Datasets:

electricsheepafrica
/

africa-synth-employment-skills-mismatch-africa-all

country stringclasses 15 values	country_code stringclasses 15 values	year int64 2.02k 2.03k	gender stringclasses 2 values	age_group stringclasses 5 values	education_level stringclasses 4 values	occupation_skill_level stringclasses 3 values	education_job_match stringclasses 3 values	technical_skills_gap stringclasses 4 values	soft_skills_gap stringclasses 4 values	digital_skills_level stringclasses 4 values	is_overqualified bool 2 classes	is_underqualified bool 2 classes	is_matched bool 2 classes	mismatch_index int64 5 100	scenario stringclasses 3 values
Nigeria	NGA	2,018	female	45-54	secondary	low_skill	matched	moderate	minor	intermediate	false	false	true	32	high_burden
Nigeria	NGA	2,021	female	15-24	primary	medium_skill	underqualified	moderate	minor	none	false	true	false	69	high_burden
Nigeria	NGA	2,021	male	15-24	none	low_skill	matched	minor	none	intermediate	false	false	true	16	high_burden
Nigeria	NGA	2,022	male	55-64	secondary	medium_skill	overqualified	none	none	basic	true	false	false	40	high_burden
Nigeria	NGA	2,025	female	25-34	primary	medium_skill	matched	none	none	none	false	false	true	25	high_burden
Nigeria	NGA	2,025	male	25-34	secondary	medium_skill	overqualified	moderate	none	intermediate	true	false	false	51	high_burden
Nigeria	NGA	2,021	female	55-64	primary	low_skill	matched	none	none	basic	false	false	true	15	high_burden
Nigeria	NGA	2,021	female	45-54	secondary	low_skill	overqualified	minor	none	none	true	false	false	58	high_burden
Nigeria	NGA	2,021	female	45-54	secondary	low_skill	overqualified	minor	minor	basic	true	false	false	54	high_burden
Nigeria	NGA	2,021	female	15-24	primary	medium_skill	matched	minor	none	none	false	false	true	33	high_burden
Nigeria	NGA	2,024	male	55-64	secondary	low_skill	overqualified	moderate	none	intermediate	true	false	false	51	high_burden
Nigeria	NGA	2,020	male	15-24	none	high_skill	underqualified	moderate	none	none	false	true	false	63	high_burden
Nigeria	NGA	2,025	male	35-44	primary	medium_skill	matched	none	none	none	false	false	true	25	high_burden
Nigeria	NGA	2,023	male	15-24	tertiary	low_skill	overqualified	minor	severe	intermediate	true	false	false	63	high_burden
Nigeria	NGA	2,020	female	45-54	primary	high_skill	underqualified	moderate	none	none	false	true	false	63	high_burden
Nigeria	NGA	2,021	female	35-44	none	low_skill	overqualified	moderate	none	basic	true	false	false	58	high_burden
Nigeria	NGA	2,019	female	15-24	none	medium_skill	matched	none	none	none	false	false	true	25	high_burden
Nigeria	NGA	2,023	female	15-24	secondary	medium_skill	underqualified	moderate	none	basic	false	true	false	53	high_burden
Nigeria	NGA	2,020	male	15-24	primary	low_skill	matched	none	none	intermediate	false	false	true	8	high_burden
Nigeria	NGA	2,025	male	35-44	primary	medium_skill	matched	none	none	none	false	false	true	25	high_burden
Nigeria	NGA	2,020	female	15-24	none	low_skill	matched	minor	none	basic	false	false	true	23	high_burden
Nigeria	NGA	2,025	female	45-54	secondary	high_skill	underqualified	minor	none	basic	false	true	false	43	high_burden
Nigeria	NGA	2,022	male	25-34	secondary	medium_skill	underqualified	minor	none	none	false	true	false	53	high_burden
Nigeria	NGA	2,024	female	55-64	none	high_skill	matched	severe	moderate	basic	false	false	true	57	high_burden
Nigeria	NGA	2,024	female	25-34	primary	medium_skill	overqualified	moderate	none	none	true	false	false	68	high_burden
Nigeria	NGA	2,025	female	45-54	primary	low_skill	matched	none	moderate	none	false	false	true	39	high_burden
Nigeria	NGA	2,023	male	25-34	secondary	medium_skill	matched	minor	severe	basic	false	false	true	45	high_burden
Nigeria	NGA	2,022	male	15-24	none	medium_skill	matched	minor	none	none	false	false	true	33	high_burden
Nigeria	NGA	2,025	female	45-54	none	low_skill	matched	minor	none	none	false	false	true	33	high_burden
Nigeria	NGA	2,022	female	55-64	primary	medium_skill	matched	minor	severe	none	false	false	true	55	high_burden
Nigeria	NGA	2,025	female	35-44	secondary	low_skill	matched	none	none	advanced	false	false	true	5	high_burden
Nigeria	NGA	2,018	male	35-44	tertiary	low_skill	overqualified	moderate	none	basic	true	false	false	58	high_burden
Nigeria	NGA	2,020	female	25-34	none	medium_skill	overqualified	moderate	minor	none	true	false	false	74	high_burden
Nigeria	NGA	2,024	male	25-34	primary	low_skill	overqualified	minor	none	none	true	false	false	58	high_burden
Nigeria	NGA	2,025	female	55-64	none	high_skill	overqualified	none	minor	none	true	false	false	56	high_burden
Nigeria	NGA	2,024	female	15-24	tertiary	medium_skill	matched	none	none	none	false	false	true	25	high_burden
Nigeria	NGA	2,023	male	15-24	none	low_skill	matched	none	none	none	false	false	true	25	high_burden
Nigeria	NGA	2,019	female	55-64	secondary	high_skill	matched	none	none	basic	false	false	true	15	high_burden
Nigeria	NGA	2,025	male	25-34	primary	medium_skill	matched	minor	none	intermediate	false	false	true	16	high_burden
Nigeria	NGA	2,018	female	15-24	secondary	low_skill	underqualified	severe	minor	basic	false	true	false	69	high_burden
Nigeria	NGA	2,022	female	45-54	primary	medium_skill	overqualified	minor	none	basic	true	false	false	48	high_burden
Nigeria	NGA	2,019	male	25-34	none	medium_skill	matched	none	none	none	false	false	true	25	high_burden
Nigeria	NGA	2,023	male	35-44	secondary	medium_skill	underqualified	severe	none	basic	false	true	false	63	high_burden
Nigeria	NGA	2,021	male	35-44	secondary	low_skill	overqualified	minor	minor	none	true	false	false	64	high_burden
Nigeria	NGA	2,019	female	15-24	secondary	low_skill	matched	none	none	intermediate	false	false	true	8	high_burden
Nigeria	NGA	2,019	female	15-24	tertiary	high_skill	underqualified	severe	minor	advanced	false	true	false	59	high_burden
Nigeria	NGA	2,025	female	55-64	primary	low_skill	matched	none	none	basic	false	false	true	15	high_burden
Nigeria	NGA	2,025	male	35-44	primary	medium_skill	overqualified	moderate	none	basic	true	false	false	58	high_burden
Nigeria	NGA	2,019	female	55-64	none	high_skill	underqualified	minor	minor	basic	false	true	false	49	high_burden
Nigeria	NGA	2,018	female	25-34	secondary	low_skill	matched	none	none	none	false	false	true	25	high_burden
Nigeria	NGA	2,025	male	15-24	primary	medium_skill	matched	none	moderate	basic	false	false	true	29	high_burden
Nigeria	NGA	2,025	female	35-44	primary	low_skill	overqualified	severe	none	basic	true	false	false	68	high_burden
Nigeria	NGA	2,025	female	15-24	secondary	low_skill	matched	none	minor	none	false	false	true	31	high_burden
Nigeria	NGA	2,018	female	35-44	primary	medium_skill	underqualified	moderate	none	advanced	false	true	false	43	high_burden
Nigeria	NGA	2,018	female	45-54	secondary	high_skill	underqualified	moderate	minor	none	false	true	false	69	high_burden
Nigeria	NGA	2,020	female	55-64	none	medium_skill	matched	minor	none	intermediate	false	false	true	16	high_burden
Nigeria	NGA	2,020	female	55-64	none	high_skill	overqualified	moderate	none	basic	true	false	false	58	high_burden
Nigeria	NGA	2,018	male	15-24	secondary	low_skill	overqualified	moderate	moderate	none	true	false	false	82	high_burden
Nigeria	NGA	2,021	male	15-24	primary	low_skill	overqualified	moderate	none	basic	true	false	false	58	high_burden
Nigeria	NGA	2,018	male	15-24	primary	low_skill	underqualified	moderate	none	basic	false	true	false	53	high_burden
Nigeria	NGA	2,018	male	25-34	secondary	medium_skill	matched	none	severe	basic	false	false	true	37	high_burden
Nigeria	NGA	2,025	female	45-54	secondary	low_skill	matched	moderate	none	basic	false	false	true	33	high_burden
Nigeria	NGA	2,022	female	25-34	tertiary	high_skill	overqualified	minor	none	basic	true	false	false	48	high_burden
Nigeria	NGA	2,023	female	15-24	primary	medium_skill	matched	none	minor	none	false	false	true	31	high_burden
Nigeria	NGA	2,019	male	55-64	secondary	medium_skill	matched	none	none	none	false	false	true	25	high_burden
Nigeria	NGA	2,019	male	35-44	secondary	low_skill	overqualified	moderate	none	intermediate	true	false	false	51	high_burden
Nigeria	NGA	2,020	female	15-24	secondary	medium_skill	overqualified	moderate	moderate	intermediate	true	false	false	65	high_burden
Nigeria	NGA	2,021	female	35-44	primary	low_skill	matched	minor	none	none	false	false	true	33	high_burden
Nigeria	NGA	2,018	male	55-64	none	medium_skill	matched	none	none	basic	false	false	true	15	high_burden
Nigeria	NGA	2,024	female	15-24	secondary	low_skill	matched	none	minor	none	false	false	true	31	high_burden
Nigeria	NGA	2,022	male	35-44	tertiary	low_skill	matched	minor	minor	intermediate	false	false	true	22	high_burden
Nigeria	NGA	2,025	female	45-54	none	medium_skill	overqualified	none	none	basic	true	false	false	40	high_burden
Nigeria	NGA	2,025	female	55-64	secondary	low_skill	matched	none	none	none	false	false	true	25	high_burden
Nigeria	NGA	2,022	female	45-54	primary	medium_skill	matched	none	moderate	none	false	false	true	39	high_burden
Nigeria	NGA	2,023	male	15-24	primary	low_skill	overqualified	none	none	basic	true	false	false	40	high_burden
Nigeria	NGA	2,020	female	45-54	secondary	low_skill	overqualified	none	none	basic	true	false	false	40	high_burden
Nigeria	NGA	2,025	male	55-64	primary	low_skill	overqualified	minor	moderate	basic	true	false	false	62	high_burden
Nigeria	NGA	2,024	male	55-64	primary	medium_skill	matched	none	none	intermediate	false	false	true	8	high_burden
Nigeria	NGA	2,023	female	25-34	primary	medium_skill	matched	moderate	none	none	false	false	true	43	high_burden
Nigeria	NGA	2,019	male	35-44	none	medium_skill	underqualified	moderate	severe	basic	false	true	false	75	high_burden
Nigeria	NGA	2,025	male	55-64	tertiary	low_skill	underqualified	moderate	moderate	basic	false	true	false	67	high_burden
Nigeria	NGA	2,024	female	45-54	primary	low_skill	overqualified	severe	severe	none	true	false	false	100	high_burden
Nigeria	NGA	2,024	female	55-64	primary	high_skill	matched	minor	none	advanced	false	false	true	13	high_burden
Nigeria	NGA	2,023	female	35-44	tertiary	medium_skill	overqualified	none	minor	advanced	true	false	false	36	high_burden
Nigeria	NGA	2,020	female	35-44	none	medium_skill	matched	none	minor	basic	false	false	true	21	high_burden
Nigeria	NGA	2,024	female	45-54	secondary	low_skill	overqualified	none	none	basic	true	false	false	40	high_burden
Nigeria	NGA	2,019	female	35-44	primary	low_skill	matched	none	moderate	basic	false	false	true	29	high_burden
Nigeria	NGA	2,018	male	25-34	secondary	medium_skill	overqualified	minor	minor	basic	true	false	false	54	high_burden
Nigeria	NGA	2,018	male	35-44	none	high_skill	matched	minor	minor	advanced	false	false	true	19	high_burden
Nigeria	NGA	2,025	female	25-34	primary	high_skill	overqualified	severe	minor	basic	true	false	false	74	high_burden
Nigeria	NGA	2,018	male	35-44	primary	high_skill	matched	none	none	none	false	false	true	25	high_burden
Nigeria	NGA	2,025	male	35-44	secondary	medium_skill	matched	moderate	minor	basic	false	false	true	39	high_burden
Nigeria	NGA	2,025	male	25-34	none	medium_skill	underqualified	moderate	minor	basic	false	true	false	59	high_burden
Nigeria	NGA	2,025	female	45-54	primary	medium_skill	matched	none	moderate	none	false	false	true	39	high_burden
Nigeria	NGA	2,018	female	25-34	secondary	medium_skill	overqualified	severe	minor	intermediate	true	false	false	67	high_burden
Nigeria	NGA	2,024	female	55-64	secondary	medium_skill	overqualified	none	minor	advanced	true	false	false	36	high_burden
Nigeria	NGA	2,024	female	55-64	none	low_skill	matched	none	none	none	false	false	true	25	high_burden
Nigeria	NGA	2,021	female	35-44	primary	medium_skill	matched	none	none	none	false	false	true	25	high_burden
Nigeria	NGA	2,025	female	35-44	primary	medium_skill	matched	none	none	basic	false	false	true	15	high_burden
Nigeria	NGA	2,019	male	15-24	primary	medium_skill	matched	none	minor	basic	false	false	true	21	high_burden

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Africa Synth Employment Skills Mismatch Africa All | Africa (Electric Sheep Africa metadata inventory)

Size category: 10K<n<100K - Formats: csv - Sector: economics_finance - 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-skills-mismatch-africa-all`
Sector	economics_finance
Topic tags	employment, labor, synthetic-data, sub-saharan-africa, skills, 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 22:54:49+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-skills-mismatch-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-skills-mismatch-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_skills_mismatch_africa_all_2026,
  title        = {Africa Synth Employment Skills Mismatch Africa All | Africa (Electric Sheep Africa metadata inventory)},
  author       = {Public dataset metadata},
  year         = {2026},
  url          = {https://huggingface.co/datasets/electricsheepafrica/africa-synth-employment-skills-mismatch-africa-all},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-synth-employment-skills-mismatch-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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