electricsheepafrica/africa-world-bank-poverty-indicators-for-niger

Niger - Poverty

Publisher: World Bank Group · Source: HDX · License: cc-by · Updated: 2026-03-27

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Abstract

Contains data from the World Bank's data portal. There is also a consolidated country dataset on HDX.

For countries with an active poverty monitoring program, the World Bank—in collaboration with national institutions, other development agencies, and civil society—regularly conducts analytical work to assess the extent and causes of poverty and inequality, examine the impact of growth and public policy, and review household survey data and measurement methods. Data here includes poverty and inequality measures generated from analytical reports, from national poverty monitoring programs, and from the World Bank’s Development Research Group which has been producing internationally comparable and global poverty estimates and lines since 1990.

Each row in this dataset represents country-level aggregates. Data was last updated on HDX on 2026-03-27. Geographic scope: NER.

Curated into ML-ready Parquet format by Electric Sheep Africa.

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Dataset Characteristics

Domain	Poverty and economic vulnerability
Unit of observation	Country-level aggregates
Rows (total)	140
Columns	8 (2 numeric, 6 categorical, 0 datetime)
Train split	112 rows
Test split	28 rows
Geographic scope	NER
Publisher	World Bank Group
HDX last updated	2026-03-27

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Variables

Geographic — country_name (Niger), country_iso3 (NER), year (range 1992.0–2022.0).

Outcome / Measurement — value (range -1.97–98.9).

Identifier / Metadata — indicator_name (Population living in slums (% of urban population), Poverty headcount ratio at $3.00 a day (2021 PPP) (% of population), Poverty gap at $8.30 a day (2021 PPP) (%)), indicator_code (EN.POP.SLUM.UR.ZS, SI.POV.DDAY, SI.POV.UMIC.GP), esa_source (HDX), esa_processed (2026-04-12).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-world-bank-poverty-indicators-for-niger")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

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Schema

Column	Type	Null %	Range / Sample Values
country_name	object	0.0%	Niger
country_iso3	object	0.0%	NER
year	int64	0.0%	1992.0 – 2022.0 (mean 2008.7214)
indicator_name	object	0.0%	Population living in slums (% of urban population), Poverty headcount ratio at $3.00 a day (2021 PPP) (% of population), Poverty gap at $8.30 a day (2021 PPP) (%)
indicator_code	object	0.0%	EN.POP.SLUM.UR.ZS, SI.POV.DDAY, SI.POV.UMIC.GP
value	float64	0.0%	-1.97 – 98.9 (mean 40.3143)
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-12

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Numeric Summary

Column	Min	Max	Mean	Median
year	1992.0	2022.0	2008.7214	2011.0
value	-1.97	98.9	40.3143	35.15

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Curation

Raw data was downloaded from HDX via the CKAN API and converted to Parquet. Column names were lowercased and standardised to snake_case. Common missing-value markers (N/A, null, none, -, unknown, no data, #N/A) were unified to NaN. The dataset was split 80/20 into train and test partitions using a fixed random seed (42) and saved as Snappy-compressed Parquet.

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Limitations

• Data originates from World Bank Group and has not been independently validated by ESA.
• Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

@dataset{hdx_africa_world_bank_poverty_indicators_for_niger,
  title     = {Niger - Poverty},
  author    = {World Bank Group},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/world-bank-poverty-indicators-for-niger},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

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Electric Sheep Africa — Africa's ML dataset infrastructure. Lagos, Nigeria.