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metadata
license: cc-by-4.0
language:
  - en
task_categories:
  - tabular-classification
  - tabular-regression
  - time-series-forecasting
multilinguality: monolingual
size_categories:
  - 1K<n<10K
tags:
  - tabular
  - asia
  - ilostat
  - informal-economy
  - ilo
  - labour
  - employment
pretty_name: >-
  Informal employment rate by sex and economic activity -- 19th ICLS (%) | Asia
  (ILOSTAT)

Informal employment rate by sex and economic activity -- 19th ICLS (%) | Asia (ILOSTAT)

🌏 1,001 observations · 18 Asia countries · 2014–2025 · Repackaged by Electric Sheep Asia

rows countries years indicators license

TL;DR

This dataset contains 1,001 observations of Informal economy data across 18 Asia countries, spanning 2014–2025, 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: Informal economy

Methodology

Data pulled directly from the ILOSTAT REST API at https://rplumber.ilo.org/data/indicator?id=EMP_5NIF_SEX_ECO_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

18 Asia countries · top rows shown below, sorted by row count:

Country Rows First year Last year
CYP 154 2014 2024
JOR 127 2017 2024
ARM 105 2018 2024
MNG 90 2019 2024
VNM 81 2020 2024
GEO 76 2020 2024
BGD 60 2022 2024
MMR 60 2017 2020
BRN 60 2014 2024
MDV 36 2016 2019
LAO 35 2017 2022
AFG 18 2021 2021
LBN 18 2019 2019
KOR 18 2019 2019
PAK 18 2025 2025
... 3 more countries

Indicators (sample)

  • EMP_5NIF_SEX_ECO_RT — Informal employment rate by sex and economic activity -- 19th ICLS (%)

Schema

Column Type Description Example
ref_area string ISO 3166-1 alpha-3 country code AFG
ref_area.label string Country name in English Afghanistan
source string ILOSTAT source code (e.g. labour force survey) BA:15715
source.label string Source name in English LFS - Labour Force Survey
indicator string ILOSTAT indicator code EMP_5NIF_SEX_ECO_RT
indicator.label string Indicator name in English Informal employment rate by sex and e…
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.) ECO_SECTOR_TOTAL
classif1.label string Economic activity (Broad sector): Total
time int64 Observation year 2021
obs_value float64 Observed indicator value (unit varies — see indicator definition) 78.882
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_S3:8
note_source.label string Repository: ILO-STATISTICS - Micro da…

Disaggregation dimensions

The following columns provide disaggregation dimensions:

  • sex (4 unique values): SEX_T, SEX_M, SEX_F, SEX_O

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-emp-5nif-sex-eco-rt-informal-employment-rate-by-sex-and-economic-activ")
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"] == "EMP_5NIF_SEX_ECO_RT"]
          .sort_values("time"))
sample.plot(x="time", y="obs_value", title="EMP_5NIF_SEX_ECO_RT")

Pivot to country × year matrix

matrix = (df[df["indicator"] == "EMP_5NIF_SEX_ECO_RT"]
          .pivot_table(index="time", columns="ref_area", values="obs_value"))
print(matrix.tail())

Citation

@misc{asia_ilo_emp_5nif_sex_eco_rt_informal_employment_rate_by_sex_and_economic_activ_2025,
  title        = {Informal employment rate by sex and economic activity -- 19th ICLS (%) | Asia (ILOSTAT)},
  author       = {International Labour Organization (ILO)},
  year         = {2025},
  url          = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_5NIF_SEX_ECO_RT},
  publisher    = {HuggingFace Datasets, repackaged by Electric Sheep Asia},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-emp-5nif-sex-eco-rt-informal-employment-rate-by-sex-and-economic-activ}}
}

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 2026-05-28 via the Electric Sheep pipeline. Source URL: https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_5NIF_SEX_ECO_RT