electricsheepafrica/africa-world-bank-aid-effectiveness-indicators-for-kenya

Kenya - Aid Effectiveness

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.

Aid effectiveness is the impact that aid has in reducing poverty and inequality, increasing growth, building capacity, and accelerating achievement of the Millennium Development Goals set by the international community. Indicators here cover aid received as well as progress in reducing poverty and improving education, health, and other measures of human welfare.

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

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

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Dataset Characteristics

Domain	Public health
Unit of observation	Country-level aggregates
Rows (total)	2,772
Columns	8 (2 numeric, 6 categorical, 0 datetime)
Train split	2,217 rows
Test split	554 rows
Geographic scope	KEN
Publisher	World Bank Group
HDX last updated	2026-03-27

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Variables

Geographic — country_name (Kenya), country_iso3 (KEN), year (range 1960.0–2025.0).

Outcome / Measurement — value (range -14569999.6948–4315971191.4062).

Identifier / Metadata — indicator_name (Net migration, Net bilateral aid flows from DAC donors, United Kingdom (current US$), Mortality rate, under-5 (per 1,000 live births)), indicator_code (SM.POP.NETM, DC.DAC.GBRL.CD, SH.DYN.MORT), esa_source (HDX), esa_processed (2026-04-09).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-world-bank-aid-effectiveness-indicators-for-kenya")
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%	Kenya
country_iso3	object	0.0%	KEN
year	int64	0.0%	1960.0 – 2025.0 (mean 1996.737)
indicator_name	object	0.0%	Net migration, Net bilateral aid flows from DAC donors, United Kingdom (current US$), Mortality rate, under-5 (per 1,000 live births)
indicator_code	object	0.0%	SM.POP.NETM, DC.DAC.GBRL.CD, SH.DYN.MORT
value	float64	0.0%	-14569999.6948 – 4315971191.4062 (mean 150554636.0282)
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-09

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Numeric Summary

Column	Min	Max	Mean	Median
year	1960.0	2025.0	1996.737	1999.0
value	-14569999.6948	4315971191.4062	150554636.0282	3675000.0715

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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_aid_effectiveness_indicators_for_kenya,
  title     = {Kenya - Aid Effectiveness},
  author    = {World Bank Group},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/world-bank-aid-effectiveness-indicators-for-kenya},
  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.