electricsheepafrica/africa-ilo-emp-pifl-sex-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: - 1K<n<10K tags: - tabular - africa - ilostat - informal-economy - ilo - labour - employment pretty_name: "Share of employment outside the formal sector by sex and education (%) | Africa (ILOSTAT)" --- # Share of employment outside the formal sector by sex and education (%) | Africa (ILOSTAT) 🌍 **8,010 observations** · **45 Africa countries** · **1999–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **8,010 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_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_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` | 1,163 | 2000 | 2024 | | `MUS` | 536 | 2012 | 2024 | | `EGY` | 482 | 2008 | 2024 | | `MLI` | 396 | 2013 | 2024 | | `AGO` | 375 | 2004 | 2025 | | `RWA` | 355 | 2017 | 2025 | | `ZMB` | 338 | 2017 | 2024 | | `SEN` | 319 | 2011 | 2024 | | `UGA` | 317 | 2010 | 2021 | | `ZWE` | 311 | 2011 | 2024 | | `BWA` | 277 | 2006 | 2024 | | `CIV` | 245 | 2012 | 2022 | | `NAM` | 204 | 2012 | 2018 | | `GMB` | 184 | 2012 | 2025 | | `BFA` | 164 | 2018 | 2024 | | ... | _30 more countries_ | | | ## Indicators (sample) - `EMP_PIFL_SEX_EDU_RT` — Share of employment outside the formal sector by sex 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_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.) | `EDU_AGGREGATE_TOTAL` | | `classif1.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-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_EDU_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_PIFL_SEX_EDU_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_PIFL_SEX_EDU_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_emp_pifl_sex_edu_rt_share_of_employment_outside_the_formal_sector_by_s_2025, title = {Share of employment outside the formal sector by sex and education (%) | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_PIFL_SEX_EDU_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-emp-pifl-sex-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_EDU_RT_This dataset contains 8,010 observations of the share of employment outside the formal sector by sex and education (%) across 45 Africa countries, spanning 1999–2025, sourced from the ILOSTAT database of the International Labour Organization (ILO) and repackaged by Electric Sheep Africa for machine learning readiness.

搜集汇总

数据集介绍

构建方式

该数据集根植于国际劳工组织劳动力统计的长期制度性积累，其原始数据源自ILOSTAT这一全球权威劳动统计数据库，并经由Electric Sheep Africa进行面向非洲数据发现场景的系统化重构。构建过程中，原始指标观测记录经标准化元数据标注、来源溯源说明与加载指引嵌入后被重新封装为Parquet格式，形成结构化且具备可复现分析潜力的语料集合，收录8010条观测记录，覆盖45个非洲国家自1999年至2025年间的正规部门外就业占比数据，按性别与教育程度两个维度进行交叉分层。

特点

数据集在覆盖面与结构设计上体现出鲜明的非洲劳动经济研究指向性。其观测单元跨越近二十六年的时间跨度与四十余个国别截面，单一指标下承载性别与教育双重分组信息，为剖析非正规经济中的结构性差异提供了充足维度。数据以表格与文本双模态呈现，体量处于千条至万条区间，文件采用Parquet列式存储格式，兼顾读取效率与类型保真。需留意的是，卡片元数据中部分字段如国家与上游发布者信息存在缺省情形，使用前须在数据文件中核验变量定义与单位。

使用方法

调用该数据集可依托Hugging Face数据集库的标准接口，以一行代码完成加载并自动获取内置划分，随后打印特征结构与样本切片以检视字段布局。当分析场景需要表格化操作时，可将首个划分转换为Pandas数据框，便于开展缺失值审视、按地理与时间维度的变量画像以及与其他Electric Sheep Africa数据集基于国别、年份与指标字段的连接。建模之前宜先确认变量定义、单位与统计方法，保留原始缺失状态直至确立可辩护的插补规则，并在可复现笔记本中同时引用原始来源背景与Hugging Face仓库地址。

背景与挑战

背景概述

非正规经济部门就业人口的界定与测度，长期构成劳动经济学与发展经济学交叉地带的核心议题。国际劳工组织（ILO）依托其ILOSTAT统计数据库，系统汇编了全球劳动市场指标，为相关实证研究奠定了数据基石。Electric Sheep Africa于2026年前后推出的该数据集，覆盖45个非洲国家、1999至2025年间共计8010条观测记录，以性别与教育程度为维度，刻画了非正规部门就业份额的分布特征。该数据集承袭ILO官方统计口径，经标准化元数据包装后，为非洲劳动市场结构转型、性别就业差距及教育回报等议题的比较研究提供了可复现的数据支撑。

当前挑战

该数据集所面临的挑战呈现多重面向。就领域问题而言，非正规就业本身概念边界模糊，跨国可比性受制于各国统计口径与抽样方法的异质性，如何在不同制度语境下保持指标一致性构成根本难题。构建过程中，Electric Sheep Africa需对ILOSTAT原始数据进行清洗、重塑与标准化，同时面临国家字段与上游发布者等元数据缺失的现实困境，加之部分年份与国别的数据稀疏性，对缺失值的合理保留与插补策略提出更高要求。上述因素共同导致数据在时间连续性与空间覆盖度上存在不均衡，直接影响到跨国比较与面板建模的可靠性。

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，该数据集凭借其覆盖45个非洲国家、跨越1999至2025年的8010条观测记录，成为刻画非正规部门就业性别与教育差异的经典数据资源。研究者通常以性别与受教育程度为分组维度，运用描述性统计与横截面比较方法，系统测度各国非正规就业占比的分布特征与历时演变趋势，进而揭示非洲劳动力市场结构转型的异质性图景。

实际应用

在政策实践层面，该数据集可服务于非洲各国劳动部门与国际发展机构对非正规就业规模的监测评估，为社会保障扩面、职业技能培训与性别包容性就业政策的设计提供量化依据。研究者亦可将其与贫困、教育投入及产业结构等指标进行跨国匹配，辅助识别非正规就业高发群体，支撑面向弱势劳动者的精准干预方案制定与效果追踪。

衍生相关工作

依托该数据集及其所属的Electric Sheep Africa开放数据目录，衍生出一系列聚焦非洲劳动力市场结构的比较研究与可复现分析工作。相关研究围绕非正规就业的性别鸿沟、教育回报差异以及跨国面板建模展开，并借助统一元数据标准与其他ILOSTAT指标数据集进行关联整合，推动了非洲经济金融领域开放数据在机器学习与计量分析中的规范化应用。