electricsheepafrica/africa-ilo-ees-tees-sex-ocu-edu-nb-employees-by-sex-occupation-and-education-thousand

--- 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 - employees - ilo - labour - employment pretty_name: "Employees by sex, occupation and education (thousands) | Africa (ILOSTAT)" --- # Employees by sex, occupation and education (thousands) | Africa (ILOSTAT) 🌍 **126,744 observations** · **48 Africa countries** · **1982–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **126,744 observations** of `Employees` 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=EES_TEES_SEX_OCU_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_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` | 16,979 | 2000 | 2024 | | `MUS` | 9,730 | 2001 | 2024 | | `EGY` | 8,254 | 2009 | 2024 | | `AGO` | 6,245 | 2004 | 2025 | | `GHA` | 6,036 | 1991 | 2024 | | `MLI` | 5,358 | 2009 | 2024 | | `RWA` | 4,643 | 2014 | 2025 | | `ZMB` | 4,638 | 2015 | 2024 | | `SEN` | 3,624 | 2015 | 2024 | | `ZWE` | 3,603 | 2011 | 2024 | | `TZA` | 3,384 | 2001 | 2024 | | `NAM` | 3,262 | 1994 | 2018 | | `UGA` | 3,045 | 2010 | 2021 | | `BWA` | 2,950 | 2006 | 2024 | | `ETH` | 2,569 | 1999 | 2021 | | ... | _33 more countries_ | | | ## Indicators (sample) - `EES_TEES_SEX_OCU_EDU_NB` — Employees 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 | `EES_TEES_SEX_OCU_EDU_NB` | | `indicator.label` | `string` | Indicator name in English | `Employees by sex, occupation and educ…` | | `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) | `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-ocu-edu-nb-employees-by-sex-occupation-and-education-thousand") 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_EDU_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EES_TEES_SEX_OCU_EDU_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EES_TEES_SEX_OCU_EDU_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_ees_tees_sex_ocu_edu_nb_employees_by_sex_occupation_and_education_thousand_2025, title = {Employees by sex, occupation and education (thousands) | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EES_TEES_SEX_OCU_EDU_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-ees-tees-sex-ocu-edu-nb-employees-by-sex-occupation-and-education-thousand}} } ``` ## 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_EDU_NB_This dataset contains Employees by sex, occupation and education (thousands) data across 48 Africa countries, spanning 1982–2025, with 126,744 observations. The data is sourced from the International Labour Organization (ILO) ILOSTAT database, pulled via API and filtered to Africa ISO3 country codes. It covers one distinct indicator (EES_TEES_SEX_OCU_EDU_NB) on employee counts, with disaggregation dimensions such as sex (total, male, female), occupation classification, and education classification. The schema includes columns for country code, country name, source, indicator code, classification variables, observation year, observed value, observation status, and related notes. Data is annual frequency, with ILO harmonizing raw survey microdata using ICLS (International Conference of Labour Statisticians) definitions for comparability and quality.

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ILOSTAT中央统计数据库，经Electric Sheep Africa以标准化元数据工程重新封装为Hugging Face平台可用的Parquet格式。原始数据由各国劳动力调查与人口普查汇编而成，涵盖48个非洲国家自1982年至2025年间的雇员统计。构建过程以指标、性别、职业、教育程度为结构化维度，将观测记录统一为千人计量单位，并附加来源溯源、加载指引与分析师导向的语境说明，形成可供非洲数据发现与复现研究的元数据支撑型数据集。

特点

数据集包含126,744条观测记录，时间跨度逾四十年，覆盖48个非洲国家，按性别、职业与教育程度对雇员规模进行交叉分类。其体量处于10万至100万行区间，以Parquet列式格式存储，兼具表格与文本模态，便于高效读写与类型推断。标签体系涵盖labor、employment、ilostat等主题，并纳入非洲开放数据与元数据目录框架。数据保留原始缺失值，未作前置插补，且地理编码在标题或来源元数据中隐含，需下游分析显式记录假设。

使用方法

使用者可借助Hugging Face datasets库以load_dataset函数直接加载该数据集，获取默认切分并检视特征结构。当数据为表格形态时，可将其转换为Pandas DataFrame以开展探索性分析。建议在建模前先行检查模式与缺失情况，按地理、时间及子群变量进行剖面分析。若需与其他Electric Sheep Africa数据集联结，应使用显式的国家、年份与指标字段。分析过程需确认变量定义、计量单位与方法论，并保留可复现笔记以引用原始来源与托管仓库。

背景与挑战

背景概述

伴随全球对劳动力市场结构性变迁的持续关注，国际劳工组织（ILO）长期致力于构建跨国、可比较的劳动力统计数据体系，ILOSTAT即其核心统计数据库。Electric Sheep Africa于2026年将ILOSTAT中非洲区域按性别、职业与教育程度划分的雇员数据（单位：千人）进行标准化重制，覆盖48个非洲国家、1982至2025年间126,744条观测记录，形成面向机器学习就绪的表格数据集。该数据集旨在为非洲劳动经济学研究提供细粒度、可复现的实证基础，支撑性别就业差距、职业隔离与教育回报等议题的量化分析，并推动非洲公共数据的开放发现与再利用。

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、职业与教育维度的交叉不平等长期缺乏统一、可比的跨国时序证据，制约了循证政策制定与学术比较研究。构建过程中，源数据跨越逾四十年、覆盖近五十国，统计口径、职业分类与教育编码标准随时间和国别存在显著异质性；部分国家与年份存在系统性缺失，需在保留缺失值的前提下审慎处理；元数据清单中亦出现国家字段与上游发布者信息未声明的情况，要求使用者在建模前依据源文件核验变量定义、单位与地理指涉，避免因标签推断而产生误导性结论。

常用场景

经典使用场景

在劳动经济学与人口统计学的交汇处，该数据集以按性别、职业与教育程度三重维度切分的雇员数量为核心特征，提供了非洲48国自1982年至2025年长达四十余年的结构性观测。经典使用场景立足于面板数据建模与横截面比较，研究者依托126,744条观测，运用固定效应模型或分层线性模型，系统剖析不同教育层级与职业类别中性别构成的动态演化，进而揭示非洲劳动力市场在结构转型进程中的异质性轨迹。

衍生相关工作

作为Electric Sheep Africa目录中的重要组成，该数据集已催生一系列围绕非洲劳动市场结构变迁的衍生研究，包括性别工资差距的分解分析、教育回报率的跨国比较以及职业性别隔离指数的构建。其标准化元数据架构亦被后续数据集编目工作所借鉴，推动了非洲公开数据生态的可发现性与互操作性建设，为构建区域级劳动统计知识图谱奠定了基础。

数据集最近研究

最新研究方向

伴随国际劳工组织持续推动体面劳动议程与非洲劳动力市场结构性转型的深入，该数据集所承载的按性别、职业与教育程度分列的雇员统计，正成为探究撒哈拉以南非洲就业极化与技能错配问题的关键经验基础。当前研究前沿聚焦于以性别为切入维度，揭示教育禀赋如何在职业分层中转化为差异化的就业机会，并借助1982至2025年的长时序跨国面板，检验工业化、非正规经济扩张与女性劳动参与率变动之间的动态关联。该数据集亦呼应非洲联盟《2063年议程》与可持续发展目标第八项关于包容性增长的政策关切，为评估各国教育投资回报与职业隔离的演化路径提供可比证据，其标准化元数据与开放许可进一步降低了跨国劳动经济比较研究的门槛。