---
license: cc-by-4.0
language:
- en
task_categories:
- tabular-classification
- tabular-regression
- time-series-forecasting
multilinguality: monolingual
size_categories:
- n<1K
tags:
- tabular
- africa
- world-bank-—-education-statistics
- education-statistics
- world-bank
- worldbank
- development-indicators
- time-series
pretty_name: "All staff compensation as % of total expenditure in tertiary public institutions (%) | Africa (World Bank — Education Statistics)"
---

# All staff compensation as % of total expenditure in tertiary public institutions (%) | Africa (World Bank — Education Statistics)

🌍 **115 observations** · **25 Africa countries** · **1998–2019** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*

![rows](https://img.shields.io/badge/rows-115-blue) ![countries](https://img.shields.io/badge/countries-25-green) ![years](https://img.shields.io/badge/years-1998–2019-orange) ![indicators](https://img.shields.io/badge/indicators-1-purple) ![license](https://img.shields.io/badge/license-cc-by-4.0-lightgrey)

## TL;DR

This dataset contains **115 observations** of `Education Statistics` data across **25 Africa countries**, spanning **1998–2019**, covering **1 distinct indicators**.

## About the source

- **Source:** [World Bank — Education Statistics](https://databank.worldbank.org/)
- **Publisher:** World Bank
- **License:** [cc-by-4.0](https://creativecommons.org/licenses/by/4.0/)
- **Topic:** Education Statistics

## Geographic coverage

25 Africa countries · top rows shown below, sorted by row count:

| Country | Rows | First year | Last year |
|---------|-----:|-----------:|----------:|
| `CIV` | 14 | 1998 | 2018 |
| `GHA` | 11 | 2001 | 2014 |
| `MLI` | 9 | 2009 | 2017 |
| `BDI` | 8 | 1999 | 2013 |
| `BEN` | 7 | 1998 | 2015 |
| `ETH` | 7 | 2009 | 2015 |
| `GIN` | 7 | 2013 | 2019 |
| `CMR` | 5 | 2008 | 2013 |
| `KEN` | 5 | 2011 | 2015 |
| `CPV` | 5 | 2004 | 2016 |
| `GMB` | 5 | 2008 | 2012 |
| `MDG` | 4 | 2013 | 2017 |
| `COM` | 4 | 1998 | 2015 |
| `CAF` | 4 | 2005 | 2010 |
| `GNB` | 4 | 2010 | 2013 |
| ... | _10 more countries_ | | |

## Indicators (sample)

- `UIS.XSPENDP.56.FDPUB.FNS` — All staff compensation as % of total expenditure in tertiary public institutions (%)

## Schema

| Column | Type | Description | Example |
|--------|------|-------------|---------|
| `indicator_id` | `string` | — | `UIS.XSPENDP.56.FDPUB.FNS` |
| `indicator_name` | `string` | — | `All staff compensation as % of total …` |
| `country_iso3` | `string` | — | `BEN` |
| `country_name` | `string` | — | `Benin` |
| `year` | `int64` | — | `2015` |
| `value` | `float64` | — | `57.47302` |

## Usage

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-worldbank-all-staff-compensation-as-of-total-expenditure-in-tertiary-public-institutions")
df = ds["train"].to_pandas()
print(df.head())
```

### Filter to one country

```python
kenya = df[df["country_iso3"] == "KEN"]
```

### Time-series for a single indicator

```python
sample = (df[df["indicator_id"] == "UIS.XSPENDP.56.FDPUB.FNS"]
          .sort_values("year"))
sample.plot(x="year", y="value", title="UIS.XSPENDP.56.FDPUB.FNS")
```

### Pivot to country × year matrix

```python
matrix = (df[df["indicator_id"] == "UIS.XSPENDP.56.FDPUB.FNS"]
          .pivot_table(index="year", columns="country_iso3", values="value"))
print(matrix.tail())
```

## Citation

```bibtex
@misc{africa_worldbank_all_staff_compensation_as_of_total_expenditure_in_tertiary_public_institutions_2019,
  title        = {All staff compensation as % of total expenditure in tertiary public institutions (%) | Africa (World Bank — Education Statistics)},
  author       = {World Bank},
  year         = {2019},
  url          = {https://databank.worldbank.org/},
  publisher    = {HuggingFace Datasets, repackaged by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-worldbank-all-staff-compensation-as-of-total-expenditure-in-tertiary-public-institutions}}
}
```

## License

Released under [cc-by-4.0](https://creativecommons.org/licenses/by/4.0/).

Original data © World Bank. When using this dataset, please cite both the original source above and the Electric Sheep Africa repackaging.

## About Electric Sheep

Electric Sheep Africa is part of the Electric Sheep mission: a unified, ML-ready data layer for Africa on HuggingFace. We pull data from authoritative open sources, normalize the schemas, package as Parquet, and publish with consistent dataset cards so researchers and developers can use `load_dataset()` to start working in seconds.

Browse the full collection: [huggingface.co/electricsheepafrica](https://huggingface.co/electricsheepafrica)

---

_Provenance: ingested 2026-06-19 via the Electric Sheep pipeline. Source URL: https://databank.worldbank.org/_