electricsheepafrica/africa-rwanda-eicv5-vup-employment-salaried-business-0931e6c0

EICV5 (VUP): Employment, salaried, business | Africa (Rwanda Data Sharing Platform - NISR)

9,180 rows - 1 Africa country - 2016-10-13-2017-10-22 - Repackaged by Electric Sheep Africa

TL;DR

This dataset packages one public table from Rwanda's official Data Sharing Platform as ML-ready Parquet. The source package is the provenance boundary: all usable source columns from this table stay together in this repo. If the public CSV omits an API-declared column, that column is included with null values and recorded in the source snapshot metadata.

About the source

• Source: EICV5 (VUP): Employment, salaried, business
• Publisher: NISR
• Portal: Rwanda Data Sharing Platform
• Dataset ID: dd81f8f1-1794-4be9-b8ba-5ddbc3935a3b
• Schema: EICV5_VUP
• Table: eicv5_vup_s6b_employement_6c_salaried_s6d_business
• Sectors: National Statistics, Social Protection
• Source tags: Microdata
• Period: 2016-10-13-2017-10-22
• Resource: CSV package
• License: CC BY 4.0
• Packaging mode: tabular_resource

Source description

Summary: This microdata table contains information on employment, including salaried employment and business, collected in the fifth Integrated Household Living Conditions Survey, known as EICV5 (Enquête Intégrale sur les Conditions de Vie des ménages). A main cross-sectional sample survey, a panel survey and a VUP (Vision Umurenge Programme) sample survey were conducted simultaneously; this microdata table relates to the VUP sample survey. Geographic Coverage: National coverage (Rwanda), including rural and urban households and allowing province- and district-level estimation of key indicators. Data are disaggregated up to the district level. Time Period: The EICV5 survey was conducted over a 12-month cycle from October 2016 to October 2017. Data collection was divided into 10 cycles in order to represent seasonality in the income and consumption data. Frequency: The EICV5 took place three years after the EICV4 was conducted (October 2013-October 2014); prior to EICV4, the survey was conducted every five years, with the first survey (EICV1) conducted in 2000/01. Population/Units: Household members Key Variables: ID number of employment, ISCO code, ISIC code, Many months in the last year work in this job, Worked most of time in the last 12 months Purpose: The main objective of the EICV5 VUP Panel Survey is to provide longitudinal data for a nationally-representative panel of households that received VUP benefits at the time of the EICV4, in order to obtain reliable estimates of trends in the socioeconomic indicators for these households. Data Quality Notes: Following the EICV5 data collection it was found that 175 of these households could not be interviewed because of a death or move outside the country. Therefore the final number of eligible EICV5 VUP panel sample households was 1,642, including 324 households from split. For more information on the EICV5 VUP Sample Survey including further details on methodology, please see the collection-level description.

Geographic coverage

1 Africa country:

Country	Rows	First year	Last year	Name
RWA	9,180	n/a	n/a	Rwanda

Indicators or Resource Contents

• This source package is published as a normalized tabular resource.

Schema

Column	Type	Description	Example
source_record_id	string	Stable row identifier assigned during Electric Sheep Africa packaging.	dd81f8f1-1794-4be9-b8ba-5ddbc3935a3b:0
country_iso3	string	ISO3 country code.	RWA
country_name	string	Country name.	Rwanda
id	int64	Row ID	1
key_17	float64	EICV5 Household unique ID	11000100.0
hhid	float64	Household ID in EICV4	110001.0
clust	float64	Cluster	11231.0
province	string	Province	Kigali City
district	string	District Name	Nyarugenge
ur	string	Urban/Rural	Rural
weight	float64	Household weight	28.08333396911621
pid_17	float64	EICV5 Personal ID	1.0
eid	float64	ID number of employment	``
s6bq3b	string	ISCO code	``
s6bq4b	string	ISIC CODE	``
s6bq5	float64	Many months in the last year work in this job	``
s6bq09	float64	How many days worked in the job in the last 7 days	``
s6bq10	float64	Many hours worked during the last 7 days	``
s6bq11	float64	How many days did work during the last 7 days of his/her last job?	``
s6bq13a	float64	Years of long period working in this job	``
s6bq13b	string	Months of long period working in this job	``
s6bq14	string	Sector for this job	``
s6bq15	string	Occupational status in each activity	``
s6cq16	string	Type of nature contract	``
s6cq17a	float64	Net salary/Wage Amoun	``
s6cq17b	string	Net salary/Wage Unit	``
s6cq19a	float64	Value of the last in-kind payments	``
s6cq19b	string	What is the value of the last in-kind payments_Unit of time_?	``
s6cq21a	float64	Subsidize Amount- benefits	``
s6cq21b	string	Subsidize Unit of time - benefits	``
s6cq23a	float64	Benefit Amount	``
s6cq23b	string	Benefit Unit of time	``
s6dq24	string	Registered with the RRA/RDB or the district, Sector authorities	``
s6dq25	string	Over the last 12 months, has sought a loan at a bank or other insitution	``
s6dq26	string	VUP Financial Service loan	``
s6dq27	float64	Average employees over the last 3 monts	``
s6dq28	float64	Owners/members work in this business	``
s6dq30	string	Greatest obstacles encountered when creqting business	``
s6dq31	string	Primary source of money used to create business	``
s6dq32a	float64	Labour expediture Amount	``
s6dq32b	string	Labour expediture Time of unit	``
s6dq33a	float64	Non labour expediture Amount	``
s6dq33b	string	Non labour expediture Time of unit	``
s6dq34a	float64	Turnover Amount	``
s6dq34b	string	Turnover Time unit	``
s0q18m	int64	Month of interview	4
s0q18y	int64	Year of interview	2017
s6bq6	string	Worked most of time in the last 12 months	``
s6bq7	string	Worked for at least 1 hour in this job during the last 7 days	``
s6bq8	string	Most working time in the last 7 days	``
s6bq12	string	Still working	``
s6cq18	string	Receive in-kind payments	``
s6cq20	string	Employer provide or subsidize housing	``
s6cq22	string	Receive any other benefits	``
s6bq99	string	More job	``
s6dq29	string	Maintain accounts egularly	``
source_provider	string	Publishing organization.	NISR
source_dataset	string	Source dataset title.	EICV5 (VUP): Employment, salaried, business
source_resource	string	Source table name.	eicv5_vup_s6b_employement_6c_salaried_s6d_business
source_package_id	string	Rwanda Data Sharing Platform dataset UUID.	dd81f8f1-1794-4be9-b8ba-5ddbc3935a3b
source_resource_id	string	Rwanda Data Sharing Platform dataset UUID.	dd81f8f1-1794-4be9-b8ba-5ddbc3935a3b
source_url	string	Original source download URL.	https://api.data.gov.rw/api/v1/datasets/public/dd81f8f1-1794-4be9-b8ba-...
license_id	string	Source license identifier.	cc-by-4.0
retrieved_at	string	UTC retrieval timestamp.	2026-07-18T16:35:18Z

Usage

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-rwanda-eicv5-vup-employment-salaried-business-0931e6c0")
df = ds["train"].to_pandas()
print(df.head())

Filter to Rwanda

rwanda = df[df["country_iso3"] == "RWA"]

Work with indicators

if "indicator_id" in df.columns:
    print(df["indicator_id"].value_counts().head())
    sample = df.sort_values([c for c in ["indicator_id", "year"] if c in df.columns])

Citation

@misc{electric_sheep_africa_africa_rwanda_eicv5_vup_employment_salaried_business_0931e6c0_2026,
  title        = {EICV5 (VUP): Employment, salaried, business | Africa (Rwanda Data Sharing Platform - NISR)},
  author       = {NISR},
  year         = {2026},
  url          = {https://api.data.gov.rw/api/v1/datasets/public/dd81f8f1-1794-4be9-b8ba-5ddbc3935a3b},
  publisher    = {HuggingFace Datasets, repackaged by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-rwanda-eicv5-vup-employment-salaried-business-0931e6c0}}
}

License

Released under CC BY 4.0.

Original data (c) NISR. When using this dataset, please cite both the original source above and the Electric Sheep Africa repackaging.

About Electric Sheep

Electric Sheep Africa is part of the Electric Sheep mission: a unified, ML-ready data layer for Africa on Hugging Face. We pull data from authoritative open sources, normalize the schemas, package as Parquet, and publish with consistent dataset cards so researchers and developers can use load_dataset() to start working in seconds.

Browse the full collection: huggingface.co/electricsheepafrica

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Provenance: ingested 2026-07-18 via the Electric Sheep pipeline. Source URL: https://api.data.gov.rw/api/v1/datasets/public/dd81f8f1-1794-4be9-b8ba-5ddbc3935a3b/download?format=csv