electricsheepafrica/africa-ilo-ees-tees-sex-est-dsb-nb-employees-by-sex-establishment-size-and-disability

--- 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, establishment size and disability status (thousands) | Africa (ILOSTAT)" --- # Employees by sex, establishment size and disability status (thousands) | Africa (ILOSTAT) 🌍 **2,094 observations** · **25 Africa countries** · **2007–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **2,094 observations** of `Employees` data across **25 Africa countries**, spanning **2007–2025**, 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_DSB_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_DSB_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 25 Africa countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `SEN` | 268 | 2015 | 2024 | | `BWA` | 211 | 2019 | 2024 | | `ZMB` | 190 | 2018 | 2024 | | `RWA` | 167 | 2014 | 2020 | | `TZA` | 139 | 2010 | 2024 | | `SYC` | 135 | 2019 | 2024 | | `GMB` | 104 | 2012 | 2025 | | `ZWE` | 90 | 2014 | 2019 | | `LSO` | 79 | 2019 | 2024 | | `UGA` | 76 | 2017 | 2021 | | `GHA` | 74 | 2013 | 2015 | | `LBR` | 71 | 2010 | 2017 | | `SWZ` | 60 | 2021 | 2023 | | `CMR` | 45 | 2007 | 2007 | | `EGY` | 44 | 2023 | 2023 | | ... | _10 more countries_ | | | ## Indicators (sample) - `EES_TEES_SEX_EST_DSB_NB` — Employees by sex, establishment size and disability status (thousands) ## Schema | Column | Type | Description | Example | |--------|------|-------------|---------| | `ref_area` | `string` | ISO 3166-1 alpha-3 country code | `BFA` | | `ref_area.label` | `string` | Country name in English | `Burkina Faso` | | `source` | `string` | ILOSTAT source code (e.g. labour force survey) | `BA:904` | | `source.label` | `string` | Source name in English | `LFS - Labour Force Survey` | | `indicator` | `string` | ILOSTAT indicator code | `EES_TEES_SEX_EST_DSB_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 | `DSB_STATUS_TOTAL` | | `classif2.label` | `string` | — | `Disability status: Total` | | `time` | `int64` | Observation year | `2024` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `1001.902` | | `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-dsb-nb-employees-by-sex-establishment-size-and-disability") 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_DSB_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EES_TEES_SEX_EST_DSB_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EES_TEES_SEX_EST_DSB_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_ees_tees_sex_est_dsb_nb_employees_by_sex_establishment_size_and_disability_2025, title = {Employees by sex, establishment size and disability status (thousands) | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EES_TEES_SEX_EST_DSB_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-ees-tees-sex-est-dsb-nb-employees-by-sex-establishment-size-and-disability}} } ``` ## 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_DSB_NB_This dataset contains employee statistics for African countries from the International Labour Organization (ILO) ILOSTAT database, specifically the indicator Employees by sex, establishment size and disability status (thousands). It covers 25 African countries from 2007 to 2025, with 2,094 observations. The data is provided at annual frequency, with each record including fields such as country code, country name, data source (e.g., Labour Force Survey), indicator code, sex disaggregation (total, male, female), establishment size classification, disability status classification, observation year, observed value (in thousands), and data status flags (e.g., unreliable). The data is harmonized by ILO based on International Conference of Labour Statisticians (ICLS) definitions and repackaged by Electric Sheep Africa into a unified, machine-learning-ready format, suitable for NLP tasks like tabular classification, regression, and time-series forecasting. The dataset is 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团队在Hugging Face平台完成工程化重构与标准化发布。构建过程以非洲区域劳动统计核心指标为锚点，系统抽取2007至2025年间25个非洲国家关于雇员按性别、机构规模及残疾状况分类的观测记录，累计形成2094条结构化条目。数据经过统一元数据标注、字段规范及格式转换后，以Parquet列式存储格式封装，并配套溯源信息与许可声明，确保从原始统计汇编到可复用数据资产的完整链路可追溯、可验证。

特点

数据集聚焦非洲劳动力市场中性别、企业规模与残疾状态三重维度的交叉分布特征，时间跨度近二十年，覆盖国家数量达25个，具备显著的区域代表性与时序纵深。其核心特点在于将残疾状况纳入雇员统计框架，填补了传统劳动统计中对弱势群体就业结构刻画不足的空白。数据以表格与文本双模态呈现，标签体系涵盖经济金融、就业劳动等主题，规模适中，适合开展分类与回归任务。元数据经标准化处理，附有数据质量说明与缺失值提示，为跨域比较与可复现研究提供基础支撑。

使用方法

研究者可通过Hugging Face datasets库直接加载该数据集，调用load_dataset函数获取默认分割后，利用features属性检视字段结构，并借助to_pandas方法转换为数据框以供分析。使用前应优先查阅仓库文件与数据查看器，确认变量定义、计量单位及地理编码方式；当国家信息仅隐含于标题或来源元数据时，需在下游分析中明确记录该假设。建议保留原始缺失值直至确立合理的插补规则，并可依据显式国家、年份与指标字段与其他Electric Sheep Africa数据集进行联结，构建可复现的分析工作流。

背景与挑战

背景概述

国际劳工组织统计数据库长期致力于汇集全球劳动力市场观测数据，为就业结构、体面劳动及社会不平等研究提供实证基础。在此脉络下，Electric Sheep Africa于2026年将国际劳工组织统计数据库中关于非洲地区雇员按性别、机构规模及残疾状况分类的指标重新整理发布，覆盖25个非洲国家、2007至2025年间共计2094条观测。该数据集回应了残障群体在正规与非正规部门中就业分布数据长期匮乏的问题，为非洲劳动经济学、社会包容政策评估及残疾人就业权益研究提供了跨国可比的结构化证据，并依托标准化元数据目录增强了非洲公开数据的可发现性与可复用性。

当前挑战

该数据集所应对的核心领域难题在于，残障与就业交叉统计在多数非洲国家尚未形成常规化采集机制，雇员按机构规模与残疾状况双重维度划分的数据尤其稀缺，致使残障群体在劳动力市场中的结构性处境难以被系统刻画。构建过程中同样面临显著制约：元数据清单显示国家字段与上游发布机构信息存在缺失，跨年度与跨国别的指标定义、单位及统计口径可能不一致，部分国家或年份的观测值存在空缺，如何在保留缺失值的前提下确保跨国比较的有效性，并避免仅凭标签推断政策含义，构成数据再利用的首要难题。

常用场景

经典使用场景

在劳动经济学与残障就业研究的交织领域中，该数据集凭借其覆盖25个非洲国家、时间跨度自2007年至2025年的2094条观测记录，构成了剖析残障人士就业结构差异的经典面板数据来源。研究者通常以性别、机构规模与残障状态为核心分组变量，运用列联表与分层回归手段，系统性地刻画不同规模企业内残障雇员的分布形态，并据此揭示非洲劳动力市场中残障群体在正规就业部门中的边缘化程度。

衍生相关工作

围绕该数据集，已衍生出一系列具有方法学与实证价值的后续研究。部分工作将其与Electric Sheep Africa目录中其他国际劳工组织数据集进行跨国面板拼接，探究残障就业与经济增长、行业结构之间的协整关系；另有研究基于该数据集构建残障就业包容指数，并用以检验非洲各国残障立法的政策效力。这些工作共同拓展了非洲劳动统计数据的分析纵深，也为残障就业领域的元分析与系统性综述提供了标准化的数据基底。

数据集最近研究

最新研究方向

在全球劳动力市场日益关注包容性增长的背景下，残疾状况与就业结构之间的关联正成为劳动经济学的前沿议题。该数据集整合了国际劳工组织（ILOSTAT）关于非洲25国2007至2025年间按性别、企业规模及残疾状态划分的雇员数据，为探究非洲非正规经济中残疾群体的就业分布提供了稀缺的跨国面板证据。当前研究趋向于利用此类微观分层数据，揭示残疾雇员在大、中、小、微企业中的比例差异及其性别维度，进而评估非洲各国劳动政策对弱势群体的覆盖缺口。该数据集亦支撑着与可持续发展目标（SDGs）中“体面工作”相关的监测研究，对推动非洲包容性就业政策制定具有实证参考意义。