electricsheepafrica/africa-ilo-emp-pifl-sex-oc2-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 occupation - ISCO level 2 (%) | Africa (ILOSTAT)" --- # Share of employment outside the formal sector by sex and occupation - ISCO level 2 (%) | Africa (ILOSTAT) 🌍 **12,555 observations** · **37 Africa countries** · **1999–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **12,555 observations** of `Informal economy` data across **37 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_OC2_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_OC2_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 37 Africa countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `ZAF` | 1,998 | 2000 | 2024 | | `MUS` | 1,145 | 2012 | 2024 | | `EGY` | 901 | 2009 | 2024 | | `RWA` | 681 | 2017 | 2025 | | `AGO` | 668 | 2019 | 2025 | | `ZMB` | 634 | 2017 | 2024 | | `ZWE` | 583 | 2011 | 2024 | | `UGA` | 490 | 2010 | 2021 | | `SEN` | 436 | 2015 | 2024 | | `BFA` | 389 | 2018 | 2024 | | `NAM` | 382 | 2012 | 2018 | | `BWA` | 357 | 2019 | 2024 | | `ETH` | 265 | 1999 | 2021 | | `CIV` | 248 | 2016 | 2019 | | `GMB` | 239 | 2012 | 2025 | | ... | _22 more countries_ | | | ## Indicators (sample) - `EMP_PIFL_SEX_OC2_RT` — Share of employment outside the formal sector by sex and occupation - ISCO level 2 (%) ## 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_OC2_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.) | `OC2_ISCO08_TOTAL` | | `classif1.label` | `string` | — | `Occupation (ISCO-08), 2 digit level: …` | | `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_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-oc2-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_OC2_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_PIFL_SEX_OC2_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_PIFL_SEX_OC2_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_emp_pifl_sex_oc2_rt_share_of_employment_outside_the_formal_sector_by_s_2025, title = {Share of employment outside the formal sector by sex and occupation - ISCO level 2 (%) | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_PIFL_SEX_OC2_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-emp-pifl-sex-oc2-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_OC2_RT_This dataset contains 12,555 observations of informal economy data across 37 Africa countries, spanning 1999–2025, covering 1 distinct indicator: Share of employment outside the formal sector by sex and occupation - ISCO level 2 (%). It is sourced from the ILOSTAT database of the International Labour Organization (ILO), harmonized for consistency, and includes disaggregation by sex and occupation, suitable for tabular classification, regression, and time-series forecasting tasks.

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）所发布的非正规部门就业统计，经Electric Sheep Africa以元数据驱动的方式重新封装为Hugging Face平台上的标准化资产。其构建以非洲37国1999年至2025年间12555条观测记录为基底，围绕性别与ISCO二级职业分类，测算正规部门以外就业人数占总就业的比重。封装过程中，原始指标口径、单位与来源信息被系统保留，同时补充了许可协议、加载指引与溯源说明，使分散的劳工统计条目转化为结构一致、可复现分析的表格化数据资源，服务于非正规经济与劳动力市场研究。

使用方法

研究者可借助Hugging Face datasets库以一行代码加载该数据集，随后通过特征查看与样本预览快速把握数据结构。若需统计分析，可将首个数据划分转换为Pandas数据框，进而开展缺失值诊断、按国家与年份的分组画像以及变量分布检验。使用时应优先确认数据文件中显式存在的国家、年份与指标字段，并留意元数据中country与upstream_publisher字段的空缺。在建模前保留原始缺失值并确立可辩护的插补规则，同时建议结合其他Electric Sheep Africa数据集，以显式地理与时间键进行联接，构建可引用原始来源与封装仓库的可复现分析流程。

背景与挑战

背景概述

非正规经济部门就业的量化测度长期构成发展经济学与劳动经济学的核心议题，其数据可得性直接制约着对非洲劳动力市场结构的实证认知。该数据集由Electric Sheep Africa工程团队于2026年基于国际劳工组织ILOSTAT数据库整理发布，涵盖37个非洲国家1999至2025年间12,555条观测记录，按性别与ISCO二级职业分类刻画非正规部门外就业占比。作为非洲公开数据目录的组成部分，该数据集以标准化元数据与Parquet格式降低了跨境比较研究的数据获取门槛，为监测非洲非正规经济规模、评估体面劳动议程进展提供了可复用的基础性证据层。

当前挑战

该数据集所回应的领域问题在于，非正规就业统计长期受制于定义分歧、抽样口径不一与报告稀疏，致使非洲跨境比较研究难以获得稳健的基准估计。构建过程中的挑战同样显著：ILOSTAT原始数据依赖各国劳动力调查的自主上报，存在指标定义与参考周期的不一致；按性别与职业交叉分层后，部分国家与年份的观测出现结构性缺失，需要审慎处理而非简单插补；元数据清单中country与upstream_publisher字段的缺失，进一步增加了来源追溯与地理编码的难度，要求使用者在建模前对变量定义、单位及覆盖范围进行独立核验。

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是刻画劳动力市场结构的关键指标。该数据集以国际标准职业分类（ISCO）第二层级为框架，按性别分列非洲37个国家1999至2025年间非正规部门就业份额，提供逾1.2万条结构化观测。经典使用场景涵盖跨国面板回归、性别差异分解、职业类别聚类以及非正规就业的时间序列趋势建模，研究者可依托其进行描述性统计、固定效应估计与跨国比较分析，进而揭示非洲劳动力市场非正规化的空间异质性与时间演化特征。

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、口径不一与覆盖有限等瓶颈，难以开展严谨的跨国比较与长时段追踪。该数据集凭借国际劳工组织标准化统计框架，统一了指标定义、国家编码与性别及职业分类维度，有效缓解了跨国可比性不足、时间序列断裂以及性别与职业交叉分析缺失等常见学术难题。其意义在于为非正规就业的驱动机制、性别不平等与职业隔离研究提供可复现的实证基础，并推动非洲劳动统计从描述性叙事向假设检验型研究转型。

实际应用

在政策实践层面，该数据集为国际组织、国家统计部门与智库评估非正规就业规模及其性别与职业分布提供量化依据。劳动监察部门可据此识别非正规就业集中度较高的职业类别，优化社会保障扩面与劳动权益保护政策的靶向设计；发展机构可将其纳入国别诊断与项目监测框架，追踪非正规化程度随时间的变化。私营部门亦可借助该数据评估特定职业劳动力供给的正式化程度，辅助市场进入与人力资源规划决策。

数据集最近研究

最新研究方向

该数据集聚焦于非洲非正规经济部门就业结构的性别与职业维度，为劳动经济学与发展研究提供了1999至2025年间37个非洲国家的12555条观测记录。当前前沿研究致力于通过ISCO二级职业分类与性别交互分析，揭示非正规就业的异质性模式，并与ILO体面劳动议程及联合国可持续发展目标8相呼应。热点议题包括非正规经济在非洲城市化与数字化转型中的演变、性别差距对劳动力市场脆弱性的影响，以及跨国可比数据在政策评估中的方法论挑战。该数据集的可复现性与开放许可特性，为构建跨国面板模型、验证结构转型理论及设计针对性社会保护政策提供了关键实证基础，对推动非洲包容性增长研究具有重要学术与政策意义。