electricsheepasia/asia-ilo-emp-xtru-sex-mts-nb-time-related-underemployment-by-sex-and-marital-st
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--- 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 - time-related-underemployment - ilo - labour - employment pretty_name: "Time-related underemployment by sex and marital status (thousands) | Asia (ILOSTAT)" --- # Time-related underemployment by sex and marital status (thousands) | Asia (ILOSTAT) 🌏 **5,351 observations** · **29 Asia countries** · **1996–2025** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **5,351 observations** of `Time-related underemployment` data across **29 Asia 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](https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_XTRU_SEX_MTS_NB) - **Publisher:** International Labour Organization (ILO) - **License:** [cc-by-4.0](https://creativecommons.org/licenses/by/4.0/) - **Topic:** Time-related underemployment ## Methodology Data pulled directly from the ILOSTAT REST API at `https://rplumber.ilo.org/data/indicator?id=EMP_XTRU_SEX_MTS_NB` 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 29 Asia countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `TUR` | 505 | 2004 | 2024 | | `CYP` | 487 | 1999 | 2020 | | `IRN` | 480 | 2005 | 2024 | | `VNM` | 378 | 2010 | 2024 | | `KHM` | 343 | 1996 | 2023 | | `THA` | 342 | 2010 | 2024 | | `KOR` | 336 | 2012 | 2025 | | `LKA` | 329 | 2010 | 2024 | | `PAK` | 262 | 2006 | 2025 | | `MNG` | 262 | 2013 | 2024 | | `PHL` | 200 | 2017 | 2023 | | `IDN` | 192 | 2016 | 2023 | | `BRN` | 188 | 2014 | 2024 | | `AFG` | 128 | 2012 | 2021 | | `JOR` | 126 | 2017 | 2024 | | ... | _14 more countries_ | | | ## Indicators (sample) - `EMP_XTRU_SEX_MTS_NB` — Time-related underemployment by sex and marital status (thousands) ## 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_XTRU_SEX_MTS_NB` | | `indicator.label` | `string` | Indicator name in English | `Time-related underemployment by sex a…` | | `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.) | `MTS_AGGREGATE_TOTAL` | | `classif1.label` | `string` | — | `Marital status (Aggregate): Total` | | `time` | `int64` | Observation year | `2021` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `588.672` | | `obs_status` | `string` | Observation status flag (e.g. provisional, unreliable) | `U` | | `obs_status.label` | `string` | — | `Unreliable` | | `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`** (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 ```python from datasets import load_dataset ds = load_dataset("electricsheepasia/asia-ilo-emp-xtru-sex-mts-nb-time-related-underemployment-by-sex-and-marital-st") df = ds["train"].to_pandas() print(df.head()) ``` ### Filter to one country ```python indonesia = df[df["ref_area"] == "IDN"] ``` ### Time-series for a single indicator ```python sample = (df[df["indicator"] == "EMP_XTRU_SEX_MTS_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_XTRU_SEX_MTS_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_XTRU_SEX_MTS_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_emp_xtru_sex_mts_nb_time_related_underemployment_by_sex_and_marital_st_2025, title = {Time-related underemployment by sex and marital status (thousands) | Asia (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_XTRU_SEX_MTS_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-emp-xtru-sex-mts-nb-time-related-underemployment-by-sex-and-marital-st}} } ``` ## License Released under [cc-by-4.0](https://creativecommons.org/licenses/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](https://huggingface.co/electricsheepasia) --- _Provenance: ingested 2026-05-26 via the Electric Sheep pipeline. Source URL: https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_XTRU_SEX_MTS_NB_
This dataset contains 5,351 observations of Time-related underemployment data across 29 Asia countries, spanning 1996 to 2025, covering 1 distinct indicator (EMP_XTRU_SEX_MTS_NB). The data is sourced from the International Labour Organization (ILO) ILOSTAT database via its REST API, filtered to Asia ISO3 country codes, and repackaged by Electric Sheep Asia. It includes detailed schema columns such as country code, source, indicator, sex disaggregation (total, male, female), marital status classification, observation year, observed value, and status flags. The data is annual frequency, with quality caveats like using ILO-selected best source for duplicate entries and non-null disaggregation columns only when published. The dataset is part of a unified, ML-ready data layer for Asia on HuggingFace.
