electricsheepafrica/africa-ilo-ees-tees-sex-ocu-ins-nb-employees-by-sex-occupation-and-public-private-sec

--- 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, occupation and public/private sector (thousands) | Africa (ILOSTAT)" --- # Employees by sex, occupation and public/private sector (thousands) | Africa (ILOSTAT) 🌍 **32,682 observations** · **47 Africa countries** · **1991–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **32,682 observations** of `Employees` data across **47 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_OCU_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_OCU_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 47 Africa countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `ZAF` | 3,734 | 2000 | 2024 | | `MUS` | 2,651 | 2003 | 2024 | | `EGY` | 2,033 | 2009 | 2024 | | `AGO` | 1,490 | 2004 | 2025 | | `GHA` | 1,357 | 1991 | 2024 | | `ZMB` | 1,276 | 2015 | 2024 | | `SYC` | 1,264 | 2014 | 2024 | | `MLI` | 1,244 | 2013 | 2024 | | `RWA` | 1,240 | 2014 | 2025 | | `ZWE` | 1,103 | 2011 | 2024 | | `TZA` | 966 | 2001 | 2024 | | `UGA` | 921 | 2010 | 2021 | | `BWA` | 897 | 2006 | 2024 | | `SEN` | 878 | 2015 | 2024 | | `NAM` | 741 | 2012 | 2018 | | ... | _32 more countries_ | | | ## Indicators (sample) - `EES_TEES_SEX_OCU_INS_NB` — Employees by sex, occupation 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_OCU_INS_NB` | | `indicator.label` | `string` | Indicator name in English | `Employees by sex, occupation and publ…` | | `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.) | `OCU_SKILL_TOTAL` | | `classif1.label` | `string` | — | `Occupation (Skill level): Total` | | `classif2` | `string` | Second classification variable where applicable | `INS_SECTOR_TOTAL` | | `classif2.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) | `U` | | `obs_status.label` | `string` | — | `Unreliable` | | `note_classif` | `float64` | — | `—` | | `note_classif.label` | `float64` | — | `—` | | `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-ocu-ins-nb-employees-by-sex-occupation-and-public-private-sec") 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_OCU_INS_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EES_TEES_SEX_OCU_INS_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EES_TEES_SEX_OCU_INS_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_ees_tees_sex_ocu_ins_nb_employees_by_sex_occupation_and_public_private_sec_2025, title = {Employees by sex, occupation 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_OCU_INS_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-ees-tees-sex-ocu-ins-nb-employees-by-sex-occupation-and-public-private-sec}} } ``` ## 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_OCU_INS_NB_This dataset contains 32,682 observations of employee statistics across 47 African countries from 1991 to 2025. The data is sourced from the ILOSTAT database of the International Labour Organization (ILO), with the core indicator being EES_TEES_SEX_OCU_INS_NB – Employees by sex, occupation and public/private sector (thousands). It includes detailed disaggregation dimensions such as sex (total, male, female), occupation classification (by skill level), and institutional sector (public/private). The data is published at an annual frequency and covers multiple African countries like South Africa, Mauritius, Egypt, etc., along with metadata on sources, observation status, and notes. It is suitable for tabular classification, regression, and time-series forecasting tasks. The dataset is repackaged by Electric Sheep Africa and licensed under cc-by-4.0.

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统计数据库（ILOSTAT），由Electric Sheep Africa团队进行系统性整理与重新封装。构建过程以非洲区域为地理边界，从ILOSTAT中提取1991至2025年间47个非洲国家的雇员统计数据，涵盖性别、职业及公共/私营部门等维度，共计32,682条观测记录。原始数据经标准化处理后，以Parquet格式存储，并配套生成统一的元数据描述，确保数据可追溯、可复现，同时保留原始发布机构的知识产权归属。

特点

数据集聚焦非洲劳动力市场，时间跨度逾三十年，覆盖47个国家，包含雇员数量（以千人为单位）在性别、职业类别以及公共与私营部门间的分布信息。其核心特点在于多维交叉分类，能够支持按性别、职业和部门属性的细分分析；数据以表格与文本模态并存，适用于分类与回归任务；体量适中，介于10K至100K观测之间，便于计算处理；所有字段均附带标准化标签，且采用开放许可CC BY 4.0，利于学术与政策研究中的共享与再利用。

使用方法

使用者可通过Hugging Face datasets库便捷加载数据，调用load_dataset函数指定仓库名称即可获取完整数据集，进而查看数据结构、特征类型及样例记录。对于表格型数据，可转换为Pandas DataFrame以进行统计分析与可视化。在建模前，建议先行检查各变量的缺失值分布与单位定义，避免因地理标识或分类标准不明确而产生推断偏差；若需与其他非洲数据集联合分析，可利用国家、年份及指标字段进行键值匹配，并始终在分析文档中注明数据来源与假设条件。

背景与挑战

背景概述

在全球劳动力市场统计体系中，国际劳工组织（ILO）主导的ILOSTAT数据库长期作为就业结构分析的核心基准，为比较各国劳动参与、职业分布与部门构成提供了标准化框架。然而，非洲地区受限于统计能力薄弱与数据碎片化，其就业结构的性别与部门差异长期难以获得系统刻画。Electric Sheep Africa于2026年发布的这一数据集，整合了ILOSTAT中47个非洲国家1991至2025年间的32,682条观察记录，以标准化元数据与Parquet格式重构了按性别、职业及公共/私营部门分类的雇员数据，为非洲劳动经济学研究提供了可复现的跨国面板基础。该数据集的核心价值在于将非洲就业性别差异与部门分割问题纳入统一分析视野，对区域劳动力市场政策评估具有基础性支撑意义。

当前挑战

该数据集所回应的领域问题，在于非洲劳动力市场中性别与部门维度的结构性不平等长期缺乏可比性证据，碎片化的国家统计体系使跨国比较研究难以开展。构建过程中，数据集面临多重挑战：ILOSTAT原始数据在非洲各国的覆盖强度极不均衡，职业分类与部门定义在各国间存在显著编码差异，需在元数据层面进行统一化处理；多个国家在时间序列上存在严重缺失，早期年份与冲突影响区域的数据稀疏性限制了面板分析的完整性；此外，公共与私营部门的划分标准在非正规经济占比较高的非洲语境下具有模糊性，跨年度口径变化亦需审慎对待。电力羊非洲的标准化流程虽提升了可发现性，但缺失值处理与变量定义仍需在源材料中进一步核验，以避免政策解读的偏差。

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，该数据集构成了剖析性别隔离与职业分层现象的基石。其经典应用场景立足于1991至2025年间47个非洲国家的32,682条观测记录，围绕按性别、职业及公私部门划分的雇员人数展开分析。研究者借此可系统性地描摹非洲劳动力市场的结构性特征，譬如女性在公共部门与私营部门中的职业分布差异，或特定职业类别中性别构成的时序演变。此类分析为理解非洲大陆就业格局的多元性与动态性提供了坚实的量化基础，亦成为比较不同国家间劳动制度差异的关键数据来源。

解决学术问题

该数据集有效回应了劳动经济学中关于性别就业差距与部门分割的长期学术争论。过往研究常受制于非洲地区微观劳动统计数据的匮乏，难以在跨国层面精确检验职业性别隔离的理论假设。此数据集通过标准化性别、职业及公私部门三重维度的雇员统计，使学者能够量化分析性别就业不平等的程度与趋势，并探究公共政策、经济结构转型对女性职业地位的影响。其意义在于将非洲劳动市场数据纳入全球比较框架，为检验歧视经济学与制度主义理论提供了宝贵的实证场域。

衍生相关工作

围绕该数据集，已衍生出一系列聚焦非洲劳动力市场性别维度的经典工作。研究者将其与ILOSTAT其他指标数据集进行链接，构建了涵盖工资、工时与就业身份的综合性分析框架，用于探讨性别收入差距的多重成因。部分文献进一步将该数据与教育统计数据结合，检验非洲女性教育成就提升与职业地位改善之间的关联强度。这些衍生研究不仅拓展了数据集的应用边界，亦推动了非洲劳动统计元数据标准化进程，为后续跨国比较研究奠定了可复用的方法论基础。