electricsheepafrica/africa-ilo-une-deap-sex-dsb-rt-unemployment-rate-by-sex-and-disability-status

--- license: cc-by-4.0 language: - en task_categories: - tabular-classification - tabular-regression - time-series-forecasting multilinguality: monolingual size_categories: - 1K<n<10K tags: - tabular - africa - ilostat - unemployment - ilo - labour - employment pretty_name: "Unemployment rate by sex and disability status (%) | Africa (ILOSTAT)" --- # Unemployment rate by sex and disability status (%) | Africa (ILOSTAT) 🌍 **1,024 observations** · **42 Africa countries** · **1998–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **1,024 observations** of `Unemployment` data across **42 Africa countries**, spanning **1998–2025**, 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=UNE_DEAP_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=UNE_DEAP_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 42 Africa countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `RWA` | 88 | 2014 | 2025 | | `GHA` | 64 | 2010 | 2024 | | `BWA` | 61 | 2009 | 2024 | | `ZWE` | 54 | 2014 | 2024 | | `ZMB` | 53 | 2015 | 2024 | | `SEN` | 46 | 2015 | 2024 | | `CIV` | 45 | 1998 | 2022 | | `TZA` | 40 | 2008 | 2024 | | `TGO` | 40 | 2006 | 2022 | | `GMB` | 33 | 2012 | 2025 | | `SYC` | 33 | 2018 | 2024 | | `BFA` | 30 | 2006 | 2024 | | `UGA` | 28 | 2010 | 2021 | | `ETH` | 27 | 2005 | 2021 | | `CMR` | 27 | 2005 | 2014 | | ... | _27 more countries_ | | | ## Indicators (sample) - `UNE_DEAP_SEX_DSB_RT` — Unemployment rate by sex and disability 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) | `AA:835` | | `source.label` | `string` | Source name in English | `PC - Population Census` | | `indicator` | `string` | ILOSTAT indicator code | `UNE_DEAP_SEX_DSB_RT` | | `indicator.label` | `string` | Indicator name in English | `Unemployment rate by sex and disabili…` | | `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 | `2014` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `9.579` | | `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` | — | `T5:1429` | | `note_indicator.label` | `string` | — | `Unemployment definition: Two criteria…` | | `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-une-deap-sex-dsb-rt-unemployment-rate-by-sex-and-disability-status") 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"] == "UNE_DEAP_SEX_DSB_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="UNE_DEAP_SEX_DSB_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "UNE_DEAP_SEX_DSB_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_une_deap_sex_dsb_rt_unemployment_rate_by_sex_and_disability_status_2025, title = {Unemployment rate by sex and disability status (%) | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=UNE_DEAP_SEX_DSB_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-une-deap-sex-dsb-rt-unemployment-rate-by-sex-and-disability-status}} } ``` ## 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=UNE_DEAP_SEX_DSB_RT_This dataset contains 1,024 observations of unemployment data across 42 African countries, spanning from 1998 to 2025, with the core indicator being Unemployment rate by sex and disability status (%). The data is sourced from the International Labour Organization (ILO) ILOSTAT database, retrieved via API and filtered to African country codes. The dataset is in tabular format, including columns such as country code, year, indicator value, sex classification (total, male, female), disability status classification, data source, and observation status. The data is harmonized by ILO based on International Conference of Labour Statisticians (ICLS) definitions and is suitable for tasks like tabular classification, regression, and time-series forecasting. The dataset is repackaged by Electric Sheep Africa as part of a unified, ML-ready data layer for Africa.

搜集汇总

数据集介绍

构建方式

该数据集依托国际劳工组织统计数据库（ILOSTAT）的原始记录，由Electric Sheep Africa团队进行工程化整理与元数据标准化，覆盖42个非洲国家1998年至2025年间的失业率观测数据，共计1024条记录。构建过程以公开统计元数据为索引基础，整合性别与残疾状况维度的分类指标，并以Parquet格式封装为面向机器学习与可复现分析的数据资产，同时附载来源溯源说明与字段级上下文注释。

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函数获取数据对象并检视其结构、特征与样本片段；当数据结构为表格型时，可调用to_pandas方法转换为数据框以展开统计建模。使用前应核验变量定义、单位与地理列，保留缺失值直至确立合理的插补规则，并建议结合显式国别、年份与指标字段与其他Electric Sheep Africa数据集进行联结分析，以构建可复现的研究工作流。

背景与挑战

背景概述

非洲大陆在劳动市场统计方面长期面临数据稀缺与碎片化的困境，残障人士的就业状况更因身份交叉性而鲜少获得系统性的量化呈现。国际劳工组织（ILO）通过ILOSTAT数据库持续汇编全球劳动统计指标，为跨国比较提供权威基准。Electric Sheep Africa于2026年基于ILOSTAT源数据，将1998至2025年间42个非洲国家的1,024条观测记录进行标准化重封装，构建了按性别与残障身份分列的失业率数据集。该数据集以CC BY 4.0许可发布，旨在为非洲劳动经济学研究、残障包容政策评估以及跨国就业不平等分析提供可复现的表格化证据基础，其核心贡献在于将性别与残障的交叉维度纳入非洲失业率的观测框架。

当前挑战

数据集所应对的领域问题在于残障与性别交叉视角下非洲失业率量化证据的长期匮乏，传统劳动统计往往忽略残障变量或仅以粗略二分法记录，难以支撑包容性就业政策的精细评估。构建过程中的挑战同样显著：ILOSTAT源数据在非洲各国的报告口径、残障定义与调查方法上存在异质性，部分年份或国家存在缺失值；性别与残障身份的分组交叉导致单元格样本量稀疏，统计稳健性受限；元数据中country与upstream_publisher字段的声明缺口亦要求使用者在建模前审慎确认地理标识与变量含义，以避免对政策含义的过度推断。

常用场景

经典使用场景

在劳动力市场计量与残疾包容性研究的交叉领域，该数据集构成了一个典型的截面-时序分析资源。研究者惯常将其用于剖析非洲各国失业率在性别与残疾身份双重维度上的分布形态，借助1998年至2025年间42个非洲国家的1024条观测记录，构建面板数据模型以检验残障群体在劳动力市场中的边缘化程度是否随性别差异而呈现系统性分化。此类场景下，数据集的表格化结构与标准化元数据使跨国比较和趋势拟合成为可能。

解决学术问题

该数据集直面劳动经济学与残疾研究中长期存在的数据稀缺问题，即非洲区域缺乏按性别与残疾状态交叉分类的失业率可比指标。它通过整合国际劳工组织统计数据库的标准化观测，为检验残疾惩罚假说、性别叠加劣势理论以及结构性排斥机制提供了可复现的实证基础，从而使得关于包容性增长与体面劳动议程的定量评估不再受制于碎片化的国别报告。

实际应用

在政策实践层面，该数据集可服务于国际发展机构与非洲各国劳工部门的监测需求，用于追踪可持续发展目标中关于充分就业与平等就业的指标进展。社会保障规划者可借助其识别残疾女性等交叉弱势群体的失业热点区域，进而优化职业康复与反歧视干预资源的配置。同时，非政府组织亦可依据其时间序列特征评估既有就业扶持政策的滞后效应。

数据集最近研究

最新研究方向

在全球劳动力市场日益关注包容性增长的背景下，性别与残障身份交叉维度的失业率数据正成为劳动经济学与残障研究领域的前沿议题。该数据集依托国际劳工组织ILOSTAT的权威统计框架，覆盖42个非洲国家1998至2025年的1024条观测值，为刻画残障女性与男性在非洲劳动力市场中的边缘化程度提供了稀缺的跨国可比证据。当前研究前沿聚焦于残障与性别双重劣势的叠加效应，即残障女性是否面临比残障男性及非残障群体更高的失业风险，这一交叉性分析视角呼应了联合国残疾人权利公约与可持续发展目标中对公平就业的监测需求。该数据集的意义在于推动非洲区域劳动政策从单一维度向交叉性评估转型，为精准识别最脆弱群体并设计差异化干预措施奠定实证基础。