electricsheepasia/asia-ilo-ees-tees-sex-geo-mts-nb-employees-by-sex-rural-urban-areas-and-marital-sta

--- license: cc-by-4.0 language: - en task_categories: - tabular-classification - tabular-regression - time-series-forecasting multilinguality: monolingual size_categories: - 10K<n<100K tags: - tabular - asia - ilostat - employees - ilo - labour - employment pretty_name: "Employees by sex, rural / urban areas and marital status (thousands) | Asia (ILOSTAT)" --- # Employees by sex, rural / urban areas and marital status (thousands) | Asia (ILOSTAT) 🌏 **23,517 observations** · **29 Asia countries** · **1970–2025** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **23,517 observations** of `Employees` data across **29 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_GEO_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_GEO_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 | |---------|-----:|-----------:|----------:| | `IDN` | 2,016 | 1996 | 2023 | | `CYP` | 1,568 | 1999 | 2020 | | `PHL` | 1,560 | 2007 | 2023 | | `ARM` | 1,390 | 2001 | 2023 | | `KHM` | 1,320 | 1996 | 2023 | | `MNG` | 1,296 | 2009 | 2024 | | `VNM` | 1,244 | 2010 | 2024 | | `PAK` | 1,233 | 2005 | 2025 | | `THA` | 1,218 | 2007 | 2024 | | `KOR` | 1,152 | 2000 | 2025 | | `TUR` | 1,042 | 2000 | 2013 | | `LKA` | 1,008 | 2010 | 2024 | | `IND` | 980 | 1994 | 2025 | | `PSE` | 834 | 2000 | 2022 | | `GEO` | 828 | 2009 | 2020 | | ... | _14 more countries_ | | | ## Indicators (sample) - `EES_TEES_SEX_GEO_MTS_NB` — Employees by sex, rural / urban areas 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_GEO_MTS_NB` | | `indicator.label` | `string` | Indicator name in English | `Employees by sex, rural / urban areas…` | | `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.) | `GEO_COV_NAT` | | `classif1.label` | `string` | — | `Area type: National` | | `classif2` | `string` | Second classification variable where applicable | `MTS_AGGREGATE_TOTAL` | | `classif2.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-geo-mts-nb-employees-by-sex-rural-urban-areas-and-marital-sta") 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_GEO_MTS_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EES_TEES_SEX_GEO_MTS_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EES_TEES_SEX_GEO_MTS_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_ees_tees_sex_geo_mts_nb_employees_by_sex_rural_urban_areas_and_marital_sta_2025, title = {Employees by sex, rural / urban areas and marital status (thousands) | Asia (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EES_TEES_SEX_GEO_MTS_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-ees-tees-sex-geo-mts-nb-employees-by-sex-rural-urban-areas-and-marital-sta}} } ``` ## 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_GEO_MTS_NB_The dataset named Employees by sex, rural / urban areas and marital status (thousands) | Asia (ILOSTAT) contains 23,517 observations across 29 Asia countries, spanning from 1970 to 2025. It focuses on a single indicator: employees by sex, rural/urban areas, and marital status (in thousands). The data is sourced from ILOSTAT, the International Labour Organizations central statistics database, which is a leading global source for labour statistics, covering indicators such as employment, unemployment, wages, and more. Data is pulled directly from the ILOSTAT REST API and filtered to Asia ISO3 country codes, with harmonization based on International Conference of Labour Statisticians (ICLS) definitions. The dataset includes columns like country code, year, indicator value, sex disaggregation, area type, and marital status, supporting tasks like tabular classification, regression, and time-series forecasting. Data quality notes include annual frequency, use of ILO-selected best source for multiple sources, and non-null disaggregation columns only when breakdowns are published. Repackaged by Electric Sheep Asia in Parquet format for machine learning readiness, it is released under the CC-BY-4.0 license.

搜集汇总

数据集介绍

构建方式

该数据集由Electric Sheep Asia基于国际劳工组织（ILO）的ILOSTAT数据库重新封装而成，原始数据通过ILOSTAT的REST API接口获取，并经筛选限定为亚洲ISO3国家代码，其底层数据源自各国劳动力调查、家庭收入调查及行政记录，由ILO统计局按照国际劳工统计学家会议（ICLS）定义进行统一协调，最终以Parquet格式发布于HuggingFace平台，确保数据可追溯且适应机器学习工作流。

特点

数据集涵盖1970至2025年间29个亚洲国家的23,517条观测记录，聚焦于按性别、城乡区域及婚姻状况分类的雇员人数（以千计），包含性别、区域类型、婚姻状态等多维分类变量，并提供观测状态与备注信息以标识数据质量，频率为年度，覆盖范围广且时间跨度长，为劳动力市场分析提供丰富的截面与时序信息。

使用方法

研究人员可通过HuggingFace的datasets库以load_dataset()函数直接加载数据集，转换为Pandas DataFrame后进行筛选、排序和透视分析，例如提取特定国家的子集、绘制单一指标的时间序列图或构建国家与年份的矩阵表，便于开展分类、回归及时间序列预测等建模任务。

背景与挑战

背景概述

国际劳工组织（ILO）自二十世纪中叶起便致力于构建全球性的劳动力统计体系，其核心数据库ILOSTAT汇集了涵盖就业、失业、工资及劳动条件等多维指标，成为劳动经济学与政策研究中不可或缺的基础设施。该数据集由Electric Sheep Asia于2025年从ILOSTAT REST API中提取并重新封装，聚焦亚洲29个国家1970至2025年间按性别、城乡地域及婚姻状况分组的雇员数量，包含23,517条观测值。其核心研究问题在于揭示亚洲地区劳动力市场中性别、地域与婚姻状态交织下的就业结构差异，为区域劳动政策制定与不平等研究提供可复用的标准化数据资源，对性别经济学与区域发展研究具有显著推动意义。

当前挑战

该数据集所回应的领域问题，在于缺乏跨国可比且细分维度一致的亚洲雇员统计数据，以支撑性别与城乡差异的量化分析。构建过程中面临多重挑战：原始数据源自各国劳动力调查与住户调查，统计口径与抽样方法各异，ILOSTAT虽经国际劳工统计学家会议定义进行调和，但断点与不可靠标志仍频繁出现；部分国家年份覆盖不连续，婚姻状况与城乡分类仅在特定年份或国家可获取，导致面板数据不平衡；此外，零值缺失与单位差异要求使用者在建模前进行细致的数据清洗与验证。

常用场景

经典使用场景

在劳动经济学与人口统计学交叉领域，该数据集最为经典的运用在于刻画亚洲地区雇员规模的性别差异与城乡分布格局。研究者常以1970至2025年间的年度观测为时序基底，借助面板回归或时间序列分解方法，考察女性劳动参与率随婚姻状态变迁的演化轨迹，并检验城乡二元结构对男女雇员数量配置的调节效应。

实际应用

政策制定者与劳动行政部门可依据该数据集监测亚洲各国雇员结构的性别失衡与城乡差距，为制定靶向性就业促进政策提供量化参照。国际发展机构亦将其用于评估减贫项目与性别平权干预在劳动市场的落地成效，企业人力资源部门则可借助时序趋势预判区域劳动力供给变化，优化用工布局与薪酬策略。

衍生相关工作

围绕该数据集已衍生出若干经典工作，包括以性别就业差距为主题的跨国面板分析、城乡劳动参与率的时空收敛性检验，以及将婚姻状态作为调节变量的女性劳动供给弹性估计。部分研究进一步将其与教育、工资等ILOSTAT指标链接，构建多维度劳动市场脆弱性指数，亦有学者据此开发亚洲劳动市场预测模型与可视化仪表板。