electricsheepafrica/africa-ilo-emp-temp-sex-ocu-edu-nb-employment-by-sex-occupation-and-education-thousan

--- license: cc-by-4.0 language: - en task_categories: - tabular-classification - tabular-regression - time-series-forecasting multilinguality: monolingual size_categories: - 100K<n<1M tags: - tabular - africa - ilostat - employment - ilo - labour pretty_name: "Employment by sex, occupation and education (thousands) | Africa (ILOSTAT)" --- # Employment by sex, occupation and education (thousands) | Africa (ILOSTAT) 🌍 **140,473 observations** · **48 Africa countries** · **1982–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **140,473 observations** of `Employment` data across **48 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=EMP_TEMP_SEX_OCU_EDU_NB) - **Publisher:** International Labour Organization (ILO) - **License:** [cc-by-4.0](https://creativecommons.org/licenses/by/4.0/) - **Topic:** Employment ## Methodology Data pulled directly from the ILOSTAT REST API at `https://rplumber.ilo.org/data/indicator?id=EMP_TEMP_SEX_OCU_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 48 Africa countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `ZAF` | 17,941 | 2000 | 2024 | | `MUS` | 10,924 | 2001 | 2024 | | `EGY` | 8,750 | 2009 | 2024 | | `AGO` | 7,030 | 2004 | 2025 | | `GHA` | 6,776 | 1991 | 2024 | | `MLI` | 5,939 | 2009 | 2024 | | `ZMB` | 5,335 | 2015 | 2024 | | `RWA` | 5,066 | 2014 | 2025 | | `ZWE` | 3,988 | 2011 | 2024 | | `SEN` | 3,786 | 2015 | 2024 | | `TZA` | 3,785 | 2001 | 2024 | | `UGA` | 3,651 | 2010 | 2021 | | `BWA` | 3,545 | 2006 | 2024 | | `NAM` | 3,501 | 1994 | 2018 | | `TGO` | 2,934 | 2010 | 2022 | | ... | _33 more countries_ | | | ## Indicators (sample) - `EMP_TEMP_SEX_OCU_EDU_NB` — Employment by sex, occupation 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 | `EMP_TEMP_SEX_OCU_EDU_NB` | | `indicator.label` | `string` | Indicator name in English | `Employment by sex, occupation and edu…` | | `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 | `EDU_AGGREGATE_TOTAL` | | `classif2.label` | `string` | — | `Education (Aggregate levels): Total` | | `time` | `int64` | Observation year | `2025` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `13984.984` | | `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-emp-temp-sex-ocu-edu-nb-employment-by-sex-occupation-and-education-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"] == "EMP_TEMP_SEX_OCU_EDU_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_TEMP_SEX_OCU_EDU_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_TEMP_SEX_OCU_EDU_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_emp_temp_sex_ocu_edu_nb_employment_by_sex_occupation_and_education_thousan_2025, title = {Employment by sex, occupation and education (thousands) | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_TEMP_SEX_OCU_EDU_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-emp-temp-sex-ocu-edu-nb-employment-by-sex-occupation-and-education-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=EMP_TEMP_SEX_OCU_EDU_NB_This dataset, titled Employment by sex, occupation and education (thousands) | Africa (ILOSTAT), contains 140,473 observations across 48 African countries, spanning the years 1982 to 2025. It is sourced from the ILOSTAT database of the International Labour Organization (ILO), a leading global source for labour statistics covering topics such as employment, unemployment, wages, and working time. The specific indicator is EMP_TEMP_SEX_OCU_EDU_NB, which represents employment disaggregated by sex, occupation, and education (in thousands). The dataset has been repackaged by Electric Sheep Africa as part of a unified, ML-ready data layer for Africa. It is organized in tabular format with columns including country code, year, sex classification, occupation and education classifications, and is suitable for tasks such as tabular classification, regression, and time-series forecasting. Data quality notes include annual frequency, use of ILO-selected best source for multiple sources, and disaggregation columns being non-null only when breakdowns are published.

搜集汇总

数据集介绍

构建方式

该数据集根植于国际劳工组织（ILO）长期运行的劳动力市场统计体系，原始数据源自ILOSTAT这一全球劳动统计的核心数据库，经Electric Sheep Africa以标准化元数据工程流程重新封装。其构建覆盖非洲48个国家，时间跨度自1982年延续至2025年，累计汇整140,473条观测记录，围绕就业人口按性别、职业与教育程度的交叉分类形成结构化表格。数据以Parquet列式格式存储，便于高效读取与批量处理，并在封装过程中保留来源标注与使用指引，使原始统计口径在再发布环节得以延续。

特点

该数据集在规模与结构上兼具广度与细度，观测总量处于十万至百万量级之间，地理范围横贯非洲大陆，时间维度逾四十年，能够支撑跨国家与跨时期的比较分析。其变量体系以性别、职业和教育三重维度为骨架，配合国别与年份标识，为劳动经济学的分组研究提供可操作的分析单元。数据以表格与文本双模态呈现，附有标准化元数据、来源说明与质量提示，标签体系涵盖就业、劳动、ILO等主题词，便于在非洲数据发现场景中被检索与复用。

使用方法

研究者可借助Hugging Face的datasets库以load_dataset函数直接加载该数据集，获取数据集的划分结构与特征字段，并按需将表格型子集转换为Pandas数据框以开展后续分析。使用前宜先检查仓库中的数据文件与查看器，核实变量定义、计量单位与缺失情况，在建模前明确国家列与年份列的显式引用，并对缺失值保留原始状态直至确立可辩护的插补规则。该数据亦可与Electric Sheep Africa目录下其他数据集按国别、年份和指标字段进行连接，用于构建可复现的分析流程。

背景与挑战

背景概述

国际劳工组织（ILO）长期致力于构建全球劳动统计标准体系，ILOSTAT作为其核心统计数据库，为衡量各国劳动力市场结构提供了权威依据。在此背景下，Electric Sheep Africa于2026年将ILOSTAT中关于非洲地区按性别、职业与教育程度分类的就业数据（以千人为单位）进行标准化整理与重新发布，形成本数据集。该数据集覆盖48个非洲国家，时间跨度自1982年至2025年，包含140473条观测记录，核心研究问题在于揭示非洲各国劳动力市场中性别、职业结构与教育水平之间的多维关联。其发布为非洲劳动经济学研究、性别平等政策评估以及教育回报率分析提供了可比性强、粒度精细的基础数据，对推动该地区循证决策具有重要价值。

当前挑战

该数据集所涉及的领域问题在于如何准确刻画非洲劳动力市场中性别、职业与教育三重维度交织下的就业分布格局，此类分析长期受制于跨国统计口径不一致、非正规部门数据缺失以及性别分类标准差异等难题。在构建过程中，原始ILOSTAT元数据存在国别标识与上游发布机构信息不完整的问题，标准化流程需在保留缺失值的前提下协调不同国家报送数据的异质性，同时需避免因标签字面含义而误读指标定义、单位与统计方法。此外，数据集覆盖范围虽广，但部分年份与国家的数据稀疏性可能影响时间序列分析与跨国比较的稳健性，要求研究者在建模前审慎核查数据结构与缺失模式。

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中，该数据集凭借国际劳工组织统计数据库的权威地位，成为刻画非洲劳动力市场结构性特征的基础性资源。其经典用法聚焦于按性别、职业与受教育程度三重维度对48个非洲国家1982至2025年间约十四万条就业观测记录进行分组统计与比较分析，研究者常借此构建面板数据结构，考察不同教育层级劳动力在职业分布上的性别差异，进而揭示非洲各国劳动力市场的分割格局与演化轨迹。

解决学术问题

该数据集缓解了长期以来非洲劳动统计研究面临的数据碎片化与口径不一问题，其标准化的指标编码和可追溯的来源信息，使得跨国家、跨年份的可比分析成为可能。学术研究借此得以检验教育扩张与就业结构转型之间的理论假设，评估性别平等在职业获得中的进展，并为人力资本理论与劳动力市场分层研究提供来自非洲大陆的系统性经验证据，对修正以发达经济体为中心的既有结论具有实质意义。

衍生相关工作

依托该数据集，研究者已开展了一系列延伸性工作，包括与非洲人口与健康调查、世界银行发展指标等外部数据源的链接分析，以及面向机器学习建模的表格分类与回归任务探索。Electric Sheep Africa围绕其构建的元数据清单与标准化文档体系，亦催生了非洲开放数据发现与治理的相关实践，为后续劳动统计数据的自动化采集、质量评估与可复现研究流程奠定了基础。