electricsheepafrica/africa-ilo-ees-tees-sex-eco-est-nb-employees-by-sex-economic-activity-and-establishme

--- 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, economic activity and establishment size (thousands) | Africa (ILOSTAT)" --- # Employees by sex, economic activity and establishment size (thousands) | Africa (ILOSTAT) 🌍 **93,841 observations** · **41 Africa countries** · **1991–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **93,841 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_ECO_EST_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_ECO_EST_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` | 18,325 | 2000 | 2024 | | `EGY` | 7,731 | 2008 | 2023 | | `AGO` | 5,712 | 2004 | 2025 | | `MLI` | 5,614 | 2013 | 2024 | | `SEN` | 3,976 | 2015 | 2024 | | `BWA` | 3,749 | 2006 | 2024 | | `ZMB` | 3,175 | 2017 | 2024 | | `RWA` | 2,936 | 2014 | 2020 | | `SYC` | 2,527 | 2019 | 2024 | | `GHA` | 2,478 | 1991 | 2015 | | `TZA` | 2,391 | 2010 | 2024 | | `CIV` | 2,249 | 2012 | 2019 | | `KEN` | 2,200 | 2019 | 2022 | | `NAM` | 2,183 | 2012 | 2016 | | `BFA` | 1,972 | 2014 | 2023 | | ... | _26 more countries_ | | | ## Indicators (sample) - `EES_TEES_SEX_ECO_EST_NB` — Employees by sex, economic activity and establishment size (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_ECO_EST_NB` | | `indicator.label` | `string` | Indicator name in English | `Employees by sex, economic activity a…` | | `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.) | `ECO_SECTOR_TOTAL` | | `classif1.label` | `string` | — | `Economic activity (Broad sector): Total` | | `classif2` | `string` | Second classification variable where applicable | `EST_AGGREGATE_TOTAL` | | `classif2.label` | `string` | — | `Establishment size (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_classif` | `string` | — | `C5:1869` | | `note_classif.label` | `string` | — | `Nonstandard economic activity: Includ…` | | `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-eco-est-nb-employees-by-sex-economic-activity-and-establishme") 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_ECO_EST_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EES_TEES_SEX_ECO_EST_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EES_TEES_SEX_ECO_EST_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_ees_tees_sex_eco_est_nb_employees_by_sex_economic_activity_and_establishme_2025, title = {Employees by sex, economic activity and establishment size (thousands) | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EES_TEES_SEX_ECO_EST_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-ees-tees-sex-eco-est-nb-employees-by-sex-economic-activity-and-establishme}} } ``` ## 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_ECO_EST_NB_This dataset contains employee data from the International Labour Organization (ILO) ILOSTAT database, specifically focused on Africa. It covers 41 African countries from 1991 to 2025, with 93,841 observations. The core indicator is Employees by sex, economic activity and establishment size (thousands), sourced via the ILOSTAT REST API and filtered by African ISO3 country codes, harmonized using International Conference of Labour Statisticians (ICLS) definitions. The dataset includes detailed columns such as country codes, indicator codes, sex disaggregation (total, male, female), economic activity and establishment size classifications, observation years, observed values, and data quality flags (e.g., unreliable data). Data is annual frequency and suitable for tasks like tabular classification, regression, and time-series forecasting. Repackaged by Electric Sheep Africa under the cc-by-4.0 license.

搜集汇总

数据集介绍

构建方式

该数据集依托国际劳工组织统计数据库（ILOSTAT）的权威劳动统计资料，经Electric Sheep Africa团队系统化采集与标准化处理而成。原始数据覆盖1991年至2025年间41个非洲国家的雇员统计信息，共计93,841条观测记录。构建过程中，团队对源数据进行了格式统一、元数据标注与结构化封装，最终以Parquet格式发布，并辅以标准化的发现元数据、加载指南及数据溯源说明，确保数据在非洲数据发现体系中的可检索性与可复用性。

特点

数据集聚焦于按性别、经济活动及机构规模分类的雇员人数（以千计），时间跨度逾三十年，空间覆盖广泛，兼具时间序列与截面维度。其数据模态涵盖表格与文本，标签体系涉及劳动、就业、经济金融等领域。数据集以CC BY 4.0许可协议开放共享，体量适中（10K至100K之间），并附有详尽的元数据说明与质量提示，为劳动经济学及区域发展研究提供了结构化的实证基础。

使用方法

研究者可通过Hugging Face数据集库直接加载该数据集，利用datasets.load_dataset函数获取数据对象并检视其结构与特征。当数据为表格形态时，可转换为Pandas DataFrame以展开分析。使用时应从仓库文件及数据查看器出发，核实变量定义与计量单位，保留缺失值直至确立合理的插补规则。在具备明确国家、年份及指标字段的情况下，可与其他Electric Sheep Africa数据集进行联结，构建可复现的分析流程并规范引用来源。

背景与挑战

背景概述

在非洲劳动力市场结构性转型与体面劳动议程持续推进的宏观语境下，国际劳工组织统计数据部门依托其全球劳动统计数据库，系统编制了按性别、经济活动门类及机构规模分列的雇员规模数据。该数据集由Electric Sheep Africa团队于2026年完成标准化整理与元数据增强，覆盖41个非洲国家、逾9.3万条观测记录，时间跨度自1991年延伸至2025年。其核心关切在于揭示非洲各国雇佣关系的性别分化与产业分布格局，为劳动经济学、发展经济学及社会政策研究提供跨国可比的面板证据，已成为区域就业结构实证分析的重要基础设施。

当前挑战

该数据集所回应的领域难题在于：非洲各国劳动统计体系发育不均衡，非正规部门占比高企，雇员界定口径与机构规模分组标准在国别间存在显著异质性，致使跨国比较面临测量等价性威胁。构建过程中，来源数据的缺失值模式复杂，部分国家年份断档严重，性别维度与经济 activity 交叉分类的稀疏单元格进一步加剧了统计推断的不确定性。此外，元数据清单中暴露的country与upstream_publisher字段缺位，要求分析者在溯源时保持审慎，避免因地理指代模糊或单位误读而产生系统性偏差。

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叠领域中，该数据集凭借其覆盖41个非洲国家、纵跨1991至2025年、共计93,841条观测的规模，成为刻画非洲就业结构变迁的核心经验来源。经典使用场景聚焦于按性别、经济活动部门与机构规模三重维度对雇员人数进行分层统计，研究者据此得以系统辨识非洲各国正规与非正规部门中性别就业差异的演化轨迹，并比较不同规模企业在吸纳劳动力方面的结构性角色。该类分析通常以面板数据模型为工具，在控制国家固定效应与时间趋势后，评估经济增长、产业结构调整与女性劳动参与率之间的动态关联，从而为理解非洲劳动力市场的异质性与分层机制提供坚实的量化基础。

实际应用

在政策实践层面，该数据集为非洲各国的就业促进战略、性别平等行动方案及中小企业扶持政策提供了可操作的证据支撑。国际组织与国别统计机构可借助其细分维度，识别女性就业集中度较高的经济部门与机构规模区间，据此设计针对性的技能培训、融资便利与劳动保障干预。企业与社会投资方亦可利用其趋势信息评估特定国别与行业的劳动力供给潜力，优化投资布局与人力规划。同时，该数据集支持构建就业脆弱性指数与性别包容性监测仪表盘，为非洲区域一体化进程中的劳动力流动治理与体面劳动议程提供常态化数据基础设施。

衍生相关工作

围绕该数据集及其所属的Electric Sheep Africa目录，已衍生出一系列具有代表性的后续工作。研究者将其与非洲其他ILOSTAT指标数据集进行横向链接，构建跨国劳动力市场综合面板，用于分析非正规就业、青年失业与性别工资差距的联动机制。部分工作以此为基础训练表格分类与回归模型，探索就业结构对经济增长的预测能力；亦有研究利用其元数据标准化特征，开发面向非洲开放数据的发现与溯源工具。这些衍生实践不仅拓展了数据集在机器学习与因果推断中的应用边界，也强化了非洲公共统计数据的可查找性、可访问性与可复用性。