electricsheepafrica/africa-ilo-emp-pifl-sex-eco-edu-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: - 100K<n<1M tags: - tabular - africa - ilostat - informal-economy - ilo - labour - employment pretty_name: "Share of employment outside the formal sector by sex, economic activity and education (%) | Africa (ILOSTAT)" --- # Share of employment outside the formal sector by sex, economic activity and education (%) | Africa (ILOSTAT) 🌍 **166,551 observations** · **45 Africa countries** · **1999–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **166,551 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_ECO_EDU_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_ECO_EDU_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` | 25,833 | 2000 | 2024 | | `EGY` | 12,806 | 2008 | 2024 | | `MUS` | 11,402 | 2012 | 2024 | | `AGO` | 8,420 | 2004 | 2025 | | `MLI` | 8,060 | 2013 | 2024 | | `RWA` | 7,775 | 2017 | 2025 | | `ZMB` | 6,460 | 2017 | 2024 | | `ZWE` | 6,299 | 2011 | 2024 | | `UGA` | 6,135 | 2010 | 2021 | | `SEN` | 5,700 | 2011 | 2024 | | `BWA` | 5,319 | 2006 | 2024 | | `CIV` | 5,287 | 2012 | 2022 | | `NAM` | 4,173 | 2012 | 2018 | | `GMB` | 3,745 | 2012 | 2025 | | `ETH` | 3,160 | 1999 | 2021 | | ... | _30 more countries_ | | | ## Indicators (sample) - `EMP_PIFL_SEX_ECO_EDU_RT` — Share of employment outside the formal sector by sex, economic activity and education (%) ## 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_ECO_EDU_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.) | `ECO_SECTOR_TOTAL` | | `classif1.label` | `string` | — | `Economic activity (Broad sector): Total` | | `classif2` | `string` | Second classification variable where applicable | `EDU_AGGREGATE_TOTAL` | | `classif2.label` | `string` | — | `Education (Aggregate levels): Total` | | `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` | `string` | — | `C3:2620` | | `note_classif.label` | `string` | — | `Nonstandard education level: Includin…` | | `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-eco-edu-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_ECO_EDU_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_PIFL_SEX_ECO_EDU_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_PIFL_SEX_ECO_EDU_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_emp_pifl_sex_eco_edu_rt_share_of_employment_outside_the_formal_sector_by_s_2025, title = {Share of employment outside the formal sector by sex, economic activity and education (%) | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_PIFL_SEX_ECO_EDU_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-emp-pifl-sex-eco-edu-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_ECO_EDU_RT_This dataset contains 166,551 observations across 45 African countries, spanning from 1999 to 2025, focusing on a single indicator: the share of employment outside the formal sector by sex, economic activity, and education (%). The data is sourced from the International Labour Organizations ILOSTAT database, retrieved via API and filtered to include only African countries. It includes detailed columns such as country codes, sex disaggregation, economic activity and education classifications, observation year, values, and data quality flags. The dataset is suitable for tabular classification, regression, and time-series forecasting tasks, designed to support research and analysis of informal economy employment trends in Africa. It is released under the CC-BY-4.0 license and repackaged by Electric Sheep Africa for machine learning readiness.

搜集汇总

数据集介绍

构建方式

该数据集源自国际劳工组织中央统计数据库，由Electric Sheep Africa对其开展系统性工程化处理，将原本分散的正式部门外就业比例指标整合为结构化的Parquet格式文件。构建过程以标准化元数据为支撑，统一了文档说明、加载指引与溯源注释，覆盖45个非洲国家自1999年至2025年的观测记录，最终形成166551条样本，旨在为非洲非正规经济研究提供可复用的数据基础。

特点

数据集聚焦非洲非正规经济领域，以正式部门外就业人口占比为核心测度，按性别、经济活动类型与教育程度三个维度加以细分，兼具时间跨度长与国别覆盖广的优势，规模处于十万至百万量级之间。数据以表格与文本双模态呈现，配有清晰的标签体系与溯源信息，并保留原始缺失值以保证分析灵活性，便于研究者开展跨国比较与纵向追踪。

使用方法

研究者可借助Hugging Face数据集加载库直接载入该数据集，获取其中的表格结构并导出为Pandas数据框以便进一步处理。使用前宜先检视仓库文件与数据查看器，确认变量定义与计量单位，并利用显式的国家、年份及指标字段进行合并分析。在建模阶段应保留缺失值直至确立合理的填补规则，同时引注原始来源与Electric Sheep Africa的元数据工程工作，以确保分析过程可复现。

背景与挑战

背景概述

非正规部门就业在全球南方经济体中的普遍存在，构成了理解劳动力市场结构与经济脆弱性的核心议题。国际劳工组织（ILO）长期致力于通过标准化统计框架追踪各成员国非正规就业的规模与分布，其ILOSTAT数据库成为跨国比较研究的基础设施。该数据集由Electric Sheep Africa于2026年基于ILOSTAT源数据整理发布，覆盖45个非洲国家、166551条观测记录，时间跨度为1999年至2025年，按性别、经济活动与教育程度三个维度分解非正规部门外就业占比。其核心研究问题在于揭示非洲各国非正规就业的结构性差异及其随时间的演变轨迹，为劳动经济学、发展经济学及社会政策评估提供可复用的高粒度面板数据，对推动非洲区域劳动力市场一体化研究与循证政策制定具有显著的方法论意义。

当前挑战

该数据集所应对的领域问题在于非正规就业统计本身的界定模糊性与跨国可比性困境：不同国家对非正规部门的操作化定义存在系统性差异，且非正规经济活动往往逃避常规劳动力调查的覆盖。构建过程中，Electric Sheep Africa面临的挑战包括：原始ILOSTAT数据在非洲各国的上报频率与指标粒度参差不齐，导致面板数据存在结构性缺失；性别、经济活动与教育三维度的交叉分类在部分年份或国家出现单元格空缺，需在不引入偏差的前提下保留缺失值以供用户自行选择插补策略；元数据清单中country与upstream_publisher字段的缺失标注，进一步增加了来源追溯与地理映射的难度；此外，将多元异构的源数据统一为parquet格式并保持分析可用性，亦需在编码与文档标准化之间取得审慎平衡。

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份额的性别差异、经济活动分布与教育梯度。研究者可借助其166551条观测记录，构建面板数据模型，系统检验1999至2025年间45个非洲国家非正规就业的结构性演变，并依托性别、经济活动门类与教育程度的三维交叉分类，揭示不同社会群体在正式部门之外的就业依附形态。

解决学术问题

该数据集有效回应了长期以来困扰非洲劳动市场研究的非正规就业测度不一致与跨国可比性不足两大难题。通过统一采用国际劳工组织ILOSTAT的标准化指标框架，它为学者提供了跨年度、跨国家的可比证据基础，使非正规就业的规模估算、趋势判定与结构分解得以在统一口径下展开，从而提升了非洲非正规经济实证研究的严谨性与可复现性。

衍生相关工作

围绕该数据集，Electric Sheep Africa已衍生出一系列面向非洲数据发现的标准化元数据工程与机器学习就绪数据集。相关经典工作涵盖非正规就业跨国比较研究、性别与劳动市场分割分析，以及依托该数据集构建的非洲劳动市场指标体系与可复现分析流程，共同推动了非洲开放数据生态在劳动经济领域的积累与传播。