electricsheepafrica/africa-ilo-emp-pifl-sex-ocu-dsb-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: - 1K<n<10K tags: - tabular - africa - ilostat - informal-economy - ilo - labour - employment pretty_name: "Employment outside the formal sector by sex, occupation and disability status (thousands) | Africa (ILOSTAT)" --- # Employment outside the formal sector by sex, occupation and disability status (thousands) | Africa (ILOSTAT) 🌍 **3,060 observations** · **31 Africa countries** · **2005–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **3,060 observations** of `Informal economy` data across **31 Africa countries**, spanning **2005–2025**, 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_DSB_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_DSB_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 31 Africa countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `RWA` | 297 | 2017 | 2025 | | `ZMB` | 258 | 2018 | 2024 | | `SEN` | 240 | 2015 | 2024 | | `ZWE` | 231 | 2014 | 2024 | | `BWA` | 223 | 2019 | 2024 | | `UGA` | 155 | 2010 | 2021 | | `GMB` | 151 | 2012 | 2025 | | `SYC` | 124 | 2019 | 2024 | | `CIV` | 121 | 2016 | 2022 | | `SWZ` | 117 | 2016 | 2023 | | `TZA` | 113 | 2014 | 2024 | | `EGY` | 86 | 2023 | 2024 | | `LBR` | 85 | 2010 | 2017 | | `ETH` | 80 | 2005 | 2021 | | `BFA` | 78 | 2022 | 2024 | | ... | _16 more countries_ | | | ## Indicators (sample) - `EMP_PIFL_SEX_OCU_DSB_NB` — Employment outside the formal sector by sex, occupation and disability status (thousands) ## Schema | Column | Type | Description | Example | |--------|------|-------------|---------| | `ref_area` | `string` | ISO 3166-1 alpha-3 country code | `BEN` | | `ref_area.label` | `string` | Country name in English | `Benin` | | `source` | `string` | ILOSTAT source code (e.g. labour force survey) | `BB:426` | | `source.label` | `string` | Source name in English | `HIES - Monitoring Survey of the Modul…` | | `indicator` | `string` | ILOSTAT indicator code | `EMP_PIFL_SEX_OCU_DSB_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 | `DSB_STATUS_TOTAL` | | `classif2.label` | `string` | — | `Disability status: Total` | | `time` | `int64` | Observation year | `2022` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `5209.906` | | `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-dsb-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_DSB_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_PIFL_SEX_OCU_DSB_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_PIFL_SEX_OCU_DSB_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_emp_pifl_sex_ocu_dsb_nb_employment_outside_the_formal_sector_by_sex_occupa_2025, title = {Employment outside the formal sector by sex, occupation and disability status (thousands) | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_PIFL_SEX_OCU_DSB_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-emp-pifl-sex-ocu-dsb-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_DSB_NB_This dataset contains 3,060 observations of informal economy data across 31 Africa countries, spanning 2005 to 2025, covering one distinct indicator: Employment outside the formal sector by sex, occupation and disability status (thousands). It is sourced from the ILOSTAT database of the International Labour Organization (ILO), processed and filtered for Africa, and intended for tasks such as tabular classification, regression, and time-series forecasting.

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）关于非洲非正规部门就业状况的官方统计汇编，由Electric Sheep Africa团队进行标准化元数据加工与Parquet格式重封装，覆盖31个非洲国家2005至2025年间共计3060条观测记录。构建过程遵循开放数据治理规范，以CC BY 4.0许可协议发布，保留原始发布机构的数据权利与来源标注，同时补充面向分析场景的加载指引、字段说明与溯源注释，形成元数据驱动的可复现数据资产。

特点

数据集聚焦非洲非正规经济中的就业结构，以性别、职业类别与残疾状况为交叉维度，记录正规部门之外就业人数的千人计量数据。其特点体现于多维度分层设计、跨国跨年度面板结构以及明确的经济金融领域标签，兼具表格与文本模态，适合开展劳动力市场异质性分析与区域比较研究。数据集以1K至10K的中等规模分布，兼顾统计推断的样本充分性与计算资源的可行性。

使用方法

研究者可通过Hugging Face datasets库调用load_dataset函数加载该数据集，利用内置的数据集查看器快速浏览分片结构与特征字段，并借助to_pandas方法转换为数据框以适配传统统计分析流程。在建模前应检视模式定义与缺失值分布，依据显式国家、年份与指标字段进行子群画像或与其他Electric Sheep Africa数据集联接，同时保留原始缺失状态直至确立可辩护的插补规则，最终在可复现笔记本中完整引用上游来源与仓库地址。

背景与挑战

背景概述

非正规经济就业的量化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，尤其在非洲大陆，非正规部门承载了绝大多数劳动力的生计来源，其规模与结构直接关乎减贫政策、社会保障扩展及体面劳动议程的成效。国际劳工组织（ILO）依托ILOSTAT数据库持续采集相关指标，该数据集即由Electric Sheep Africa于2026年对ILOSTAT非洲区域数据进行工程化重整而形成，涵盖31个非洲国家、2005至2025年间3060条观测，按性别、职业与残疾状况细分非正规部门外就业规模（以千人为单位）。数据集以CC BY 4.0协议开放，为非洲非正规经济研究提供了标准化的跨国可比面板，对追踪劳动力市场边缘化群体的就业动态具有基础性参考价值。

当前挑战

该数据集所回应的领域难题在于非正规就业统计长期面临概念界定歧异、跨国口径不一及弱势群体（女性、残疾劳动者）数据系统性缺失的困境，致使非正规部门就业规模的可靠比较与趋势研判困难重重。构建过程中，数据整合亦遭遇多重制约：元数据中关键字段如国别与上游发布机构存在缺失，需依赖标题与来源信息进行审慎推断；ILOSTAT原始数据的采集频率与覆盖范围在非洲各国间参差不齐，跨年度观测的均衡性难以保证；残疾状况与职业交叉分类的稀疏性进一步限制了细分维度的深入分析。上述因素共同要求使用者在建模前充分核查变量定义、单位口径与缺失模式，避免对标签含义作过度推断。

常用场景

经典使用场景

在非正规经济研究领域，该数据集凭借其覆盖三十一个非洲国家、逾三千条观测值的结构化记录，成为刻画非正规部门就业形态的经典数据资源。研究者通常以性别、职业类别与残疾状况为分层变量，系统剖析不同社会群体在非正规经济中的参与差异，进而揭示劳动力市场分割的结构性特征。该数据集的时间跨度自2005年延伸至2025年，为追踪非正规就业的长期演变趋势提供了难得的纵向比较基础。

衍生相关工作

围绕该数据集，已衍生出一系列聚焦非洲非正规经济与弱势群体就业的衍生性研究。部分工作将其与Electric Sheep Africa目录中的其他ILOSTAT派生数据集进行跨国、跨指标融合，构建更为综合的非洲劳动力市场数据库；另有研究以此为基础，开发非正规就业预测模型或性别就业差距的分解分析工具。这些衍生工作持续拓展着该数据集在劳动统计标准化与非洲数据基础设施建设方面的影响力。

数据集最近研究

最新研究方向

在全球南方劳动治理议题持续升温的背景下，非正规部门就业的性别与职业分化成为发展经济学与劳动社会学交叉领域的前沿方向。该数据集覆盖31个非洲国家2005至2025年非正规部门就业的性别、职业与残疾状况维度，为探究结构性不平等提供了稀缺的纵向证据。当前研究正从总量测度转向对残障群体劳动边缘化的异质性刻画，并与非洲大陆自贸区劳动标准协调、ILO体面劳动议程等热点议题形成呼应。其价值在于打通微观就业结构与宏观政策评估之间的数据壁垒，为包容性增长与性别敏感型社会保护研究奠定实证基础。