electricsheepafrica/africa-ilo-sdg-u552-noc-rt-sdg-indicator-5-5-2-proportion-of-women-in-manager

--- 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 - employment - ilo - labour pretty_name: "SDG indicator 5.5.2 - Proportion of women in managerial positions -- 19th ICLS (%) | Africa (ILOSTAT)" --- # SDG indicator 5.5.2 - Proportion of women in managerial positions -- 19th ICLS (%) | Africa (ILOSTAT) 🌍 **69 observations** · **23 Africa countries** · **2012–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **69 observations** of `Employment` data across **23 Africa countries**, spanning **2012–2025**, 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_U552_NOC_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=SDG_U552_NOC_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 23 Africa countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `RWA` | 9 | 2017 | 2025 | | `ZMB` | 8 | 2017 | 2024 | | `AGO` | 7 | 2019 | 2025 | | `BWA` | 6 | 2019 | 2024 | | `ZWE` | 5 | 2019 | 2024 | | `GHA` | 5 | 2017 | 2024 | | `GMB` | 3 | 2018 | 2025 | | `UGA` | 3 | 2017 | 2021 | | `TZA` | 3 | 2012 | 2024 | | `KEN` | 3 | 2019 | 2022 | | `CIV` | 2 | 2016 | 2019 | | `SWZ` | 2 | 2021 | 2023 | | `SYC` | 2 | 2023 | 2024 | | `LSO` | 2 | 2019 | 2024 | | `EGY` | 1 | 2024 | 2024 | | ... | _8 more countries_ | | | ## Indicators (sample) - `SDG_U552_NOC_RT` — SDG indicator 5.5.2 - Proportion of women in managerial positions -- 19th ICLS (%) ## 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 | `SDG_U552_NOC_RT` | | `indicator.label` | `string` | Indicator name in English | `SDG indicator 5.5.2 - Proportion of w…` | | `time` | `int64` | Observation year | `2025` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `16.544` | | `obs_status` | `string` | Observation status flag (e.g. provisional, unreliable) | `B` | | `obs_status.label` | `string` | — | `Break in series` | | `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…` | ## 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-u552-noc-rt-sdg-indicator-5-5-2-proportion-of-women-in-manager") 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_U552_NOC_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="SDG_U552_NOC_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "SDG_U552_NOC_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_sdg_u552_noc_rt_sdg_indicator_5_5_2_proportion_of_women_in_manager_2025, title = {SDG indicator 5.5.2 - Proportion of women in managerial positions -- 19th ICLS (%) | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=SDG_U552_NOC_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-sdg-u552-noc-rt-sdg-indicator-5-5-2-proportion-of-women-in-manager}} } ``` ## 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_U552_NOC_RT_This dataset contains 69 observations of employment data across 23 Africa countries, spanning from 2012 to 2025, covering 1 distinct indicator: SDG indicator 5.5.2 - Proportion of women in managerial positions (based on the 19th International Conference of Labour Statisticians definition, in percentage). The data is sourced from the International Labour Organizations (ILO) ILOSTAT database, retrieved via REST API and filtered to Africa country codes, focusing on topics such as employment, labour statistics, and Sustainable Development Goals (SDG). It is structured in tabular format with columns including country code, country name, data source, indicator code, indicator label, year, observed value, observation status, and related notes, suitable for tasks like tabular classification, regression, and time-series forecasting. The dataset is repackaged by Electric Sheep Africa to facilitate machine learning applications for Africa data.

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与联合国可持续发展目标监测框架，由Electric Sheep Africa对原始数据进行标准化重包装而成。构建过程以国际劳工组织第19届国际劳工统计学家会议通过的就业定义为准绳，从ILOSTAT及联合国SDG数据源中提取非洲地区女性担任管理职务的比例指标，限定时间跨度为2012至2025年，覆盖23个非洲国家，最终形成69条观测记录，并以parquet格式存储，附带标准化的元数据标签与溯源说明。

使用方法

研究者可通过Hugging Face数据集加载接口直接获取数据，利用datasets库载入后查看特征结构并转换为Pandas数据框以便分析。使用时应从仓库文件与数据查看器出发，确认变量定义与单位，保留缺失值直至建立可辩护的插补规则。在建模前需检查数据模式与缺失情况，可按地理、时间和子群变量进行剖面分析，亦可与其他非洲数据集依据国家、年份和指标字段进行连接，并确保在分析中明确记录地理假设与引用原始来源。

背景与挑战

背景概述

性别平等与妇女赋权是联合国可持续发展目标的核心议题之一，其中SDG 5.5.2指标聚焦于女性在管理岗位中的占比，用以衡量女性在职场决策层的代表性。该数据集由Electric Sheep Africa工程团队于2026年整理发布，源自国际劳工组织ILOSTAT数据库及联合国SDG数据，覆盖23个非洲国家、69条观测记录，时间跨度为2012至2025年。数据集以标准化元数据封装为Parquet格式，为非洲区域劳动力市场性别差异研究提供了可复现的公开数据基础，对推动非洲经济金融领域的性别包容性政策分析具有参考价值。

当前挑战

该数据集所回应的领域问题在于消除性别差距与促进女性职场领导力，其挑战在于非洲各国劳动统计体系发展不均衡，非正式就业占比高，女性管理岗位的界定与抽样口径存在国别差异。构建过程中，原始ILOSTAT数据在部分国家与年份存在缺失，且元数据中未声明ISO3国别编码，需依赖标题及来源信息推断地理覆盖，增加了数据整合与时间序列比较的复杂性。此外，69条观测的样本量偏小，限制了跨国面板分析与因果推断的稳健性，要求研究者在建模前审慎处理缺失值并验证指标定义的一致性。

常用场景

经典使用场景

在性别平等与劳动力市场研究的交叉领域，该数据集构成了探究非洲女性管理职位占比动态的重要证据基础。研究者借助其中涵盖二十三个非洲国家、跨越二零一二年至二零二五年的六十九项观测记录，以面板数据回归、趋势分解及跨国比较等经典计量手段，系统考察女性在管理岗位中的代表性演变轨迹。这一数据集尤其适用于追踪联合国可持续发展目标第五点五之二项指标在非洲区域的落实进展，并为刻画性别职业隔离的结构性特征提供可复现的量化素材。

解决学术问题

该数据集直面发展经济学与性别研究中长期存在的证据碎片化困境，即非洲地区女性管理参与度缺乏统一口径、可比时序与跨国横向对照的问题。通过整合国际劳工组织统计数据库与联合国可持续发展目标数据框架，其部分弥合了区域劳动统计中性别维度数据稀缺的缺口，使研究者得以在统一指标定义下检验女性管理代表性同经济增长、教育扩张及制度变迁之间的关联假设。其意义在于为非洲性别平等议题的实证研究提供了标准化的数据锚点，亦为全球比较视野下的相关分析贡献了区域证据。

实际应用

在政策制定与企业治理的实践场域中，该数据集可为非洲各国性别平等政策的成效评估提供量化参照，辅助国际组织监测可持续发展目标在管理层的落实进度。企业与社会责任研究机构亦可借此辨识女性晋升通道受阻的高频区域与时段，进而设计更具针对性的干预方案。咨询机构与智库在撰写非洲劳动力市场性别差距报告时，可将此数据作为基础事实层，支撑投资尽调、包容性增长评估及人力资源战略规划等多元应用场景。

数据集最近研究

最新研究方向

在非洲劳动力市场性别平等议题持续升温的背景下，该数据集聚焦于可持续发展目标5.5.2指标，即女性在管理岗位中的占比，基于国际劳工组织第19届国际劳动统计学家会议标准，覆盖23个非洲国家2012至2025年的69项观测记录。当前前沿研究借助此类跨国面板数据，探讨女性管理参与率与经济增长、制度环境及教育投入之间的动态关联，并利用机器学习方法进行缺失值插补与趋势预测。相关研究为非洲性别包容性政策制定提供实证依据，亦响应联合国2030议程中对女性领导力赋权的全球呼吁，具有显著的学术价值与政策意义。