---
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
- tabular-regression
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- economics
- gender
- men
- social-media-data
- socioeconomics
- women
- afg
- alb
- dza
- and
- ago
pretty_name: "Cross Gender Ties"
dataset_info:
  splits:
    - name: train
      num_examples: 142
    - name: test
      num_examples: 35
---

# Cross Gender Ties

**Publisher:** AI for Good at Meta · **Source:** [HDX](https://data.humdata.org/dataset/cross-gender-ties) · **License:** `cc-by` · **Updated:** 2026-02-04

---

## Abstract

Data from the paper "Cross-Gender Social Ties Around the World", which is available [here](https://drew-johnston.com/files/cross_gender_ties/Cross-Gender_Social_Ties_Around_the_World.pdf).

Each row in this dataset represents first-level administrative unit observations. Data was last updated on HDX on 2026-02-04. Geographic scope: **AFG, ALB, DZA, AND, AGO, ARG, ARM, AUS, and 173 others**.

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

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Humanitarian and development data |
| **Unit of observation** | First-level administrative unit observations |
| **Rows (total)** | 178 |
| **Columns** | 16 (10 numeric, 6 categorical, 0 datetime) |
| **Train split** | 142 rows |
| **Test split** | 35 rows |
| **Geographic scope** | AFG, ALB, DZA, AND, AGO, ARG, ARM, AUS, and 173 others |
| **Publisher** | AI for Good at Meta |
| **HDX last updated** | 2026-02-04 |

---

## Variables

**Geographic** — `region_id` (JM, RO, AM), `region_name` (JM, RO, AM), `country` (JM, RO, AM).

**Identifier / Metadata** — `esa_source` (HDX), `esa_processed` (2026-04-05).

**Other** — `level` (country), `cgfr_5` (range 0.0661–0.8291), `cgfr_10` (range 0.0653–0.8507), `cgfr_25` (range 0.0661–0.9263), `cgfr_50` (range 0.0682–1.0636) and 6 others.

---

## Quick Start

```python
from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-cross-gender-ties")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `region_id` | object | 0.6% | JM, RO, AM |
| `region_name` | object | 0.6% | JM, RO, AM |
| `country` | object | 0.6% | JM, RO, AM |
| `level` | object | 0.0% | country |
| `cgfr_5` | float64 | 0.0% | 0.0661 – 0.8291 (mean 0.541) |
| `cgfr_10` | float64 | 0.0% | 0.0653 – 0.8507 (mean 0.5278) |
| `cgfr_25` | float64 | 0.0% | 0.0661 – 0.9263 (mean 0.5344) |
| `cgfr_50` | float64 | 0.0% | 0.0682 – 1.0636 (mean 0.5598) |
| `cgfr_75` | float64 | 0.0% | 0.0695 – 1.1722 (mean 0.5818) |
| `cgfr_100` | float64 | 0.0% | 0.0704 – 1.2563 (mean 0.6002) |
| `cgfr_125` | float64 | 0.0% | 0.0712 – 1.3226 (mean 0.616) |
| `cgfr_150` | float64 | 0.0% | 0.0718 – 1.3758 (mean 0.6296) |
| `cgfr_175` | float64 | 0.0% | 0.0723 – 1.4198 (mean 0.6415) |
| `cgfr_200` | float64 | 0.0% | 0.0728 – 1.4564 (mean 0.6521) |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-05 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `cgfr_5` | 0.0661 | 0.8291 | 0.541 | 0.5595 |
| `cgfr_10` | 0.0653 | 0.8507 | 0.5278 | 0.543 |
| `cgfr_25` | 0.0661 | 0.9263 | 0.5344 | 0.5373 |
| `cgfr_50` | 0.0682 | 1.0636 | 0.5598 | 0.5555 |
| `cgfr_75` | 0.0695 | 1.1722 | 0.5818 | 0.5695 |
| `cgfr_100` | 0.0704 | 1.2563 | 0.6002 | 0.5891 |
| `cgfr_125` | 0.0712 | 1.3226 | 0.616 | 0.6032 |
| `cgfr_150` | 0.0718 | 1.3758 | 0.6296 | 0.6164 |
| `cgfr_175` | 0.0723 | 1.4198 | 0.6415 | 0.6271 |
| `cgfr_200` | 0.0728 | 1.4564 | 0.6521 | 0.6363 |

---

## 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 AI for Good at Meta and has not been independently validated by ESA.
- Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
- This dataset spans 181 countries; geographic and methodological inconsistencies across national boundaries may affect cross-country comparability.
- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/cross-gender-ties) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_cross_gender_ties,
  title     = {Cross Gender Ties},
  author    = {AI for Good at Meta},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/cross-gender-ties},
  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.*