "emission-category"] + ) + + +def _pathway_shares(test_data, gini_df): + result = per_capita_adjusted_gini( + population_ts=test_data["population"], + gdp_ts=test_data["gdp"], + gini_s=gini_df, + first_allocation_year=ALLOCATION_YEAR, + preserve_first_allocation_year_shares=True, + **COMMON, + ) + return result.relative_shares_pathway_emissions[str(ALLOCATION_YEAR)].droplevel( + ["unit", "emission-category"] + ) + + +class TestPreWindowSnapshotUsesGini: + def test_budget_shares_respond_to_gini(self, test_data): + observed = _budget_shares(test_data, test_data["gini"]) + flattened = _budget_shares(test_data, _flat_gini(test_data["gini"], 0.9)) + assert (observed - flattened).abs().max() > 1e-6, ( + "changing every Gini value left the allocation untouched, so the " + "pre-window capability snapshot ignored the Gini adjustment" + ) + + def test_pathway_shares_respond_to_gini(self, test_data): + observed = _pathway_shares(test_data, test_data["gini"]) + flattened = _pathway_shares(test_data, _flat_gini(test_data["gini"], 0.9)) + assert (observed - flattened).abs().max() > 1e-6 + + def test_budget_and_pathway_agree_on_the_pre_window_snapshot(self, test_data): + """Both sides read the snapshot the same way, Gini adjustment included.""" + pd.testing.assert_series_equal( + _pathway_shares(test_data, test_data["gini"]).sort_index(), + _budget_shares(test_data, test_data["gini"]).sort_index(), + check_names=False, + ) + + def test_income_floor_still_matters_at_a_pre_window_reference_year(self, test_data): + """With no floor, capability is Gini-independent by construction. + + So a floor of zero is not evidence the adjustment ran — this checks the + floor changes the answer on this branch, which it can only do through + the Gini-adjusted capability. + """ + without_floor = per_capita_adjusted_gini_budget( + population_ts=test_data["population"], + gdp_ts=test_data["gdp"], + gini_s=test_data["gini"], + allocation_year=ALLOCATION_YEAR, + preserve_allocation_year_shares=True, + **{**COMMON, "income_floor": 0.0}, + ).relative_shares_cumulative_emission[str(ALLOCATION_YEAR)] + + with_floor = _budget_shares(test_data, test_data["gini"]) + difference = abs(with_floor.to_numpy() - without_floor.to_numpy()).max() + assert difference > 1e-6 + + def test_an_in_window_reference_year_is_unaffected(self, test_data): + """Guard against the fix disturbing the branch that already worked.""" + in_window = {**COMMON, "capability_reference_year": ALLOCATION_YEAR} + result = per_capita_adjusted_gini_budget( + population_ts=test_data["population"], + gdp_ts=test_data["gdp"], + gini_s=test_data["gini"], + allocation_year=ALLOCATION_YEAR, + preserve_allocation_year_shares=True, + **in_window, + ) + shares = result.relative_shares_cumulative_emission[str(ALLOCATION_YEAR)] + assert shares.sum() == pytest.approx(1.0) diff --git a/tests/unit/test_gini_coverage.py b/tests/unit/test_gini_coverage.py new file mode 100644 index 0000000..99e1d75 --- /dev/null +++ b/tests/unit/test_gini_coverage.py @@ -0,0 +1,164 @@ +"""Analysis-country membership does not depend on Gini, and missing Gini is explicit. + +A country without a Gini value used to be dropped from every allocation, not +just the inequality-adjusted ones. These tests hold the two halves of the fix in +place: Gini is out of the membership intersection, and a country without a value +is named and marked as imputed rather than disappearing. +""" + +from __future__ import annotations + +import pandas as pd +import pytest + +from fair_shares.library.exceptions import ConfigurationError, DataProcessingError +from fair_shares.library.preprocessing import ( + complete_gini, + compute_analysis_countries, + create_coverage_summary, + gini_missing_policy, +) + +YEARS = [str(y) for y in range(1990, 2024)] +COUNTRIES = ["AUT", "DEU", "SAU"] + + +def _timeseries(countries, unit="billion"): + return pd.DataFrame( + [[1.0] * len(YEARS) for _ in countries], + index=pd.MultiIndex.from_tuples( + [(c, unit) for c in countries], names=["iso3c", "unit"] + ), + columns=YEARS, + ) + + +def _emissions(countries): + return pd.DataFrame( + [[1.0] * len(YEARS) for _ in countries], + index=pd.MultiIndex.from_tuples( + [(c, "MtCO2", "co2-ffi") for c in countries], + names=["iso3c", "unit", "emission-category"], + ), + columns=YEARS, + ) + + +def _gini(values: dict[str, float]): + return pd.DataFrame( + {"gini": list(values.values())}, + index=pd.MultiIndex.from_tuples( + [(c, "unitless") for c in values], names=["iso3c", "unit"] + ), + ) + + +class TestAnalysisCountriesIgnoreGini: + def test_a_country_without_gini_stays_in_the_analysis(self): + countries = compute_analysis_countries( + {"co2-ffi": _emissions(COUNTRIES)}, + _timeseries(COUNTRIES), + _timeseries(COUNTRIES, unit="million"), + _gini({"AUT": 0.3, "DEU": 0.32}), + ) + assert countries == set(COUNTRIES) + + def test_the_country_set_does_not_change_with_the_gini_source(self): + args = ( + {"co2-ffi": _emissions(COUNTRIES)}, + _timeseries(COUNTRIES), + _timeseries(COUNTRIES, unit="million"), + ) + wiid = compute_analysis_countries(*args, _gini({"AUT": 0.3, "SAU": 0.45})) + wdi = compute_analysis_countries(*args, _gini({"AUT": 0.29, "DEU": 0.31})) + assert wiid == wdi == set(COUNTRIES) + + def test_missing_gdp_still_removes_a_country(self): + countries = compute_analysis_countries( + {"co2-ffi": _emissions(COUNTRIES)}, + _timeseries(["AUT", "DEU"]), + _timeseries(COUNTRIES, unit="million"), + _gini({c: 0.3 for c in COUNTRIES}), + ) + assert countries == {"AUT", "DEU"} + + +class TestCompleteGini: + def test_fallback_mean_names_every_country_and_reports_imputation(self): + gini_complete, imputed = complete_gini( + _gini({"AUT": 0.30, "DEU": 0.34}), set(COUNTRIES) + ) + assert imputed == {"SAU"} + assert set(gini_complete.index.get_level_values("iso3c")) == { + *COUNTRIES, + "ROW", + } + assert gini_complete.loc[("SAU", "unitless"), "gini"] == pytest.approx(0.32) + assert gini_complete.loc[("ROW", "unitless"), "gini"] == pytest.approx(0.32) + + def test_the_mean_uses_analysis_countries_only(self): + gini_complete, _ = complete_gini( + _gini({"AUT": 0.30, "DEU": 0.34, "BRA": 0.90}), {"AUT", "DEU", "SAU"} + ) + assert gini_complete.loc[("SAU", "unitless"), "gini"] == pytest.approx(0.32) + + def test_nothing_changes_when_every_country_has_a_value(self): + values = {"AUT": 0.30, "DEU": 0.34, "SAU": 0.45} + gini_complete, imputed = complete_gini(_gini(values), set(COUNTRIES)) + assert imputed == set() + for iso3c, value in values.items(): + assert gini_complete.loc[(iso3c, "unitless"), "gini"] == value + assert gini_complete.loc[("ROW", "unitless"), "gini"] == pytest.approx( + sum(values.values()) / 3 + ) + + def test_strict_refuses_to_impute(self): + with pytest.raises(DataProcessingError, match="SAU"): + complete_gini( + _gini({"AUT": 0.30, "DEU": 0.34}), set(COUNTRIES), policy="strict" + ) + + def test_strict_is_fine_when_nothing_is_missing(self): + gini_complete, imputed = complete_gini( + _gini({c: 0.3 for c in COUNTRIES}), set(COUNTRIES), policy="strict" + ) + assert imputed == set() + assert ("ROW", "unitless") in gini_complete.index + + def test_no_gini_at_all_is_an_error(self): + with pytest.raises(DataProcessingError, match="No Gini coefficient data"): + complete_gini(_gini({"BRA": 0.5}), set(COUNTRIES)) + + def test_an_unknown_policy_is_rejected(self): + with pytest.raises(ConfigurationError, match="Unknown Gini missing-value"): + complete_gini(_gini({"AUT": 0.3}), {"AUT"}, policy="guess") + + +class TestPolicyFromConfig: + def test_the_default_applies_when_the_config_predates_the_setting(self): + assert gini_missing_policy({"general": {}}) == "fallback-mean" + assert gini_missing_policy({}) == "fallback-mean" + + def test_a_configured_policy_is_used(self): + assert ( + gini_missing_policy({"general": {"gini_missing_policy": "strict"}}) + == "strict" + ) + + +class TestCoverageSummary: + def test_gini_imputed_marks_analysis_countries_without_a_value(self, tmp_path): + summary = create_coverage_summary( + analysis_countries=set(COUNTRIES), + emissions_data={"co2-ffi": _emissions(COUNTRIES)}, + gdp=_timeseries(COUNTRIES), + population=_timeseries(COUNTRIES, unit="million"), + gini=_gini({"AUT": 0.30, "DEU": 0.34}), + region_mapping=pd.DataFrame({"iso3c": [*COUNTRIES, "BRA"]}), + output_dir=tmp_path, + ) + by_country = summary.set_index("iso3c") + assert by_country.loc["SAU", "gini_imputed"] + assert not by_country.loc["AUT", "gini_imputed"] + # Outside the analysis, so it is in ROW rather than imputed. + assert not by_country.loc["BRA", "gini_imputed"] diff --git a/tests/unit/test_input_validation.py b/tests/unit/test_input_validation.py index 461ec55..b45ba14 100644 --- a/tests/unit/test_input_validation.py +++ b/tests/unit/test_input_validation.py @@ -7,6 +7,7 @@ from __future__ import annotations import pytest +from pydantic import ValidationError from fair_shares.library.config.models import GiniDataParameters from fair_shares.library.exceptions import AllocationError, ConfigurationError @@ -43,47 +44,31 @@ def test_error_message_includes_parameter_name(self): validate_year_parameter("2020", "first_allocation_year") -class TestGiniYearBoundsValidation: - """Test gini_year bounds validation in config models.""" - - def test_valid_gini_year(self): - """Valid gini_year within bounds should not raise.""" - # Boundary values - GiniDataParameters(world_key="WORLD", gini_year=1900) - GiniDataParameters(world_key="WORLD", gini_year=2100) - - # Middle values - GiniDataParameters(world_key="WORLD", gini_year=2000) - GiniDataParameters(world_key="WORLD", gini_year=2015) - - def test_gini_year_below_1900_raises(self): - """gini_year below 1900 should raise ConfigurationError.""" - with pytest.raises( - ConfigurationError, match="gini_year must be between 1900 and 2100" - ): - GiniDataParameters(world_key="WORLD", gini_year=1899) - - with pytest.raises( - ConfigurationError, match="gini_year must be between 1900 and 2100" - ): - GiniDataParameters(world_key="WORLD", gini_year=1800) - - def test_gini_year_above_2100_raises(self): - """gini_year above 2100 should raise ConfigurationError.""" - with pytest.raises( - ConfigurationError, match="gini_year must be between 1900 and 2100" - ): - GiniDataParameters(world_key="WORLD", gini_year=2101) - - with pytest.raises( - ConfigurationError, match="gini_year must be between 1900 and 2100" - ): - GiniDataParameters(world_key="WORLD", gini_year=2200) - - def test_error_message_includes_value(self): - """Error message should include the invalid year value.""" - with pytest.raises(ConfigurationError, match="got 1850"): - GiniDataParameters(world_key="WORLD", gini_year=1850) - - with pytest.raises(ConfigurationError, match="got 2150"): - GiniDataParameters(world_key="WORLD", gini_year=2150) +class TestGiniSelectionValidation: + """Test Gini source parameter validation in config models.""" + + def test_valid_parameters(self): + """A selection rule with or without a year window is accepted.""" + GiniDataParameters(selection="latest-high-quality") + GiniDataParameters(selection="latest-available", year_window=[2015, 2023]) + GiniDataParameters(selection="latest-available", year_window=[1900, 2100]) + + def test_unknown_selection_rejected(self): + """A selection rule no notebook implements must not validate.""" + with pytest.raises(ValidationError): + GiniDataParameters(selection="single-year") + + def test_year_window_needs_two_years(self): + with pytest.raises(ConfigurationError, match="\\[first_year, last_year\\]"): + GiniDataParameters(selection="latest-available", year_window=[2015]) + + def test_year_window_bounds(self): + with pytest.raises(ConfigurationError, match="between 1900 and 2100"): + GiniDataParameters(selection="latest-available", year_window=[1850, 2023]) + + with pytest.raises(ConfigurationError, match="between 1900 and 2100"): + GiniDataParameters(selection="latest-available", year_window=[2015, 2150]) + + def test_year_window_order(self): + with pytest.raises(ConfigurationError, match="out of order"): + GiniDataParameters(selection="latest-available", year_window=[2023, 2015])