electricsheepafrica/africa-ilo-emp-pifl-sex-age-rt-share-of-employment-outside-the-formal-sector-by-s

--- 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 - informal-economy - ilo - labour - employment pretty_name: "Share of employment outside the formal sector by sex and age (%) | Africa (ILOSTAT)" --- # Share of employment outside the formal sector by sex and age (%) | Africa (ILOSTAT) 🌍 **15,699 observations** · **45 Africa countries** · **1999–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **15,699 observations** of `Informal economy` data across **45 Africa countries**, spanning **1999–2025**, 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=EMP_PIFL_SEX_AGE_RT) - **Publisher:** International Labour Organization (ILO) - **License:** [cc-by-4.0](https://creativecommons.org/licenses/by/4.0/) - **Topic:** Informal economy ## Methodology Data pulled directly from the ILOSTAT REST API at `https://rplumber.ilo.org/data/indicator?id=EMP_PIFL_SEX_AGE_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 45 Africa countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `ZAF` | 2,025 | 2000 | 2024 | | `MUS` | 972 | 2012 | 2024 | | `EGY` | 972 | 2008 | 2024 | | `MLI` | 810 | 2013 | 2024 | | `AGO` | 729 | 2004 | 2025 | | `RWA` | 729 | 2017 | 2025 | | `SEN` | 648 | 2011 | 2024 | | `ZMB` | 648 | 2017 | 2024 | | `ZWE` | 567 | 2011 | 2024 | | `UGA` | 567 | 2010 | 2021 | | `BWA` | 567 | 2006 | 2024 | | `CIV` | 486 | 2012 | 2022 | | `NAM` | 405 | 2012 | 2018 | | `BFA` | 324 | 2018 | 2024 | | `GMB` | 324 | 2012 | 2025 | | ... | _30 more countries_ | | | ## Indicators (sample) - `EMP_PIFL_SEX_AGE_RT` — Share of employment outside the formal sector by sex and age (%) ## 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 | `EMP_PIFL_SEX_AGE_RT` | | `indicator.label` | `string` | Indicator name in English | `Share of employment outside the forma…` | | `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.) | `AGE_YTHADULT_YGE15` | | `classif1.label` | `string` | — | `Age (Youth, adults): 15+` | | `time` | `int64` | Observation year | `2025` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `80.588` | | `obs_status` | `string` | Observation status flag (e.g. provisional, unreliable) | `B` | | `obs_status.label` | `string` | — | `Break in series` | | `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-emp-pifl-sex-age-rt-share-of-employment-outside-the-formal-sector-by-s") 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"] == "EMP_PIFL_SEX_AGE_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_PIFL_SEX_AGE_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_PIFL_SEX_AGE_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_emp_pifl_sex_age_rt_share_of_employment_outside_the_formal_sector_by_s_2025, title = {Share of employment outside the formal sector by sex and age (%) | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_PIFL_SEX_AGE_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-emp-pifl-sex-age-rt-share-of-employment-outside-the-formal-sector-by-s}} } ``` ## 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=EMP_PIFL_SEX_AGE_RT_This dataset contains 15,699 observations of informal economy data across 45 Africa countries, spanning 1999–2025, covering the indicator Share of employment outside the formal sector by sex and age (%). The data is sourced from ILOSTAT, the ILOs central statistics database, and repackaged by Electric Sheep Africa. It is provided in tabular format with columns for country codes, years, sex and age disaggregation, observed values, and metadata, suitable for tabular classification, regression, and time-series forecasting tasks. The dataset is part of a unified, ML-ready data layer for Africa on HuggingFace.

搜集汇总

数据集介绍

构建方式

该数据集由Electric Sheep Africa基于国际劳工组织统计数据库的原始记录进行工程化重整与标准化发布。构建过程以非洲地区非正规经济就业统计为核心，整合1999至2025年间45个非洲国家的15699条观测数据，涵盖性别与年龄双重维度的非正规部门就业比例指标。数据经统一元数据标注、格式转换后以parquet格式存储，并附带来源溯源说明与分析导向的上下文信息，形成可直接用于机器学习任务的表格化数据集。

使用方法

使用者可通过Hugging Face数据集加载接口直接获取数据，以Python代码调用load_dataset函数载入指定仓库，进而获取数据分割、特征结构与样本预览。对于表格型数据，可转换为Pandas数据框以便进行统计分析与建模。建议在使用前检查变量定义与单位，明确国家、年份与指标字段，保留缺失值以待合理的插补策略，并引用原始来源与Electric Sheep Africa仓库信息以确保分析的可复现性。

背景与挑战

背景概述

非正规经济就业的测度始终是发展经济学与劳动经济学的核心议题，尤其在非洲大陆，非正规部门承载了绝大多数劳动力的生计。国际劳工组织（ILO）长期通过ILOSTAT数据库系统性地收集并标准化各国劳动力市场统计，为跨国比较研究提供了权威基础。Electric Sheep Africa于2026年将ILOSTAT中按性别与年龄分组的非洲非正规就业占比数据进行工程化整理，覆盖45个非洲国家、1999至2025年间共计15,699条观测记录，以CC BY 4.0许可在Hugging Face平台发布。该数据集为研究者提供了探索非洲非正规就业性别差异与年龄分化的结构化入口，对理解发展中国家劳动力市场分层具有重要参考价值。

当前挑战

该数据集所回应的领域问题在于，非正规就业占比的精确测度本身即构成重大挑战——各国对非正规部门的界定标准不一，调查方法与抽样框架差异显著，导致跨国可比性受限。构建过程中，原始ILOSTAT数据的元数据缺失问题尤为突出，国家字段与上游发布机构信息在标准化流程中未能完整保留，ISO3地理编码亦未声明，迫使下游分析者依赖标题或来源元数据推断地理归属。此外，1999至2025年间部分国家与年份的观测值存在系统性缺失，缺失机制不明，若未加审慎处理即行插补，可能引入偏误。数据结构中隐含的性别与年龄分组维度在缺乏明确变量定义的情况下，亦增加了建模时误读之风险。

常用场景

经典使用场景

在劳动经济学与发展经济学领域，非正规部门就业占比的性别与年龄差异始终是理解非洲劳动力市场结构的关键切口。该数据集汇聚了45个非洲国家自1999年至2025年间共计15,699条观察记录，围绕非正规就业份额这一核心指标，按性别与年龄组别进行系统刻画，为研究者提供了长时段、跨地域的标准化面板数据。经典使用场景涵盖非正规就业的性别鸿沟测度、青年与中老年劳动力非正规化程度的代际比较，以及各国非正规经济规模的时序演变分析。研究者可借助该数据集构建跨国比较框架，识别非正规就业占比在人口学维度上的异质性模式。

解决学术问题

长期以来，非洲非正规经济研究受制于数据碎片化与口径不一，难以开展严谨的跨国比较与趋势推断。该数据集依托国际劳工组织统计数据库的权威来源，以统一指标定义与标准化元数据回应了这一困境，使研究者得以在一致框架下考察非正规就业的性别分化与年龄梯度。其学术意义在于，为检验劳动力市场二元结构理论、非正规化与贫困关联假说提供了可复现的实证基础，亦为探讨结构性转型过程中正规部门吸纳能力不足等议题提供了量化依据，推动了非洲劳动统计研究的规范化与累积性知识生产。

实际应用

在政策实践层面，该数据集为国际组织、各国劳工部门及发展机构提供了评估非正规就业态势的量化工具。社会保障体系设计者可依据性别与年龄维度的非正规就业分布，精准识别覆盖盲区与优先干预群体；就业促进项目可参照青年非正规就业占比的时序变化，动态调整职业培训与正规化激励策略。同时，该数据集亦服务于世界银行、国际劳工组织等机构的国别诊断与区域监测报告编制，为非正规经济治理、体面劳动议程推进及包容性增长政策制定提供数据支撑。

数据集最近研究

最新研究方向

非洲非正规就业的性别与年龄分层研究正成为劳动经济学与发展经济学的交叉前沿。该数据集覆盖45国1999至2025年的非正规部门就业占比，为量化结构性转型中的脆弱性提供长时序面板证据。近期研究聚焦于非正规经济对性别工资差距的代际传递、青年劳动力市场边缘化，以及数字经济下零工经济对传统非正规就业的替代与重塑。在全球推动体面劳动与可持续发展目标背景下，该数据支撑政策评估与跨国比较，对理解非洲劳动力市场韧性及社会保障扩面具有关键意义。