electricsheepafrica/africa-ilo-ees-tees-sex-ins-nb-employees-by-sex-and-public-private-sector-thousan

--- 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 - africa - ilostat - employees - ilo - labour - employment pretty_name: "Employees by sex and public/private sector (thousands) | Africa (ILOSTAT)" --- # Employees by sex and public/private sector (thousands) | Africa (ILOSTAT) 🌍 **2,627 observations** · **48 Africa countries** · **1991–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **2,627 observations** of `Employees` data across **48 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_INS_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_INS_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 48 Africa countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `ZAF` | 225 | 2000 | 2024 | | `MUS` | 189 | 2003 | 2024 | | `EGY` | 147 | 2008 | 2024 | | `TUN` | 126 | 2005 | 2021 | | `TZA` | 108 | 2001 | 2024 | | `GHA` | 101 | 1991 | 2024 | | `MLI` | 99 | 2013 | 2024 | | `AGO` | 96 | 2004 | 2025 | | `RWA` | 90 | 2014 | 2025 | | `ZMB` | 84 | 2015 | 2024 | | `SYC` | 81 | 2014 | 2024 | | `UGA` | 78 | 2010 | 2021 | | `ZWE` | 75 | 2011 | 2024 | | `SEN` | 72 | 2011 | 2024 | | `BWA` | 72 | 2006 | 2024 | | ... | _33 more countries_ | | | ## Indicators (sample) - `EES_TEES_SEX_INS_NB` — Employees by sex and public/private sector (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_INS_NB` | | `indicator.label` | `string` | Indicator name in English | `Employees by sex and public/private s…` | | `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.) | `INS_SECTOR_TOTAL` | | `classif1.label` | `string` | — | `Institutional sector: 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) | `B` | | `obs_status.label` | `string` | — | `Break in series` | | `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-ins-nb-employees-by-sex-and-public-private-sector-thousan") 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_INS_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EES_TEES_SEX_INS_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EES_TEES_SEX_INS_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_ees_tees_sex_ins_nb_employees_by_sex_and_public_private_sector_thousan_2025, title = {Employees by sex and public/private sector (thousands) | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EES_TEES_SEX_INS_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-ees-tees-sex-ins-nb-employees-by-sex-and-public-private-sector-thousan}} } ``` ## 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_INS_NB_This dataset contains 2,627 observations of employees data across 48 Africa countries, spanning 1991–2025, covering 1 distinct indicator: Employees by sex and public/private sector (thousands). The data is sourced from ILOSTAT, the ILOs central statistics database, pulled via REST API and filtered to Africa ISO3 country codes. It is structured in tabular format with columns such as country code, year, sex disaggregation (total, male, female), indicator value, and observation status, suitable for tasks like tabular classification, regression, and time-series forecasting. The dataset has been repackaged by Electric Sheep Africa for ML-ready use.

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）的中央统计数据库ILOSTAT，经Electric Sheep Africa团队标准化处理后以Parquet格式发布于Hugging Face平台。原始数据由ILOSTAT汇编，涵盖非洲48个国家在1991年至2025年间的男性与女性在公共及私营部门的雇员人数（以千为单位）。Electric Sheep Africa在构建过程中对元数据进行了规范化的索引与注释，补充了加载指南、溯源说明及面向分析师的上下文信息，形成包含2627条观测记录的结构化表格数据集。

使用方法

研究者可通过Hugging Face的datasets库以单行代码加载该数据集，获取各分割的表格结构与特征信息，并借助to_pandas方法转换为数据框以供进一步分析。在建模前应先行检视数据文件的模式定义、缺失值分布与度量单位，依据显式国家字段进行地理维度的聚合与比较。该数据集适合开展按性别、部门及年份分组的描述性统计与趋势分析，亦可与其他Electric Sheep Africa数据集通过国家、年份及指标字段进行连接，构建可复现的劳动力市场研究笔记本。

背景与挑战

背景概述

伴随非洲劳动力市场结构转型与性别平等议题的持续深化，捕捉公有与私营部门就业的性别分布，已成为发展经济学与国际劳工治理的核心关切。国际劳工组织中央统计数据库（ILOSTAT）长期承担全球劳动统计的权威汇编职能，为跨国比较提供基准。在此背景下，Electric Sheep Africa于2026年对ILOSTAT源数据实施标准化工程，构建了涵盖48个非洲国家、1991至2025年间2627条观测值的就业数据集。该数据集以百万计的单位记录按性别与公私部门划分的雇员规模，为探究非洲正规就业的性别分化、公共部门吸纳能力及私营部门扩张效应提供了可复现的微观证据基础，亦为机器学习驱动的表格建模与跨国面板分析奠定了数据底座。

当前挑战

该数据集所回应的领域问题在于：如何以统一口径量化非洲各国就业结构的性别差异及部门归属，从而支撑跨国比较与政策评估。然而，其构建过程面临多重挑战。其一，源数据源自多国劳动力调查与行政记录，各国统计能力参差，指标定义、覆盖范围与抽样方法存在异质性，致使跨国可比性受限。其二，时间跨度长达三十余年，部分国家与年份存在系统性缺失，缺失机制非随机，直接插补易引入偏误。其三，元数据中country与upstream_publisher字段标注不全，地理实体与发布主体需依赖外部资料回溯确认。其四，公私部门划分在不同法律与制度语境下边界模糊，单位与统计口径的歧义可能影响建模结果的稳健性。

常用场景

经典使用场景

在劳动经济学与性别研究的交叉领域，该数据集凭借其覆盖48个非洲国家、纵贯1991至2025年的2627条观测，成为刻画公共与私营部门就业性别结构的经典素材。研究者惯常将其用于按年份、国别与性别维度绘制就业人数的时间序列，借助面板数据方法识别非洲劳动力市场中公共部门女性就业比例的演变轨迹。依托parquet格式的轻量化存储与标准化的指标元数据，该数据集亦被广泛纳入可复现的分析流水线，用以支撑跨国比较与分组统计推断。

解决学术问题

该数据集直面的核心学术问题在于非洲地区公共与私营部门就业性别差异的系统性量化。过往研究常受制于跨国劳动统计口径不一、时间覆盖零散等障碍，而此数据集以统一的指标框架整合了ILO中央统计数据库的原始记录，并明确标注缺失值与国家字段，为检验性别隔离理论、公共部门雇佣的性别平等效应等命题提供了可追溯的经验基础，其意义在于将碎片化的非洲就业统计转化为可供计量建模的结构化证据。

实际应用

在政策分析与国际发展实务中，该数据集为劳动部门、国际组织及研究机构评估非洲各国公共部门性别雇佣政策的实效提供了量化参照。使用者可借助其国别与年份字段，追踪特定国家在公共部门女性就业规模上的变化，进而辅助制定针对性的就业平等干预措施。同时，其tabular与text混合模态及标准化的加载指引，便于快速接入仪表盘与监测系统，为劳动力市场性别主流化议程提供数据支撑。

数据集最近研究

最新研究方向

伴随国际劳工组织统计数据库（ILOSTAT）持续扩容，基于非洲48国1991—2025年公私部门男女雇员规模数据的劳动经济学研究，正由传统的劳动力总量刻画转向性别结构与部门构成的精细化解析。前沿议题聚焦于公共部门女性就业比例提升对性别薪酬差距的收敛效应、非正规经济扩张背景下正规部门雇员统计口径的校准，以及跨国面板与时间序列方法在非洲异质性劳动力市场中的适用性改进。此项经Electric Sheep Africa标准化封装的数据资产，为非洲就业政策评估、性别平等监测及可持续发展目标（SDG 8）进展追踪提供了可复现的实证基础，对填补全球南方劳动统计地理盲区具有重要价值。