Datasets:

electricsheepafrica
/

africa-synth-employment-labor-force-participation-africa-all

country stringclasses 15 values	country_code stringclasses 15 values	year int64 2.02k 2.03k	gender stringclasses 2 values	age_group stringclasses 10 values	urban_rural stringclasses 2 values	education_level stringclasses 4 values	in_labor_force bool 2 classes	labor_force_status stringclasses 7 values	employment_type stringclasses 5 values	weekly_hours int64 1 59	is_underemployed bool 2 classes	scenario stringclasses 3 values
Nigeria	NGA	2,018	female	45-49	urban	primary	false	student	not_applicable	54	false	high_burden
Nigeria	NGA	2,022	female	50-54	rural	secondary	true	employed	full_time	46	false	high_burden
Nigeria	NGA	2,019	female	50-54	rural	primary	true	employed	part_time	28	true	high_burden
Nigeria	NGA	2,025	male	55-59	rural	primary	false	homemaker	not_applicable	45	false	high_burden
Nigeria	NGA	2,018	female	35-39	rural	secondary	true	employed	full_time	46	false	high_burden
Nigeria	NGA	2,022	male	50-54	rural	tertiary	false	homemaker	not_applicable	49	false	high_burden
Nigeria	NGA	2,025	male	15-19	urban	tertiary	true	employed	full_time	37	false	high_burden
Nigeria	NGA	2,022	female	60-64	urban	none	false	homemaker	not_applicable	53	false	high_burden
Nigeria	NGA	2,024	female	35-39	rural	tertiary	true	employed	full_time	40	false	high_burden
Nigeria	NGA	2,024	male	55-59	urban	tertiary	true	employed	self_employed	51	false	high_burden
Nigeria	NGA	2,022	male	40-44	rural	none	true	employed	part_time	5	true	high_burden
Nigeria	NGA	2,024	female	15-19	rural	secondary	false	homemaker	not_applicable	45	false	high_burden
Nigeria	NGA	2,024	male	45-49	urban	primary	true	employed	self_employed	37	false	high_burden
Nigeria	NGA	2,018	male	55-59	urban	tertiary	true	employed	part_time	10	true	high_burden
Nigeria	NGA	2,022	female	60-64	rural	primary	false	student	not_applicable	36	false	high_burden
Nigeria	NGA	2,020	male	20-24	urban	tertiary	true	employed	part_time	25	true	high_burden
Nigeria	NGA	2,025	male	35-39	rural	primary	true	employed	self_employed	37	false	high_burden
Nigeria	NGA	2,022	male	60-64	urban	primary	false	other_inactive	not_applicable	24	true	high_burden
Nigeria	NGA	2,021	female	35-39	urban	tertiary	false	discouraged	not_applicable	58	false	high_burden
Nigeria	NGA	2,024	male	50-54	urban	none	false	homemaker	not_applicable	23	true	high_burden
Nigeria	NGA	2,023	male	50-54	urban	tertiary	true	employed	self_employed	43	false	high_burden
Nigeria	NGA	2,021	female	45-49	urban	primary	true	employed	full_time	44	false	high_burden
Nigeria	NGA	2,024	male	50-54	urban	none	true	employed	full_time	42	false	high_burden
Nigeria	NGA	2,019	female	50-54	urban	primary	true	employed	part_time	32	true	high_burden
Nigeria	NGA	2,020	male	20-24	rural	primary	false	student	not_applicable	39	false	high_burden
Nigeria	NGA	2,020	male	45-49	urban	primary	true	employed	full_time	49	false	high_burden
Nigeria	NGA	2,025	male	40-44	rural	tertiary	true	employed	self_employed	37	false	high_burden
Nigeria	NGA	2,022	male	30-34	rural	primary	false	homemaker	not_applicable	24	true	high_burden
Nigeria	NGA	2,019	female	50-54	rural	tertiary	true	unemployed	not_applicable	50	false	high_burden
Nigeria	NGA	2,025	male	25-29	urban	none	true	employed	self_employed	42	false	high_burden
Nigeria	NGA	2,025	male	25-29	rural	secondary	true	employed	casual	31	true	high_burden
Nigeria	NGA	2,019	male	40-44	urban	primary	false	other_inactive	not_applicable	37	false	high_burden
Nigeria	NGA	2,018	female	55-59	rural	none	true	employed	self_employed	55	false	high_burden
Nigeria	NGA	2,020	female	35-39	urban	tertiary	false	homemaker	not_applicable	23	true	high_burden
Nigeria	NGA	2,018	female	60-64	urban	none	false	student	not_applicable	24	true	high_burden
Nigeria	NGA	2,023	male	30-34	rural	secondary	true	employed	self_employed	54	false	high_burden
Nigeria	NGA	2,020	female	35-39	rural	primary	true	employed	full_time	37	false	high_burden
Nigeria	NGA	2,018	female	60-64	urban	primary	false	homemaker	not_applicable	40	false	high_burden
Nigeria	NGA	2,021	male	55-59	rural	none	false	student	not_applicable	48	false	high_burden
Nigeria	NGA	2,021	male	40-44	rural	none	true	employed	self_employed	35	false	high_burden
Nigeria	NGA	2,020	female	40-44	urban	tertiary	true	employed	self_employed	56	false	high_burden
Nigeria	NGA	2,024	male	50-54	urban	secondary	false	homemaker	not_applicable	36	false	high_burden
Nigeria	NGA	2,019	female	50-54	rural	primary	false	student	not_applicable	38	false	high_burden
Nigeria	NGA	2,023	male	25-29	rural	primary	true	employed	self_employed	51	false	high_burden
Nigeria	NGA	2,025	female	50-54	rural	primary	true	employed	self_employed	36	false	high_burden
Nigeria	NGA	2,025	female	25-29	urban	secondary	true	employed	self_employed	29	true	high_burden
Nigeria	NGA	2,025	male	55-59	urban	primary	true	employed	full_time	43	false	high_burden
Nigeria	NGA	2,025	male	60-64	rural	secondary	true	employed	full_time	37	false	high_burden
Nigeria	NGA	2,022	male	50-54	urban	none	true	employed	self_employed	26	true	high_burden
Nigeria	NGA	2,024	male	55-59	urban	tertiary	false	other_inactive	not_applicable	40	false	high_burden
Nigeria	NGA	2,022	male	35-39	urban	tertiary	true	employed	full_time	38	false	high_burden
Nigeria	NGA	2,020	male	15-19	urban	tertiary	true	employed	full_time	42	false	high_burden
Nigeria	NGA	2,021	female	45-49	urban	tertiary	false	other_inactive	not_applicable	53	false	high_burden
Nigeria	NGA	2,020	female	40-44	rural	secondary	false	student	not_applicable	45	false	high_burden
Nigeria	NGA	2,024	female	30-34	rural	tertiary	true	employed	self_employed	54	false	high_burden
Nigeria	NGA	2,019	male	15-19	urban	primary	true	employed	full_time	42	false	high_burden
Nigeria	NGA	2,022	male	50-54	urban	primary	true	employed	casual	35	false	high_burden
Nigeria	NGA	2,023	female	55-59	urban	tertiary	true	employed	self_employed	33	true	high_burden
Nigeria	NGA	2,019	female	40-44	rural	tertiary	true	employed	full_time	44	false	high_burden
Nigeria	NGA	2,022	female	40-44	rural	tertiary	false	student	not_applicable	25	true	high_burden
Nigeria	NGA	2,022	female	40-44	urban	secondary	true	unemployed	not_applicable	57	false	high_burden
Nigeria	NGA	2,019	female	35-39	rural	primary	false	other_inactive	not_applicable	51	false	high_burden
Nigeria	NGA	2,018	male	50-54	rural	none	true	employed	part_time	4	true	high_burden
Nigeria	NGA	2,018	female	35-39	urban	secondary	false	discouraged	not_applicable	40	false	high_burden
Nigeria	NGA	2,022	male	25-29	rural	none	true	employed	full_time	48	false	high_burden
Nigeria	NGA	2,021	female	50-54	urban	tertiary	false	homemaker	not_applicable	50	false	high_burden
Nigeria	NGA	2,023	female	55-59	rural	secondary	true	employed	full_time	39	false	high_burden
Nigeria	NGA	2,025	male	15-19	rural	secondary	true	employed	part_time	15	true	high_burden
Nigeria	NGA	2,025	female	30-34	rural	secondary	true	unemployed	not_applicable	39	false	high_burden
Nigeria	NGA	2,018	female	55-59	rural	primary	false	retired	not_applicable	24	true	high_burden
Nigeria	NGA	2,018	male	35-39	rural	none	true	employed	self_employed	47	false	high_burden
Nigeria	NGA	2,021	male	60-64	urban	primary	true	unemployed	not_applicable	37	false	high_burden
Nigeria	NGA	2,023	female	40-44	urban	primary	false	homemaker	not_applicable	59	false	high_burden
Nigeria	NGA	2,024	female	15-19	urban	secondary	false	retired	not_applicable	59	false	high_burden
Nigeria	NGA	2,022	male	45-49	rural	tertiary	true	employed	self_employed	21	true	high_burden
Nigeria	NGA	2,024	male	15-19	rural	primary	true	employed	full_time	44	false	high_burden
Nigeria	NGA	2,024	female	60-64	rural	tertiary	false	student	not_applicable	29	true	high_burden
Nigeria	NGA	2,023	female	60-64	rural	none	false	discouraged	not_applicable	50	false	high_burden
Nigeria	NGA	2,021	male	35-39	urban	none	true	employed	self_employed	24	true	high_burden
Nigeria	NGA	2,018	female	35-39	urban	tertiary	true	employed	self_employed	49	false	high_burden
Nigeria	NGA	2,022	female	50-54	rural	secondary	false	student	not_applicable	22	true	high_burden
Nigeria	NGA	2,019	female	35-39	rural	primary	true	employed	casual	26	true	high_burden
Nigeria	NGA	2,022	male	40-44	rural	primary	true	employed	casual	45	false	high_burden
Nigeria	NGA	2,022	male	35-39	rural	secondary	true	unemployed	not_applicable	47	false	high_burden
Nigeria	NGA	2,019	male	35-39	rural	secondary	true	employed	full_time	41	false	high_burden
Nigeria	NGA	2,025	male	15-19	urban	secondary	false	retired	not_applicable	37	false	high_burden
Nigeria	NGA	2,025	male	20-24	urban	tertiary	true	employed	full_time	38	false	high_burden
Nigeria	NGA	2,020	male	35-39	urban	none	true	employed	self_employed	29	true	high_burden
Nigeria	NGA	2,022	female	40-44	rural	secondary	false	homemaker	not_applicable	32	true	high_burden
Nigeria	NGA	2,019	male	40-44	rural	secondary	true	employed	self_employed	34	true	high_burden
Nigeria	NGA	2,019	female	55-59	rural	secondary	false	homemaker	not_applicable	40	false	high_burden
Nigeria	NGA	2,022	female	30-34	urban	none	true	employed	full_time	36	false	high_burden
Nigeria	NGA	2,018	male	40-44	urban	primary	true	employed	full_time	37	false	high_burden
Nigeria	NGA	2,021	female	50-54	rural	primary	true	employed	full_time	40	false	high_burden
Nigeria	NGA	2,022	male	40-44	rural	none	false	homemaker	not_applicable	59	false	high_burden
Nigeria	NGA	2,024	female	20-24	rural	tertiary	false	student	not_applicable	35	false	high_burden
Nigeria	NGA	2,023	male	50-54	rural	primary	true	employed	casual	26	true	high_burden
Nigeria	NGA	2,025	female	25-29	rural	tertiary	true	employed	full_time	49	false	high_burden
Nigeria	NGA	2,020	male	55-59	rural	none	true	employed	full_time	41	false	high_burden
Nigeria	NGA	2,019	female	60-64	urban	secondary	true	employed	part_time	8	true	high_burden

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Africa Synth Employment Labor Force Participation Africa All | Africa (Electric Sheep Africa metadata inventory)

Size category: 10K<n<100K - Formats: csv - Sector: demographics_social - Engineered by Electric Sheep Africa

TL;DR

This dataset is part of the Electric Sheep Africa catalog on Hugging Face. It is indexed for African data discovery with standardized metadata, loading guidance, provenance notes, and analyst-oriented context.

What This Dataset Covers

Public datasets help analysts inspect structured evidence, build reproducible workflows, and compare patterns across domains.

Dataset context from the existing Hugging Face card: ⚠️ Synthetic dataset — Parameterized from published SSA literature, not real observations. Not suitable for empirical analysis or policy inference.

Dataset Profile

Field	Value
Hugging Face repo	`electricsheepafrica/africa-synth-employment-labor-force-participation-africa-all`
Sector	demographics_social
Topic tags	employment, labor, synthetic-data, sub-saharan-africa, labor-force, synthetic
Modalities	`tabular`, `text`
Formats	`csv`
Size category	`10K<n<100K`
Countries	Africa-wide or source-defined African coverage
ISO3 coverage	`not declared`
Last modified on HF	`2026-04-14 22:54:29+00:00`
Inventory snapshot	`2026-07-16T16:00:34Z`

How To Read This Dataset

Start from the repository files and the dataset viewer when available.
Treat the README context as a fast orientation layer; confirm variable definitions and units in the data files before modeling.
Use explicit country columns when present. When geography is only implied by the title or source metadata, document that assumption in downstream analysis.
Preserve missing values until you have a defensible imputation rule.

Usage

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-synth-employment-labor-force-participation-africa-all")
print(ds)

split_name = next(iter(ds))
table = ds[split_name]
print(table.features)
print(table[:3])

Convert To Pandas When Tabular

from datasets import Dataset

first_split = ds[next(iter(ds))]
if isinstance(first_split, Dataset):
    df = first_split.to_pandas()
    print(df.head())

Data Quality Notes

This card was standardized from the Electric Sheep Africa Hugging Face metadata inventory.
Exact schema, row counts, and source files should be inspected in the repository data files.
Metadata gaps from the inventory: country, upstream_publisher.
Do not infer policy meaning from labels alone; confirm definitions, units, and methods in the source material.

Source And Provenance

Source context: Electric Sheep Africa metadata inventory
Publisher/source attribution: Public dataset metadata
License: CC BY 4.0
Hugging Face URL: https://huggingface.co/datasets/electricsheepafrica/africa-synth-employment-labor-force-participation-africa-all
Inventory retrieved at: 2026-07-16T16:00:34Z

Suggested Analyses

Inspect schema and missingness before modeling.
Profile variables by geography, time, and subgroup columns where present.
Join with other Electric Sheep Africa datasets using explicit country, year, and indicator fields when available.
Build reproducible notebooks that cite both the original source context and the Electric Sheep Africa Hugging Face repo.

Citation

@misc{electric_sheep_africa_africa_synth_employment_labor_force_participation_africa_all_2026,
  title        = {Africa Synth Employment Labor Force Participation Africa All | Africa (Electric Sheep Africa metadata inventory)},
  author       = {Public dataset metadata},
  year         = {2026},
  url          = {https://huggingface.co/datasets/electricsheepafrica/africa-synth-employment-labor-force-participation-africa-all},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-synth-employment-labor-force-participation-africa-all}}
}

License

Released under CC BY 4.0.

Original source rights remain with the original publisher or data provider. Electric Sheep Africa engineering standardizes discovery metadata, documentation, and usage guidance for analysis on Hugging Face.

About Electric Sheep Africa

Electric Sheep Africa publishes ML-ready African public datasets on Hugging Face.

Provenance: metadata-backed README standardized 2026-08-12 by the Electric Sheep Africa README system. Inventory source: catalog/esa_metadata_inventory/master_metadata.jsonl.

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