electricsheepasia/asia-ilo-ees-tees-sex-mts-nb-employees-by-sex-and-marital-status-thousands

--- 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 - employees - ilo - labour - employment pretty_name: "Employees by sex and marital status (thousands) | Asia (ILOSTAT)" --- # Employees by sex and marital status (thousands) | Asia (ILOSTAT) 🌏 **9,837 observations** · **33 Asia countries** · **1970–2025** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **9,837 observations** of `Employees` data across **33 Asia countries**, spanning **1970–2025**, 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=EES_TEES_SEX_MTS_NB) - **Publisher:** International Labour Organization (ILO) - **License:** [cc-by-4.0](https://creativecommons.org/licenses/by/4.0/) - **Topic:** Employees ## Methodology Data pulled directly from the ILOSTAT REST API at `https://rplumber.ilo.org/data/indicator?id=EES_TEES_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 33 Asia countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `IDN` | 672 | 1996 | 2023 | | `PHL` | 660 | 2001 | 2023 | | `KOR` | 615 | 2000 | 2025 | | `TUR` | 612 | 2000 | 2024 | | `CYP` | 527 | 1999 | 2020 | | `IRN` | 488 | 2005 | 2024 | | `KHM` | 473 | 1996 | 2023 | | `ARM` | 471 | 2001 | 2023 | | `MNG` | 459 | 2000 | 2024 | | `THA` | 432 | 2000 | 2024 | | `PAK` | 411 | 2005 | 2025 | | `VNM` | 408 | 2010 | 2024 | | `IND` | 336 | 1994 | 2025 | | `LKA` | 336 | 2010 | 2024 | | `ISR` | 312 | 2012 | 2024 | | ... | _18 more countries_ | | | ## Indicators (sample) - `EES_TEES_SEX_MTS_NB` — Employees 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 | `EES_TEES_SEX_MTS_NB` | | `indicator.label` | `string` | Indicator name in English | `Employees by sex and marital status (…` | | `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) | `1709.649` | | `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`** (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 ```python from datasets import load_dataset ds = load_dataset("electricsheepasia/asia-ilo-ees-tees-sex-mts-nb-employees-by-sex-and-marital-status-thousands") 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"] == "EES_TEES_SEX_MTS_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EES_TEES_SEX_MTS_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EES_TEES_SEX_MTS_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_ees_tees_sex_mts_nb_employees_by_sex_and_marital_status_thousands_2025, title = {Employees by sex and marital status (thousands) | Asia (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EES_TEES_SEX_MTS_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-ees-tees-sex-mts-nb-employees-by-sex-and-marital-status-thousands}} } ``` ## 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=EES_TEES_SEX_MTS_NB_This dataset contains Employees by sex and marital status (thousands) indicator data from the International Labour Organization (ILO) ILOSTAT database, covering 33 Asian countries from 1970 to 2025, with a total of 9,837 observations. Repackaged by Electric Sheep Asia, it is provided in tabular format suitable for tasks such as tabular classification, regression, and time-series forecasting. The dataset includes columns for country codes, years, sex disaggregation, marital status classification, observed values, data sources, and comes with data quality notes and usage examples.

搜集汇总

数据集介绍

构建方式

该数据集源自国际劳工组织（ILO）的ILOSTAT中央统计数据库，通过其REST API接口直接提取指标EES_TEES_SEX_MTS_NB的原始数据，并依据ISO3国家代码筛选出亚洲地区33个国家的记录。ILOSTAT采用国际劳工统计学家会议（ICLS）定义对各国劳动力调查等微观数据进行统一协调，数据集保留了来源标识以确保可追溯性，最终由Electric Sheep Asia重新打包为Parquet格式并发布。

特点

数据集涵盖1970年至2025年间亚洲33个国家的9837条观测记录，以年度频率呈现按性别和婚姻状况分类的雇员人数（单位：千人）。核心字段包括参考地区、来源信息、指标代码、性别分类、婚姻状况分类、时间及观测值，并附有观测状态和质量注释，支持表格分类、回归及时间序列预测等多类任务，具有明确的许可协议和引用规范。

使用方法

研究者可通过HuggingFace的datasets库以load_dataset函数加载数据集，转换为Pandas DataFrame后进行筛选、排序和可视化。例如，可针对特定国家（如印度尼西亚）或指标进行子集提取，亦可透视生成国家与年份的矩阵，便于开展时间序列分析、跨国比较或机器学习建模，所有操作均基于标准化字段和清晰的元数据。

背景与挑战

背景概述

国际劳工组织（ILO）于1970年代起持续编纂全球劳动力统计数据库ILOSTAT，旨在为就业、失业及体面劳动等核心议题提供可比性强的跨国面板数据。该数据集由Electric Sheep Asia于2025年重新封装，覆盖33个亚洲国家，收录了按性别与婚姻状况分类的雇员数量（千人），时间跨度为1970至2025年，共9,837条观测。作为ILOSTAT指标EES_TEES_SEX_MTS_NB的区域化子集，它为探究亚洲劳动力市场中性别与婚姻状态的交互作用提供了标准化的微观基础，对劳动经济学、社会人口学及区域发展研究具有重要参考价值。

当前挑战

该数据集所应对的核心领域问题在于：在婚姻状况与性别维度上精确量化亚洲各国雇员规模，以揭示劳动力参与的结构性差异。构建过程中面临多重挑战：其一，ILOSTAT原始数据源自各国劳动力调查与行政记录，其抽样设计、覆盖范围与统计口径存在显著异质性，需依赖国际劳工统计学家会议（ICLS）定义进行协调；其二，部分国家时间序列存在断点，如方法论修订或调查中断，导致跨年比较受限；其三，性别分类中除男、女外仅含总量，缺乏对非二元性别的系统记录；其四，部分观测值被标记为“不可靠”或“临时性”，影响推断稳健性。这些因素共同构成了数据质量与跨国可比性的持续挑战。

常用场景

经典使用场景

在劳动经济学与人口统计学的交叉研究中，该数据集最经典的使用场景是构建跨国家、跨性别的婚姻状况与就业关联分析。研究者借助其1970至2025年覆盖33个亚洲国家的面板结构，可系统考察已婚、未婚、离异或丧偶等不同婚姻状态人群的雇员数量变动趋势。通过性别与婚姻状况的双重分层，能够揭示亚洲劳动力市场中婚姻角色对就业参与率的差异化影响，尤其适用于时间序列预测与国别比较研究。

实际应用

在政策实践中，该数据集为亚洲各国劳动与社会保障部门识别脆弱就业群体提供了量化依据。决策者可借助性别与婚姻状况交叉的雇员规模数据，评估单亲家庭、丧偶女性或未婚青年等群体的就业支持需求，进而优化职业培训、托幼服务与家庭补贴等干预措施。国际组织亦可利用其跨国可比性，监测区域婚姻相关就业差距的演变，为促进体面劳动与性别平等的可持续发展目标提供循证支撑。

衍生相关工作

基于该数据集，已衍生出一系列聚焦亚洲劳动力市场结构变迁的经典工作。典型方向包括利用其长时序特征进行婚姻就业差距的分解分析，或结合其他ILO指标开展性别就业弹性的跨国回归。Electric Sheep Asia的标准化封装进一步促进了机器学习社区的应用，如构建婚姻状况就业率的预测模型、聚类识别国别就业模式，以及开发交互式可视化工具，推动了劳动统计数据的开放科学与可复现研究。