Datasets:

electricsheepafrica
/

africa-morocco-mpi

annotations_creators:
  - no-annotation
language_creators:
  - found
language:
  - en
license: other
multilinguality:
  - monolingual
size_categories:
  - n<1K
source_datasets:
  - original
task_categories:
  - tabular-classification
  - tabular-regression
  - other
task_ids: []
tags:
  - africa
  - humanitarian
  - hdx
  - electric-sheep-africa
  - development
  - education
  - health
  - indicators
  - mortality
  - nutrition
  - poverty
  - socioeconomics
  - mar
pretty_name: Morocco Multidimensional Poverty Index
dataset_info:
  splits:
    - name: train
      num_examples: 10
    - name: test
      num_examples: 2

Morocco Multidimensional Poverty Index

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

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: MAR.

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

Dataset Characteristics


Domain	Public health
Unit of observation	Country-level aggregates
Rows (total)	13
Columns	13 (5 numeric, 6 categorical, 0 datetime)
Train split	10 rows
Test split	2 rows
Geographic scope	MAR
Publisher	Oxford Poverty & Human Development Initiative
HDX last updated	2026-03-05

Variables

Geographic — country_iso3 (MAR), admin_1_pcode (MA001, MA002, MA003), admin_1_name (Ed Dakhla-Oued ed Dahab, Laâyoune-Sakia El Hamra, Béni Mellal-Khénifra), intensity_of_deprivation (range 35.1001–49.3576), vulnerable_to_poverty (range 7.363–16.2484) and 2 others.

Temporal — start_date, end_date.

Outcome / Measurement — headcount_ratio (range 2.6908–14.2242).

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

Other — mpi (range 0.0101–0.0702).

Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-morocco-mpi")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

Schema

Column	Type	Null %	Range / Sample Values
`country_iso3`	object	0.0%	MAR
`admin_1_pcode`	object	23.1%	MA001, MA002, MA003
`admin_1_name`	object	7.7%	Ed Dakhla-Oued ed Dahab, Laâyoune-Sakia El Hamra, Béni Mellal-Khénifra
`mpi`	float64	0.0%	0.0101 – 0.0702 (mean 0.0274)
`headcount_ratio`	float64	0.0%	2.6908 – 14.2242 (mean 6.4793)
`intensity_of_deprivation`	float64	0.0%	35.1001 – 49.3576 (mean 40.6764)
`vulnerable_to_poverty`	float64	0.0%	7.363 – 16.2484 (mean 11.2527)
`in_severe_poverty`	float64	0.0%	0.0465 – 6.3548 (mean 1.469)
`survey`	object	0.0%	PAPFAM
`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

Numeric Summary

Column	Min	Max	Mean	Median
`mpi`	0.0101	0.0702	0.0274	0.0234
`headcount_ratio`	2.6908	14.2242	6.4793	5.76
`intensity_of_deprivation`	35.1001	49.3576	40.6764	39.9578
`vulnerable_to_poverty`	7.363	16.2484	11.2527	11.1372
`in_severe_poverty`	0.0465	6.3548	1.469	1.1207

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.

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.
Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

Citation

@dataset{hdx_africa_morocco_mpi,
  title     = {Morocco Multidimensional Poverty Index},
  author    = {Oxford Poverty & Human Development Initiative},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/morocco-mpi},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

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