electricsheepafrica/africa-ilo-sdg-0552-noc-rt-sdg-indicator-5-5-2-proportion-of-women-in-senior

--- 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 senior and middle management positions (%) | Africa (ILOSTAT)" --- # SDG indicator 5.5.2 - Proportion of women in senior and middle management positions (%) | Africa (ILOSTAT) 🌍 **188 observations** · **40 Africa countries** · **2000–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **188 observations** of `Employment` data across **40 Africa countries**, spanning **2000–2025**, 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_0552_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_0552_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 40 Africa countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `ZAF` | 25 | 2000 | 2024 | | `MUS` | 22 | 2001 | 2024 | | `EGY` | 15 | 2009 | 2024 | | `AGO` | 9 | 2009 | 2025 | | `ZMB` | 9 | 2015 | 2024 | | `RWA` | 8 | 2017 | 2025 | | `SYC` | 7 | 2014 | 2024 | | `BWA` | 7 | 2011 | 2024 | | `SEN` | 6 | 2015 | 2024 | | `ZWE` | 6 | 2011 | 2024 | | `NAM` | 6 | 2010 | 2018 | | `TZA` | 6 | 2001 | 2024 | | `TUN` | 5 | 2009 | 2021 | | `UGA` | 5 | 2012 | 2021 | | `GHA` | 4 | 2006 | 2017 | | ... | _25 more countries_ | | | ## Indicators (sample) - `SDG_0552_NOC_RT` — SDG indicator 5.5.2 - Proportion of women in senior and middle management positions (%) ## 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_0552_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) | `16.202` | | `obs_status` | `string` | Observation status flag (e.g. provisional, unreliable) | `U` | | `obs_status.label` | `string` | — | `Unreliable` | | `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-0552-noc-rt-sdg-indicator-5-5-2-proportion-of-women-in-senior") 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_0552_NOC_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="SDG_0552_NOC_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "SDG_0552_NOC_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_sdg_0552_noc_rt_sdg_indicator_5_5_2_proportion_of_women_in_senior_2025, title = {SDG indicator 5.5.2 - Proportion of women in senior and middle management positions (%) | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=SDG_0552_NOC_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-sdg-0552-noc-rt-sdg-indicator-5-5-2-proportion-of-women-in-senior}} } ``` ## 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_0552_NOC_RT_This dataset contains 188 observations across 40 African countries from 2000 to 2025, covering 1 specific indicator: SDG indicator 5.5.2 - Proportion of women in senior and middle management positions (%). The data is sourced from the International Labour Organization (ILO) ILOSTAT statistics database, focusing on employment topics, particularly womens representation in management positions in Africa. The dataset includes fields such as country codes, country names, data sources, indicator codes, years, observed values, and observation status, making it suitable for tabular classification, regression, and time-series forecasting tasks. The data is harmonized by ILO using International Conference of Labour Statisticians (ICLS) definitions and flagged with sources for traceability.

搜集汇总

数据集介绍

构建方式

该数据集围绕联合国可持续发展目标5.5.2，聚焦于非洲地区女性在中高级管理职位中的占比，由Electric Sheep Africa基于国际劳工组织统计数据库与联合国可持续发展目标公开数据进行系统性整编。构建过程中，原始数据经统一清洗、标准化元数据标注与格式转换，最终以Parquet格式发布，覆盖四十个非洲国家、时间跨度自2000年至2025年，共含一百八十八项观测记录，并保留缺失值以待后续分析处理。

特点

数据集以经济学与金融领域为背景，兼具表格与文本双重模态，规模属于n<1K的小型数据类别，采用CC BY 4.0开放许可。其突出特点在于聚焦非洲区域女性管理参与度的长期演变，涵盖单一明确指标，配套提供来源溯源信息、标准化标签体系与分析指引，便于跨国比较与时间序列探索，同时支持多语言环境下的单向英文标注。

使用方法

使用者可通过Hugging Face的datasets库调用load_dataset函数指定仓库名称加载数据，并访问首个切分以查看特征结构与样本内容。对于表格型数据，可进一步转换为Pandas数据框以便统计分析。实际建模前应核验变量定义与单位，确认国家与年份字段，对缺失值采取审慎处理，并建议结合其他非洲公开数据集进行跨国或跨领域联合分析，同时引用原始来源与Electric Sheep Africa仓库信息。

背景与挑战

背景概述

性别平等是联合国可持续发展目标的核心议题之一，其中目标5.5明确呼吁确保妇女在政治、经济与公共生活各决策层级中的充分参与和平等机会。SDG指标5.5.2以女性在中高级管理岗位中的占比为度量，为监测这一目标提供了关键量化依据。该数据集由Electric Sheep Africa于2026年基于国际劳工组织ILOSTAT及联合国SDG数据源整理发布，覆盖40个非洲国家、188条观测记录，时间跨度自2000年至2025年，属经济学与金融领域的小规模表格数据集。其核心价值在于将分散的劳工统计数据标准化为机器学习可用的格式，为非洲性别平等与劳动力市场研究提供可复现的数据基础设施，对区域政策评估和学术研究具有基础性支撑意义。

当前挑战

该数据集所对应的领域问题在于如何准确刻画并比较不同非洲国家女性管理职位参与度的长期演变，这要求数据兼顾跨国可比性、时间连续性与企业内部职级定义的统一性，而各国劳动统计口径和报告规范的差异构成了根本性障碍。构建过程中，研究者面临源数据缺失值普遍、国家标识与上游发布机构等元数据字段不完整、覆盖年份在国家间不均衡等问题，加之样本量不足千条，难以支撑精细的因果推断或高维建模。如何在保留缺失信息的前提下进行稳健的插补与跨国对齐，是该数据集在实证分析中亟待应对的挑战。

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汇地带，该数据集常被用于刻画非洲各国女性在中高级管理岗位中的参与程度及其时序变迁。研究者借助其跨国面板结构，开展横截面比较与纵向趋势分析，检验女性管理代表性同经济增长、教育扩张及制度环境之间的关联，并以此为基点评估可持续发展目标5.5.2在非洲区域的落实进度。

衍生相关工作

围绕该数据集，衍生出一系列非洲劳动力市场与性别议题的经典研究，包括基于ILOSTAT与联合国SDG数据的跨国比较分析、女性管理参与同经济增长关系的面板计量工作，以及面向政策评估的国别案例研究。Electric Sheep Africa对其进行的标准化重整与元数据增强，亦催生了若干开放数据再利用项目，推动了非洲社会经济数据基础设施的建设与共享。

数据集最近研究

最新研究方向

在联合国可持续发展目标（SDGs）监测框架与非洲劳动力市场性别平等议题深度交织的背景下，该数据集所承载的SDG指标5.5.2——女性在中高级管理岗位中的占比，正成为性别经济学与劳动计量研究的前沿切入点。当前研究借助ILOSTAT跨国可比数据，聚焦2000至2025年间40个非洲国家的管理岗位性别构成演变，探索结构性障碍、制度变迁与女性经济赋权之间的动态关联。伴随非洲大陆自贸区建设与数字化转型浪潮，学术界日益关注女性管理参与度如何影响企业治理效能与包容性增长，相关成果为政策制定者优化性别配额、技能培训与反歧视法规提供实证基础，亦为全球南方性别平等路径比较研究贡献关键证据。