electricsheepafrica/africa-ilo-sdg-b852-sex-dsb-rt-sdg-indicator-8-5-2-unemployment-rate-by-sex-and-d

--- license: cc-by-4.0 language: - en task_categories: - tabular-classification - tabular-regression - time-series-forecasting multilinguality: monolingual size_categories: - n<1K tags: - tabular - africa - ilostat - unemployment - ilo - labour - employment pretty_name: "SDG indicator 8.5.2 - Unemployment rate by sex and disability status -- 19th ICLS (%) | Africa (ILOSTAT)" --- # SDG indicator 8.5.2 - Unemployment rate by sex and disability status -- 19th ICLS (%) | Africa (ILOSTAT) 🌍 **459 observations** · **20 Africa countries** · **2016–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **459 observations** of `Unemployment` data across **20 Africa countries**, spanning **2016–2025**, 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=SDG_B852_SEX_DSB_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=SDG_B852_SEX_DSB_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 20 Africa countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `RWA` | 81 | 2017 | 2025 | | `BWA` | 52 | 2019 | 2024 | | `ZMB` | 52 | 2018 | 2024 | | `ZWE` | 45 | 2019 | 2024 | | `GHA` | 42 | 2017 | 2024 | | `GMB` | 24 | 2018 | 2025 | | `MWI` | 18 | 2020 | 2024 | | `TZA` | 18 | 2020 | 2024 | | `LSO` | 18 | 2019 | 2024 | | `UGA` | 17 | 2017 | 2021 | | `SWZ` | 17 | 2021 | 2023 | | `SYC` | 12 | 2023 | 2024 | | `NGA` | 9 | 2019 | 2019 | | `EGY` | 9 | 2024 | 2024 | | `CIV` | 9 | 2016 | 2016 | | ... | _5 more countries_ | | | ## Indicators (sample) - `SDG_B852_SEX_DSB_RT` — SDG indicator 8.5.2 - Unemployment rate by sex and disability status -- 19th ICLS (%) ## Schema | Column | Type | Description | Example | |--------|------|-------------|---------| | `ref_area` | `string` | ISO 3166-1 alpha-3 country code | `BWA` | | `ref_area.label` | `string` | Country name in English | `Botswana` | | `source` | `string` | ILOSTAT source code (e.g. labour force survey) | `BX:15710` | | `source.label` | `string` | Source name in English | `HS - Multi-Topic Household Survey` | | `indicator` | `string` | ILOSTAT indicator code | `SDG_B852_SEX_DSB_RT` | | `indicator.label` | `string` | Indicator name in English | `SDG indicator 8.5.2 - Unemployment ra…` | | `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.) | `DSB_STATUS_TOTAL` | | `classif1.label` | `string` | — | `Disability status: Total` | | `time` | `int64` | Observation year | `2024` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `28.002` | | `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-sdg-b852-sex-dsb-rt-sdg-indicator-8-5-2-unemployment-rate-by-sex-and-d") 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"] == "SDG_B852_SEX_DSB_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="SDG_B852_SEX_DSB_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "SDG_B852_SEX_DSB_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_sdg_b852_sex_dsb_rt_sdg_indicator_8_5_2_unemployment_rate_by_sex_and_d_2025, title = {SDG indicator 8.5.2 - Unemployment rate by sex and disability status -- 19th ICLS (%) | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=SDG_B852_SEX_DSB_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-sdg-b852-sex-dsb-rt-sdg-indicator-8-5-2-unemployment-rate-by-sex-and-d}} } ``` ## 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=SDG_B852_SEX_DSB_RT_This dataset contains SDG indicator 8.5.2 (Unemployment rate by sex and disability status) data from the International Labour Organizations ILOSTAT database, specifically for Africa. It covers 20 African countries from 2016 to 2025, with 459 observations. The core indicator is the unemployment rate (in percentage), disaggregated by sex (total, male, female) and disability status. Data is sourced directly from the ILOSTAT REST API, filtered to African ISO3 country codes, and harmonized using International Conference of Labour Statisticians (ICLS) definitions for consistency and comparability. The dataset is suitable for tasks such as tabular classification, regression, and time-series forecasting, and can be used to analyze unemployment trends in Africa, support Sustainable Development Goal (SDG) research, or labor market analysis.

搜集汇总

数据集介绍

构建方式

该数据集立足联合国可持续发展目标监测框架，聚焦非洲区域体面劳动议题，由Electric Sheep Africa对国际劳工组织统计数据库及联合国可持续发展目标公开数据进行系统性整编与标准化封装。构建过程以国际劳工组织第19届国际劳工统计学家会议决议为统计口径基准，提取2016至2025年间20个非洲国家的失业率观测记录，经结构化清洗后形成459条涵盖性别与残疾状况维度的表格数据，并以Parquet格式发布，同时配套标准化元数据、来源溯源说明与分析场景指引，以支撑非洲公开数据的可发现性与可复现研究。

特点

数据集以可持续发展目标指标8.5.2为核心锚点，系统呈现非洲地区按性别与残疾状况分列的失业率水平，具有鲜明的劳动经济学与残障包容政策交叉属性。其规模精炼而结构清晰，变量维度同时覆盖地理单元、时间序列与人群亚组，便于开展跨国家、跨年度的比较分析。数据以表格与文本双模态承载，采用开放许可协议发布，并附带来源归属与元数据说明，在保持原始统计口径的同时兼顾机器学习工作流的兼容性，为非洲劳动市场不平等研究提供了可靠的微观证据基础。

使用方法

研究者可借助Hugging Face数据集加载接口直接获取数据，并通过内置的数据集查看器快速浏览整体结构与字段特征。在具体分析中，建议先行检查变量定义、单位与缺失值分布，再依据显式国家字段和时间字段开展分组统计、趋势刻画或亚组差异检验。针对表格型数据，可将其转换为Pandas数据框以便与其它非洲公开数据集进行基于国家、年份与指标字段的联结分析。建模前应保留缺失值并审慎确立插补规则，同时在下游成果中完整引用原始来源与Electric Sheep Africa的工程化贡献，以确保研究过程的透明性与可追溯性。

背景与挑战

背景概述

伴随可持续发展议程的深入推进，体面劳动与包容性增长成为全球经济治理的核心议题。联合国可持续发展目标第8.5.2项指标聚焦于按性别与残疾状况分列的失业率，旨在揭示劳动力市场中长期存在的结构性不平等。该数据集由Electric Sheep Africa工程化整理，源自国际劳工组织ILOSTAT数据库与联合国SDG数据，覆盖2016至2025年间20个非洲国家的459条观测记录，以标准化元数据与Parquet格式发布，为非洲劳动经济研究提供了可复现的跨国比较基础，对监测弱势群体就业状况、评估区域劳动力市场政策成效具有重要参考价值。

当前挑战

该数据集所回应的核心难题在于，如何以统一口径刻画非洲各国在性别与残疾维度上的失业差异，而这一领域长期受制于定义不一致、统计能力参差与数据稀疏等现实约束。构建过程中面临多重挑战：各国对残疾状况的界定与采集标准存在显著异质性，19届国际劳工统计学家会议标准的采纳程度不一；部分国家在特定年份与分组的观测值缺失，需在保留缺失信息与维持分析完整性之间审慎权衡；元数据中地理与来源字段尚存缺口，变量单位与统计方法需回溯原始出处方能确认。上述因素共同制约着跨国比较的稳健性与纵向趋势推断的可靠性。

常用场景

经典使用场景

在劳动经济学与可持续发展目标监测领域，该数据集最经典的使用场景是作为面板数据源，对非洲二十国2016至2025年间按性别与残疾状况分列的失业率进行跨时空比较分析。研究者借助其标准化的国别、年份与指标字段，构建二元或多元回归模型，考察性别差异与残疾状态在失业风险中的交互效应，并检验第19届国际劳工统计学家会议标准在非洲区域的数据一致性。

解决学术问题

该数据集回应了非洲劳动力市场中弱势群体就业状况长期缺乏可比较分列证据的学术困境。传统失业统计往往忽视残疾维度与性别交叉性，致使相关不平等测度难以量化。此数据集以统一指标框架弥补了分列数据缺失，为性别与残疾交叉脆弱性研究、可持续发展目标进展评估以及跨国劳动政策比较提供了可复现的经验基础，推动了包容性就业议题的实证深化。

衍生相关工作

围绕该数据集，Electric Sheep Africa系列目录衍生出多类相关经典工作，包括非洲失业率的国别比较分析、性别就业差距的时序建模以及残疾包容性劳动指标的可视化仪表板。这些工作以显式国别与年份字段为基础，联动其他非洲公共数据集，形成了以ILOSTAT与联合国可持续发展目标数据为核心的开放数据生态，支撑机器学习就绪型非洲数据集的持续构建与方法复用。