electricsheepafrica/africa-ilo-emp-pifl-sex-age-geo-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, age and rural / urban areas (%) | Africa (ILOSTAT)" --- # Share of employment outside the formal sector by sex, age and rural / urban areas (%) | Africa (ILOSTAT) 🌍 **21,393 observations** · **41 Africa countries** · **1999–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **21,393 observations** of `Informal economy` data across **41 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_GEO_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_GEO_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 41 Africa countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `ZAF` | 2,340 | 2008 | 2024 | | `EGY` | 1,620 | 2008 | 2024 | | `MLI` | 1,350 | 2013 | 2024 | | `AGO` | 1,247 | 2004 | 2025 | | `RWA` | 1,215 | 2017 | 2025 | | `ZMB` | 1,080 | 2017 | 2024 | | `SEN` | 1,080 | 2011 | 2024 | | `UGA` | 945 | 2010 | 2021 | | `ZWE` | 945 | 2011 | 2024 | | `CIV` | 810 | 2012 | 2022 | | `NAM` | 675 | 2012 | 2018 | | `GMB` | 540 | 2012 | 2025 | | `NER` | 540 | 2011 | 2022 | | `BFA` | 540 | 2018 | 2024 | | `COD` | 405 | 2005 | 2020 | | ... | _26 more countries_ | | | ## Indicators (sample) - `EMP_PIFL_SEX_AGE_GEO_RT` — Share of employment outside the formal sector by sex, age and rural / urban areas (%) ## 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_GEO_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+` | | `classif2` | `string` | Second classification variable where applicable | `GEO_COV_NAT` | | `classif2.label` | `string` | — | `Area type: National` | | `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) | `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-emp-pifl-sex-age-geo-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_GEO_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_PIFL_SEX_AGE_GEO_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_PIFL_SEX_AGE_GEO_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_emp_pifl_sex_age_geo_rt_share_of_employment_outside_the_formal_sector_by_s_2025, title = {Share of employment outside the formal sector by sex, age and rural / urban areas (%) | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_PIFL_SEX_AGE_GEO_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-emp-pifl-sex-age-geo-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_GEO_RT_This dataset contains 21,393 observations across 41 African countries from 1999 to 2025, focusing on the indicator Share of employment outside the formal sector by sex, age and rural/urban areas (%). The data is sourced from the International Labour Organization (ILO) ILOSTAT database, providing detailed statistics on informal economy employment. The dataset includes fields such as country code, country name, data source, indicator code, sex disaggregation (total, male, female), age classification, rural/urban classification, observation year, observed value, observation status, and related notes. The data is harmonized by ILO using International Conference of Labour Statisticians (ICLS) definitions and is suitable for labor market analysis, economic research, and machine learning tasks (e.g., tabular classification, regression, time-series forecasting). Repackaged by Electric Sheep Africa in Parquet format for easy loading via HuggingFaces `load_dataset()`.

搜集汇总

数据集介绍

构建方式

该数据集脱胎于国际劳工组织统计数据库（ILOSTAT）所收录的非洲非正规部门就业数据，由Electric Sheep Africa团队以元数据驱动的方式加以标准化整编。整编过程遵循可复现的工程规范，对原始来源中关于性别、年龄组及城乡地域的就业比例记录进行统一编码，转换为Parquet格式并配以结构化的发现层元数据。数据覆盖41个非洲国家，时间跨度自1999年至2025年，共汇集约21,393条观测，涉及单一非正规就业指标。每条记录均保留了来源出处、许可信息及变量定义线索，未对缺失值做任何填补，以维持原始统计口径的真实性。

特点

数据集以表格与文本双重模态呈现，体量介于一万至十万行之间，属于中等规模的非洲劳动力市场专题数据。其核心特征在于对非正规部门就业比例按性别、年龄和城乡维度进行了细致分解，并附有标准化的元数据标签，便于非洲数据发现与跨库关联。数据以单一指标为主线，结构简洁，缺失值予以保留，地理覆盖为非洲全域或来源界定的范围，ISO3国家编码未做显式声明，需在使用中加以推断并记录。许可协议为CC BY 4.0，兼顾开放获取与来源署名要求。

使用方法

使用者可通过Hugging Face datasets库以一行代码加载该数据集，并借助数据集查看器快速浏览文件结构与字段类型。加载后可按需选取相应分割，将表格部分转换为Pandas数据框以便进一步分析。在建模前，建议先审视架构、缺失模式及变量分布，按地理、时间与子群维度进行profiling。若需与其他Electric Sheep Africa数据集融合，应依据显式的国家、年份与指标字段进行连接。所有下游分析均宜引用原始来源与Hugging Face仓库，并对国家字段的推断假设予以明示。

背景与挑战

背景概述

非洲非正规部门就业长期构成劳动力市场的主体，其规模与性别、年龄及城乡分布密切相关。国际劳工组织（ILO）通过ILOSTAT数据库持续采集相关指标，为全球劳动统计提供基准。Electric Sheep Africa于2026年将ILOSTAT中非洲区域非正规就业占比数据整理为标准化数据集，覆盖41个非洲国家、1999至2025年间共计21,393条观测，按性别、年龄组和城乡区域细分。该数据集为探究非洲非正规经济结构变迁、劳动力市场脆弱性及政策干预效果提供了可复用的结构化证据，并成为非洲数据发现目录中的重要组成。

当前挑战

该数据集所回应的领域问题在于刻画非正规就业在人口子群与空间维度上的异质性，此类指标通常受制于各国定义差异、抽样口径不一及时间序列不连续，跨国可比性始终是劳动统计的核心难题。构建过程中，数据整理者面临元数据缺失（如国家字段与上游发布者未完整声明）、地理信息仅由标题隐含、以及需在保留缺失值的前提下避免过度推断等挑战；同时，非正规就业测量对调查工具与参考期高度敏感，若无原始方法说明，直接比较不同国家或年份的数值可能产生误导。

常用场景

经典使用场景

在劳动经济学与非正规经济研究领域，该数据集最经典的使用场景在于刻画非洲各国非正规部门就业的性别差异、年龄分层与城乡空间分布。研究者依托1999至2025年间41个非洲国家的21,393条观测记录，得以系统比较不同人口群体在正规部门之外就业的比例演变，进而揭示非洲劳动力市场结构性分割的时空特征。

解决学术问题

该数据集有效回应了非洲非正规经济测度中长期存在的数据碎片化与可比性不足问题。通过标准化整合国际劳工组织统计数据库的指标，它为学者检验非正规就业与性别不平等、城乡发展差距及经济周期之间的理论假设提供了统一的分析基础，推动了比较劳动制度研究从描述性叙述向可复现实证的范式转变。

衍生相关工作

围绕该数据集，Electric Sheep Africa目录衍生出一系列面向非洲数据发现的标准化元数据工程实践，并催生了与非正规经济、劳动统计相关的机器学习建模工作。研究者借助其表格分类与回归任务接口，开展了非正规就业预测、区域差异聚类及跨国面板分析等延伸研究，逐步形成了非洲劳动市场数据开放共享的方法论积累。