electricsheepeurope/europe-ilo-emp-care-sex-oc2-nb-care-employment-by-sex-and-occupation-isco-level-2

Care employment by sex and occupation - ISCO level 2 (thousands) | Europe (ILOSTAT)

🇪🇺 19,710 observations · 15 Europe countries · 1996–2025 · Repackaged by Electric Sheep Europe

TL;DR

This dataset contains 19,710 observations of Paid care workers data across 15 Europe countries, spanning 1996–2025, covering 1 distinct indicators.

About the source

ILOSTAT is the ILO's central statistics database, the leading global source for labour statistics. It compiles indicators across employment, unemployment, wages, working time, child labour, informal economy, social protection, occupational injuries, and SDG decent work targets — drawing on national labour force surveys, household income surveys, establishment surveys, and administrative records. Coverage spans 200+ economies, with the ILO's Department of Statistics responsible for harmonisation.

• Source: ILOSTAT
• Publisher: International Labour Organization (ILO)
• License: cc-by-4.0
• Topic: Paid care workers

Methodology

Data pulled directly from the ILOSTAT REST API at https://rplumber.ilo.org/data/indicator?id=EMP_CARE_SEX_OC2_NB and filtered to Europe ISO3 country codes. ILOSTAT harmonises raw survey microdata using ICLS (International Conference of Labour Statisticians) definitions; sources are flagged in the source.label column for traceability.

Geographic coverage

15 Europe countries · top rows shown below, sorted by row count:

Country	Rows	First year	Last year
CHE	3,004	1996	2025
GBR	2,111	2005	2025
FRA	2,089	2005	2024
GRC	1,636	2006	2025
AUT	1,525	2011	2025
PRT	1,439	2011	2025
SVK	1,381	1998	2023
CZE	1,352	2011	2024
MKD	1,133	2006	2025
BIH	877	2006	2024
SRB	814	2007	2019
ALB	726	2010	2024
BLR	718	2016	2024
ITA	713	2014	2020
ESP	192	2001	2011

Indicators (sample)

• EMP_CARE_SEX_OC2_NB — Care employment by sex and occupation - ISCO level 2 (thousands)

Schema

Column	Type	Description	Example
ref_area	string	ISO 3166-1 alpha-3 country code	ALB
ref_area.label	string	Country name in English	Albania
source	string	ILOSTAT source code (e.g. labour force survey)	BA:480
source.label	string	Source name in English	LFS - Labour Force Survey
indicator	string	ILOSTAT indicator code	EMP_CARE_SEX_OC2_NB
indicator.label	string	Indicator name in English	Care employment by sex and occupation…
sex	string	Disaggregation by sex (SEX_T = total, SEX_M = male, SEX_F = female)	SEX_T
sex.label	string	—	Total
classif1	string	First classification variable (age, education, status, etc.)	OC2_ISCO08_TOTAL
classif1.label	string	—	Occupation (ISCO-08), 2 digit level: …
time	int64	Observation year	2024
obs_value	float64	Observed indicator value (unit varies — see indicator definition)	114.723
obs_status	string	Observation status flag (e.g. provisional, unreliable)	U
obs_status.label	string	—	Unreliable
note_classif	string	—	—
note_classif.label	string	—	—
note_indicator	string	—	I11:264
note_indicator.label	string	—	Break in series: Methodology revised
note_source	string	—	R1:3513
note_source.label	string	—	Repository: ILO-STATISTICS - Micro da…

Disaggregation dimensions

The following columns provide disaggregation dimensions:

• sex (3 unique values): SEX_T, SEX_M, SEX_F

Data quality & caveats

• Data is annual frequency. Some indicators also publish monthly or quarterly series — those are not included here.
• When an indicator has multiple sources for the same country×year, the ILO-selected 'best source' is used.
• Disaggregation columns (sex, classif1, classif2) are non-null only when the indicator publishes that breakdown.

Usage

from datasets import load_dataset

ds = load_dataset("electricsheepeurope/europe-ilo-emp-care-sex-oc2-nb-care-employment-by-sex-and-occupation-isco-level-2")
df = ds["train"].to_pandas()
print(df.head())

Filter to one country

germany = df[df["ref_area"] == "DEU"]

Time-series for a single indicator

sample = (df[df["indicator"] == "EMP_CARE_SEX_OC2_NB"]
          .sort_values("time"))
sample.plot(x="time", y="obs_value", title="EMP_CARE_SEX_OC2_NB")

Pivot to country × year matrix

matrix = (df[df["indicator"] == "EMP_CARE_SEX_OC2_NB"]
          .pivot_table(index="time", columns="ref_area", values="obs_value"))
print(matrix.tail())

Citation

@misc{europe_ilo_emp_care_sex_oc2_nb_care_employment_by_sex_and_occupation_isco_level_2_2025,
  title        = {Care employment by sex and occupation - ISCO level 2 (thousands) | Europe (ILOSTAT)},
  author       = {International Labour Organization (ILO)},
  year         = {2025},
  url          = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_CARE_SEX_OC2_NB},
  publisher    = {HuggingFace Datasets, repackaged by Electric Sheep Europe},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-emp-care-sex-oc2-nb-care-employment-by-sex-and-occupation-isco-level-2}}
}

License

Released under cc-by-4.0.

Original data © International Labour Organization (ILO). When using this dataset, please cite both the original source above and the Electric Sheep Europe repackaging.

About Electric Sheep

Electric Sheep Europe is part of the Electric Sheep mission: a unified, ML-ready data layer for Europe on HuggingFace. 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/electricsheepeurope

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Provenance: ingested 2026-05-28 via the Electric Sheep pipeline. Source URL: https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_CARE_SEX_OC2_NB