electricsheepafrica/africa-ilo-emp-pifl-sex-age-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, age and public/private sector (%) | Africa (ILOSTAT)" --- # Share of employment outside the formal sector by sex, age and public/private sector (%) | Africa (ILOSTAT) 🌍 **18,705 observations** · **45 Africa countries** · **1999–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **18,705 observations** of `Informal economy` data across **45 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_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_AGE_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 45 Africa countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `ZAF` | 2,250 | 2000 | 2024 | | `MUS` | 1,149 | 2012 | 2024 | | `EGY` | 1,131 | 2008 | 2024 | | `MLI` | 900 | 2013 | 2024 | | `UGA` | 887 | 2010 | 2021 | | `AGO` | 861 | 2004 | 2025 | | `RWA` | 855 | 2017 | 2025 | | `ZWE` | 832 | 2011 | 2024 | | `SEN` | 803 | 2011 | 2024 | | `BWA` | 776 | 2006 | 2024 | | `ZMB` | 720 | 2017 | 2024 | | `CIV` | 560 | 2012 | 2022 | | `NAM` | 457 | 2012 | 2018 | | `SYC` | 435 | 2019 | 2024 | | `BFA` | 405 | 2018 | 2024 | | ... | _30 more countries_ | | | ## Indicators (sample) - `EMP_PIFL_SEX_AGE_INS_RT` — Share of employment outside the formal sector by sex, age 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_AGE_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.) | `AGE_YTHADULT_YGE15` | | `classif1.label` | `string` | — | `Age (Youth, adults): 15+` | | `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-age-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_AGE_INS_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_PIFL_SEX_AGE_INS_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_PIFL_SEX_AGE_INS_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_emp_pifl_sex_age_ins_rt_share_of_employment_outside_the_formal_sector_by_s_2025, title = {Share of employment outside the formal sector by sex, age and public/private sector (%) | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_PIFL_SEX_AGE_INS_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-emp-pifl-sex-age-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_AGE_INS_RT_This dataset contains informal economy data from the International Labour Organizations (ILO) ILOSTAT database, specifically the indicator Share of employment outside the formal sector by sex, age and public/private sector (%). It covers 45 African countries from 1999 to 2025, with 18,705 observations. Data is pulled directly from the ILOSTAT REST API and filtered to Africa ISO3 country codes. The dataset includes disaggregation dimensions such as sex (total, male, female), age groups, and institutional sector, along with data quality flags (e.g., unreliable). Original sources include national labour force surveys, household income surveys, and others, harmonized by ILO using International Conference of Labour Statisticians (ICLS) definitions. The dataset is repackaged by Electric Sheep Africa for machine learning readiness.

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（ILO）的ILOSTAT中央统计数据库，由Electric Sheep Africa团队进行标准化再包装，旨在系统整合非洲区域非正规经济部门的就业数据。其核心构建逻辑聚焦于按性别、年龄组及公共/私营部门维度，量化正式部门以外就业人数的比例，从而刻画非洲劳动力市场的结构性特征。数据采集覆盖45个非洲国家，时间跨度为1999年至2025年，总计18,705条观测记录，每条记录均对应特定国家、年份、性别、年龄及部门类别的指标值。通过统一元数据标注与来源溯源，该数据集在保留原始统计口径的同时，提升了跨国家比较的可用性与可复现性。

使用方法

使用该数据集时，可借助Hugging Face datasets库直接加载，通过load_dataset函数获取数据对象，并利用features属性查看字段结构，以切片方式预览样本记录。对于表格化分析，可将指定拆分转换为Pandas数据框，便于开展统计描述与建模。在分析前需核验变量定义、单位及缺失模式，避免仅凭标签推断政策含义。建议在涉及地理或时间维度的分析中，明确使用国家、年份等显式字段，并保留缺失值直至确立合理的插补规则。该数据集还可与Electric Sheep Africa目录中其他数据集进行连接，以构建更丰富的非洲社会经济分析工作流。

背景与挑战

背景概述

非正规经济就业的测度长期以来构成劳动经济学与发展经济学交汇处的关键议题，国际劳工组织（ILO）依托ILOSTAT数据库持续采集相关统计指标。本数据集由Electric Sheep Africa于2026年标准化发布，基于ILOSTAT来源，涵盖45个非洲国家、1999至2025年间共18,705条观测记录，系统刻画按性别、年龄及公私部门分类的非正规部门就业占比。该数据集为探究非洲劳动力市场非正规化进程、性别与年龄维度下的就业脆弱性提供了跨国可比的结构化证据，对推动非洲就业政策评估与实证研究具有基础性价值。

当前挑战

该数据集所回应的领域问题在于如何以统一口径度量非洲各国非正规就业的规模与结构，其难点源于非正规部门定义在各国统计实践中的显著异质性。构建过程中的挑战亦不容忽视：元数据清单显示国家与上游发布者字段存在缺失，地理归属需依赖标题或来源信息进行推断；部分年份与子群体观测值稀疏，缺失机制可能并非随机。分析者须在明确单位与变量定义的前提下审慎处理缺失值，避免因标签化解读而引致政策含义的误判。

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，该数据集构成了刻画非洲劳动力市场结构性特征的基础性资源。其经典使用场景在于以性别、年龄及公共或私营部门为分组维度，系统测算并比较45个非洲国家1999至2025年间非正规部门就业份额的时序演变与横截面差异。研究者常借此构建面板数据模型，识别非正规就业在不同人口群体中的分布规律，并利用Parquet格式的高效读取特性，将数据无缝接入Python分析流程，开展跨国别、跨时期的描述性统计与可视化探索。

解决学术问题

该数据集有效回应了非洲非正规经济研究中长期存在的测度不一致与跨国可比性不足等难题。通过整合国际劳工组织统一口径的统计数据，它为学者提供了标准化、长时段且覆盖广泛国别的观测样本，使得关于非正规就业规模与结构的跨国比较研究得以在一致的方法论框架下展开。其意义在于推动了对非正规部门性别差异、年龄分层及部门归属等议题的实证检验，为理解发展中国家劳动力市场二元结构提供了可靠的经验基础。

实际应用

在政策分析与实务操作层面，该数据集为国际组织、政府机构及发展智库评估非洲非正规就业状况提供了量化依据。使用者可依据国别与年份维度提取非正规就业份额指标，辅助制定面向非正规从业者的社会保障扩面策略、职业技能培训计划及劳动市场监管政策。同时，数据亦可嵌入世界银行、非洲开发银行等机构的经济监测报告，支撑区域劳动力市场诊断与可持续发展目标的进展追踪。

数据集最近研究

最新研究方向

在非洲劳动力市场结构转型的宏大叙事中，非正规经济部门就业占比的性别与年龄异质性正成为发展经济学与劳动经济学的交叉前沿议题。该数据集依托国际劳工组织ILOSTAT的权威统计框架，覆盖45个非洲国家自1999年至2025年的18705条观测记录，为刻画非正规就业的动态演化提供了稀缺的长时序面板基础。当前研究热点集中于运用该数据检验结构性转型理论在非洲情境下的适用性，并借助性别与年龄分层变量揭示非正规部门中的脆弱性分布格局。此类分析对理解非洲城镇化进程中的生计韧性、社会保障覆盖面扩展及包容性增长政策设计具有显著的实证支撑意义，亦为跨国比较研究提供了标准化、可复现的数据基础设施。