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

---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- other
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- indicators
- socioeconomics
- swz
pretty_name: "Eswatini - Social Protection and Labor"
dataset_info:
splits:
- name: train
num_examples: 3138
- name: test
num_examples: 784
---

# Eswatini - Social Protection and Labor

**Publisher:** World Bank Group · **Source:** [HDX](https://data.humdata.org/dataset/world-bank-social-protection-and-labor-indicators-for-eswatini) · **License:** `cc-by` · **Updated:** 2026-03-27

---

## Abstract

Contains data from the World Bank's [data portal](http://data.worldbank.org/). There is also a [consolidated country dataset](https://data.humdata.org/dataset/world-bank-combined-indicators-for-eswatini) 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: **SWZ**.

*Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).*

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Humanitarian and development data |
| **Unit of observation** | Country-level aggregates |
| **Rows (total)** | 3,923 |
| **Columns** | 8 (2 numeric, 6 categorical, 0 datetime) |
| **Train split** | 3,138 rows |
| **Test split** | 784 rows |
| **Geographic scope** | SWZ |
| **Publisher** | World Bank Group |
| **HDX last updated** | 2026-03-27 |

---

## Variables

**Geographic** — `country_name` (Eswatini), `country_iso3` (SWZ), `year` (range 1966.0–2025.0).

**Outcome / Measurement** — `value` (range 0.0–489846.0).

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

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-world-bank-social-protection-and-labor-indicators-for-eswatini")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()

print(train.shape)
train.head()
```

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `country_name` | object | 0.0% | Eswatini |
| `country_iso3` | object | 0.0% | SWZ |
| `year` | int64 | 0.0% | 1966.0 – 2025.0 (mean 2009.5733) |
| `indicator_name` | object | 0.0% | Labor force participation rate, female (% of female population ages 15+) (modeled ILO estimate), Labor force participation rate, total (% of total population ages 15+) (modeled ILO estimate), Labor force, total |
| `indicator_code` | object | 0.0% | SL.TLF.CACT.FE.ZS, SL.TLF.CACT.ZS, SL.TLF.TOTL.IN |
| `value` | float64 | 0.0% | 0.0 – 489846.0 (mean 7419.3065) |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-10 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `year` | 1966.0 | 2025.0 | 2009.5733 | 2010.0 |
| `value` | 0.0 | 489846.0 | 7419.3065 | 26.105 |

---

## 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.

---

## 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](https://data.humdata.org/dataset/world-bank-social-protection-and-labor-indicators-for-eswatini) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_world_bank_social_protection_and_labor_indicators_for_eswatini,
title = {Eswatini - Social Protection and Labor},
author = {World Bank Group},
year = {2026},
url = {https://data.humdata.org/dataset/world-bank-social-protection-and-labor-indicators-for-eswatini},
note = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}
```

---

*[Electric Sheep Africa](https://huggingface.co/electricsheepafrica) — Africa's ML dataset infrastructure. Lagos, Nigeria.*