electricsheepafrica/africa-ilo-ged-xlu1-sex-hht-chl-rt-prime-age-unemployment-rate-by-sex-household-type

--- 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 - unemployment - ilo - labour - employment pretty_name: "Prime-age unemployment rate by sex, household type and presence of children (%) | Africa (ILOSTAT)" --- # Prime-age unemployment rate by sex, household type and presence of children (%) | Africa (ILOSTAT) 🌍 **16,957 observations** · **44 Africa countries** · **2000–2024** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **16,957 observations** of `Unemployment` data across **44 Africa countries**, spanning **2000–2024**, 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=GED_XLU1_SEX_HHT_CHL_RT) - **Publisher:** International Labour Organization (ILO) - **License:** [cc-by-4.0](https://creativecommons.org/licenses/by/4.0/) - **Topic:** Unemployment ## Methodology Data pulled directly from the ILOSTAT REST API at `https://rplumber.ilo.org/data/indicator?id=GED_XLU1_SEX_HHT_CHL_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 44 Africa countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `TUN` | 1,616 | 2005 | 2023 | | `EGY` | 1,055 | 2008 | 2024 | | `MUS` | 951 | 2014 | 2024 | | `RWA` | 898 | 2014 | 2024 | | `ZMB` | 828 | 2015 | 2023 | | `GHA` | 824 | 2000 | 2022 | | `BWA` | 700 | 2006 | 2023 | | `UGA` | 677 | 2010 | 2021 | | `NAM` | 581 | 2012 | 2018 | | `AGO` | 581 | 2004 | 2019 | | `KEN` | 566 | 2005 | 2022 | | `MLI` | 512 | 2009 | 2022 | | `TZA` | 495 | 2008 | 2024 | | `NGA` | 416 | 2011 | 2019 | | `BFA` | 405 | 2006 | 2024 | | ... | _29 more countries_ | | | ## Indicators (sample) - `GED_XLU1_SEX_HHT_CHL_RT` — Prime-age unemployment rate by sex, household type and presence of children (%) ## 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) | `BB:16199` | | `source.label` | `string` | Source name in English | `HIES - Survey on Expenditure, Revenue…` | | `indicator` | `string` | ILOSTAT indicator code | `GED_XLU1_SEX_HHT_CHL_RT` | | `indicator.label` | `string` | Indicator name in English | `Prime-age unemployment rate by sex, h…` | | `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.) | `HHT_AGGREGATE_TOTAL` | | `classif1.label` | `string` | — | `Household type: Total` | | `classif2` | `string` | Second classification variable where applicable | `CHL_AGET6_TOTAL` | | `classif2.label` | `string` | — | `Presence of children under age 6: Total` | | `time` | `int64` | Observation year | `2019` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `9.546` | | `obs_status` | `string` | Observation status flag (e.g. provisional, unreliable) | `U` | | `obs_status.label` | `string` | — | `Unreliable` | | `note_indicator` | `float64` | — | `—` | | `note_indicator.label` | `float64` | — | `—` | | `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-ged-xlu1-sex-hht-chl-rt-prime-age-unemployment-rate-by-sex-household-type") 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"] == "GED_XLU1_SEX_HHT_CHL_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="GED_XLU1_SEX_HHT_CHL_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "GED_XLU1_SEX_HHT_CHL_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_ged_xlu1_sex_hht_chl_rt_prime_age_unemployment_rate_by_sex_household_type_2024, title = {Prime-age unemployment rate by sex, household type and presence of children (%) | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2024}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=GED_XLU1_SEX_HHT_CHL_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-ged-xlu1-sex-hht-chl-rt-prime-age-unemployment-rate-by-sex-household-type}} } ``` ## 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=GED_XLU1_SEX_HHT_CHL_RT_This dataset contains 16,957 observations of unemployment data across 44 Africa countries, spanning 2000–2024, covering 1 distinct indicator: Prime-age unemployment rate by sex, household type and presence of children (%). It is sourced from the ILOSTAT database of the International Labour Organization (ILO), harmonized and filtered for African countries. The data is provided in tabular format with dimensions such as country code, year, indicator value, sex disaggregation, household type, and presence of children, suitable for tasks like tabular classification, regression, and time-series forecasting.

搜集汇总

数据集介绍

构建方式

该数据集源自国际劳工组织统计数据库（ILOSTAT）的原始调查数据，经Electric Sheep Africa团队系统性封装与元数据标准化后发布。数据采集覆盖44个非洲国家，时间跨度为2000年至2024年，合计16,957条观测记录，以Parquet列式格式存储，兼顾存储效率与分析性能。构建过程中保留了源数据中按性别、家庭类型及子女 Presence 分类的原始编码，并纳入标准化元数据清单，确保来源可追溯、版本可核查。

使用方法

研究者可通过Hugging Face datasets库以一行代码加载数据集，自动获取默认拆分与特征结构。建议在建模前利用to_pandas方法转换为数据框，检查各变量缺失模式与取值分布，尤其关注性别、家庭类型等分类字段的编码一致性。跨国比较或面板分析时，应显式引用国家与年份字段，并避免对缺失值进行未经论证的插补。数据可与其他Electric Sheep Africa数据集按国家、年份及指标字段联结，以构建更丰富的分析框架。

背景与挑战

背景概述

非洲大陆的劳动力市场长期受制于结构性失业与性别分工的交互影响，家庭结构在劳动供给决策中扮演着关键角色。国际劳工组织（ILO）依托ILOSTAT中央统计数据库，系统采集了全球劳动统计指标，其中非洲地区的数据覆盖尤为值得关注。Electric Sheep Africa于2026年将该数据集标准化发布于Hugging Face平台，涵盖44个非洲国家、2000至2024年间约16,957条观察记录，以性别、家庭类型及子女存在状况为维度刻画壮年失业率。该数据集填补了非洲家庭异质性失业分析的空白，为劳动经济学、性别研究与区域政策评估提供了细粒度证据。

当前挑战

该数据集所面对的首要领域难题在于，失业率的性别差异与家庭结构效应在非洲各国呈现高度异质性，传统宏观失业指标难以揭示照料责任、家庭类型对劳动参与的差异化约束。构建过程中的挑战集中于数据标准化环节：原始ILOSTAT数据在各国汇报口径、家庭类型分类与性别编码上存在不一致，部分国家存在年份缺失与指标定义漂移。元数据中country与upstream_publisher字段的缺漏进一步加大了溯源与跨国比较的难度。此外，缺失值的处理需审慎对待，避免因不当插补而扭曲家庭类型间的失业率差异。

常用场景

经典使用场景

在劳动经济学与家庭人口学的交叉研究中，该数据集凭借其按性别、家庭类型及子女在场情况细分的黄金年龄失业率指标，成为探究非洲劳动力市场结构性差异的经典素材。研究者常以面板数据回归、分组比较或纵向追踪的方式，考察不同家庭结构中男女失业风险的异质性，尤其关注育儿责任对女性就业参与率的抑制效应。数据集覆盖44个非洲国家、2000至2024年的16957条观测，为跨国别、跨时段的比较分析提供了扎实的表格化基础，亦可用于构建分类或回归模型以识别失业脆弱群体。

解决学术问题

该数据集直击非洲劳动统计中长期存在的碎片化与粒度不足问题，弥补了多数宏观失业指标未能交叉刻画性别、家庭形态与子女抚养状态的缺陷。通过标准化ILOSTAT原始数据，它为学术界检验“母职惩罚”假说、家庭生计策略与劳动力供给弹性等议题提供了可复现的量化证据。其意义在于将家庭内部异质性引入失业分析框架，推动非洲就业政策研究从总量视角转向结构性精准识别，并为后续跨国比较与因果推断奠定数据基础。

实际应用

在国际组织与非洲各国劳动部门的政策实践中，该数据集可用于监测黄金年龄人口失业率的性别与家庭类型差距，辅助设计面向单亲家庭、多子女家庭等特定群体的就业扶持措施。发展金融机构亦可借助其细分指标评估项目干预对弱势家庭就业状况的改善效果。同时，该数据集适合与Electric Sheep Africa目录中的其他非洲社会经济数据集进行国别、年份与指标字段的联接，支撑可复现的分析报告与数据看板，服务于劳动力市场监测与民生政策评估。

数据集最近研究

最新研究方向

伴随非洲劳动力市场结构转型与家庭形态变迁的交叉议题升温，基于该数据集的前沿研究正从单一失业率测度转向性别—家庭类型—子女 presence 三重维度的精细化分层分析。研究者借助覆盖44个非洲国家、2000至2024年的16,957条观测，探索育儿责任与家庭角色如何差异化塑造壮年男女的失业风险，进而检验性别不平等在家庭内部的传导机制。该方向呼应了国际劳工组织对非标准就业与非正式照料经济的持续关切，为非洲包容性增长政策、家庭友好型劳动力市场干预以及性别响应型社会保障设计提供了可复用的实证基础，亦推动开放数据驱动的比较劳动经济学发展。