electricsheepafrica/africa-ilo-eap-dwap-sex-age-mts-rt-labour-force-participation-rate-by-sex-age-and-mar

--- 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 - labour-force - ilo - labour - employment pretty_name: "Labour force participation rate by sex, age and marital status (%) | Africa (ILOSTAT)" --- # Labour force participation rate by sex, age and marital status (%) | Africa (ILOSTAT) 🌍 **123,435 observations** · **49 Africa countries** · **1982–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **123,435 observations** of `Labour force` 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=EAP_DWAP_SEX_AGE_MTS_RT) - **Publisher:** International Labour Organization (ILO) - **License:** [cc-by-4.0](https://creativecommons.org/licenses/by/4.0/) - **Topic:** Labour force ## Methodology Data pulled directly from the ILOSTAT REST API at `https://rplumber.ilo.org/data/indicator?id=EAP_DWAP_SEX_AGE_MTS_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` | 10,691 | 2000 | 2024 | | `MUS` | 8,768 | 2001 | 2024 | | `EGY` | 7,083 | 2008 | 2024 | | `TUN` | 6,423 | 2005 | 2023 | | `MLI` | 4,936 | 2009 | 2024 | | `GHA` | 4,884 | 1991 | 2024 | | `RWA` | 4,187 | 2014 | 2025 | | `AGO` | 4,171 | 2004 | 2025 | | `BWA` | 4,053 | 1996 | 2024 | | `SYC` | 3,689 | 2014 | 2024 | | `ZMB` | 3,553 | 2015 | 2024 | | `TZA` | 3,307 | 2001 | 2024 | | `SEN` | 3,174 | 2011 | 2024 | | `CIV` | 2,957 | 1998 | 2022 | | `ZWE` | 2,951 | 2011 | 2024 | | ... | _34 more countries_ | | | ## Indicators (sample) - `EAP_DWAP_SEX_AGE_MTS_RT` — Labour force participation rate by sex, age and marital status (%) ## 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 | `EAP_DWAP_SEX_AGE_MTS_RT` | | `indicator.label` | `string` | Indicator name in English | `Labour force participation rate by se…` | | `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 | `MTS_AGGREGATE_TOTAL` | | `classif2.label` | `string` | — | `Marital status (Aggregate): Total` | | `time` | `int64` | Observation year | `2025` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `74.342` | | `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-eap-dwap-sex-age-mts-rt-labour-force-participation-rate-by-sex-age-and-mar") 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"] == "EAP_DWAP_SEX_AGE_MTS_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EAP_DWAP_SEX_AGE_MTS_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EAP_DWAP_SEX_AGE_MTS_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_eap_dwap_sex_age_mts_rt_labour_force_participation_rate_by_sex_age_and_mar_2025, title = {Labour force participation rate by sex, age and marital status (%) | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EAP_DWAP_SEX_AGE_MTS_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-eap-dwap-sex-age-mts-rt-labour-force-participation-rate-by-sex-age-and-mar}} } ``` ## 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=EAP_DWAP_SEX_AGE_MTS_RT_This dataset is a tabular dataset containing labour market statistics for 49 African countries from 1982 to 2025, with the core indicator being Labour force participation rate by sex, age and marital status (%). It includes 123,435 observations sourced from the International Labour Organization (ILO) ILOSTAT database, retrieved via API and harmonized. The data covers fields such as country codes, indicator classifications, sex (total, male, female), age groups, marital status, year, observed values, and data quality flags, suitable for machine learning tasks like classification, regression, or time-series forecasting.

搜集汇总

数据集介绍

构建方式

该数据集系基于国际劳工组织中央统计数据库（ILOSTAT）的原始记录，由Electric Sheep Africa团队以标准化元数据工程方式重新封装而成。构建过程涵盖对源数据的采集、清洗与结构化转换，最终以Parquet列式存储格式发布，形成覆盖49个非洲国家、时间跨度为1982年至2025年、共计123435条观测值的面板数据集。数据字段涵盖性别、年龄组与婚姻状况等人口学维度，并附有标准化元数据说明、数据加载指引及溯源注释，为非洲劳动参与率的跨域比较与可复现分析提供结构化基础。

特点

数据集聚焦于非洲区域劳动参与率这一劳动经济学核心议题，具有显著的时空广度与人口学细分深度。其覆盖范围横跨49个非洲国家逾四十年，按性别、年龄与婚姻状况多维度交叉呈现劳动参与率百分比，样本量达十万量级，兼顾宏观趋势识别与微观异质性刻画。数据以表格与文本双模态呈现，附有标准化标签体系与来源溯源信息，便于在非洲数据发现框架内进行检索、复用与跨数据集关联，为劳动市场结构变迁研究提供兼具广度与颗粒度的经验素材。

使用方法

研究者可依托Hugging Face数据集库，通过datasets.load_dataset接口直接加载该数据集，并以数据集查看器初步审视schema与缺失值分布。对于表格型分析，可将首个数据划分转换为Pandas数据框，进行按地理、时间与人口学子群的分组剖析。在使用过程中，应依据显式国家字段界定分析单元，对地理信息仅隐含于标题或来源元数据的情形加以明确标注，并在缺乏可靠依据时审慎处理缺失值。建议结合其他Electric Sheep Africa数据集，通过国家、年份与指标字段实施联结，构建可复现的分析笔记本，并同时引用原始来源与封装仓库。

背景与挑战

背景概述

伴随非洲大陆劳动力市场结构性转型的持续深化，性别、年龄与婚姻状况维度下的劳动参与率差异，已成为发展经济学与劳动经济学交叉领域的核心议题。国际劳工组织（ILO）通过ILOSTAT数据库系统汇集各国劳动力调查数据，为跨国比较提供权威基准。Electric Sheep Africa于2026年将该数据集标准化为Hugging Face平台上的可复现资源，覆盖49个非洲国家、1982年至2025年间的123,435条观测记录。该数据集弥补了非洲劳动统计元数据碎片化的缺陷，为探究婚姻与性别交互作用下劳动供给行为的长期演变提供了关键实证基础。

当前挑战

该数据集所应对的领域问题在于，非洲各国劳动力调查在指标定义、抽样方法与报告标准上长期存在异质性，致使跨国比较面临口径不一致与数据缺失的系统性障碍。构建过程中的挑战体现为多源异构数据的清洗与对齐，包括婚姻状况分类的本土化差异、非正式部门就业的统计盲区，以及部分国家时间序列的断裂。此外，元数据中country与upstream_publisher字段的缺失，要求使用者在建模前必须审慎核实地理标识与变量单位，避免因标签推断而引入政策误读。

常用场景

经典使用场景

在劳动经济学与人口统计学的实证研究中，该数据集通常被用于刻画非洲地区劳动力参与率的性别、年龄与婚姻状况差异及其时序演变。研究者借助涵盖49个非洲国家、1982至2025年逾十二万条观测的纵向截面数据，构建性别—年龄—婚姻状况三维分组下的劳动供给画像，进而识别不同生命周期阶段与婚姻状态对劳动参与决策的异质性影响。此类分析为理解非洲劳动力市场结构变迁提供了基础性的描述性证据。

解决学术问题

该数据集有效回应了非洲劳动统计长期存在的碎片化与可比性不足问题。通过统一指标口径与标准化元数据，它使跨国、跨时期的劳动参与率比较成为可能，缓解了因统计口径差异导致的实证偏差。在学术层面，它为检验婚姻状况对女性劳动供给的抑制效应、年龄—参与率倒U型关系等经典假说提供了可靠面板基础，推动了非洲劳动市场研究的规范化与可复现性。

衍生相关工作

基于该数据集及Electric Sheep Africa元数据编目体系，衍生出一系列面向非洲劳动市场的可复现分析工作，包括跨国劳动参与率分解研究、性别差距时序追踪以及与其他非洲社会经济数据集的联合建模。这些工作依托标准化元数据与Parquet格式，促进了非洲公开数据的互操作性与分析流水线建设，为后续机器学习驱动的劳动市场预测研究奠定了基础。