electricsheepafrica/africa-ilo-emp-pifl-sex-ocu-ins-nb-employment-outside-the-formal-sector-by-sex-occupa

--- 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: "Employment outside the formal sector by sex, occupation and public/private sector (thousan | Africa (ILOSTAT)" --- # Employment outside the formal sector by sex, occupation and public/private sector (thousan | Africa (ILOSTAT) 🌍 **17,163 observations** · **43 Africa countries** · **1999–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **17,163 observations** of `Informal economy` data across **43 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_OCU_INS_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_OCU_INS_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 43 Africa countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `ZAF` | 2,412 | 2000 | 2024 | | `MUS` | 1,183 | 2012 | 2024 | | `EGY` | 1,043 | 2009 | 2024 | | `AGO` | 883 | 2004 | 2025 | | `MLI` | 850 | 2013 | 2024 | | `ZWE` | 802 | 2011 | 2024 | | `UGA` | 788 | 2010 | 2021 | | `RWA` | 736 | 2017 | 2025 | | `ZMB` | 724 | 2017 | 2024 | | `BWA` | 678 | 2006 | 2024 | | `SEN` | 593 | 2015 | 2024 | | `NAM` | 482 | 2012 | 2018 | | `SYC` | 405 | 2019 | 2024 | | `BFA` | 378 | 2018 | 2024 | | `CIV` | 374 | 2016 | 2022 | | ... | _28 more countries_ | | | ## Indicators (sample) - `EMP_PIFL_SEX_OCU_INS_NB` — Employment outside the formal sector by sex, occupation and public/private sector (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_OCU_INS_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.) | `OCU_SKILL_TOTAL` | | `classif1.label` | `string` | — | `Occupation (Skill level): Total` | | `classif2` | `string` | Second classification variable where applicable | `INS_SECTOR_TOTAL` | | `classif2.label` | `string` | — | `Institutional sector: 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` | `float64` | — | `—` | | `note_classif.label` | `float64` | — | `—` | | `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-ocu-ins-nb-employment-outside-the-formal-sector-by-sex-occupa") 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_OCU_INS_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_PIFL_SEX_OCU_INS_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_PIFL_SEX_OCU_INS_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_emp_pifl_sex_ocu_ins_nb_employment_outside_the_formal_sector_by_sex_occupa_2025, title = {Employment outside the formal sector by sex, occupation and public/private sector (thousan | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_PIFL_SEX_OCU_INS_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-emp-pifl-sex-ocu-ins-nb-employment-outside-the-formal-sector-by-sex-occupa}} } ``` ## 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_OCU_INS_NB_This dataset contains 17,163 observations of Informal economy data across 43 Africa countries, spanning 1999–2025, covering 1 distinct indicator: Employment outside the formal sector by sex, occupation and public/private sector (thousands). The data is sourced from the ILOSTAT database of the International Labour Organization (ILO), processed and filtered for Africa, and is suitable for tabular classification, regression, or time-series forecasting tasks. It includes fields such as country code, year, observed value, sex disaggregation, occupation classification, institutional sector classification, and data quality notes.

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）中关于非正规经济部门就业状况的原始统计记录，由Electric Sheep Africa团队进行系统化整理与元数据增强。构建过程以非洲区域为地理边界，筛选出覆盖43个非洲国家、时间跨度为1999年至2025年的非正规部门就业观测数据，涉及性别、职业类别及公共/私营部门等多维分类变量。原始数据经标准化清洗后以Parquet格式重新封装，并附加以可发现性为导向的元数据标签、加载指南与溯源说明，最终形成17,163条观测记录的表格化数据集，面向非洲数据探索与可重复分析场景。

特点

该数据集在结构上呈现典型的表格化特征，兼具分类与回归任务的双重适用性，涵盖经济学与金融领域的劳动市场信息。其核心特点在于聚焦非洲区域非正规经济部门的就业分布，以性别、职业和部门属性为交叉分析维度，形成多层级的面板数据结构。数据集规模处于一万至十万条之间，语言为英文，采用CC BY 4.0开放许可，便于学术研究与政策分析中的自由使用。元数据标注涵盖非正规经济、劳动力、就业等主题标签，并保留缺失值以支持后续审慎的插补决策，为分析者提供透明且可追溯的数据基础。

使用方法

研究者可借助Hugging Face数据集库以编程方式加载该数据，通过load_dataset函数指定仓库路径获取数据对象，进而检视数据划分、特征结构与样本内容。对于表格化分析需求，可将数据集转换为Pandas数据框以便进行统计建模与可视化探索。使用过程中建议优先查阅仓库文件与数据查看器以确认变量定义与单位，利用显式的国家、年份和指标字段进行跨数据集连接，并在建模前系统评估缺失模式。分析流程应引用原始来源与Electric Sheep Africa仓库，确保方法可复现且结论具备溯源依据。

背景与挑战

背景概述

非正规部门就业长期以来是发展中国家劳动力市场研究的核心议题，其规模与结构直接关乎社会保障覆盖面与生计脆弱性的评估。国际劳工组织（ILO）依托ILOSTAT数据库持续采集全球劳动统计，为跨国比较提供基准数据。非洲作为非正规就业占比最高的区域，相关证据的碎片化严重制约了政策研判的精度。Electric Sheep Africa于2026年发布的本数据集，汇编了43个非洲国家1999至2025年间17,163条观察值，按性别、职业及公私部门维度刻画非正规部门外的就业分布。该数据集为劳动经济学与非洲发展研究提供了标准化的跨国面板基础，其影响力在于打通了ILO官方统计与非洲数据发现生态之间的衔接路径。

当前挑战

从领域问题看，非正规就业的界定与度量本身存在显著的概念模糊性，各国统计口径的差异使跨国可比性面临根本性挑战，性别与职业维度的交叉分类进一步加剧了样本稀疏问题。就构建过程而言，本数据集以元数据标准化为主要工程路径，面临上游出版方与国别字段缺失的现实约束，非洲各国统计能力参差导致时间序列覆盖不均衡，1999至2025年间部分年份与国家的观察值存在系统性空缺。如何在不引入主观推断的前提下保留原始缺失结构，并确保职业分类与公私部门归属在跨年度间的一致性，构成该数据集在建模分析前需要审慎处理的先决难题。

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，该数据集最为经典的应用场景在于刻画非洲各国非正规部门就业的性别与职业结构差异。研究者借助其覆盖43个非洲国家、1999至2025年、逾一万七千条观测的时序截面数据，按性别、职业类别及公共/私营部门维度对正规部门以外就业人口进行分层比较，进而揭示女性在非正规经济中的过度集中现象以及职业隔离的演变轨迹，为跨国比较分析提供统一口径的量化基础。

解决学术问题

该数据集有效回应了非洲非正规就业统计中长期存在的口径不一与国别数据碎片化问题。通过ILOSTAT标准化指标与Electric Sheep Africa元数据编排，研究者得以在统一框架下检验非正规就业的性别差距、部门构成及其与经济发展水平的关系，弥补了既有文献多聚焦单一国家或短期观察的不足，为理解发展中国家劳动力市场二元结构提供了可复现的实证依据，亦推动了非正规经济测量方法的学术讨论。

衍生相关工作

该数据集衍生了一系列围绕非洲劳动力市场与性别经济学的后续研究。基于Electric Sheep Africa元数据清单，研究者将其与非洲其他ILOSTAT指标数据集进行跨国面板合并，开展非正规就业与贫困、教育及经济增长的关联分析；亦有工作利用其职业与部门细分维度，构建性别职业隔离指数并检验结构性转型假说。这些衍生研究共同拓展了非洲非正规经济实证文献的边界。