electricsheepafrica/africa-world-bank-aid-effectiveness-indicators-for-south-sudan

---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: cc-by-4.0
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- tabular-classification
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- aid-effectiveness
- indicators
- ssd
pretty_name: "South Sudan - Aid Effectiveness"
dataset_info:
splits:
- name: train
num_examples: 671
- name: test
num_examples: 167
---

# South Sudan - Aid Effectiveness

**Publisher:** World Bank Group · **Source:** [HDX](https://data.humdata.org/dataset/world-bank-aid-effectiveness-indicators-for-south-sudan) · **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-south-sudan) on HDX.

Aid effectiveness is the impact that aid has in reducing poverty and inequality, increasing growth, building capacity, and accelerating achievement of the Millennium Development Goals set by the international community. Indicators here cover aid received as well as progress in reducing poverty and improving education, health, and other measures of human welfare.

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

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

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Public health |
| **Unit of observation** | Country-level aggregates |
| **Rows (total)** | 839 |
| **Columns** | 8 (2 numeric, 6 categorical, 0 datetime) |
| **Train split** | 671 rows |
| **Test split** | 167 rows |
| **Geographic scope** | SSD |
| **Publisher** | World Bank Group |
| **HDX last updated** | 2026-03-27 |

---

## Variables

**Geographic** — `country_name` (South Sudan), `country_iso3` (SSD), `year` (range 1960.0–2025.0).

**Outcome / Measurement** — `value` (range -8232298.851–2566326904.2969).

**Identifier / Metadata** — `indicator_name` (Net migration, Mortality rate, under-5 (per 1,000 live births), Maternal mortality ratio (modeled estimate, per 100,000 live births)), `indicator_code` (SM.POP.NETM, SH.DYN.MORT, SH.STA.MMRT), `esa_source` (HDX), `esa_processed` (2026-04-10).

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-world-bank-aid-effectiveness-indicators-for-south-sudan")
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% | South Sudan |
| `country_iso3` | object | 0.0% | SSD |
| `year` | int64 | 0.0% | 1960.0 – 2025.0 (mean 2011.733) |
| `indicator_name` | object | 0.0% | Net migration, Mortality rate, under-5 (per 1,000 live births), Maternal mortality ratio (modeled estimate, per 100,000 live births) |
| `indicator_code` | object | 0.0% | SM.POP.NETM, SH.DYN.MORT, SH.STA.MMRT |
| `value` | float64 | 0.0% | -8232298.851 – 2566326904.2969 (mean 178050566.2794) |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-10 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `year` | 1960.0 | 2025.0 | 2011.733 | 2015.0 |
| `value` | -8232298.851 | 2566326904.2969 | 178050566.2794 | 1389999.9857 |

---

## 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-aid-effectiveness-indicators-for-south-sudan) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_world_bank_aid_effectiveness_indicators_for_south_sudan,
title = {South Sudan - Aid Effectiveness},
author = {World Bank Group},
year = {2026},
url = {https://data.humdata.org/dataset/world-bank-aid-effectiveness-indicators-for-south-sudan},
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.*