electricsheepafrica/africa-ilo-ees-tees-sex-age-nb-employees-by-sex-and-age-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 age (thousands) | Africa (ILOSTAT)" --- # Employees by sex and age (thousands) | Africa (ILOSTAT) 🌍 **24,880 observations** · **49 Africa countries** · **1982–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **24,880 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_AGE_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_AGE_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` | 2,100 | 2000 | 2024 | | `MUS` | 2,013 | 1990 | 2024 | | `EGY` | 1,344 | 2008 | 2024 | | `TUN` | 1,173 | 2005 | 2021 | | `MLI` | 984 | 2009 | 2024 | | `AGO` | 921 | 2004 | 2025 | | `GHA` | 915 | 1991 | 2024 | | `BWA` | 864 | 1990 | 2024 | | `RWA` | 840 | 2014 | 2025 | | `SYC` | 779 | 1990 | 2024 | | `ZMB` | 753 | 2015 | 2024 | | `SEN` | 669 | 2011 | 2024 | | `ZWE` | 588 | 2011 | 2024 | | `NAM` | 588 | 1994 | 2018 | | `UGA` | 584 | 2010 | 2021 | | ... | _34 more countries_ | | | ## Indicators (sample) - `EES_TEES_SEX_AGE_NB` — Employees by sex and age (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_AGE_NB` | | `indicator.label` | `string` | Indicator name in English | `Employees by sex and age (thousands)` | | `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.) | `AGE_YTHADULT_YGE15` | | `classif1.label` | `string` | — | `Age (Youth, adults): 15+` | | `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…` | ## 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-age-nb-employees-by-sex-and-age-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_AGE_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EES_TEES_SEX_AGE_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EES_TEES_SEX_AGE_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_ees_tees_sex_age_nb_employees_by_sex_and_age_thousands_2025, title = {Employees by sex and age (thousands) | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EES_TEES_SEX_AGE_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-ees-tees-sex-age-nb-employees-by-sex-and-age-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_AGE_NB_This dataset contains 24,880 observations of Employees data across 49 Africa countries, spanning 1982 to 2025, covering 1 distinct indicator. The data is sourced from the International Labour Organization (ILO)s ILOSTAT database, pulled via REST API and filtered to Africa ISO3 country codes. It includes employees by sex and age (in thousands), providing structured tabular data suitable for tasks such as tabular classification, regression, and time-series forecasting. The dataset has been repackaged by Electric Sheep Africa and published in Parquet format for machine learning readiness.

搜集汇总

数据集介绍

构建方式

该数据集隶属于Electric Sheep Africa在Hugging Face平台构建的非洲公共数据目录，其底层数据源自国际劳工组织核心统计数据库ILOSTAT，经标准化元数据工程整理后重新发布。原始统计资料由各国劳动力调查与行政记录汇总而成，覆盖49个非洲国家并跨越1982年至2025年，共汇集24,880条观测记录。Electric Sheep Africa在保留原始指标定义、单位与来源信息的基础上，以Parquet格式统一封装，并补充数据发现、溯源说明与分析导向的上下文信息，形成可供机器学习任务直接调用的表格型数据集。

使用方法

研究者可借助Hugging Face datasets库以load_dataset函数直接加载该数据集，并通过查看数据特征与样本切片快速把握结构。对于表格型分析，可将首个数据分割转换为Pandas数据框，进而开展缺失值检查、变量分布刻画以及按地理、时间与人口子群的剖面分析。使用时应先核实变量定义与单位，保留缺失值直至确立合理的插补规则，并可依据显式的国家、年份与指标字段与其他Electric Sheep Africa数据集进行连接，构建可复现的分析流程并规范引用原始来源与再发布仓库。

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体系，ILOSTAT作为其核心数据库，为就业结构变迁研究提供了权威基准。非洲地区劳动力市场数据长期存在碎片化与覆盖不足的困境，Electric Sheep Africa于2026年将该数据库中的非洲雇员性别与年龄分布数据标准化发布为Hugging Face数据集，涵盖49个非洲国家、1982至2025年间共计24,880条观测记录，聚焦雇员人数（千人）这一单一指标。该数据集以标准化元数据与可复现工作流为支撑，为非洲劳动经济学、性别就业差距及人口转型研究提供了跨时空可比的结构化证据，弥合了区域劳动统计在机器学习就绪性方面的关键缺口。

当前挑战

该数据集所应对的核心领域问题在于揭示非洲各国雇员性别与年龄结构的长期演变，但非洲劳动统计体系普遍面临数据稀疏、调查频率不一及非正规就业难以捕捉等固有障碍。构建过程中，源数据在国别与年份维度存在大量缺失值，且元数据清单中country字段缺失、上游发布者信息未明，需依赖标题与来源上下文进行地理推断，增加了下游分析的歧义风险。此外，指标定义与单位换算在不同国家间可能存在隐性差异，性别与年龄组的分类标准亦未必完全统一，若直接建模而不加辨析，易导致跨群组比较的偏误，对缺失值处理规则与变量对齐策略提出了审慎的方法论要求。

常用场景

经典使用场景

在劳动经济学与非洲区域发展研究的交汇处，该数据集凭借其覆盖四十九个非洲国家、跨越1982至2025年的两万四千余条观测，成为刻画就业性别与年龄结构的经典面板数据源。研究者通常将其用于按性别与年龄组别分解各国雇员规模的时序演变，构建劳动参与率、就业弹性等衍生指标，并借助固定效应模型或面板回归比较不同国家与时期之间的结构性差异，从而揭示非洲劳动力市场的人口学轮廓。

解决学术问题

该数据集有效缓解了非洲劳动统计长期存在的碎片化与口径不一问题。通过统一整理国际劳工组织数据库中的雇员指标，并以标准化元数据与持久化格式加以封装，它为学术研究提供了可复现、可比较的跨国证据基础，使学者能够系统检验性别就业差距、青年劳动市场融入以及人口转型对就业结构的影响等议题，进而为区域劳动政策评估与跨国实证比较提供严谨的数据支撑。

实际应用

在现实政策与商业分析层面，该数据集可服务于国际组织、政府部门与咨询机构的劳动力市场监测与规划工作。使用者能够据此识别特定国家或年龄性别群体在就业总量中的占比变化，辅助制定面向青年或女性的就业促进措施，亦可用于企业进入非洲市场时的人口与用工环境评估，并通过与其他社会经济数据集联结，支撑区域发展报告与投资尽调中的定量论证。

数据集最近研究

最新研究方向

伴随非洲劳动力市场结构性转型议题的持续升温，基于国际劳工组织ILOSTAT数据库的性别与年龄维度就业分析正成为发展经济学与劳动经济学的交汇前沿。该数据集涵盖1982至2025年间49个非洲国家逾两万条雇员观测记录，为探究青年就业困境、女性劳动参与率变迁以及非正规经济中年龄分层等热点问题提供了长时序、跨国别的实证基础。当前研究趋向于将该类表格数据与人口普查、教育统计等多源异构信息进行链接，以识别性别与年龄交互作用下的就业脆弱性模式，并借助机器学习方法提升缺失值插补与趋势预测的稳健性。其开放许可与标准化元数据设计，亦推动了非洲数据主权意识下可复现研究的范式演进，为政策评估与可持续发展目标监测注入实证动能。