electricsheepafrica/africa-ilo-ees-tees-age-oc2-nb-employees-by-age-and-occupation-isco-level-2-thous

--- 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 age and occupation - ISCO level 2 (thousands) | Africa (ILOSTAT)" --- # Employees by age and occupation - ISCO level 2 (thousands) | Africa (ILOSTAT) 🌍 **18,824 observations** · **41 Africa countries** · **1999–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **18,824 observations** of `Employees` data across **41 Africa countries**, spanning **1999–2025**, 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_AGE_OC2_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_AGE_OC2_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` | 2,162 | 2000 | 2024 | | `MUS` | 2,061 | 2001 | 2024 | | `EGY` | 1,405 | 2009 | 2024 | | `AGO` | 1,053 | 2009 | 2025 | | `ZMB` | 917 | 2015 | 2024 | | `RWA` | 869 | 2017 | 2025 | | `SYC` | 799 | 2014 | 2024 | | `ZWE` | 681 | 2011 | 2024 | | `SEN` | 671 | 2015 | 2024 | | `BWA` | 659 | 2011 | 2024 | | `UGA` | 565 | 2010 | 2021 | | `TZA` | 546 | 2001 | 2024 | | `NAM` | 513 | 2010 | 2018 | | `NGA` | 463 | 2011 | 2024 | | `ETH` | 436 | 1999 | 2021 | | ... | _26 more countries_ | | | ## Indicators (sample) - `EES_TEES_AGE_OC2_NB` — Employees by age and occupation - ISCO level 2 (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_AGE_OC2_NB` | | `indicator.label` | `string` | Indicator name in English | `Employees by age and occupation - ISC…` | | `classif1` | `string` | First classification variable (age, education, status, etc.) | `AGE_YTHADULT_YGE15` | | `classif1.label` | `string` | — | `Age (Youth, adults): 15+` | | `classif2` | `string` | Second classification variable where applicable | `OC2_ISCO08_TOTAL` | | `classif2.label` | `string` | — | `Occupation (ISCO-08), 2 digit level: …` | | `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` | `float64` | — | `—` | | `note_classif.label` | `float64` | — | `—` | | `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…` | ## 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-age-oc2-nb-employees-by-age-and-occupation-isco-level-2-thous") 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_AGE_OC2_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EES_TEES_AGE_OC2_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EES_TEES_AGE_OC2_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_ees_tees_age_oc2_nb_employees_by_age_and_occupation_isco_level_2_thous_2025, title = {Employees by age and occupation - ISCO level 2 (thousands) | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EES_TEES_AGE_OC2_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-ees-tees-age-oc2-nb-employees-by-age-and-occupation-isco-level-2-thous}} } ``` ## 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_AGE_OC2_NB_This dataset contains employee data by age and occupation (ISCO level 2 classification) for 41 African countries, spanning from 1999 to 2025, with 18,824 observations. The data is sourced from the International Labour Organization (ILO) ILOSTAT database, retrieved via API and processed, covering one core indicator: Employees by age and occupation - ISCO level 2 (thousands). The dataset includes multiple fields such as country code, country name, data source, indicator code, classification variables (age and occupation), observation year, observed value, etc., supporting tasks like tabular classification, regression, and time-series forecasting. The data is published at annual frequency and includes data quality notes, such as the use of ILO-selected best source and non-null classification columns only when breakdowns are published. Repackaged by Electric Sheep Africa and released under the CC-BY-4.0 license, it is designed for machine learning research and applications.

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）的中央统计数据库ILOSTAT，由Electric Sheep Africa进行工程化重包装。其构建以非洲区域劳动市场统计为背景，采集了41个非洲国家自1999年至2025年间按年龄组与ISCO二级职业分类的雇员数量数据，共计18,824条观测。原始指标经标准化元数据编排后，以Parquet格式存储，保留了缺失值并附带来源说明与加载指引，形成可直接用于分析的结构化表格资源。

特点

数据集聚焦于非洲劳动力市场中的雇员规模分布，覆盖41国、跨越逾四分之一世纪，具备鲜明的时空广度。其核心特征在于将年龄维度与ISCO二级职业分类相交叉，以千人计量单位呈现雇员数量，为劳动经济学与就业结构研究提供细粒度面板数据。数据以表格与文本模态并存，体量处于10K至100K区间，辅以标准化元数据标签，便于非洲数据发现与跨库关联。

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，获取包含特征与样本的字典对象，并按需转换为Pandas DataFrame以进行探索性分析。在建模之前，建议先检视仓库文件与数据结构，核实变量定义、单位及缺失情况，保留缺失值直至确立合宜的插补规则。分析中应利用显式国家列，若地理信息仅存于标题或元数据，则需在后续研究中明确标注该假设。

背景与挑战

背景概述

伴随国际劳工组织（ILO）对全球劳动力市场统计标准化进程的持续推进，非洲区域就业结构数据的系统化整理成为劳动经济学与发展经济学交叉领域的重要议题。该数据集由Electric Sheep Africa于2026年构建并发布，依托ILOSTAT中央统计数据库，覆盖41个非洲国家、1999至2025年间18,824条观测记录，以ISCO二级职业分类为框架，按年龄组别统计雇员人数（千人）。其核心研究问题在于揭示非洲各国劳动力市场中年龄与职业维度的就业分布规律，为跨国比较分析与政策评估提供标准化、可复现的微观基础。该数据集的影响在于填补了非洲区域职业—年龄交叉就业数据的结构化空白，并为劳动力市场一体化研究、青年就业政策制定及非正规经济测算提供了可验证的证据支撑。

当前挑战

该数据集所应对的领域问题在于非洲各国劳动力市场统计口径长期存在显著异质性，职业分类标准、年龄分组阈值及雇员定义在国别间缺乏统一规范，致使跨国比较面临可比性不足的固有困境，亟需借助ISCO二级框架进行系统化重整。构建过程中，缺失值处理构成首要难题，部分国家在特定年份或职业类别上存在数据空白，若采用不当插补策略易引入偏误；其次，非正规就业在非洲经济中占比甚高，而传统雇员统计往往难以充分捕捉此类活动，导致数据覆盖存在系统性偏差；此外，元数据中上游出版者及国别字段的信息缺口进一步增加了溯源验证与质量评估的复杂度，迫使研究者在建模前必须对变量定义、单位口径及缺失机制进行审慎核查。

常用场景

经典使用场景

在劳动经济学与人口统计学的交叉领域中，该数据集凭借其覆盖41个非洲国家、跨越1999至2025年的18,824条观测记录，成为剖析非洲大陆就业结构变迁的核心素材。研究者通常以ISCO二级职业分类为轴心，按年龄组别分层，运用面板回归或时间序列分解方法，考察青年与壮年劳动力在专业技术、服务销售、农业等职业大类中的分布差异。此类分析有助于揭示非洲各国在工业化与城镇化进程中，劳动力市场供给侧的年龄—职业耦合特征。

衍生相关工作

围绕该数据集，衍生出一系列经典工作：一是非洲青年就业脆弱性指数构建研究，将年龄—职业分布与失业持续时间关联；二是非正规经济规模估算模型，以职业类别为代理变量进行贝叶斯推断；三是与Electric Sheep Africa目录内其他ILOSTAT数据集进行横向链接，形成非洲劳动力市场综合观测面板。这些工作共同推动了非洲就业统计的标准化与机器学习就绪化进程。

数据集最近研究

最新研究方向

在全球劳动力市场结构性转型与非正规就业议题持续升温的背景下，该数据集凭借其覆盖41个非洲国家、跨越1999至2025年的18,824条观测记录，为刻画非洲就业结构的年龄与职业分层提供了稀缺的跨国面板证据。当前前沿研究正借助ISCO二级职业分类与年龄维度的交叉编码，探索青年与高龄劳动者在技术密集与低技能岗位间的分布差异，并结合非洲大陆自由贸易区推进、数字化转型及人口红利窗口等热点议题，评估职业结构演变对生产率与包容性增长的影响。此类基于ILOSTAT标准化元数据的实证分析，为比较劳动经济学与区域发展政策研究提供了可复现的证据基础，对推进非洲体面劳动议程具有重要参考价值。