README.md

metadata

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 · 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.

---

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

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.

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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 for the publisher's own methodology notes and caveats.

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Citation

@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)}
}

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Electric Sheep Africa — Africa's ML dataset infrastructure. Lagos, Nigeria.