electricsheepafrica/africa-financial-services-liberia

---
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:
- tabular-regression
- other
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- economics
- indicators
- lbr
pretty_name: "Liberia - Financial Sector"
dataset_info:
splits:
- name: train
num_examples: 1856
- name: test
num_examples: 464
---

# Liberia - Financial Sector

**Publisher:** World Bank Group · **Source:** [HDX](https://data.humdata.org/dataset/world-bank-financial-sector-indicators-for-liberia) · **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-liberia) on HDX.

An economy's financial markets are critical to its overall development. Banking systems and stock markets enhance growth, the main factor in poverty reduction. Strong financial systems provide reliable and accessible information that lowers transaction costs, which in turn bolsters resource allocation and economic growth. Indicators here include the size and liquidity of stock markets; the accessibility, stability, and efficiency of financial systems; and international migration and workers\ remittances, which affect growth and social welfare in both sending and receiving countries.

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

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

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Poverty and economic vulnerability |
| **Unit of observation** | Country-level aggregates |
| **Rows (total)** | 2,321 |
| **Columns** | 8 (2 numeric, 6 categorical, 0 datetime) |
| **Train split** | 1,856 rows |
| **Test split** | 464 rows |
| **Geographic scope** | LBR |
| **Publisher** | World Bank Group |
| **HDX last updated** | 2026-03-27 |

---

## Variables

**Geographic** — `country_name` (Liberia), `country_iso3` (LBR), `year` (range 1960.0–2025.0).

**Outcome / Measurement** — `value` (range -2309981241.25–2309981241.25).

**Identifier / Metadata** — `indicator_name` (Domestic credit to private sector (% of GDP), Net migration, DEC alternative conversion factor (LCU per US$)), `indicator_code` (SM.POP.NETM, PA.NUS.ATLS, PA.NUS.FCRF), `esa_source` (HDX), `esa_processed` (2026-04-27).

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-financial-services-liberia")
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% | Liberia |
| `country_iso3` | object | 0.0% | LBR |
| `year` | int64 | 0.0% | 1960.0 – 2025.0 (mean 2002.679) |
| `indicator_name` | object | 0.0% | Domestic credit to private sector (% of GDP), Net migration, DEC alternative conversion factor (LCU per US$) |
| `indicator_code` | object | 0.0% | SM.POP.NETM, PA.NUS.ATLS, PA.NUS.FCRF |
| `value` | float64 | 0.0% | -2309981241.25 – 2309981241.25 (mean 27429205.8524) |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-27 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `year` | 1960.0 | 2025.0 | 2002.679 | 2006.0 |
| `value` | -2309981241.25 | 2309981241.25 | 27429205.8524 | 13.1297 |

---

## 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-financial-sector-indicators-for-liberia) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_financial_services_liberia,
title = {Liberia - Financial Sector},
author = {World Bank Group},
year = {2026},
url = {https://data.humdata.org/dataset/world-bank-financial-sector-indicators-for-liberia},
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.*