electricsheepafrica/africa-synth-textile-garment-production-all

⚠️ Synthetic dataset — Parameterized from published SSA literature, not real observations. Not suitable for empirical analysis or policy inference.

African Textile & Garment Production Dataset

Synthetic panel dataset of textile and garment manufacturing across eight Sub-Saharan African countries.

Overview

Property	Value
Countries	Ethiopia, Kenya, Madagascar, Mauritius, South Africa, Nigeria, Lesotho, Tanzania
Years	2015–2025
Scenarios	baseline, agip_grow, global_downturn
Records per scenario	~80,000 (10,000 per country)
Total records	~240,000
License	CC-BY-4.0

Variables

Variable	Type	Description
record_id	string	Unique UUID
country	string	Country name
year	int	Production year (2015–2025)
factory_id	string	Factory identifier
product_type	string	woven / knit / denim / garment
production_volume_units	int	Units produced
capacity_utilization_pct	float	Capacity utilization (0–100)
export_orders_pct	float	Share of orders for export (0–100)
workforce_size	int	Number of workers
average_wage_usd	float	Average monthly wage (USD)
compliance_audit_score	float	Social/compliance audit score (0–100)
lead_time_days	int	Order-to-delivery lead time
defect_rate_pct	float	Product defect rate
export_destination	string	US / EU / Regional
investment_usd	float	Annual capital investment (USD)
local_content_pct	float	Local raw-material content (0–100)
scenario	string	Scenario label

Scenarios

• baseline — Historical trend continuation.
• agip_grow — Africa Growth & Integration Programme: 8–20% capacity and export uplift per country, reflecting AGOA and AfCFTA policy expansion.
• global_downturn — External demand shock: 20–30% contraction in capacity utilization and exports.

Key Design Notes

• Ethiopia Hawassa Industrial Park factories are modelled at ~60% mean capacity utilization, reflecting real-world ramp-up constraints.
• AGOA beneficiary country export shares to the US are elevated (40–60%) vs non-beneficiaries.
• Lesotho is weighted toward denim production.
• Mauritius has the highest wages and compliance scores, reflecting its mature EPZ ecosystem.
• Nigeria has the lowest capacity utilization and export share, reflecting infrastructure challenges.

Files

data/
  baseline.csv
  agip_grow.csv
  global_downturn.csv
  all_scenarios.csv
generate_dataset.py
validate_dataset.py
requirements.txt

Usage

from datasets import load_dataset
ds = load_dataset("electricsheepafrica/african-textile-garment-production")

Reproduction

pip install -r requirements.txt
python generate_dataset.py
python validate_dataset.py

Citation

@dataset{african_textile_garment_production_2026,
  title={African Textile & Garment Production Dataset},
  author={Electric Sheep Africa},
  year={2026},
  license={cc-by-4.0}
}