electricsheepafrica/africa-ilo-emp-pifl-sex-eco-edu-nb-employment-outside-the-formal-sector-by-sex-econom

--- 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: "Employment outside the formal sector by sex, economic activity and education (thousands) | Africa (ILOSTAT)" --- # Employment outside the formal sector by sex, economic activity and education (thousands) | Africa (ILOSTAT) 🌍 **167,824 observations** · **45 Africa countries** · **1999–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **167,824 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_NB) - **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_NB` 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` | 25,905 | 2000 | 2024 | | `EGY` | 13,004 | 2008 | 2024 | | `MUS` | 11,466 | 2012 | 2024 | | `AGO` | 8,502 | 2004 | 2025 | | `MLI` | 8,195 | 2013 | 2024 | | `RWA` | 7,797 | 2017 | 2025 | | `ZMB` | 6,552 | 2017 | 2024 | | `ZWE` | 6,338 | 2011 | 2024 | | `UGA` | 6,177 | 2010 | 2021 | | `SEN` | 5,739 | 2011 | 2024 | | `BWA` | 5,381 | 2006 | 2024 | | `CIV` | 5,363 | 2012 | 2022 | | `NAM` | 4,181 | 2012 | 2018 | | `GMB` | 3,755 | 2012 | 2025 | | `ETH` | 3,170 | 1999 | 2021 | | ... | _30 more countries_ | | | ## Indicators (sample) - `EMP_PIFL_SEX_ECO_EDU_NB` — Employment outside the formal sector by sex, economic activity and education (thousands) ## 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_NB` | | `indicator.label` | `string` | Indicator name in English | `Employment outside the formal sector …` | | `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) | `11270.18` | | `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-nb-employment-outside-the-formal-sector-by-sex-econom") 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_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_PIFL_SEX_ECO_EDU_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_PIFL_SEX_ECO_EDU_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_emp_pifl_sex_eco_edu_nb_employment_outside_the_formal_sector_by_sex_econom_2025, title = {Employment outside the formal sector by sex, economic activity and education (thousands) | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_PIFL_SEX_ECO_EDU_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-emp-pifl-sex-eco-edu-nb-employment-outside-the-formal-sector-by-sex-econom}} } ``` ## 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_NB_The dataset is titled Employment outside the formal sector by sex, economic activity and education (thousands) | Africa (ILOSTAT). It is a statistical dataset focusing on informal economy employment in Africa, containing 167,824 observations across 45 African countries, spanning the years 1999 to 2025 with annual frequency. The core indicator is EMP_PIFL_SEX_ECO_EDU_NB, which measures employment outside the formal sector disaggregated by sex, economic activity, and education (in thousands). Data is sourced from the ILOSTAT database of the International Labour Organization (ILO), retrieved via API and harmonized using International Conference of Labour Statisticians (ICLS) definitions. The dataset schema includes columns such as country code (ref_area), indicator code (indicator), sex disaggregation (sex), economic activity and education classifications (classif1 and classif2), observation year (time), observed value (obs_value), along with source, observation status, and notes for traceability. It supports multi-dimensional breakdowns, including by sex (total, male, female). Repackaged by Electric Sheep Africa, this dataset is part of a unified, ML-ready data layer for Africa, designed for easy access and analysis by researchers and developers.

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核心统计数据库ILOSTAT关于非洲非正规部门就业的权威汇编，经Electric Sheep Africa以标准化元数据框架重新封装而成。构建过程以ILOSTAT原始统计记录为基底，覆盖四十五个非洲国家自一九九九年迄二零二五年的观测数据，按性别、经济活动门类与教育程度三重维度交叉汇总，以千人为计量单位记录正规部门以外就业人口规模。Electric Sheep Africa在保留源数据语义结构的前提下，统一了字段命名、地理标识与时间编码，并以Parquet列式格式存储，使逾十六万条观测记录兼具可追溯的出处信息与面向机器学习工作流的就绪形态。

特点

该数据集以高度的维度结构化与跨域可比性见长，其核心特征在于将非正规经济就业这一发展经济学关键议题拆解为性别、行业与教育三重可分析切面，为刻画非洲劳动力市场的结构性差异提供了细粒度证据。数据集覆盖国家范围广、时间跨度长，逾十六万条记录在百千至百万量级之间，兼顾统计深度与计算可行性。字段设计遵循整洁数据原则，缺失值予以保留而未作主观填补，配合标准化的加载指引与溯源说明，使其既能服务于描述性统计，亦可支撑计量建模与跨国比较研究。

使用方法

研究者可借助Hugging Face datasets库以单行代码加载该数据集，在获取数据划分后检视特征结构并预览样本记录。对于表格型分析任务，可将指定划分转换为Pandas数据框，以便开展缺失模式诊断、按地理与时间维度的变量画像，以及与其他Electric Sheep Africa数据集的显式联结。建模之前宜先行核验变量定义与计量单位，并审慎处理未声明的地理编码字段。在引用与复现层面，建议同时标注ILOSTAT原始出处与该Hugging Face仓库链接，以确保研究过程的可追溯性与可重复性。

背景与挑战

背景概述

非正规经济中的就业形态长期构成非洲劳动力市场统计的盲区，国际劳工组织（ILO）维护的ILOSTAT数据库虽为全球劳工统计的权威来源，但非洲区域按性别、经济活动与教育程度细分的数据长期分散且口径不一。2026年，Electric Sheep Africa基于ILOSTAT原始数据构建了该数据集，覆盖45个非洲国家、1999至2025年间167,824条观测记录，聚焦于非正规部门以外就业人口的规模测算。该数据集回应了非洲非正规经济量化研究中跨国可比数据匮乏的核心问题，为劳动经济学、发展经济学及社会政策评估提供了标准化的分析基础，其元数据工程实践亦为非洲公共数据的可发现性与可复用性树立了参照。

当前挑战

该数据集所面对的首要领域问题在于，非正规就业本身边界模糊，各国对“非正规部门”的界定与统计口径存在系统性差异，致使跨国比较与趋势推断面临概念一致性的根本挑战。在构建层面，源数据来自多国劳动力调查的异质上报，观测年份不连续、变量层级不齐、缺失值广泛分布，元数据中country与upstream_publisher字段的缺失进一步削弱了溯源链条的完整性；同时，按性别、经济活动与教育程度三重维度交叉细分导致部分单元格样本量稀疏，估计稳定性存疑。如何在保留缺失结构的前提下建立可辩护的插补与聚合规则，并避免从标签本身引申政策含义，构成了该数据集在分析与建模中的持续挑战。

常用场景

经典使用场景

在劳动经济学与发展经济学的实证研究中，该数据集最为经典的用途在于刻画非洲非正规部门就业的性别差异与教育梯度。研究者依托国际劳工组织统计司提供的标准化劳动力调查数据，按性别、经济活动门类与教育程度三个维度对非正规就业人数进行分层比较，从而揭示女性在非正规经济中的过度集中现象以及低教育群体对非正规生计的高度依赖。此类面板数据结构使得跨年度的趋势分析与跨国比较成为可能，为理解非洲劳动力市场二元结构提供了可复现的量化基础。

衍生相关工作

围绕该数据集已衍生出一系列相关研究，涵盖非正规就业的性别差距分解、教育扩展对非正规部门规模的影响评估以及非洲劳动力市场一体化的比较分析。部分工作将其与收入、贫困及社会保护数据链接，构建多维度的脆弱就业指标体系。这些衍生研究进一步推动了国际劳工组织统计框架在非洲的本地化应用，并催生了面向政策模拟的可计算一般均衡模型与微观计量评估工具。

数据集最近研究

最新研究方向

在非洲非正规经济持续扩张的宏观背景下，该数据集凭借覆盖45国、跨1999至2025年的16万余条观测，正推动劳动经济学与非正规部门研究的范式转向。前沿探索聚焦于性别、经济活动与教育三重维度下的非正规就业异质性，力图揭示结构性不平等如何在时序中演化。结合国际劳工组织统计口径与机器学习可复现流程，研究者得以系统评估教育禀赋对非正规就业路径的调节效应，并检验性别分化在行业间的稳健性。此类分析为非洲包容性增长政策、社会保障扩展及人力资本投资优先序提供了关键的实证基座，亦为跨国比较与元分析构筑了标准化数据桥梁。