license: cc-by-4.0
language:
- en
task_categories:
- tabular-classification
- tabular-regression
- time-series-forecasting
multilinguality: monolingual
size_categories:
- n<1K
tags:
- tabular
- asia
- ilostat
- employment
- ilo
- labour
pretty_name: >-
SDG indicator 5.5.2 - Proportion of women in senior and middle management
positions -- 19t | Asia (ILOSTAT)
SDG indicator 5.5.2 - Proportion of women in senior and middle management positions -- 19t | Asia (ILOSTAT)
🌏 88 observations · 19 Asia countries · 2013–2025 · Repackaged by Electric Sheep Asia
TL;DR
This dataset contains 88 observations of Employment data across 19 Asia countries, spanning 2013–2025, 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: Employment
Methodology
Data pulled directly from the ILOSTAT REST API at https://rplumber.ilo.org/data/indicator?id=SDG_B552_NOC_RT and filtered to Asia 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
19 Asia countries · top rows shown below, sorted by row count:
| Country | Rows | First year | Last year |
|---|---|---|---|
CYP |
11 | 2014 | 2024 |
ISR |
11 | 2014 | 2024 |
JPN |
10 | 2014 | 2023 |
MNG |
6 | 2019 | 2024 |
JOR |
6 | 2017 | 2023 |
ARE |
5 | 2017 | 2022 |
GEO |
5 | 2020 | 2024 |
VNM |
5 | 2020 | 2024 |
BRN |
4 | 2014 | 2024 |
SGP |
4 | 2021 | 2024 |
KHM |
4 | 2019 | 2023 |
MMR |
4 | 2017 | 2020 |
BGD |
3 | 2022 | 2024 |
TLS |
3 | 2013 | 2022 |
LAO |
2 | 2017 | 2022 |
| ... | 4 more countries |
Indicators (sample)
SDG_B552_NOC_RT— SDG indicator 5.5.2 - Proportion of women in senior and middle management positions -- 19th ICLS (%)
Schema
| Column | Type | Description | Example |
|---|---|---|---|
ref_area |
string |
ISO 3166-1 alpha-3 country code | ARE |
ref_area.label |
string |
Country name in English | United Arab Emirates |
source |
string |
ILOSTAT source code (e.g. labour force survey) | BA:716 |
source.label |
string |
Source name in English | LFS - Labour Force Survey |
indicator |
string |
ILOSTAT indicator code | SDG_B552_NOC_RT |
indicator.label |
string |
Indicator name in English | SDG indicator 5.5.2 - Proportion of w… |
time |
int64 |
Observation year | 2022 |
obs_value |
float64 |
Observed indicator value (unit varies — see indicator definition) | 23.464 |
obs_status |
string |
Observation status flag (e.g. provisional, unreliable) | B |
obs_status.label |
string |
— | Break in series |
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… |
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("electricsheepasia/asia-ilo-sdg-b552-noc-rt-sdg-indicator-5-5-2-proportion-of-women-in-senior")
df = ds["train"].to_pandas()
print(df.head())
Filter to one country
indonesia = df[df["ref_area"] == "IDN"]
Time-series for a single indicator
sample = (df[df["indicator"] == "SDG_B552_NOC_RT"]
.sort_values("time"))
sample.plot(x="time", y="obs_value", title="SDG_B552_NOC_RT")
Pivot to country × year matrix
matrix = (df[df["indicator"] == "SDG_B552_NOC_RT"]
.pivot_table(index="time", columns="ref_area", values="obs_value"))
print(matrix.tail())
Citation
@misc{asia_ilo_sdg_b552_noc_rt_sdg_indicator_5_5_2_proportion_of_women_in_senior_2025,
title = {SDG indicator 5.5.2 - Proportion of women in senior and middle management positions -- 19t | Asia (ILOSTAT)},
author = {International Labour Organization (ILO)},
year = {2025},
url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=SDG_B552_NOC_RT},
publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia},
howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-sdg-b552-noc-rt-sdg-indicator-5-5-2-proportion-of-women-in-senior}}
}
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 Asia repackaging.
About Electric Sheep
Electric Sheep Asia is part of the Electric Sheep mission: a unified, ML-ready data layer for Asia 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/electricsheepasia
Provenance: ingested 2026-05-25 via the Electric Sheep pipeline. Source URL: https://www.ilo.org/shinyapps/bulkexplorer/?id=SDG_B552_NOC_RT