README.md

---
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
- covid-19
- demographics
- eastern-africa
- economics
- employment
- geodata
- indicators
- civ
- ken
- moz
- nga
- zaf
pretty_name: "Economic Impact of COVID-19 in Sub-Saharan Africa"
dataset_info:
  splits:
    - name: train
      num_examples: 1940
    - name: test
      num_examples: 485
---

# Economic Impact of COVID-19 in Sub-Saharan Africa

**Publisher:** Mobile Accord, Inc. (GeoPoll) · **Source:** [HDX](https://data.humdata.org/dataset/economic-impact-of-covid-19-in-sub-saharan-africa) · **License:** `cc-by` · **Updated:** 2025-09-26

---

## Abstract

This data looks at the impact of COVID-19 on employment, income, ability to pay expenses, and more in Côte D'Ivoire, Kenya, Mozambique Nigeria, and South Africa. Data is nationally representative by age, gender, and location, and is broken down by job type and formal or informal workers. Please contact us for data broken down by province or more information on the methodology.

Each row in this dataset represents country-level aggregates. Data was last updated on HDX on 2025-09-26. Geographic scope: **CIV, KEN, MOZ, NGA, ZAF**.

*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)** | 2,426 |
| **Columns** | 27 (2 numeric, 25 categorical, 0 datetime) |
| **Train split** | 1,940 rows |
| **Test split** | 485 rows |
| **Geographic scope** | CIV, KEN, MOZ, NGA, ZAF |
| **Publisher** | Mobile Accord, Inc. (GeoPoll) |
| **HDX last updated** | 2025-09-26 |

---

## Variables

**Geographic** — `country` (Ivory Coast (Cote D'Ivoire), Kenya, South Africa), `employmenttype` (Unemployed, Employed full time, Student), `jobtype` (Educator, Small business owner/employee, Large business), `informal_work_type` (Seller, Agriculture, Other), `monthlyincome` (0-100000, 0-150000, 0-15000) and 11 others.

**Demographic** — `age_group` (36+, 26-35, 18-25), `gender` (Male, Female).

**Outcome / Measurement** — `incomechange`.

**Identifier / Metadata** — `aid`, `covidloans`, `esa_source`, `esa_processed`.

**Other** — `informalworker` (Yes, No, Not sure), `jobloss` (Yes, No-I’m still able to work, Prefer not to say), `jobregain` (Yes, Don't know, No), `lengthsurvival`.

---

## Quick Start

```python
from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-economic-impact-of-covid-19-in-sub-saharan-africa")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `country` | object | 0.0% | Ivory Coast (Cote D'Ivoire), Kenya, South Africa |
| `age_group` | object | 0.0% | 36+, 26-35, 18-25 |
| `gender` | object | 0.0% | Male, Female |
| `employmenttype` | object | 0.0% | Unemployed, Employed full time, Student |
| `jobtype` | object | 65.6% | Educator, Small business owner/employee, Large business |
| `informalworker` | object | 49.5% | Yes, No, Not sure |
| `informal_work_type` | object | 73.5% | Seller, Agriculture, Other |
| `jobloss` | object | 46.9% | Yes, No-I’m still able to work, Prefer not to say |
| `jobregain` | object | 68.6% | Yes, Don't know, No |
| `monthlyincome` | object | 46.9% | 0-100000, 0-150000, 0-15000 |
| `monthlyincome_bracket` | float64 | 47.2% | 1.0 – 11.0 (mean 1.6846) |
| `incomechange` | object | 47.0% |  |
| `expenseresponsibility` | object | 0.0% |  |
| `lengthsurvival` | object | 21.8% |  |
| `moneyforexpenses` | object | 21.8% |  |
| `concernexpenses` | object | 21.8% |  |
| `expense_concern_rating` | float64 | 21.8% | 1.0 – 5.0 (mean 3.9612) |
| `monthlyneed` | object | 20.5% |  |
| `toppriority` | object | 19.0% |  |
| `lowpriority` | object | 19.0% |  |
| `aid` | object | 0.0% |  |
| `covidloans` | object | 0.0% |  |
| `mobilemoneyactivity` | object | 0.0% |  |
| `mobilemoneydeposit` | object | 0.0% |  |
| `governmentpriority` | object | 0.0% |  |
| `esa_source` | object | 0.0% |  |
| `esa_processed` | object | 0.0% |  |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `monthlyincome_bracket` | 1.0 | 11.0 | 1.6846 | 1.0 |
| `expense_concern_rating` | 1.0 | 5.0 | 3.9612 | 5.0 |

---

## 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`. 8 column(s) with >80% missing values were removed: `otherjob`, `aidsource_aid_organisations`, `aidsource_charities_donations`, `aidsource_friends_family`, `aidsource_government`, `aidsource_not_sure`.... 74 exact duplicate rows were removed. 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 Mobile Accord, Inc. (GeoPoll) and has not been independently validated by ESA.
- Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
- The following columns have >20% missing values and should be treated with caution in modelling: `jobtype`, `informalworker`, `informal_work_type`, `jobloss`, `jobregain`, `monthlyincome`, `monthlyincome_bracket`, `incomechange`....
- This dataset spans 5 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/economic-impact-of-covid-19-in-sub-saharan-africa) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_economic_impact_of_covid_19_in_sub_saharan_africa,
  title     = {Economic Impact of COVID-19 in Sub-Saharan Africa},
  author    = {Mobile Accord, Inc. (GeoPoll)},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/economic-impact-of-covid-19-in-sub-saharan-africa},
  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.*