electricsheepafrica/africa-ilo-ees-tees-sex-edu-nb-employees-by-sex-and-education-thousands

--- 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 and education (thousands) | Africa (ILOSTAT)" --- # Employees by sex and education (thousands) | Africa (ILOSTAT) 🌍 **11,814 observations** · **49 Africa countries** · **1982–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **11,814 observations** of `Employees` data across **49 Africa countries**, spanning **1982–2025**, 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_EDU_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_EDU_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 49 Africa countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `ZAF` | 1,176 | 2000 | 2024 | | `MUS` | 928 | 2001 | 2024 | | `EGY` | 654 | 2008 | 2024 | | `GHA` | 520 | 1991 | 2024 | | `MLI` | 514 | 2009 | 2024 | | `AGO` | 483 | 2004 | 2025 | | `RWA` | 417 | 2014 | 2025 | | `ZMB` | 388 | 2015 | 2024 | | `SEN` | 358 | 2011 | 2024 | | `TUN` | 339 | 2005 | 2021 | | `BWA` | 338 | 2006 | 2024 | | `TGO` | 318 | 2006 | 2022 | | `UGA` | 310 | 2010 | 2021 | | `TZA` | 298 | 2001 | 2024 | | `BFA` | 298 | 2006 | 2024 | | ... | _34 more countries_ | | | ## Indicators (sample) - `EES_TEES_SEX_EDU_NB` — Employees by sex and education (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_EDU_NB` | | `indicator.label` | `string` | Indicator name in English | `Employees by sex and education (thous…` | | `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.) | `EDU_AGGREGATE_TOTAL` | | `classif1.label` | `string` | — | `Education (Aggregate levels): 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` | — | `C3:3710` | | `note_classif.label` | `string` | — | `Nonstandard education level: Includin…` | | `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-edu-nb-employees-by-sex-and-education-thousands") 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_EDU_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EES_TEES_SEX_EDU_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EES_TEES_SEX_EDU_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_ees_tees_sex_edu_nb_employees_by_sex_and_education_thousands_2025, title = {Employees by sex and education (thousands) | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EES_TEES_SEX_EDU_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-ees-tees-sex-edu-nb-employees-by-sex-and-education-thousands}} } ``` ## 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_EDU_NB_This dataset contains Employees by sex and education (thousands) data for Africa from the ILOs ILOSTAT database, covering 49 African countries from 1982 to 2025, with 11,814 observations and 1 distinct indicator (EES_TEES_SEX_EDU_NB). Data is sourced via the ILOSTAT REST API and harmonized for use in tabular classification, regression, and time-series forecasting tasks.

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）的核心统计数据库ILOSTAT，由Electric Sheep Africa团队进行工程化整理与标准化重发布，以支持非洲劳动市场的可复现研究。原始数据涵盖1982年至2025年间49个非洲国家的就业人员统计，按性别与教育程度分类，单位为千人。构建过程遵循开放数据规范，将分散的ILOSTAT指标统一为Parquet格式，并配以标准化元数据，包括国家覆盖、指标定义与来源注释，最终形成包含11,814条观测的结构化表格数据集。

使用方法

研究者可借助Hugging Face datasets库直接加载数据集，通过load_dataset函数获取数据对象，并查看其特征结构与样本记录。对于表格分析，可将数据集转换为Pandas DataFrame，便于执行描述统计、缺失值检验与分组汇总。使用时应优先检查模式与缺失情况，明确国家、年份与指标字段，避免仅凭标签推断政策含义。建议结合其他Electric Sheep Africa数据集，以显式国家、年份与指标字段进行连接，构建可复现的分析流程，并引用原始ILOSTAT来源与重发布仓库。

背景与挑战

背景概述

伴随全球劳动统计体系日趋精细化，按性别与受教育程度分列的就业结构数据成为衡量区域劳动力市场公平与人力资本配置的关键依据。国际劳工组织长期运营的ILOSTAT数据库汇聚各国报送的劳动力调查与行政记录，构成跨国可比劳动统计的权威基础。Electric Sheep Africa于2026年前后对该来源进行标准化再包装，形成覆盖49个非洲国家、1982至2025年间11814条观测的非洲就业结构数据集，聚焦雇员规模按性别与教育维度的分布。该数据集为探究非洲女性劳动参与、教育回报及结构转型提供了可复用的分析底本，在发展经济学与劳动经济学领域具有基础性参考价值。

当前挑战

该数据集所应对的领域难题在于非洲劳动统计长期存在的碎片化与口径异质性：各国调查年份不一、教育分类体系互不兼容、非正规就业大量遗漏，致使按性别与教育交叉分列的雇员指标难以直接跨国比较。构建过程中的挑战则集中于元数据缺失与语义对齐：清单显示国家代码与上游发布者字段未予声明，地理范围仅由标题与来源元数据隐含指涉，迫使用户在缺乏显式标识的条件下自行推断归属；同时缺失值处理、教育层级归并及单位一致性均需在数据文件层面加以核验，方可避免因标签误读而衍生失当的政策推论。

常用场景

经典使用场景

在劳动经济学与教育社会学的交叉研究中，该数据集构成探究非洲劳动力市场性别结构与人力资本配置的基石性资源。研究者依托其涵盖49个非洲国家、时间跨度自1982年至2025年的11,814条观测，得以系统刻画不同教育层级下男女雇员数量的长期演变轨迹。该数据集以千人为计量单位，按性别与教育程度交叉分组，为分析女性劳动参与率随教育扩张的响应弹性、中等教育群体在就业结构中的相对份额变化等议题提供了统一口径的跨国比较框架，是非洲区域就业统计领域极为典型的截面与面板分析素材。

解决学术问题

该数据集有效回应了非洲劳动统计中性别与教育维度长期缺乏统一、可复现跨国面板的学术困境。既有研究常因各国统计口径异质、年份断裂而难以开展横向比较，本数据集以标准化元数据整合ILOSTAT来源指标，使研究者能够检验教育性别平等政策与就业结构转型之间的因果关联，评估不同教育层级劳动力供给对经济增长的贡献差异。其意义在于将碎片化的国别统计转化为可计算的结构化证据，为非洲人力资本积累与劳动力市场分割理论提供实证基础，并对区域发展政策的效果评估产生可累积的学术影响。

实际应用

在实际应用层面，该数据集服务于国际组织、政策研究机构与开发金融机构的循证决策需求。使用者可据此监测非洲各国女性就业结构随教育普及的动态变化，识别高学历群体就业不足或性别差距持续存在的重点国家，进而为职业教育投入、女性经济赋权项目及就业促进政策的设计提供量化依据。其表格化格式便于与人口、产业及贸易数据联结，支撑劳动力供给预测、技能缺口评估与国别营商环境分析等实务场景，亦可作为机器学习建模中社会经济特征工程的高质量输入。

数据集最近研究

最新研究方向

在非洲劳动力市场结构性转型与教育性别平等议题持续升温的背景下，该数据集依托ILOSTAT权威劳动统计框架，覆盖1982至2025年间49个非洲国家按性别与受教育程度分列的雇员规模数据，为劳动经济学与发展经济学交叉研究提供了长时序、跨国别的量化基础。近期前沿研究聚焦于教育分层如何通过性别通道影响非洲各国正规就业参与率，尤其关注中等教育扩张与女性雇员占比变动之间的非线性关系，以及不同教育层级在劳动力市场中的回报异质性。该数据集亦被用于检验撒哈拉以南非洲国家间就业结构收敛假说，并与ILO体面劳动议程及非洲联盟2063年议程中的技能发展目标形成呼应，对评估教育政策干预的就业效应及缩小性别就业差距具有重要参考价值。