electricsheepafrica/africa-ilo-emp-pifl-sex-ocu-est-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 establishment size (thousands) | Africa (ILOSTAT)" --- # Employment outside the formal sector by sex, occupation and establishment size (thousands) | Africa (ILOSTAT) 🌍 **34,688 observations** · **37 Africa countries** · **2000–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **34,688 observations** of `Informal economy` data across **37 Africa countries**, spanning **2000–2025**, 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_EST_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_EST_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 37 Africa countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `ZAF` | 8,216 | 2000 | 2024 | | `EGY` | 2,723 | 2009 | 2023 | | `AGO` | 2,170 | 2004 | 2025 | | `MLI` | 2,038 | 2013 | 2024 | | `SEN` | 1,641 | 2015 | 2024 | | `BWA` | 1,572 | 2006 | 2024 | | `ZMB` | 1,341 | 2017 | 2024 | | `NAM` | 1,236 | 2012 | 2018 | | `RWA` | 929 | 2017 | 2020 | | `SYC` | 855 | 2019 | 2024 | | `BFA` | 837 | 2018 | 2024 | | `MDG` | 756 | 2012 | 2022 | | `GMB` | 755 | 2012 | 2025 | | `COD` | 715 | 2005 | 2020 | | `ZWE` | 707 | 2011 | 2019 | | ... | _22 more countries_ | | | ## Indicators (sample) - `EMP_PIFL_SEX_OCU_EST_NB` — Employment outside the formal sector by sex, occupation and establishment size (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_EST_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 | `EST_AGGREGATE_TOTAL` | | `classif2.label` | `string` | — | `Establishment size (Aggregate): 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-est-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_EST_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_PIFL_SEX_OCU_EST_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_PIFL_SEX_OCU_EST_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_emp_pifl_sex_ocu_est_nb_employment_outside_the_formal_sector_by_sex_occupa_2025, title = {Employment outside the formal sector by sex, occupation and establishment size (thousands) | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_PIFL_SEX_OCU_EST_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-emp-pifl-sex-ocu-est-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_EST_NB_This dataset contains 34,688 observations of informal economy employment data across 37 Africa countries, spanning 2000–2025, covering 1 distinct indicator: Employment outside the formal sector by sex, occupation and establishment size (thousands). The data is sourced from ILOSTAT, the ILOs central statistics database, retrieved via API and filtered to Africa ISO3 country codes. It is provided in tabular format with columns including country code, source, indicator, sex disaggregation, time, observed value, and data quality flags, suitable for tasks like tabular classification, regression, and time-series forecasting. The data is harmonised by ILO using International Conference of Labour Statisticians (ICLS) definitions and includes caveats on data quality (e.g., observation status).

搜集汇总

数据集介绍

构建方式

该数据集来源于国际劳工组织统计数据库（ILOSTAT），作为全球劳动力市场统计的权威来源，其原始数据经Electric Sheep Africa标准化处理后，以Parquet格式重新发布。数据覆盖34,688条观测记录，涉及37个非洲国家，时间跨度为2000年至2025年，每条记录均标注性别、职业类别及机构规模三个维度，以千人为计量单位，系统刻画非正规部门以外的就业分布状况。

特点

该数据集以非洲区域非正规经济就业为核心议题，聚焦正式部门之外就业人口的结构特征，兼具时间序列与跨国比较的双重分析价值。数据标注涵盖性别、职业与机构规模三重变量，可支撑多维交叉分析，并保留原始缺失值以确保后续处理的可追溯性。其标准化元数据与明确的知识共享许可协议，为非洲劳动力市场研究提供了可复现、可引用的结构化证据基础。

使用方法

研究者可通过Hugging Face datasets库加载该数据集，利用内置的split机制获取数据表，并借助features属性查看变量结构及样本预览。当数据以表格形式呈现时，可转换为Pandas DataFrame以支持进一步的统计建模与可视化分析。在建模前应先行检查数据模式与缺失值分布，必要时依据国家、年份及指标字段与其他非洲数据集进行关联，并在分析中明确标注地理范围假设与来源出处。

背景与挑战

背景概述

非正规经济部门的就业规模与结构长期是发展经济学与劳动经济学的核心议题，尤其在非洲大陆，非正规就业往往占据总就业的绝大部分，却因统计口径不一而难以准确刻画。国际劳工组织（ILO）依托ILOSTAT数据库，系统汇集了按性别、职业及机构规模分列的非正规部门外就业估算数据，覆盖37个非洲国家、时间跨度自2000年至2025年，共计34,688条观测。Electric Sheep Africa于2026年对该数据集进行元数据标准化与重封装，以Hugging Face为发布平台，提升了非洲劳动统计数据的可发现性与可复用性，为比较劳动制度、性别差距与非正规性研究提供了重要基础。

当前挑战

该数据集所应对的领域问题在于：非正规就业的跨国可比测度长期受制于定义分歧、抽样偏差与报告口径差异，亟需统一框架下的定量证据。构建过程中的挑战则体现为多维度的数据治理难题：其一，元数据存在国别与上游出版者字段缺失，需在分析前审慎处理；其二，覆盖范围虽广但时间序列不均衡，部分国家年份稀疏，影响面板建模的稳健性；其三，性别、职业与机构规模交叉分类导致单元格内样本量不均，缺失机制复杂，直接插补或删除易引入偏误；其四，非正规性概念本身在不同国家统计实践中边界模糊，标签含义需回溯ILO原始方法论文档方能确认。

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用途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。研究者依托2000至2025年间37个非洲国家逾三万条观测记录，按性别、职业类别与机构规模三个维度交叉剖析正规部门之外的就业分布形态。此类分析常借助列联表与分组回归，揭示女性在低规模机构与特定职业中的集聚现象，进而为理解非洲劳动力市场的二元结构提供可复现的量化基础。

衍生相关工作

围绕该数据集已衍生出若干延续性工作，包括非洲非正规就业跨国比较研究、性别就业差距的时序分解分析，以及将本数据集与其他Electric Sheep Africa目录下劳工统计资源联结构建的区域劳动力市场面板。这些工作普遍沿用其国别、年份与指标字段作为联结键，形成以国际劳工组织统计口径为基准的可复现研究生态，并推动非洲非正规经济数据的开放获取与二次利用。

数据集最近研究

最新研究方向

在非洲非正规经济持续扩张的背景下，该数据集以性别、职业与机构规模三重维度刻画正规部门外就业的分布格局，为劳动经济学前沿研究提供了稀缺的跨国面板证据。近年来，国际劳工组织与世界银行相继警示非正规就业对社会保障覆盖与生产率增长的抑制效应，学界愈发关注非正规部门内部的异质性结构。基于该数据集，研究者可深入探究性别职业隔离、微型经营单位吸纳能力以及非正规就业与贫困脆弱性的关联机制，亦可用于校准非洲劳动力市场模型或评估结构性转型政策。其34,688条观测覆盖37国、横跨四分之一世纪，为比较制度分析与因果推断提供了坚实的数据基座。