Datasets:

electricsheepafrica
/

africa-synth-financial-inclusion-mobile-money-adoption-africa-all

country string	year int64	urban_rural string	gender string	age int64	age_group string	income_quintile string	account_ownership int64	active_use int64	provider string	n_transactions_monthly int64	avg_transaction_value_usd float64	transaction_types string	digital_literacy_score int64	years_active int64	scenario string
Uganda	2,020	rural	female	70	55+	middle	1	0	MTN Mobile Money	0	0	none	2	5	low_burden
Mali	2,024	urban	female	46	45-54	fourth	0	0	none	0	0	none	5	0	low_burden
Rwanda	2,018	rural	male	51	45-54	fourth	0	0	none	0	0	none	3	0	low_burden
Malawi	2,021	urban	male	36	35-44	lowest	1	1	TNM Mpamba	13	107.82	loan_repayment,cash_deposit,airtime_purchase,bill_payment	7	10	low_burden
Malawi	2,024	rural	female	68	55+	lowest	0	0	none	0	0	none	10	0	low_burden
DRC	2,025	urban	female	19	15-24	fourth	0	0	none	0	0	none	7	0	low_burden
Uganda	2,020	rural	female	31	25-34	highest	0	0	none	0	0	none	10	0	low_burden
Niger	2,023	urban	female	74	55+	highest	1	1	Airtel Money	15	62.63	merchant_payment,cash_withdrawal	5	12	low_burden
Ghana	2,019	urban	female	44	35-44	highest	1	1	Vodafone Cash	13	79.01	loan_repayment,airtime_purchase,cash_deposit	9	5	low_burden
Uganda	2,018	rural	female	25	15-24	middle	1	0	Airtel Money	0	0	none	3	4	low_burden
Zambia	2,020	urban	female	39	35-44	highest	0	0	none	0	0	none	8	0	low_burden
South Africa	2,025	urban	female	30	25-34	second	0	0	none	0	0	none	10	0	low_burden
Mali	2,023	rural	female	46	45-54	fourth	0	0	none	0	0	none	7	0	low_burden
Mali	2,021	urban	male	29	25-34	highest	1	0	Moov Money	0	0	none	10	7	low_burden
Niger	2,020	rural	male	38	35-44	fourth	1	0	Airtel Money	0	0	none	7	1	low_burden
Mali	2,019	rural	male	69	55+	second	0	0	none	0	0	none	6	0	low_burden
Zambia	2,020	urban	female	19	15-24	second	0	0	none	0	0	none	9	0	low_burden
Uganda	2,019	rural	female	60	55+	second	0	0	none	0	0	none	4	0	low_burden
DRC	2,019	urban	male	19	15-24	second	0	0	none	0	0	none	5	0	low_burden
Malawi	2,023	rural	female	42	35-44	fourth	0	0	none	0	0	none	10	0	low_burden
Zambia	2,019	urban	male	55	45-54	fourth	0	0	none	0	0	none	10	0	low_burden
Ethiopia	2,018	urban	male	72	55+	lowest	0	0	none	0	0	none	10	0	low_burden
Nigeria	2,022	urban	female	30	25-34	fourth	1	0	PalmPay	0	0	none	10	3	low_burden
Mozambique	2,020	rural	male	18	15-24	second	0	0	none	0	0	none	4	0	low_burden
Mali	2,021	rural	male	60	55+	middle	0	0	none	0	0	none	3	0	low_burden
Rwanda	2,019	rural	female	68	55+	second	0	0	none	0	0	none	4	0	low_burden
Malawi	2,019	rural	male	48	45-54	middle	0	0	none	0	0	none	6	0	low_burden
DRC	2,021	rural	female	18	15-24	fourth	1	0	M-Pesa	0	0	none	4	2	low_burden
Senegal	2,023	rural	female	45	35-44	fourth	0	0	none	0	0	none	8	0	low_burden
Malawi	2,021	rural	female	38	35-44	fourth	0	0	none	0	0	none	1	0	low_burden
Malawi	2,022	rural	female	42	35-44	second	1	0	Airtel Money	0	0	none	10	4	low_burden
Ghana	2,022	urban	male	18	15-24	fourth	0	0	none	0	0	none	7	0	low_burden
Uganda	2,020	rural	female	34	25-34	lowest	1	0	MTN Mobile Money	0	0	none	5	8	low_burden
Senegal	2,020	rural	male	74	55+	middle	0	0	none	0	0	none	5	0	low_burden
Mozambique	2,021	urban	male	17	15-24	middle	1	1	e-Mola	15	152.04	bill_payment,international_remittance	7	4	low_burden
Kenya	2,018	urban	female	69	55+	fourth	1	0	M-Pesa	0	0	none	7	6	low_burden
DRC	2,023	urban	male	46	45-54	highest	0	0	none	0	0	none	5	0	low_burden
DRC	2,022	rural	female	50	45-54	second	0	0	none	0	0	none	4	0	low_burden
Tanzania	2,020	urban	male	71	55+	fourth	0	0	none	0	0	none	5	0	low_burden
Rwanda	2,020	rural	female	28	25-34	second	0	0	none	0	0	none	3	0	low_burden
Nigeria	2,020	rural	male	74	55+	middle	1	1	Paga	7	57.34	person_to_person,savings	3	9	low_burden
Mali	2,023	rural	male	31	25-34	fourth	0	0	none	0	0	none	6	0	low_burden
Niger	2,024	urban	female	35	25-34	fourth	1	1	Moov Money	14	131.27	loan_repayment,cash_withdrawal,international_remittance	2	11	low_burden
Ethiopia	2,018	rural	male	41	35-44	highest	1	0	telebirr	0	0	none	5	6	low_burden
Uganda	2,021	rural	female	51	45-54	fourth	0	0	none	0	0	none	5	0	low_burden
South Africa	2,018	urban	male	34	25-34	second	1	1	FNB eWallet	10	102.05	merchant_payment,airtime_purchase	3	6	low_burden
Malawi	2,021	urban	female	62	55+	highest	1	1	TNM Mpamba	18	53.7	person_to_person,airtime_purchase,cash_withdrawal,bill_payment	6	6	low_burden
Mozambique	2,019	urban	female	16	15-24	highest	0	0	none	0	0	none	9	0	low_burden
Niger	2,020	urban	female	54	45-54	second	0	0	none	0	0	none	9	0	low_burden
Mali	2,021	rural	female	54	45-54	lowest	0	0	none	0	0	none	4	0	low_burden
Mali	2,020	rural	male	47	45-54	second	0	0	none	0	0	none	6	0	low_burden
Nigeria	2,021	rural	female	51	45-54	fourth	0	0	none	0	0	none	1	0	low_burden
Ethiopia	2,024	rural	female	59	55+	highest	0	0	none	0	0	none	4	0	low_burden
Zambia	2,024	rural	female	69	55+	fourth	1	0	MTN Mobile Money	0	0	none	7	8	low_burden
Zambia	2,018	rural	female	28	25-34	second	1	0	Airtel Money	0	0	none	6	8	low_burden
Kenya	2,021	rural	female	43	35-44	lowest	1	1	Airtel Money	7	198.38	cash_withdrawal,airtime_purchase,person_to_person,cash_deposit	10	7	low_burden
Senegal	2,024	urban	female	48	45-54	middle	1	1	Wave	8	144.59	savings,loan_repayment,merchant_payment,person_to_person,airtime_purchase	9	6	low_burden
Nigeria	2,022	rural	male	35	25-34	second	0	0	none	0	0	none	10	0	low_burden
Mali	2,019	urban	female	73	55+	lowest	0	0	none	0	0	none	6	0	low_burden
Niger	2,022	rural	female	45	35-44	lowest	0	0	none	0	0	none	1	0	low_burden
Mozambique	2,019	rural	female	50	45-54	lowest	0	0	none	0	0	none	6	0	low_burden
Mali	2,018	rural	male	26	25-34	lowest	1	0	Moov Money	0	0	none	10	10	low_burden
Ethiopia	2,023	urban	female	39	35-44	fourth	0	0	none	0	0	none	3	0	low_burden
South Africa	2,022	rural	female	52	45-54	second	0	0	none	0	0	none	10	0	low_burden
Malawi	2,024	urban	female	40	35-44	second	0	0	none	0	0	none	2	0	low_burden
Tanzania	2,020	rural	male	61	55+	middle	0	0	none	0	0	none	7	0	low_burden
Mozambique	2,024	urban	male	23	15-24	highest	1	0	M-Pesa	0	0	none	10	15	low_burden
Ethiopia	2,021	rural	male	24	15-24	fourth	0	0	none	0	0	none	4	0	low_burden
Uganda	2,023	rural	male	35	25-34	middle	0	0	none	0	0	none	3	0	low_burden
Zambia	2,018	urban	female	34	25-34	fourth	1	0	MTN Mobile Money	0	0	none	10	9	low_burden
Mali	2,018	urban	male	63	55+	fourth	0	0	none	0	0	none	5	0	low_burden
Nigeria	2,024	rural	female	67	55+	fourth	0	0	none	0	0	none	7	0	low_burden
Malawi	2,024	urban	male	43	35-44	middle	1	1	Airtel Money	5	123.46	international_remittance,savings	3	14	low_burden
Uganda	2,025	urban	male	45	35-44	middle	1	1	MTN Mobile Money	5	141.25	savings,merchant_payment,cash_withdrawal	8	7	low_burden
Niger	2,020	urban	female	32	25-34	second	0	0	none	0	0	none	8	0	low_burden
Zambia	2,018	urban	female	46	45-54	second	0	0	none	0	0	none	7	0	low_burden
Tanzania	2,025	rural	male	57	55+	second	1	1	M-Pesa	8	25.58	cash_withdrawal,bill_payment,savings,person_to_person,cash_deposit	8	17	low_burden
Ghana	2,022	rural	male	58	55+	second	0	0	none	0	0	none	5	0	low_burden
Senegal	2,019	urban	female	73	55+	second	1	0	Wave	0	0	none	8	8	low_burden
Niger	2,024	rural	female	49	45-54	middle	0	0	none	0	0	none	5	0	low_burden
South Africa	2,025	urban	male	35	25-34	middle	0	0	none	0	0	none	10	0	low_burden
Malawi	2,020	urban	female	19	15-24	lowest	0	0	none	0	0	none	8	0	low_burden
Nigeria	2,024	rural	male	56	55+	second	0	0	none	0	0	none	3	0	low_burden
DRC	2,024	urban	female	63	55+	second	0	0	none	0	0	none	10	0	low_burden
Rwanda	2,022	rural	male	48	45-54	fourth	0	0	none	0	0	none	4	0	low_burden
Niger	2,023	rural	female	70	55+	second	0	0	none	0	0	none	10	0	low_burden
Uganda	2,020	rural	female	34	25-34	lowest	1	0	MTN Mobile Money	0	0	none	10	11	low_burden
Tanzania	2,025	rural	male	18	15-24	lowest	0	0	none	0	0	none	7	0	low_burden
Mali	2,025	urban	male	52	45-54	middle	1	0	Moov Money	0	0	none	6	1	low_burden
DRC	2,025	urban	male	22	15-24	lowest	1	1	M-Pesa	12	75.31	savings,cash_deposit,merchant_payment	5	2	low_burden
Malawi	2,023	urban	male	20	15-24	middle	0	0	none	0	0	none	10	0	low_burden
DRC	2,018	rural	male	57	55+	middle	0	0	none	0	0	none	3	0	low_burden
DRC	2,020	rural	male	41	35-44	highest	1	0	M-Pesa	0	0	none	7	1	low_burden
Ethiopia	2,021	urban	female	51	45-54	middle	0	0	none	0	0	none	10	0	low_burden
Mozambique	2,025	urban	male	59	55+	lowest	0	0	none	0	0	none	3	0	low_burden
Tanzania	2,022	urban	female	37	35-44	second	1	1	Airtel Money	12	40.49	bill_payment,international_remittance,loan_repayment	3	14	low_burden
Ghana	2,022	rural	male	51	45-54	fourth	1	0	Vodafone Cash	0	0	none	10	5	low_burden
Niger	2,019	rural	female	47	45-54	second	0	0	none	0	0	none	10	0	low_burden
Rwanda	2,024	rural	male	49	45-54	highest	0	0	none	0	0	none	4	0	low_burden
Nigeria	2,020	rural	female	57	55+	middle	0	0	none	0	0	none	3	0	low_burden

End of preview. Expand in Data Studio

Africa Synth Financial Inclusion Mobile Money Adoption Africa All | Africa (Electric Sheep Africa metadata inventory)

Size category: 10K<n<100K - Formats: csv - Sector: economics_finance - Engineered by Electric Sheep Africa

TL;DR

This dataset is part of the Electric Sheep Africa catalog on Hugging Face. It is indexed for African data discovery with standardized metadata, loading guidance, provenance notes, and analyst-oriented context.

What This Dataset Covers

Public datasets help analysts inspect structured evidence, build reproducible workflows, and compare patterns across domains.

Dataset context from the existing Hugging Face card: ⚠️ Synthetic dataset — Parameterized from published SSA literature, not real observations. Not suitable for empirical analysis or policy inference. Mobile Money Adoption in Africa Synthetic dataset modeling mobile money adoption patterns across 15 Sub-Saharan African countries from 2018-2025. Dataset Description This dataset simulates individual-level mobile money adoption and usage patterns, capturing the rapid growth of mobile financial services in Africa. It reflects… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-synth-financial-inclusion-mobile-money-adoption-africa-all.

Dataset Profile

Field	Value
Hugging Face repo	`electricsheepafrica/africa-synth-financial-inclusion-mobile-money-adoption-africa-all`
Sector	economics_finance
Topic tags	financial-inclusion, fintech, synthetic-data, sub-saharan-africa, mobile-money, synthetic
Modalities	`tabular`, `text`
Formats	`csv`
Size category	`10K<n<100K`
Countries	Africa-wide or source-defined African coverage
ISO3 coverage	`not declared`
Last modified on HF	`2026-04-14 22:52:33+00:00`
Inventory snapshot	`2026-07-16T16:00:34Z`

How To Read This Dataset

Start from the repository files and the dataset viewer when available.
Treat the README context as a fast orientation layer; confirm variable definitions and units in the data files before modeling.
Use explicit country columns when present. When geography is only implied by the title or source metadata, document that assumption in downstream analysis.
Preserve missing values until you have a defensible imputation rule.

Usage

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-synth-financial-inclusion-mobile-money-adoption-africa-all")
print(ds)

split_name = next(iter(ds))
table = ds[split_name]
print(table.features)
print(table[:3])

Convert To Pandas When Tabular

from datasets import Dataset

first_split = ds[next(iter(ds))]
if isinstance(first_split, Dataset):
    df = first_split.to_pandas()
    print(df.head())

Data Quality Notes

This card was standardized from the Electric Sheep Africa Hugging Face metadata inventory.
Exact schema, row counts, and source files should be inspected in the repository data files.
Metadata gaps from the inventory: country, upstream_publisher.
Do not infer policy meaning from labels alone; confirm definitions, units, and methods in the source material.

Source And Provenance

Source context: Electric Sheep Africa metadata inventory
Publisher/source attribution: Public dataset metadata
License: CC BY 4.0
Hugging Face URL: https://huggingface.co/datasets/electricsheepafrica/africa-synth-financial-inclusion-mobile-money-adoption-africa-all
Inventory retrieved at: 2026-07-16T16:00:34Z

Suggested Analyses

Inspect schema and missingness before modeling.
Profile variables by geography, time, and subgroup columns where present.
Join with other Electric Sheep Africa datasets using explicit country, year, and indicator fields when available.
Build reproducible notebooks that cite both the original source context and the Electric Sheep Africa Hugging Face repo.

Citation

@misc{electric_sheep_africa_africa_synth_financial_inclusion_mobile_money_adoption_africa_all_2026,
  title        = {Africa Synth Financial Inclusion Mobile Money Adoption Africa All | Africa (Electric Sheep Africa metadata inventory)},
  author       = {Public dataset metadata},
  year         = {2026},
  url          = {https://huggingface.co/datasets/electricsheepafrica/africa-synth-financial-inclusion-mobile-money-adoption-africa-all},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-synth-financial-inclusion-mobile-money-adoption-africa-all}}
}

License

Released under CC BY 4.0.

Original source rights remain with the original publisher or data provider. Electric Sheep Africa engineering standardizes discovery metadata, documentation, and usage guidance for analysis on Hugging Face.

About Electric Sheep Africa

Electric Sheep Africa publishes ML-ready African public datasets on Hugging Face.

Provenance: metadata-backed README standardized 2026-08-12 by the Electric Sheep Africa README system. Inventory source: catalog/esa_metadata_inventory/master_metadata.jsonl.

Downloads last month: 50