electricsheepafrica/africa-ilo-emp-pifl-sex-age-ocu-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, age and occupation (%) | Africa (ILOSTAT)" --- # Share of employment outside the formal sector by sex, age and occupation (%) | Africa (ILOSTAT) 🌍 **75,220 observations** · **43 Africa countries** · **1999–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **75,220 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_AGE_OCU_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_AGE_OCU_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` | 11,781 | 2000 | 2024 | | `MUS` | 5,144 | 2012 | 2024 | | `EGY` | 4,852 | 2009 | 2024 | | `AGO` | 3,999 | 2004 | 2025 | | `MLI` | 3,728 | 2013 | 2024 | | `RWA` | 3,279 | 2017 | 2025 | | `ZMB` | 3,223 | 2017 | 2024 | | `ZWE` | 2,874 | 2011 | 2024 | | `UGA` | 2,717 | 2010 | 2021 | | `BWA` | 2,535 | 2006 | 2024 | | `SEN` | 2,494 | 2015 | 2024 | | `NAM` | 2,201 | 2012 | 2018 | | `BFA` | 1,760 | 2018 | 2024 | | `CIV` | 1,676 | 2016 | 2022 | | `GMB` | 1,623 | 2012 | 2025 | | ... | _28 more countries_ | | | ## Indicators (sample) - `EMP_PIFL_SEX_AGE_OCU_RT` — Share of employment outside the formal sector by sex, age and occupation (%) ## 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_AGE_OCU_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.) | `AGE_YTHADULT_YGE15` | | `classif1.label` | `string` | — | `Age (Youth, adults): 15+` | | `classif2` | `string` | Second classification variable where applicable | `OCU_SKILL_TOTAL` | | `classif2.label` | `string` | — | `Occupation (Skill level): 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-age-ocu-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_AGE_OCU_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_PIFL_SEX_AGE_OCU_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_PIFL_SEX_AGE_OCU_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_emp_pifl_sex_age_ocu_rt_share_of_employment_outside_the_formal_sector_by_s_2025, title = {Share of employment outside the formal sector by sex, age and occupation (%) | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_PIFL_SEX_AGE_OCU_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-emp-pifl-sex-age-ocu-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_AGE_OCU_RT_This dataset contains 75,220 observations of informal economy data across 43 Africa countries, spanning 1999–2025, covering 1 distinct indicator: Share of employment outside the formal sector by sex, age and occupation (%). It is sourced from ILOSTAT, the ILOs central statistics database, pulled via API and filtered to Africa ISO3 country codes. The dataset includes columns such as country code, country name, source, indicator code, sex disaggregation, age and occupation classifications, observation year, observed value, and status flags, with notes on data quality and usage examples.

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），由Electric Sheep Africa团队进行元数据标准化与重封装。原始数据涵盖1999年至2025年间43个非洲国家的非正规经济部门就业统计，以性别、年龄和职业为细分维度，共计75,220条观测记录。构建过程保留原始数据文件的变量定义与单位，仅统一了元数据描述、加载指引与溯源说明，并以Parquet格式重新发布，便于分析人员直接调用。

使用方法

研究人员可通过Hugging Face的datasets库加载该数据集，获取默认分割后利用to_pandas方法转换为数据框，进而按国家、年份和人口学子组进行描述性统计或建模分析。建议在使用前检查架构与缺失模式，结合显式国家列进行地理聚合，并与其他Electric Sheep Africa数据集通过国家、年份和指标字段进行联接，以构建可复现的分析工作流。

背景与挑战

背景概述

非正规部门就业在全球劳动力市场中占据显著份额，尤其撒哈拉以南非洲地区，其规模与结构对理解经济发展路径、社会保障覆盖及劳动力市场脆弱性具有核心意义。国际劳工组织（ILO）长期致力于测度非正规就业，其ILOSTAT数据库成为跨国比较的权威基准。该数据集由Electric Sheep Africa团队于2026年基于ILOSTAT原始统计资料整理并标准化发布，涵盖43个非洲国家、1999至2025年间的75,220条观测记录，按性别、年龄与职业维度拆解非正规部门就业占比。该数据集为非洲非正规经济研究提供了跨时空、多维度的可比性基础，对劳动经济学、发展政策评估及社会保护分析具有重要支撑价值。

当前挑战

非正规就业领域长期面临界定模糊与测度口径不统一的困境，不同国家对非正规部门边界的操作化定义存在差异，致使跨国比较与时间序列分析潜藏系统性偏差。该数据集构建过程中的挑战同样显著：原始ILOSTAT数据中性别、年龄与职业维度的交叉分层常伴随样本稀疏与缺失值问题，元数据中地理字段的标注不完整进一步增加了国别归属的不确定性；跨年度数据在指标口径与分类标准上可能存在断裂，需要审慎的协调处理。此外，如何在保持数据原始性的同时提供充分的文档指引，以支持可复现的分析流程，亦构成持续的工程挑战。

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，该数据集最经典的使用场景在于跨国比较分析。研究者依托其涵盖43个非洲国家、跨越1999至2025年的75,220条观测记录，按性别、年龄与职业维度系统剖析正规部门之外就业比重的时空演变。此类分析通常借助面板数据模型与分层回归方法，揭示不同人口群体在非正规经济中的分布差异，进而勾勒非洲劳动力市场结构的整体图景。

解决学术问题

该数据集有效回应了非洲非正规就业测度长期存在的可比性难题。过往研究多依赖国别调查，指标口径参差、时序断裂，难以支撑严谨的跨国推断。本数据集以ILOSTAT标准化框架整合多国数据，为检验非正规就业与性别不平等、年龄分层及职业隔离之间的理论关联提供了统一经验基础，推动了劳动市场二元结构理论的实证深化。

实际应用

在政策实践层面，该数据集为国际组织与非洲各国劳工部门提供了量化依据。决策者可据此识别非正规就业高发群体与区域，设计面向青年、女性及低技能劳动者的社会保障扩展方案与正规化激励政策。同时，该数据亦服务于世界银行、国际劳工组织等机构的国别评估与项目监测，助力就业政策干预的精准投放与效果追踪。

数据集最近研究

最新研究方向

伴随非洲非正规经济在劳动力市场中占据的结构性地位日益受到学界关注，围绕该数据集的前沿探索正从静态描述性统计转向多维异质性刻画与动态演变建模。研究者利用性别、年龄与职业的交叉分类，揭示非正规就业在青年群体与女性劳动者中的集聚效应，并将其与城市化进程、跨境贸易波动及数字普惠金融的渗透相勾连，考察结构性转型对非正规部门规模的非线性冲击。该数据集在ILOSTAT权威统计框架下覆盖43个非洲国家逾四分之一世纪的观测，为跨国比较、面板因果推断与机器学习分类任务提供了稀缺的长时序证据，亦为评估国际劳工标准落地成效、设计面向非正规就业者的社会保障政策提供了关键的数据基础设施。