electricsheepafrica/africa-future-of-business-survey-aggregated-data

Small Business Surveys - Aggregated Data

Publisher: AI for Good at Meta · Source: HDX · License: cc-by · Updated: 2026-03-26

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Abstract

More than 200 million businesses use Facebook globally. The goal of our Small Business Surveys has been to learn about the unique perspectives, challenges and opportunities of small and medium-sized businesses (SMBs). Through 2022, the Future of Business Survey and the Global State of Small Business (GSoSB) Survey were conducted in partnership with the World Bank and Organisation for Economic Cooperation and Development.

Aggregated country level data for each survey wave is available to the public on HDX and controlled access microdata is available to Data for Good at Meta partners. Please visit https://ai.meta.com/ai-for-good/datasets/small-business-surveys/ to apply for access to microdata or contact aiforgood@meta.com for any questions.

This dataset regularly updated until 2023-03-24 and is no longer expecting additional data.

Each row in this dataset represents tabular records. Data was last updated on HDX on 2026-03-26. Geographic scope: AGO, ARG, AUS, BGD, BEL, BEN, BFA, BDI, and 84 others.

Curated into ML-ready Parquet format by Electric Sheep Africa.

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Dataset Characteristics

Domain	Water, sanitation and hygiene (wash)
Unit of observation	Tabular records
Rows (total)	61
Columns	5 (0 numeric, 5 categorical, 0 datetime)
Train split	48 rows
Test split	12 rows
Geographic scope	AGO, ARG, AUS, BGD, BEL, BEN, BFA, BDI, and 84 others
Publisher	AI for Good at Meta
HDX last updated	2026-03-26

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Variables

Geographic — question_text (What are the most important challenges your business currently faces? (Please select up to two), Does your business have outside financing? By outside financing, we mean financing from someone who does not own or work for the company. (Select all that apply), Why did you start or join this business? (Please select up to three reasons that are most important to you)).

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

Other — variable (bus_age, edu, emp_chl_concern_working_hours), possible_options (Yes, No, Don't know, No, Yes, Between 1 and 2 years ago, Between 2 and 5 years ago, Less than 1 year ago, More than 5 years ago).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-future-of-business-survey-aggregated-data")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

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Schema

Column	Type	Null %	Range / Sample Values
variable	object	0.0%	bus_age, edu, emp_chl_concern_working_hours
question_text	object	0.0%	What are the most important challenges your business currently faces? (Please select up to two), Does your business have outside financing? By outside financing, we mean financing from someone who does not own or work for the company. (Select all that apply), Why did you start or join this business? (Please select up to three reasons that are most important to you)
possible_options	object	0.0%	Yes, No, Don't know, No, Yes, Between 1 and 2 years ago, Between 2 and 5 years ago, Less than 1 year ago, More than 5 years ago
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-05

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Numeric Summary

Column	Min	Max	Mean	Median
No numeric columns.

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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.

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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 92 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_future_of_business_survey_aggregated_data,
  title     = {Small Business Surveys - Aggregated Data},
  author    = {AI for Good at Meta},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/future-of-business-survey-aggregated-data},
  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.