electricsheepafrica/africa-ilo-ees-tees-sex-est-mts-nb-employees-by-sex-establishment-size-and-marital-st

--- 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 - africa - ilostat - employees - ilo - labour - employment pretty_name: "Employees by sex, establishment size and marital status (thousands) | Africa (ILOSTAT)" --- # Employees by sex, establishment size and marital status (thousands) | Africa (ILOSTAT) 🌍 **42,769 observations** · **41 Africa countries** · **1991–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **42,769 observations** of `Employees` data across **41 Africa countries**, spanning **1991–2025**, 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_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_EST_MTS_NB` and filtered to Africa 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 41 Africa countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `ZAF` | 8,687 | 2000 | 2024 | | `EGY` | 3,303 | 2008 | 2023 | | `MLI` | 2,730 | 2013 | 2024 | | `AGO` | 2,496 | 2004 | 2025 | | `SEN` | 2,032 | 2015 | 2024 | | `BWA` | 1,771 | 2006 | 2024 | | `RWA` | 1,339 | 2014 | 2020 | | `GHA` | 1,233 | 1991 | 2015 | | `SYC` | 1,165 | 2019 | 2024 | | `ZMB` | 1,143 | 2017 | 2024 | | `TZA` | 1,119 | 2010 | 2024 | | `BFA` | 1,049 | 2014 | 2024 | | `KEN` | 1,016 | 2019 | 2022 | | `CIV` | 968 | 2012 | 2019 | | `NAM` | 962 | 2012 | 2016 | | ... | _26 more countries_ | | | ## Indicators (sample) - `EES_TEES_SEX_EST_MTS_NB` — Employees by sex, establishment size and marital status (thousands) ## Schema | Column | Type | Description | Example | |--------|------|-------------|---------| | `ref_area` | `string` | ISO 3166-1 alpha-3 country code | `AGO` | | `ref_area.label` | `string` | Country name in English | `Angola` | | `source` | `string` | ILOSTAT source code (e.g. labour force survey) | `BA:13951` | | `source.label` | `string` | Source name in English | `LFS - Employment Survey` | | `indicator` | `string` | ILOSTAT indicator code | `EES_TEES_SEX_EST_MTS_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 | `MTS_AGGREGATE_TOTAL` | | `classif2.label` | `string` | — | `Marital status (Aggregate): Total` | | `time` | `int64` | Observation year | `2025` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `3788.156` | | `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` | | `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("electricsheepafrica/africa-ilo-ees-tees-sex-est-mts-nb-employees-by-sex-establishment-size-and-marital-st") df = ds["train"].to_pandas() print(df.head()) ``` ### Filter to one country ```python kenya = df[df["ref_area"] == "KEN"] ``` ### Time-series for a single indicator ```python sample = (df[df["indicator"] == "EES_TEES_SEX_EST_MTS_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EES_TEES_SEX_EST_MTS_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EES_TEES_SEX_EST_MTS_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_ees_tees_sex_est_mts_nb_employees_by_sex_establishment_size_and_marital_st_2025, title = {Employees by sex, establishment size and marital status (thousands) | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EES_TEES_SEX_EST_MTS_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-ees-tees-sex-est-mts-nb-employees-by-sex-establishment-size-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 Africa repackaging. ## About Electric Sheep Electric Sheep Africa is part of the Electric Sheep mission: a unified, ML-ready data layer for Africa 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/electricsheepafrica](https://huggingface.co/electricsheepafrica) --- _Provenance: ingested 2026-05-26 via the Electric Sheep pipeline. Source URL: https://www.ilo.org/shinyapps/bulkexplorer/?id=EES_TEES_SEX_EST_MTS_NB_This dataset contains Employees by sex, establishment size and marital status (thousands) data from the International Labour Organization (ILO) ILOSTAT database, specifically for Africa. It includes 42,769 observations across 41 African countries spanning 1991 to 2025, focusing on one core indicator: number of employees (in thousands), disaggregated by dimensions such as sex (total, male, female), establishment size (total), and marital status (total). The data is sourced via the ILOSTAT REST API, normalized, and stored in tabular format with columns including country code, country name, data source, indicator code, time, observed value, and status. It is suitable for tasks like tabular classification, regression, and time-series forecasting, providing machine learning-ready data for African labour market research.

搜集汇总

数据集介绍

构建方式

该数据集源于国际劳工组织中央统计数据库（ILOSTAT），由Electric Sheep Africa团队进行标准化重封装。构建过程以公开发布的非洲区域劳动统计资料为基础，提取1991年至2025年间41个非洲国家的雇员数据，形成42,769条观测记录。数据经过元数据编目与格式转换，以Parquet列式存储方式重新组织，并配套标准化的发现元数据、加载指引与溯源说明，从而在保留原始统计口径的同时提升数据集的可发现性与可复用性。

特点

数据集聚焦于非洲劳动市场中的雇员规模，按性别、机构规模及婚姻状况进行交叉分类，以千人为计量单位呈现。其覆盖范围横跨41个非洲国家，时间跨度自1991年延伸至2025年，总计42,769条观测，属于中等规模表格数据集。数据模态兼具表格与文本，以Parquet格式分发，便于高效读取与列式分析。数据集附带明确的许可协议、主题标签及分析导向的上下文说明，有助于研究者快速定位变量含义并开展跨国、跨期比较研究。

使用方法

使用者可通过Hugging Face datasets库直接加载该数据集，调用load_dataset函数并指定仓库名称即可获取数据对象，随后可查看各分片的特征结构与示例记录。对于表格型任务，可将首个分片转换为Pandas数据框以便进行探索性分析与建模。在应用过程中，建议先检查模式与缺失值分布，确认变量定义与计量单位，并利用显式的国家、年份及指标字段与其他Electric Sheep Africa数据集进行联接，从而构建可复现的分析流程。

背景与挑战

背景概述

非洲劳动力市场长期面临数据碎片化与可比性不足的困境，国际劳工组织（ILO）依托ILOSTAT数据库持续汇编成员国劳动力统计，为跨国比较提供基准。该数据集由Electric Sheep Africa于2026年标准化发布，整合了1991至2025年间41个非洲国家共42,769条雇员观测记录，按性别、机构规模与婚姻状况分类，旨在揭示非洲正式就业结构的异质性。作为开放数据目录的一部分，它降低了非洲劳动经济研究的准入门槛，为政策评估与学术分析提供了可复用的结构化证据。

当前挑战

该数据集所回应的核心领域问题在于刻画非洲雇员在性别、机构规模与婚姻状况维度上的分布规律，此类细粒度交叉统计在多数非洲国家统计体系中长期缺失，制约了劳动力市场性别差距与就业脆弱性的实证研究。构建过程中的挑战同样显著：ILOSTAT原始数据存在国别报告口径不一、年份覆盖不均衡、婚姻状况分类标准差异等问题，加之元数据中country与upstream_publisher字段缺失，要求使用者在建模前审慎核验变量定义与单位，并保留缺失值以待可辩护的插补规则。

常用场景

经典使用场景

在劳动经济学与非洲发展研究的交叉领域中，该数据集凭借其对41个非洲国家1991至2025年间42,769条观测记录的系统性整合，构成了分析正规与非正规部门就业结构的核心经验底座。研究者通常以性别、企业规模与婚姻状况为分组变量，借助列联表分析与面板回归模型，刻画不同人口群体在雇佣关系中的分布特征与演变轨迹。此类数据亦常被用于构建劳动力市场分割指数的基准参照，为跨国比较研究提供统一口径的量化依据。

解决学术问题

该数据集有效回应了非洲劳动力市场研究中长期存在的数据碎片化与口径不一致问题，使得跨国、跨时期的雇佣结构比较得以在统一框架下展开。其覆盖性别、企业规模与婚姻状况的多维交叉特征，为检验婚姻状况对女性劳动参与的影响、企业规模与就业正规性之间的关联等经典假说提供了可复现的证据基础。数据的时间跨度允许研究者识别结构性转折，从而推动非洲就业变迁的理论解释从静态描述走向动态推断。

衍生相关工作

围绕该数据集，Electric Sheep Africa持续构建非洲公共数据目录，衍生出一系列以标准化元数据为特征的关联数据集，涵盖工资、工时与非正规就业等主题，形成可互操作的劳动统计资源网络。研究者基于其结构特征开发了跨国就业脆弱性指数与性别差距测度工具，部分工作进一步将ILOSTAT原始指标与家庭调查微观数据链接，拓展了多源数据融合的方法论路径，为非洲劳动统计的开放科学实践积累了可复用的技术范式。