electricsheepafrica/africa-ilo-emp-pifl-sex-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: - n<1K tags: - tabular - africa - ilostat - informal-economy - ilo - labour - employment pretty_name: "Share of employment outside the formal sector by sex (%) | Africa (ILOSTAT)" --- # Share of employment outside the formal sector by sex (%) | Africa (ILOSTAT) 🌍 **582 observations** · **45 Africa countries** · **1999–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **582 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_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_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` | 75 | 2000 | 2024 | | `MUS` | 36 | 2012 | 2024 | | `EGY` | 36 | 2008 | 2024 | | `MLI` | 30 | 2013 | 2024 | | `AGO` | 27 | 2004 | 2025 | | `RWA` | 27 | 2017 | 2025 | | `SEN` | 24 | 2011 | 2024 | | `ZMB` | 24 | 2017 | 2024 | | `ZWE` | 21 | 2011 | 2024 | | `UGA` | 21 | 2010 | 2021 | | `BWA` | 21 | 2006 | 2024 | | `CIV` | 18 | 2012 | 2022 | | `NAM` | 15 | 2012 | 2018 | | `BFA` | 12 | 2018 | 2024 | | `SYC` | 12 | 2019 | 2024 | | ... | _30 more countries_ | | | ## Indicators (sample) - `EMP_PIFL_SEX_RT` — Share of employment outside the formal sector by sex (%) ## 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_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` | | `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) | `B` | | `obs_status.label` | `string` | — | `Break in series` | | `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-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_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_PIFL_SEX_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_PIFL_SEX_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_emp_pifl_sex_rt_share_of_employment_outside_the_formal_sector_by_s_2025, title = {Share of employment outside the formal sector by sex (%) | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_PIFL_SEX_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-emp-pifl-sex-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_RT_This dataset contains 582 observations of informal economy employment data across 45 African countries, spanning the years 1999 to 2025, covering 1 distinct indicator: the share of employment outside the formal sector by sex (%). The data is sourced from the ILOSTAT database of the International Labour Organization (ILO) and repackaged by Electric Sheep Africa for machine learning readiness. It includes columns such as country code, country name, data source, indicator code, sex disaggregation, observation year, observed value, observation status, and notes, enabling filtering and analysis by country, year, sex, and other dimensions.

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统计数据库（ILOSTAT）的正式就业部门外就业比例指标，由Electric Sheep Africa团队进行工程化整理与重包装。构建过程遵循标准化元数据框架，将原始ILOSTAT数据转换为Parquet格式，并统一了变量命名、地理标识及时间范围。数据集覆盖45个非洲国家，时间跨度为1999年至2025年，共收录582条观测记录，每条记录均以性别为分组维度，反映非正式部门就业份额的百分比数值。整理过程中保留了原始缺失值，未作插补处理，并附带来源注释与数据质量说明，以确保分析的可追溯性。

特点

数据集聚焦于非洲非正式经济领域的性别差异，提供按性别分列的正式部门外就业比例，具有明确的区域与主题针对性。其数据规模小于1000条，结构紧凑，以表格与文本混合模态呈现，便于快速加载与探索。元数据标签体系完整，涵盖劳动就业、非正式经济、经济金融等关键词，并标注了CC BY 4.0开放许可。数据集特别强调了来源透明性，保留上游出版者与许可证信息，同时记录了元数据缺项，如国家字段与上游发布者字段的缺失状态，为分析者提供了清晰的数据质量边界。

使用方法

研究人员可通过Hugging Face数据集库直接加载该数据集，利用Python中的datasets.load_dataset函数获取数据对象，并检查其分割、特征与样本结构。对于表格型数据，可转换为Pandas DataFrame进行统计分析与可视化。使用时应从仓库文件与数据查看器入手，确认变量定义、单位与地理编码，避免仅凭标签推断政策含义。建议在建模前检查缺失值分布，保留缺失状态直至确立合理的插补规则。该数据集可与其他Electric Sheep Africa数据集通过国家、年份与指标字段进行联接，以构建更具解释力的分析框架。

背景与挑战

背景概述

非正规经济部门就业规模的性别差异，长期构成发展经济学与劳动经济学的核心议题。国际劳工组织（ILO）依托ILOSTAT统计数据库，系统汇集了各国劳动力调查中关于非正规就业的标准化指标，为跨国比较提供了权威基准。在此背景下，Electric Sheep Africa于2026年将ILOSTAT中非洲区域按性别划分的非正规部门就业占比数据整理发布至Hugging Face平台，覆盖45个非洲国家、582条观测记录，时间跨度为1999年至2025年。该数据集以表格化格式封装，服务于非洲劳动力市场结构变迁、性别就业不平等及非正规经济动态的实证研究，为机器学习驱动的政策分析提供了可复现的数据基础。

当前挑战

该数据集所回应的领域难题，在于非正规就业的界定与测度本身存在显著的跨国异质性：各国劳动力调查对非正规部门的操作化定义、抽样框架与覆盖范围不尽一致，导致跨国比较易受口径差异干扰。在构建层面，数据整理者面临非洲国家统计能力参差不齐所带来的数据稀疏与缺失问题，部分年份与国家的观测值缺失需谨慎处理，不宜轻率插补。同时，按性别分列的指标在部分国家存在系统性漏报，且元数据中上游出版方与国家字段的标注缺口，增加了源流追溯与变量定义确认的难度。如何在保留缺失结构的前提下进行稳健的统计建模，构成该数据集应用中的主要方法论挑战。

常用场景

经典使用场景

在劳动经济学与非正规经济研究的交汇处，该数据集以性别为分组维度，刻画了非洲四十五国自1999年至2025年间非正规部门就业占比的演变轨迹。研究者借此得以开展跨国比较分析，考察不同经济体在正规化进程中的性别差异格局，并借助时间序列观察政策干预前后非正规就业比重的结构性变动。其经典用法在于构建面板数据模型，将非正规就业占比作为被解释变量，纳入制度质量、教育水平与产业结构等协变量，从而揭示非正规经济持续存在的深层机制。

实际应用

在政策实践层面，该数据集为国际组织与非洲各国劳工部门提供了监测非正规就业规模及其性别分布的量化依据。社会保障机构可据此识别非正规就业女性集中度较高的国家与时期，从而优化缴费型社保方案的覆盖策略。发展金融机构在评估普惠金融与技能培训项目的投放优先级时，亦可借助该数据定位非正规就业占比居高不下且性别差距显著的区域。此外，咨询机构与智库在撰写非洲劳动力市场国别报告时，常以此数据作为核心指标来源。

衍生相关工作

以该数据集为基础，Electric Sheep Africa系列已衍生出多个关联数据产品，涵盖非正规经济其他维度指标及非洲劳动力市场的多主题汇编。研究者利用其标准化元数据与parquet格式，构建了可复现的分析流水线，并与国际劳工组织ILOSTAT原始数据库进行交叉验证。部分学术工作将其与非洲家庭调查微观数据链接，用以检验宏观非正规就业占比与个体就业选择之间的一致性，亦有研究将其纳入机器学习特征工程，用于预测非洲各国非正规就业的演变趋势。