Global poverty & inequality signals (Gini and poverty headcount trajectories, anomaly flags)
Country-level signals derived from World Bank poverty and inequality indicators (Gini index, poverty headcount at $3.00 and $4.20 a day, 2021 PPP): 10-year point changes, OLS trend slopes over trailing survey years, 3-sigma anomaly flags, 5-year linear-extrapolation forecasts, per-year cross-country ranks, a poverty-improvement flag and a high-inequality flag. All rows are normalized to ISO alpha-3 country_code so they join cleanly with country macro data. Raw data: World Bank Poverty and Inequality Platform (keyless API, non-commercial terms).
Qualité
Attribution
World Bank Poverty and Inequality Platform (derived signals by Frontier Data Hub)
Schéma
| Colonne | Type | Description |
|---|---|---|
| date | string | Survey year, reported as January 1 of the year (World Bank API field date). |
| country | string | |
| country_code | string | |
| series_id | string | World Bank indicator code: SI.POV.GINI (Gini index), SI.POV.DDAY (poverty headcount ratio at $3.00 a day, 2021 PPP), SI.POV.LMIC (poverty headcount ratio at $4.20 a day, 2021 PPP). |
| series_label | string | Official World Bank indicator name as published in the indicator metadata. |
| value | float | Indicator value for the survey year: Gini index (0 = perfect equality, 100 = perfect inequality) or poverty headcount ratio as a percentage of the population. See the World Bank indicator metadata for full methodology. |
| change_10y_pp | float | |
| trend_slope | float | |
| anomaly_flag | integer | |
| forecast_1m | float | |
| rank | integer | |
| improvement_flag | integer | |
| high_inequality_flag | integer |
Exemple de lignes
| date | country | country_code | series_id | series_label | value | change_10y_pp | trend_slope | anomaly_flag | forecast_1m | rank | improvement_flag | high_inequality_flag |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1963-01-01 | United States | USA | SI.POV.DDAY | Poverty headcount ratio at $3.00 a day (2021 PPP) (% of population) | 1.6 | — | — | 0 | — | 1 | — | — |
| 1963-01-01 | United States | USA | SI.POV.GINI | Gini index | 36.7 | — | — | 0 | — | 1 | — | 0 |
| 1963-01-01 | United States | USA | SI.POV.LMIC | Poverty headcount ratio at $4.20 a day (2021 PPP) (% of population) | 2.1 | — | — | 0 | — | 1 | — | — |
| 1964-01-01 | United States | USA | SI.POV.DDAY | Poverty headcount ratio at $3.00 a day (2021 PPP) (% of population) | 1.5 | — | — | 0 | — | 1 | — | — |
| 1964-01-01 | United States | USA | SI.POV.GINI | Gini index | 37.4 | — | — | 0 | — | 1 | — | 0 |
Télécharger un échantillon
Téléchargez l'échantillon complet de ce jeu de données (lignes d'exemple, pas le jeu complet).
Utiliser avec un LLM
Dirigez n’importe quel LLM vers le point d’accès des métadonnées — la documentation ci-dessus est aussi lisible par machine (JSON-LD + Croissant).
cURL
curl "https://datazimuts.com/v1/datasets/poverty_inequality_signals/global_poverty_inequality_signals" | jq '{title, rows, columns_count, license}'Python
import requests
ds = requests.get("https://datazimuts.com/v1/datasets/poverty_inequality_signals/global_poverty_inequality_signals").json()
print(ds["title"], ds["rows"], "rows")
# Sample rows for an LLM context window
for row in ds.get("sample_rows", [])[:5]:
print(row)Point d’accès API: https://datazimuts.com/v1/datasets/poverty_inequality_signals/global_poverty_inequality_signals
Astuce : récupérez /llms.txt pour le catalogue complet lisible par machine.