electricsheepafrica/africa-ilo-emp-pifl-sex-ocu-ins-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 public/private sector | Africa (ILOSTAT)" --- # Share of employment outside the formal sector by sex, occupation and public/private sector | Africa (ILOSTAT) 🌍 **17,150 observations** · **43 Africa countries** · **1999–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **17,150 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_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_INS_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` | 2,412 | 2000 | 2024 | | `MUS` | 1,182 | 2012 | 2024 | | `EGY` | 1,043 | 2009 | 2024 | | `AGO` | 883 | 2004 | 2025 | | `MLI` | 848 | 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` | 399 | 2019 | 2024 | | `BFA` | 378 | 2018 | 2024 | | `CIV` | 372 | 2016 | 2022 | | ... | _28 more countries_ | | | ## Indicators (sample) - `EMP_PIFL_SEX_OCU_INS_RT` — Share of employment outside the formal sector by sex, occupation and public/private sector (%) ## 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_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 | `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) | `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-ins-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_INS_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_PIFL_SEX_OCU_INS_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_PIFL_SEX_OCU_INS_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_emp_pifl_sex_ocu_ins_rt_share_of_employment_outside_the_formal_sector_by_s_2025, title = {Share of employment outside the formal sector by sex, occupation and public/private sector | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_PIFL_SEX_OCU_INS_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-emp-pifl-sex-ocu-ins-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_INS_RT_This dataset contains informal economy employment share data for Africa from the International Labour Organization (ILO) ILOSTAT database, with 17,150 observations across 43 African countries spanning 1999–2025. The core indicator is Share of employment outside the formal sector by sex, occupation and public/private sector (%) (EMP_PIFL_SEX_OCU_INS_RT). Data is pulled directly from the ILOSTAT REST API and filtered to Africa ISO3 country codes. It provides disaggregation across dimensions such as sex (total, male, female), occupation classification (by skill level), and institutional sector (public/private). Data is harmonized by ILO using International Conference of Labour Statisticians (ICLS) definitions and includes source and quality flags. Suitable for tabular classification, regression, and time-series forecasting tasks.

搜集汇总

数据集介绍

构建方式

该数据集由Electric Sheep Africa团队从国际劳工组织（ILOSTAT）的统计数据中提取并重组而成，聚焦于非洲地区非正规经济部门的就业状况。原始数据经标准化处理，涵盖43个非洲国家自1999年至2025年的17150条观测记录，以表格形式存储为Parquet文件。构建过程中保留了源数据的性别、职业及公共/私营部门等分类维度，并通过元数据清单进行系统化编目，确保数据的可追溯性与可发现性。

特点

数据集以非洲非正规就业为核心议题，系统刻画了不同性别、职业类别及部门属性下正规部门以外就业份额的分布特征。其特点在于跨时段长、覆盖国家广泛且分类维度细致，能够支持对非正规经济结构演变的纵向比较与横向分析。数据以表格与文本模态呈现，便于统计建模与可视化探索，同时附带标准化元数据，降低了跨领域研究的整合门槛。

使用方法

研究者可通过Hugging Face的datasets库直接加载数据集，利用内置的分割接口获取表格数据并转换为Pandas数据框，以便进行缺失值检查、变量剖面分析及地理与时间维度的子群比较。使用时应明确国家、年份与指标字段，结合其他非洲数据集进行联合分析，并在建模前确认原始定义与单位，保留缺失值直至制定合理的插补规则。

背景与挑战

背景概述

非正规就业的测度长期以来构成劳动经济学与发展经济学的重要议题，尤其在非洲大陆，非正规部门吸纳了绝大多数劳动力，其规模与结构直接关乎生计脆弱性、社会保障覆盖及税收基础。国际劳工组织（ILO）通过ILOSTAT数据库系统汇集各国劳动力调查数据，为跨国比较提供基准。Electric Sheep Africa于2026年将ILOSTAT中关于非洲非正规就业的指标重新整理发布，覆盖43个非洲国家、1999至2025年共17,150条观测，按性别、职业及公私部门维度细分。该数据集为研究者提供了审视非洲非正规就业性别差异与职业分布的长时段面板资料，并为非洲数据发现与可复现分析奠定了基础。

当前挑战

该数据集所应对的核心领域问题在于非正规就业统计的跨国可比性困境：各国劳动力调查在非正规部门定义、抽样框架与职业编码上长期存在异质性，致使跨国比较面临测量误差与口径不一致的风险。构建过程中的挑战则集中于元数据整合与标准化：原始ILOSTAT指标名称冗长且编码含义需从源文件确认，数据集中存在国家与上游发布者等元数据缺口，需在保留缺失值的前提下审慎处理。此外，部分观测的年份跨度与性别、职业交叉分类导致单元格稀疏，对建模时的缺失机制假设与插补策略提出了更高要求，分析者需在明确变量定义与单位后方能进行可靠的推断。

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差异与职业结构异质性。研究者依托国际劳工组织（ILO）标准化统计框架，以1999至2025年43个非洲国家的17150条观测记录为基底，系统比较不同性别、职业类别及公私部门属性下非正规就业份额的时序演变。此类分析常借助面板数据模型或分层回归策略，揭示非正规经济在非洲劳动力市场中的结构性特征，为跨国比较与区域趋势识别提供实证基础。

解决学术问题

该数据集有效回应了非正规就业测量中长期存在的口径不一与跨国可比性不足问题。通过统一采用ILOSTAT指标定义，并覆盖性别、职业与部门三重维度，它为检验非正规就业的性别鸿沟假说、部门分割理论及职业隔离机制提供了可复现的数据支撑。其长时段覆盖能力使得研究者能够区分结构性趋势与周期性波动，从而深化对非洲劳动力市场二元性的理解，并对既有基于单一国家或短期截面数据的研究结论进行稳健性检验。

衍生相关工作

围绕该数据集，已衍生出一系列经典研究工作，包括非洲非正规经济规模估算与动态追踪、性别就业差距的分解分析、以及公私部门工资溢价与非正规化关联的实证检验。部分研究将其与世界银行企业调查、非洲家庭调查网络等微观数据链接，构建多源融合的劳动市场分析框架。此外，Electric Sheep Africa元数据目录的标准化工程亦催生了面向非洲开放数据的可发现性工具与可复现研究流程，推动了区域数据基础设施的建设。