electricsheepafrica/africa-comoros-mpi

Comoros Multidimensional Poverty Index

Publisher: Oxford Poverty & Human Development Initiative · Source: HDX · License: other-pd-nr · Updated: 2026-03-05

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Abstract

The global Multidimensional Poverty Index provides the only comprehensive measure available for non-income poverty, which has become a critical underpinning of the SDGs. The global Multidimensional Poverty Index (MPI) measures multidimensional poverty in over 100 developing countries, using internationally comparable datasets and is updated annually. The measure captures the acute deprivations that each person faces at the same time using information from 10 indicators, which are grouped into three equally weighted dimensions: health, education, and living standards. Critically, the MPI comprises variables that are already reported under the Demographic Health Surveys (DHS), the Multi-Indicator Cluster Surveys (MICS) and in some cases, national surveys.

The subnational multidimensional poverty data from the data tables are published by the Oxford Poverty and Human Development Initiative (OPHI), University of Oxford. For the details of the global MPI methodology, please see the latest Methodological Notes found here.

Each row in this dataset represents country-level aggregates. Data was last updated on HDX on 2026-03-05. Geographic scope: COM.

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)	4
Columns	13 (5 numeric, 6 categorical, 0 datetime)
Train split	3 rows
Test split	0 rows
Geographic scope	COM
Publisher	Oxford Poverty & Human Development Initiative
HDX last updated	2026-03-05

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Variables

Geographic — country_iso3 (COM), admin_1_pcode (KM2, KM3), admin_1_name (Ndzuwani, Ngazidja, Mwali), intensity_of_deprivation (range 39.4017–46.4114), vulnerable_to_poverty (range 17.9546–22.6857) and 2 others.

Temporal — start_date, end_date.

Outcome / Measurement — headcount_ratio (range 11.8452–26.2713).

Identifier / Metadata — esa_source (HDX), esa_processed (2026-04-04).

Other — mpi (range 0.0467–0.1219).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-comoros-mpi")
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_iso3	object	0.0%	COM
admin_1_pcode	object	50.0%	KM2, KM3
admin_1_name	object	25.0%	Ndzuwani, Ngazidja, Mwali
mpi	float64	0.0%	0.0467 – 0.1219 (mean 0.0907)
headcount_ratio	float64	0.0%	11.8452 – 26.2713 (mean 20.7829)
intensity_of_deprivation	float64	0.0%	39.4017 – 46.4114 (mean 43.0923)
vulnerable_to_poverty	float64	0.0%	17.9546 – 22.6857 (mean 20.1308)
in_severe_poverty	float64	0.0%	1.8491 – 9.837 (mean 6.0351)
survey	object	0.0%	MICS
start_date	datetime64[ns, UTC]	0.0%
end_date	datetime64[ns, UTC]	0.0%
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-04

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Numeric Summary

Column	Min	Max	Mean	Median
mpi	0.0467	0.1219	0.0907	0.0972
headcount_ratio	11.8452	26.2713	20.7829	22.5075
intensity_of_deprivation	39.4017	46.4114	43.0923	43.278
vulnerable_to_poverty	17.9546	22.6857	20.1308	19.9415
in_severe_poverty	1.8491	9.837	6.0351	6.2271

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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. 2 column(s) were cast from string to numeric or datetime based on parse-success rate (>85% threshold). 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 Oxford Poverty & Human Development Initiative and has not been independently validated by ESA.
• Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
• The following columns have >20% missing values and should be treated with caution in modelling: admin_1_pcode, admin_1_name.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

@dataset{hdx_africa_comoros_mpi,
  title     = {Comoros Multidimensional Poverty Index},
  author    = {Oxford Poverty & Human Development Initiative},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/comoros-mpi},
  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.