electricsheepafrica/africa-ilo-emp-temp-sex-age-edu-nb-employment-by-sex-age-and-education-thousands

--- 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, age and education (thousands) | Africa (ILOSTAT)" --- # Employment by sex, age and education (thousands) | Africa (ILOSTAT) 🌍 **187,044 observations** · **50 Africa countries** · **1982–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **187,044 observations** of `Employment` data across **50 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_AGE_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_AGE_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 50 Africa countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `ZAF` | 18,468 | 2000 | 2024 | | `MUS` | 13,392 | 2001 | 2024 | | `EGY` | 10,817 | 2008 | 2024 | | `GHA` | 7,900 | 1991 | 2024 | | `MLI` | 7,342 | 2009 | 2024 | | `AGO` | 6,975 | 2004 | 2025 | | `TZA` | 6,466 | 2001 | 2024 | | `ZMB` | 6,049 | 2015 | 2024 | | `RWA` | 6,029 | 2014 | 2025 | | `TUN` | 6,000 | 1994 | 2023 | | `BWA` | 5,214 | 2006 | 2024 | | `SEN` | 4,993 | 2011 | 2024 | | `UGA` | 4,926 | 2010 | 2021 | | `TGO` | 4,731 | 2006 | 2022 | | `ZWE` | 4,554 | 2011 | 2024 | | ... | _35 more countries_ | | | ## Indicators (sample) - `EMP_TEMP_SEX_AGE_EDU_NB` — Employment by sex, age 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_AGE_EDU_NB` | | `indicator.label` | `string` | Indicator name in English | `Employment by sex, age and education …` | | `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+` | | `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-age-edu-nb-employment-by-sex-age-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"] == "EMP_TEMP_SEX_AGE_EDU_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_TEMP_SEX_AGE_EDU_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_TEMP_SEX_AGE_EDU_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_emp_temp_sex_age_edu_nb_employment_by_sex_age_and_education_thousands_2025, title = {Employment by sex, age and education (thousands) | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_TEMP_SEX_AGE_EDU_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-emp-temp-sex-age-edu-nb-employment-by-sex-age-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=EMP_TEMP_SEX_AGE_EDU_NB_This dataset is named Employment by sex, age and education (thousands) | Africa (ILOSTAT) and contains 187,044 observations across 50 African countries, spanning from 1982 to 2025. The data is sourced from the International Labour Organization (ILO)s ILOSTAT database, the leading global source for labour statistics. The dataset includes one core indicator: EMP_TEMP_SEX_AGE_EDU_NB, which represents employment by sex, age and education in thousands. Data is pulled directly from the ILOSTAT REST API and filtered to Africa ISO3 country codes. ILOSTAT harmonises raw survey microdata using International Conference of Labour Statisticians (ICLS) definitions, with sources flagged in the `source.label` column for traceability. The dataset is tabular and includes columns such as country code, country name, data source, indicator, sex disaggregation, age and education classifications, observation year, observed value, observation status flags, etc. Regarding data quality, the data is annual frequency, and when an indicator has multiple sources for the same country×year, the ILO-selected best source is used. Disaggregation columns (e.g., sex, age, education) are non-null only when the indicator publishes that breakdown. The dataset is suitable for tasks like tabular classification, regression, and time-series forecasting.

搜集汇总

数据集介绍

构建方式

该数据集以国际劳工组织中央统计数据库（ILOSTAT）为上游来源，由Electric Sheep Africa团队对原始就业统计资料进行系统性重新封装。构建过程遵循元数据驱动的工程规范，将跨越1982年至2025年的非洲区域就业观测记录整理为统一的表格结构，并以Parquet格式存储。原始数据经字段对齐、标准化标签映射和溯源信息补全后形成可复现的分析资产，涵盖50个非洲国家的18.7万余条观测，每条记录对应特定性别、年龄组与教育程度组合下的就业规模（以千人为单位），并保留明确的来源归属与许可声明。

使用方法

研究者可借助Hugging Face datasets库直接加载该数据集，通过load_dataset函数获取默认划分并检视特征结构，随后利用to_pandas方法转换为数据框以适配常规分析流程。在建模之前，建议优先检查各字段的缺失模式与变量定义，避免在单位或分类标准不明的情况下作出推断。当涉及地理维度分析时，应使用显式的国家列；若地理信息仅由标题或来源元数据隐含，则需在下游分析中明确记录这一假设。该数据集亦可与Electric Sheep Africa目录中的其他数据集基于国家、年份和指标字段进行联接，以支持更广泛的比较研究。

背景与挑战

背景概述

国际劳工组织（ILO）自二十世纪中叶以来持续构建全球劳动力统计体系，ILOSTAT作为其核心数据库，长期为就业结构分析提供权威基准。2026年，Electric Sheep Africa对ILOSTAT中非洲区域按性别、年龄与教育程度分列的就业数据进行系统性重包装，形成覆盖50个非洲国家、187,044条观测值、时间跨度自1982年至2025年的结构化数据集。该数据集的核心研究问题在于揭示非洲劳动力市场中教育回报与人口结构差异的交互效应，为发展经济学与劳动政策评估提供可复现的微观证据基础，其标准化元数据组织方式亦为非洲开放数据生态的互操作性树立了实践范式。

当前挑战

该数据集所应对的领域问题在于，非洲各国劳动力统计长期面临口径不一、报告缺失与教育分类异构等结构性障碍，使得跨国比较与趋势推断尤为困难。构建过程中，重包装者须在保留ILOSTAT原始指标语义的前提下，协调不同国家与年份间的教育层级映射、性别与年龄分组粒度差异，以及大量缺失值所导致的建模偏差风险。此外，元数据中上游出版者与国家标识的缺位，进一步增加了来源追溯与地理归属的不确定性，要求下游分析在采用明确国家字段的同时，对隐含地理假设保持审慎并加以文档化。

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汇处，该数据集以国际劳工组织统计数据库为基石，承载了1982至2025年间五十个非洲国家按性别、年龄与教育程度分列的就业人口观测，体量逾十八万条。研究者常以此构建面板数据模型，考察不同人口群体就业结构的时序演变与跨国差异，并借助交叉列联与回归分析揭示教育回报、性别鸿沟及青年就业困境等议题。其经典用法在于将人口学维度与劳动市场结果相联结，为非洲劳动力供给结构的量化刻画提供细粒度证据。

解决学术问题

长期以来，非洲劳动统计面临数据碎片化与口径不一的困境，跨国比较研究常受制于样本覆盖面窄和分类标准异质。该数据集通过统一整理国际劳工组织标准化指标，缓解了教育—年龄—性别三维交叉就业信息稀缺的问题，使学者得以检验人力资本理论在非洲情境中的适用性，并评估结构性转型对各类人口群体就业参与的影响。其意义在于为跨国计量分析提供可比框架，推动非洲劳动市场研究从个案描述走向系统比较，亦为循证政策讨论奠定数据基础。

实际应用

在政策制定与实务领域，该数据集可服务于国家就业规划、职业教育资源配置及性别平等监测等任务。国际组织与非洲各国劳工部门可据此识别青年、女性及低教育群体就业不足的高风险区域，设计针对性培训与就业促进项目。发展金融机构亦能结合时序趋势评估投资项目对当地劳动力吸纳的潜在效应。数据以parquet格式发布并附载入指引，便于分析师在可复现工作流中快速调用，降低从原始统计到决策参考之间的技术门槛。

数据集最近研究

最新研究方向

在非洲劳动力市场结构转型与人力资本积累的宏大叙事中，该数据集凭借覆盖五十国、纵贯1982至2025年的十八万余条观测，为解析性别、年龄与教育维度下就业分布的动态演化提供了稀缺的跨国面板基础。当前前沿研究正依托此类微观异质性信息，聚焦于人口红利窗口期内青年与女性劳动参与率的收敛机制，以及教育错配对非正规就业韧性的长期影响。国际劳工组织统计口径与元数据标准化实践的融合，亦推动着可复现机器学习流程在劳动经济学中的落地，为撒哈拉以南非洲结构性失业治理与技能政策评估注入实证动能。