electricsheepafrica/africa-ilo-emp-dwap-sex-age-edu-rt-employment-to-population-ratio-by-sex-age-and-educ

--- 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-to-population ratio by sex, age and education (%) | Africa (ILOSTAT)" --- # Employment-to-population ratio by sex, age and education (%) | Africa (ILOSTAT) 🌍 **185,189 observations** · **49 Africa countries** · **1982–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **185,189 observations** of `Employment` 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=EMP_DWAP_SEX_AGE_EDU_RT) - **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_DWAP_SEX_AGE_EDU_RT` 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` | 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` | 6,451 | 2001 | 2024 | | `ZMB` | 6,049 | 2015 | 2024 | | `RWA` | 6,029 | 2014 | 2025 | | `TUN` | 5,973 | 2005 | 2023 | | `BWA` | 5,211 | 2006 | 2024 | | `SEN` | 4,993 | 2011 | 2024 | | `UGA` | 4,920 | 2010 | 2021 | | `TGO` | 4,731 | 2006 | 2022 | | `ZWE` | 4,554 | 2011 | 2024 | | ... | _34 more countries_ | | | ## Indicators (sample) - `EMP_DWAP_SEX_AGE_EDU_RT` — Employment-to-population ratio by sex, age and education (%) ## 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_DWAP_SEX_AGE_EDU_RT` | | `indicator.label` | `string` | Indicator name in English | `Employment-to-population ratio by sex…` | | `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) | `66.617` | | `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-dwap-sex-age-edu-rt-employment-to-population-ratio-by-sex-age-and-educ") 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_DWAP_SEX_AGE_EDU_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_DWAP_SEX_AGE_EDU_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_DWAP_SEX_AGE_EDU_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_emp_dwap_sex_age_edu_rt_employment_to_population_ratio_by_sex_age_and_educ_2025, title = {Employment-to-population ratio by sex, age and education (%) | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_DWAP_SEX_AGE_EDU_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-emp-dwap-sex-age-edu-rt-employment-to-population-ratio-by-sex-age-and-educ}} } ``` ## 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 2026-05-25 via the Electric Sheep pipeline. Source URL: https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_DWAP_SEX_AGE_EDU_RT_This dataset is a tabular dataset on the employment-to-population ratio by sex, age, and education (%) for African countries, sourced from the International Labour Organization (ILO) ILOSTAT database. It contains 185,189 observations covering 49 African countries from 1982 to 2025, focusing on one key indicator: EMP_DWAP_SEX_AGE_EDU_RT (Employment-to-population ratio by sex, age and education). The data is retrieved via the ILOSTAT REST API and harmonized using International Conference of Labour Statisticians (ICLS) definitions. The dataset includes detailed columns such as country codes, indicator codes, sex disaggregation (total, male, female), age and education classifications, observation year, observed values, and data quality flags. It is suitable for tabular classification, regression, and time-series forecasting tasks, designed to provide machine learning-ready data for studying African labor markets.

搜集汇总

数据集介绍

构建方式

该数据集源于国际劳工组织（ILO）的中央统计数据库ILOSTAT，后者作为全球劳工统计的权威来源，系统汇编了各国劳动力市场指标。Electric Sheep Africa在此基础上对非洲区域数据进行重新打包与工程化处理，将其转化为符合机器学习工作流的Parquet格式，并标准化了发现元数据、文档说明与使用指南。数据覆盖49个非洲国家，时间跨度为1982年至2025年，总计185,189条观测记录，聚焦于按性别、年龄和教育程度划分的就业人口比率指标。

特点

数据集以表格与文本混合模态呈现，规模介于十万至百万行之间，采用CC BY 4.0开放许可，便于学术研究与商业应用。其核心特征在于多维度的分组结构，涵盖性别、年龄组和教育水平三个关键人口学维度，为分析非洲劳动力市场的结构性差异提供了细粒度证据。数据经过元数据标准化处理，附带来源溯源说明与分析师导向的上下文注释，但部分字段如国家与上游发布者的元数据存在缺失，需在建模前予以确认。

使用方法

研究人员可通过Hugging Face的datasets库以单行代码加载数据集，并借助内置查看器快速浏览数据结构。加载后可利用features属性检视变量类型，或通过to_pandas方法将表格数据转换为数据框以支持后续分析。使用时应从仓库文件出发核对变量定义与单位，保留缺失值直至确立合理的插补规则，在地理信息仅由标题或来源元数据隐含时须明确记录该假设。建议在建模前系统检查模式与缺失情况，并依据国家、年份与指标字段与其他非洲数据集进行联接。

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可比的劳动力市场统计体系，ILOSTAT即是其汇聚各国就业、失业与劳动参与数据的核心平台。在此框架下，Electric Sheep Africa于2026年将非洲地区按性别、年龄与教育程度分列的就业人口比指标整理发布，覆盖49国、185,189条观测，时间跨度自1982年至2025年。该数据集回应了非洲劳动力市场结构性不平等的研究需求，为考察教育回报、性别差距与年龄分层就业差异提供了可复现的跨国面板基础，对发展经济学与劳动政策评估具有基础性支撑价值。

当前挑战

就业人口比受非正式经济广泛存在、自雇与家庭帮工统计口径模糊等因素影响，在非洲语境下尤其难以精确测度，导致跨国与跨期可比性面临系统性偏差。教育程度的分类标准在各国间缺乏统一映射，性别与年龄分组的覆盖也随年份和国别显著波动。数据构建过程中，原始上报资料的质量参差、缺失与口径变更频繁，需在保留缺失值的同时谨慎处理，避免因不当插补而扭曲结构性差异。如何在不牺牲透明度的前提下支持建模与政策分析，构成该数据集持续面对的方法论挑战。

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，该数据集常被用于刻画性别、年龄与教育水平三重维度下就业人口比率的时空分异格局。研究者借助185189条观测记录与49个非洲国家1982至2025年的长时段面板数据，构建跨国比较分析框架，考察不同人口群体劳动参与程度的演变轨迹。此类分析通常以年份为时间轴、以国家为截面单元，通过分组统计与趋势拟合揭示就业结构的异质性特征。

解决学术问题

该数据集有效回应了非洲劳动力市场研究中长期存在的数据碎片化与口径不一难题。通过ILOSTAT统一统计标准，它使学者得以在可比框架下检验教育扩张对就业吸纳能力的影响、性别就业差距的收敛性以及青年与壮年劳动力参与率的差异化响应。其意义在于为跨国面板回归、队列分析及劳动力市场分层研究提供了可靠的经验基础，推动了非洲就业问题从描述性讨论向因果推断的范式转变。

衍生相关工作

围绕该数据集，已衍生出一系列非洲劳动力市场比较研究与计量建模工作。部分研究将其与教育成就、人口结构及宏观经济指标进行跨库联结，构建多维度就业质量指数；另有工作利用其时间序列特征开展就业弹性的国别估计。Electric Sheep Africa对元数据的标准化整理亦促进了非洲开放数据生态的互操作性，使该数据集成为区域劳动统计知识生产链条中的关键节点。