electricsheepafrica/africa-world-bank-social-protection-and-labor-indicators-for-zimbabwe

Zimbabwe - Social Protection and Labor

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.

The supply of labor available in an economy includes people who are employed, those who are unemployed but seeking work, and first-time job-seekers. Not everyone who works is included: unpaid workers, family workers, and students are often omitted, while some countries do not count members of the armed forces. Data on labor and employment are compiled by the International Labour Organization (ILO) from labor force surveys, censuses, establishment censuses and surveys, and administrative records such as employment exchange registers and unemployment insurance schemes.

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

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

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Dataset Characteristics

Domain	Humanitarian and development data
Unit of observation	Country-level aggregates
Rows (total)	5,064
Columns	8 (2 numeric, 6 categorical, 0 datetime)
Train split	4,051 rows
Test split	1,012 rows
Geographic scope	ZWE
Publisher	World Bank Group
HDX last updated	2026-03-27

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Variables

Geographic — country_name (Zimbabwe), country_iso3 (ZWE), year (range 1982.0–2025.0).

Outcome / Measurement — value (range -0.1839–7619557.0).

Identifier / Metadata — indicator_name (Labor force, female (% of total labor force), Labor force participation rate, female (% of female population ages 15-64) (modeled ILO estimate), Labor force participation rate, total (% of total population ages 15+) (modeled ILO estimate)), indicator_code (SL.TLF.TOTL.FE.ZS, SL.TLF.ACTI.FE.ZS, SL.TLF.CACT.ZS), esa_source (HDX), esa_processed (2026-04-10).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-world-bank-social-protection-and-labor-indicators-for-zimbabwe")
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%	Zimbabwe
country_iso3	object	0.0%	ZWE
year	int64	0.0%	1982.0 – 2025.0 (mean 2012.4741)
indicator_name	object	0.0%	Labor force, female (% of total labor force), Labor force participation rate, female (% of female population ages 15-64) (modeled ILO estimate), Labor force participation rate, total (% of total population ages 15+) (modeled ILO estimate)
indicator_code	object	0.0%	SL.TLF.TOTL.FE.ZS, SL.TLF.ACTI.FE.ZS, SL.TLF.CACT.ZS
value	float64	0.0%	-0.1839 – 7619557.0 (mean 86356.6646)
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-10

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Numeric Summary

Column	Min	Max	Mean	Median
year	1982.0	2025.0	2012.4741	2017.0
value	-0.1839	7619557.0	86356.6646	20.3525

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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_social_protection_and_labor_indicators_for_zimbabwe,
  title     = {Zimbabwe - Social Protection and Labor},
  author    = {World Bank Group},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/world-bank-social-protection-and-labor-indicators-for-zimbabwe},
  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.