electricsheepafrica/africa-governance-all

Control of Corruption

Publisher: World Bank · Source: OpenAfrica · License: cc-by · Updated: 2023-01-23

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Abstract

Control of Corruption captures perceptions of the extent to which public power is exercised for private gain, including both petty and grand forms of corruption, as well as "capture" of the state by elites and private interests. Estimate gives the country's score on the aggregate indicator, in units of a standard normal distribution, i.e. ranging from approximately -2.5 to 2.5.

Each row in this dataset represents tabular records. Data was last updated on OpenAfrica on 2023-01-23. Geographic scope: BENIN, BOTSWANA, CAPE-VERDE, ETHIOPIA, KENYA, NIGERIA, SENEGAL, SOUTH-AFRICA, and 4 others.

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

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Dataset Characteristics

Domain	Humanitarian and development data
Unit of observation	Tabular records
Rows (total)	70
Columns	27 (23 numeric, 4 categorical, 0 datetime)
Train split	56 rows
Test split	14 rows
Geographic scope	BENIN, BOTSWANA, CAPE-VERDE, ETHIOPIA, KENYA, NIGERIA, SENEGAL, SOUTH-AFRICA, and 4 others
Publisher	World Bank
OpenAfrica last updated	2023-01-23

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Variables

Identifier / Metadata — unnamed_1 (Control of Corruption: Estimate, Control of Corruption: Percentile Rank, Control of Corruption: Number of Sources), unnamed_2 (range -1.24–1996.0), unnamed_3 (range -1.1658–1998.0), unnamed_4 (range -1.3246–2000.0), unnamed_5 (range -1.5021–2002.0) and 21 others.

Other — control_of_corruption_1996_2021 (Benin, Botswana, Cabo Verde).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-governance-all")
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
control_of_corruption_1996_2021	object	1.4%	Benin, Botswana, Cabo Verde
unnamed_1	object	4.3%	Control of Corruption: Estimate, Control of Corruption: Percentile Rank, Control of Corruption: Number of Sources
unnamed_2	float64	4.3%	-1.24 – 1996.0 (mean 49.4544)
unnamed_3	float64	4.3%	-1.1658 – 1998.0 (mean 49.328)
unnamed_4	float64	4.3%	-1.3246 – 2000.0 (mean 49.2738)
unnamed_5	float64	4.3%	-1.5021 – 2002.0 (mean 49.0986)
unnamed_6	float64	4.3%	-1.4177 – 2003.0 (mean 49.838)
unnamed_7	float64	4.3%	-1.3834 – 2004.0 (mean 50.409)
unnamed_8	float64	4.3%	-1.3876 – 2005.0 (mean 50.491)
unnamed_9	float64	4.3%	-1.1913 – 2006.0 (mean 52.155)
unnamed_10	float64	4.3%	-1.3298 – 2007.0 (mean 52.26)
unnamed_11	float64	4.3%	-1.4275 – 2008.0 (mean 51.9305)
unnamed_12	float64	4.3%	-1.1503 – 2009.0 (mean 51.3054)
unnamed_13	float64	4.3%	-1.1967 – 2010.0 (mean 50.9721)
unnamed_14	float64	4.3%	-1.2046 – 2011.0 (mean 51.2121)
unnamed_15	float64	4.3%	-1.5034 – 2012.0 (mean 50.4249)
unnamed_16	float64	4.3%	-1.4888 – 2013.0 (mean 51.1353)
unnamed_17	float64	4.3%	-1.4626 – 2014.0 (mean 51.8034)
unnamed_18	float64	4.3%	-1.483 – 2015.0 (mean 52.5657)
unnamed_19	float64	4.3%	-1.546 – 2016.0 (mean 52.9573)
unnamed_20	float64	4.3%	-1.5452 – 2017.0 (mean 51.6021)
unnamed_21	float64	4.3%	-1.4395 – 2018.0 (mean 52.1678)
unnamed_22	float64	4.3%
unnamed_23	float64	4.3%
unnamed_24	float64	4.3%
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-28

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Numeric Summary

Column	Min	Max	Mean	Median
unnamed_2	-1.24	1996.0	49.4544	4.0
unnamed_3	-1.1658	1998.0	49.328	4.0
unnamed_4	-1.3246	2000.0	49.2738	4.0
unnamed_5	-1.5021	2002.0	49.0986	5.0
unnamed_6	-1.4177	2003.0	49.838	7.0
unnamed_7	-1.3834	2004.0	50.409	10.0
unnamed_8	-1.3876	2005.0	50.491	11.0
unnamed_9	-1.1913	2006.0	52.155	13.0
unnamed_10	-1.3298	2007.0	52.26	14.0
unnamed_11	-1.4275	2008.0	51.9305	13.0
unnamed_12	-1.1503	2009.0	51.3054	14.0
unnamed_13	-1.1967	2010.0	50.9721	14.0
unnamed_14	-1.2046	2011.0	51.2121	15.0
unnamed_15	-1.5034	2012.0	50.4249	12.7962
unnamed_16	-1.4888	2013.0	51.1353	15.0

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Curation

Raw data was downloaded from OpenAfrica 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 World Bank 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 12 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{openafrica_africa_governance_all,
  title     = {Control of Corruption},
  author    = {World Bank},
  year      = {2023},
  url       = {https://open.africa/dataset/control-of-corruption},
  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.