electricsheepasia/asia-ilo-ees-tees-sex-est-geo-nb-employees-by-sex-establishment-size-and-rural-urba

--- 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, establishment size and rural / urban areas (thousands) | Asia (ILOSTAT)" --- # Employees by sex, establishment size and rural / urban areas (thousands) | Asia (ILOSTAT) 🌏 **10,088 observations** · **21 Asia countries** · **2000–2025** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **10,088 observations** of `Employees` data across **21 Asia countries**, spanning **2000–2025**, 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_EST_GEO_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_EST_GEO_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 21 Asia countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `MNG` | 1,736 | 2003 | 2024 | | `THA` | 1,296 | 2010 | 2024 | | `PAK` | 1,242 | 2006 | 2025 | | `PSE` | 1,187 | 2010 | 2022 | | `JOR` | 936 | 2017 | 2024 | | `LKA` | 630 | 2010 | 2024 | | `GEO` | 585 | 2020 | 2024 | | `TUR` | 504 | 2000 | 2013 | | `TLS` | 275 | 2001 | 2013 | | `ARM` | 270 | 2009 | 2017 | | `VNM` | 210 | 2019 | 2020 | | `TJK` | 198 | 2007 | 2009 | | `KHM` | 198 | 2012 | 2019 | | `BGD` | 153 | 2010 | 2017 | | `NPL` | 135 | 2008 | 2017 | | ... | _6 more countries_ | | | ## Indicators (sample) - `EES_TEES_SEX_EST_GEO_NB` — Employees by sex, establishment size and rural / urban areas (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_EST_GEO_NB` | | `indicator.label` | `string` | Indicator name in English | `Employees by sex, establishment size …` | | `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.) | `EST_AGGREGATE_TOTAL` | | `classif1.label` | `string` | — | `Establishment size (Aggregate): Total` | | `classif2` | `string` | Second classification variable where applicable | `GEO_COV_NAT` | | `classif2.label` | `string` | — | `Area type: National` | | `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`** (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-ees-tees-sex-est-geo-nb-employees-by-sex-establishment-size-and-rural-urba") 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_EST_GEO_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EES_TEES_SEX_EST_GEO_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EES_TEES_SEX_EST_GEO_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_ees_tees_sex_est_geo_nb_employees_by_sex_establishment_size_and_rural_urba_2025, title = {Employees by sex, establishment size and rural / urban areas (thousands) | Asia (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EES_TEES_SEX_EST_GEO_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-ees-tees-sex-est-geo-nb-employees-by-sex-establishment-size-and-rural-urba}} } ``` ## 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_EST_GEO_NB_This dataset contains 10,088 observations of employees data across 21 Asia countries, spanning 2000–2025, disaggregated by sex, establishment size, and rural/urban areas (in thousands). It covers one primary indicator (EES_TEES_SEX_EST_GEO_NB) and is sourced from the ILOSTAT database of the International Labour Organization (ILO), harmonized using ILO statistical standards. The dataset includes detailed disaggregation dimensions (e.g., sex, establishment size, area type) and metadata columns (e.g., country code, year, observed value, data status), suitable for tabular classification, regression, and time-series forecasting tasks.

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接口直接拉取指标代码为EES_TEES_SEX_EST_GEO_NB的原始数据，并依据亚洲ISO3国家代码进行筛选。ILOSTAT依据国际劳工统计学家会议（ICLS）定义对各国劳动力调查、家庭收入调查及行政记录等微观数据进行统一协调，源数据在source.label列中标注以确保可追溯性。Electric Sheep Asia对拉取后的数据进行了标准化重打包，形成了覆盖亚洲21个国家、时间跨度为2000至2025年、共计10,088条观测值的结构化表格数据集。

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，并转换为Pandas DataFrame进行后续分析。典型操作包括按ref_area字段筛选特定国家（如印度尼西亚IDN），或针对指标EES_TEES_SEX_EST_GEO_NB按时间排序绘制单一指标的时间序列图。用户亦可利用pivot_table方法将数据重塑为国家与年份的交叉矩阵，便于开展跨国比较或面板数据分析。该数据集遵循CC-BY-4.0许可协议，使用时需同时引用国际劳工组织原始来源及Electric Sheep Asia的重打包工作。

背景与挑战

背景概述

国际劳工组织（ILO）长期致力于全球劳动力市场统计数据的编纂与标准化工作，其核心数据库ILOSTAT汇集了来自各国劳动力调查、家庭收入调查及行政记录的多源数据，覆盖逾200个经济体。在此背景下，Electric Sheep Asia于2025年对ILOSTAT中“按性别、机构规模及城乡区域划分的雇员人数”指标进行重新打包，构建了覆盖21个亚洲国家、时间跨度为2000至2025年、包含10,088条观测值的结构化数据集。该数据集聚焦亚洲地区雇员分布的性别差异、企业规模效应与城乡空间异质性，为劳动经济学、性别研究和区域发展政策分析提供了重要的实证基础，其遵循ICLS定义进行数据协调的方法论亦增强了跨国比较的可信度。

当前挑战

该数据集所回应的领域问题在于如何系统刻画亚洲劳动力市场中雇员分布的多元结构性特征，包括性别区隔、机构规模分层与城乡二元格局之间的交互影响，此类分析长期受制于跨国数据口径不一致与细分维度缺失。构建过程中的核心挑战涉及多个层面：原始调查微数据在各国间的抽样设计、覆盖范围与变量定义存在显著异质性，需依赖ICLS标准进行事后协调；部分国家时间序列断裂，观测状态标注为“不可靠”或“方法修订”的样本需谨慎处理；城乡与机构规模的交叉分类导致单元格样本稀疏，估计值稳定性存疑；数据源标签与注释体系复杂，追溯与验证成本较高。这些因素共同构成了该数据集在精细化劳动统计分析与建模应用中的关键约束。

常用场景

经典使用场景

在劳动经济学与区域发展研究的经典范式中，该数据集最常被用于刻画亚洲各国雇员规模在性别、企业规模及城乡维度上的结构性差异。研究者可借助其2000至2025年的年度序列，构建面板数据模型，系统考察不同性别雇员在微型、小型、中型与大型企业中的分布动态，以及农村与城市区域间的雇佣形态分化。此类分析往往以时序趋势与横截面比较为轴心，揭示亚洲劳动力市场在工业化与城镇化进程中的异质性特征。

解决学术问题

该数据集有效回应了劳动统计领域长期存在的若干学术难题，包括非正规部门就业的性别差异测度、企业规模对雇佣性别结构的影响机制，以及城乡二元结构下雇员分布的时空演变。通过提供ILO统一协调的多国可比数据，它缓解了跨国劳动研究中的口径不一致与数据缺失问题，为检验劳动力市场分割理论、性别不平等假说提供了坚实的经验基础，进而推动了亚洲劳动统计标准化与实证研究的纵深发展。

实际应用

在政策制定与企业战略层面，该数据集可服务于劳动监察部门评估不同规模企业的性别雇佣合规状况，辅助国际组织设计针对农村女性雇员的技能培训与就业促进项目。咨询机构亦可利用其进行区域劳动力市场风险评估，为跨国企业在亚洲的用工布局提供国别与城乡差异化参考。其年度更新机制与开放许可，进一步降低了实证分析与商业决策的数据获取门槛。

数据集最近研究

最新研究方向

在全球劳动统计加速向高分辨率性别与空间维度演进的背景下，该数据集凭借其涵盖21个亚洲国家逾万条员工观测记录，正推动着劳动经济学与空间计量交叉领域的前沿探索。依托国际劳工组织统一的ICLS定义框架，研究者得以将企业规模与城乡二元结构纳入性别就业差异的分析视域，进而揭示结构性不平等在亚洲劳动力市场中的复杂作用机制。近期相关研究聚焦于利用该面板数据构建性别就业弹性的时空异质性模型，并结合时间序列预测方法评估城乡产业政策对女性就业的差异化效应。该数据集亦为机器学习驱动的劳动力市场细分与政策仿真提供了标准化、可复现的数据基础，对实现体面劳动与性别平等的可持续发展目标具有实质性的监测与评估价值。