electricsheepafrica/africa-ilo-emp-pifl-sex-ocu-mts-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, occupation and marital status (%) | Africa (ILOSTAT)" --- # Share of employment outside the formal sector by sex, occupation and marital status (%) | Africa (ILOSTAT) 🌍 **61,829 observations** · **43 Africa countries** · **1999–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **61,829 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_MTS_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_OCU_MTS_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 43 Africa countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `ZAF` | 10,200 | 2000 | 2024 | | `EGY` | 4,498 | 2009 | 2024 | | `MUS` | 4,033 | 2012 | 2024 | | `MLI` | 3,092 | 2013 | 2024 | | `AGO` | 3,030 | 2004 | 2025 | | `RWA` | 2,857 | 2017 | 2025 | | `ZMB` | 2,648 | 2017 | 2024 | | `ZWE` | 2,514 | 2011 | 2024 | | `BWA` | 2,197 | 2006 | 2024 | | `UGA` | 1,977 | 2010 | 2021 | | `SEN` | 1,967 | 2015 | 2024 | | `NAM` | 1,965 | 2012 | 2018 | | `BFA` | 1,454 | 2018 | 2024 | | `CIV` | 1,395 | 2016 | 2022 | | `GMB` | 1,241 | 2012 | 2025 | | ... | _28 more countries_ | | | ## Indicators (sample) - `EMP_PIFL_SEX_OCU_MTS_RT` — Share of employment outside the formal sector by sex, occupation and marital status (%) ## 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_MTS_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.) | `OCU_SKILL_TOTAL` | | `classif1.label` | `string` | — | `Occupation (Skill level): Total` | | `classif2` | `string` | Second classification variable where applicable | `MTS_AGGREGATE_TOTAL` | | `classif2.label` | `string` | — | `Marital status (Aggregate): 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) | `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-mts-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_OCU_MTS_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_PIFL_SEX_OCU_MTS_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_PIFL_SEX_OCU_MTS_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_emp_pifl_sex_ocu_mts_rt_share_of_employment_outside_the_formal_sector_by_s_2025, title = {Share of employment outside the formal sector by sex, occupation and marital status (%) | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_PIFL_SEX_OCU_MTS_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-emp-pifl-sex-ocu-mts-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_OCU_MTS_RT_This dataset contains 61,829 observations of Informal economy data across 43 Africa countries, spanning 1999–2025, covering 1 distinct indicator. The indicator is EMP_PIFL_SEX_OCU_MTS_RT — Share of employment outside the formal sector by sex, occupation and marital status (%). Data is sourced from the International Labour Organization (ILO) ILOSTAT database, pulled directly from its REST API and filtered to Africa ISO3 country codes. Repackaged by Electric Sheep Africa, it provides a unified, ML-ready data layer with schema including country codes, indicators, observed values, time, disaggregation dimensions (e.g., sex, occupation, marital status), and data quality flags. The dataset is suitable for tabular classification, regression, and time-series forecasting tasks, enabling analysis of informal employment trends in Africa.

搜集汇总

数据集介绍

构建方式

该数据集源出国际劳工组织统计数据库（ILOSTAT）关于非正规经济部门就业状况的权威调查，经Electric Sheep Africa以标准化元数据框架进行重新封装与编目，形成面向非洲区域的可复现数据资产。其构建过程保留了原始统计口径下的性别、职业与婚姻状况三维分类变量，并通过Parquet列式存储格式加以组织，最终汇聚为涵盖43个非洲国家、1999年至2025年共61,829条观测记录的结构化表格，为劳动经济学与非正规部门研究提供了跨国可比的基础数据支撑。

特点

数据集以非正规部门就业占比为核心测度，系统刻画了性别、职业类别与婚姻状况交叉维度下的劳动力市场分层特征，时间跨度逾二十五年，空间覆盖逾四十个非洲国家，兼具纵向时序深度与横向国别广度。数据以表格与文本双模态呈现，体量居于万级至十万级区间，辅以标准化元数据标签及开放许可协议，便于研究者进行缺失值诊断、变量剖面分析与跨国比较，构成非洲非正规经济实证研究中兼具规范性与可发现性的数据资源。

使用方法

研究者可借助Hugging Face数据集加载接口直接获取该数据集，并以Parquet格式读取全部表结构；在建模之前，宜先检视仓库数据文件中的变量定义、计量单位与缺失模式，必要时将首个数据切分转换为Pandas数据框以开展探索性分析。当进行跨国或跨指标整合时，应显式使用国家、年份与指标字段实施连接，并对地理标识仅由标题暗示的情形予以明确记录，从而确保分析流程的可追溯性与结论的稳健性。

背景与挑战

背景概述

非正规经济部门就业的量化测度，长期以来构成发展经济学与劳动经济学交叉领域的基础性议题。国际劳工组织（ILO）依托其ILOSTAT中央统计数据库，持续采集并发布涵盖性别、职业与婚姻状况等多维分组的非正规就业指标。Electric Sheep Africa于2026年将这一数据资源系统整理为面向非洲区域的分析就绪数据集，覆盖43个非洲国家、时间跨度自1999年至2025年，共计61,829条观测。该数据集为审视非洲劳动力市场结构转型、非正规部门性别差异及职业分布提供了可复用的实证基础，对非洲经济政策评估与比较研究具有重要参考价值。

当前挑战

从领域问题看，非正规就业的界定与测度本身充满争议，不同国家在调查口径、抽样方法与统计标准上的异质性，使跨国比较面临系统性偏差风险。就构建过程而言，该数据集面临多重挑战：非洲各国劳动力调查的覆盖频率与质量参差不齐，性别、职业与婚姻状况的交叉分组导致部分单元格存在缺失或稀疏观测；元数据中上游出版方与国家字段的缺失，要求下游分析者在建模前自行核验变量定义与单位；时间序列跨越二十余年，指标定义可能随国际标准修订而变动，若不加甄别地合并使用，易引入结构性断裂。

常用场景

经典使用场景

在非洲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正规部门就业的结构性特征。研究者依托国际劳工组织统计数据库（ILOSTAT）的权威框架，以性别、职业类别与婚姻状况为分层维度，系统测算43个非洲国家自1999年至2025年间正规部门之外就业份额的分布与演变。此类分析通常结合面板数据模型与分组比较方法，考察不同人口学属性群体在非正规就业市场中的异质性暴露程度，为理解非洲劳动力市场二元结构提供量化基础。

解决学术问题

该数据集有效回应了非洲非正规就业研究中长期存在的测度口径不一与跨国可比性不足等核心难题。通过统一指标定义、覆盖范围与时间跨度，研究者得以在一致框架下检验性别差异、职业隔离与婚姻状态对非正规就业参与的影响机制，进而修正以往因样本碎片化而产生的偏误结论。其意义在于为劳动经济学、发展经济学与社会分层研究提供可复现的实证素材，推动关于非正规部门吸纳能力与劳动力市场脆弱性的理论对话。

衍生相关工作

围绕该数据集已衍生出一系列具有代表性的后续研究。部分学者将其与非洲家庭调查、劳动力调查微观数据链接，构建多源融合的非正规就业测算框架；亦有研究以此为基础，采用机器学习方法对非正规就业份额进行缺失值插补与短期预测。此外，Electric Sheep Africa元数据目录的标准化工程促进了该数据集与其它非洲社会经济数据集的联合分析，推动形成以开放数据驱动的非洲劳动力市场研究生态。