electricsheepafrica/africa-ilo-emp-pifl-sex-ocu-dsb-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: - 1K<n<10K tags: - tabular - africa - ilostat - informal-economy - ilo - labour - employment pretty_name: "Share of employment outside the formal sector by sex, occupation and disability status (%) | Africa (ILOSTAT)" --- # Share of employment outside the formal sector by sex, occupation and disability status (%) | Africa (ILOSTAT) 🌍 **3,058 observations** · **31 Africa countries** · **2005–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **3,058 observations** of `Informal economy` data across **31 Africa countries**, spanning **2005–2025**, 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_DSB_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_DSB_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 31 Africa countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `RWA` | 297 | 2017 | 2025 | | `ZMB` | 258 | 2018 | 2024 | | `SEN` | 240 | 2015 | 2024 | | `ZWE` | 231 | 2014 | 2024 | | `BWA` | 223 | 2019 | 2024 | | `UGA` | 155 | 2010 | 2021 | | `GMB` | 151 | 2012 | 2025 | | `SYC` | 122 | 2019 | 2024 | | `CIV` | 121 | 2016 | 2022 | | `SWZ` | 117 | 2016 | 2023 | | `TZA` | 113 | 2014 | 2024 | | `EGY` | 86 | 2023 | 2024 | | `LBR` | 85 | 2010 | 2017 | | `ETH` | 80 | 2005 | 2021 | | `BFA` | 78 | 2022 | 2024 | | ... | _16 more countries_ | | | ## Indicators (sample) - `EMP_PIFL_SEX_OCU_DSB_RT` — Share of employment outside the formal sector by sex, occupation and disability status (%) ## Schema | Column | Type | Description | Example | |--------|------|-------------|---------| | `ref_area` | `string` | ISO 3166-1 alpha-3 country code | `BEN` | | `ref_area.label` | `string` | Country name in English | `Benin` | | `source` | `string` | ILOSTAT source code (e.g. labour force survey) | `BB:426` | | `source.label` | `string` | Source name in English | `HIES - Monitoring Survey of the Modul…` | | `indicator` | `string` | ILOSTAT indicator code | `EMP_PIFL_SEX_OCU_DSB_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 | `DSB_STATUS_TOTAL` | | `classif2.label` | `string` | — | `Disability status: Total` | | `time` | `int64` | Observation year | `2022` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `95.682` | | `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-dsb-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_DSB_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_PIFL_SEX_OCU_DSB_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_PIFL_SEX_OCU_DSB_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_emp_pifl_sex_ocu_dsb_rt_share_of_employment_outside_the_formal_sector_by_s_2025, title = {Share of employment outside the formal sector by sex, occupation and disability status (%) | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_PIFL_SEX_OCU_DSB_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-emp-pifl-sex-ocu-dsb-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_DSB_RT_This dataset contains 3,058 observations of informal economy employment data across 31 African countries, spanning from 2005 to 2025. The core indicator is Share of employment outside the formal sector by sex, occupation and disability status (%) (ILOSTAT code: EMP_PIFL_SEX_OCU_DSB_RT). Sourced from the International Labour Organizations ILOSTAT database, the data is retrieved via API and harmonized, covering dimensions such as employment, sex, occupation classification, disability status, and time periods. It is designed to provide statistical insights into informal employment in Africa, supporting labor market analysis, economic research, and machine learning applications.

搜集汇总

数据集介绍

构建方式

该数据集以国际劳工组织统计数据库（ILOSTAT）为上游来源，经Electric Sheep Africa进行元数据标准化与重封装而成。其原始指标记录非正规部门以外就业人口占比，按性别、职业及残疾状况分类，覆盖31个非洲国家2005年至2025年的时间序列。构建过程中保留了原始观测的缺失值结构，统一了数据格式为Parquet，并依据CC BY 4.0许可协议发布。数据以表格与文本混合模态呈现，包含3058条观测记录，属于经济学与金融领域，旨在为非洲非正规经济研究提供可复现、可追溯的结构化证据基础。

使用方法

使用者可通过Hugging Face datasets库以一行代码加载该数据集，获取数据集对象后查看分片结构、特征字段与样本记录。对于表格型分片，可调用to_pandas方法转换为DataFrame以进行后续统计分析。建议在建模前先行检查各变量的缺失模式与取值分布，按地理、时间及子群维度进行变量画像。若需与其他Electric Sheep Africa数据集整合，可利用国家、年份与指标字段进行显式连接。使用中应保持缺失值原状，除非有充分依据的插补规则，并注意在分析中核实原始定义与计量单位，以避免对标签的过度解读。

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构成发展中国家劳动力市场研究的核心议题，其测量质量直接关系到社会保障覆盖、税收基数估算与体面劳动议程的推进。国际劳工组织（ILO）依托ILOSTAT数据库持续汇编各国劳动力调查数据，为跨时段、跨国家的非正规就业比较提供权威依据。本数据集由Electric Sheep Africa于2026年标准化发布，源自ILOSTAT，涵盖31个非洲国家2005至2025年间3058条观测记录，按性别、职业与残疾状况细分正规部门以外就业占比，采用CC BY 4.0许可并以Parquet格式分发，为非洲非正规经济研究提供了可复现的跨国面板基础。

当前挑战

非正规就业统计本身面临界定标准不一、跨国可比性不足与调查覆盖率参差等固有困难，残疾状况维度的测量更因各国调查工具差异而尤为脆弱。本数据集在构建中亦需应对元数据缺口（国家标识与上游发布机构未予声明）、部分变量单位与定义须回溯原始来源方可确认、以及跨国面板中普遍存在的缺失值与时间序列不均衡等问题。如何在缺乏明确地理编码的情况下维持分析假设的可追溯性，并在建模前妥善处理缺失机制，构成使用该数据集的核心方法论挑战。

常用场景

经典使用场景

在劳动经济学与发展经济学交叉领域，非正规部门就业规模的测度长期依赖家户调查与劳动力抽样数据。该数据集汇聚31个非洲国家自2005年至2025年间共计3058条观测，按性别、职业类别与残疾状况对正规部门以外就业占比进行系统刻画，构成非洲非正规经济比较研究的核心面板资源。研究者可据此开展跨国家、跨时期的非正规就业结构分析，识别性别鸿沟与职业隔离在非正规部门中的表现形式，并借助残疾状况维度审视劳动力市场边缘化的叠加效应。

解决学术问题

囿于非洲各国统计口径参差与时间序列断裂，学界对非正规就业的跨国比较长期受制于数据可比性不足的痼疾。该数据集依托国际劳工组织集中统计数据库的标准化框架，将性别、职业与残疾状况三重维度整合于统一指标之下，有效回应了非正规就业测度中分类粒度粗疏、弱势群体可见度低等学术关切。其意义在于为检验劳动力市场二元结构理论、评估结构性转型的包容性提供了可复现的实证基础，并推动非洲非正规经济研究从国别叙事迈向区域比较。

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监测非正规就业动态、设计社会保障扩面方案提供了量化依据。发展机构可依据性别与残疾状况分项数据识别最需干预的目标群体，工会与民间组织亦可借助时序趋势评估非正规化进程的加剧或缓解。结合明确的国家与年份字段，分析者能够将该数据与贫困、教育、产业结构等指标进行联结，支撑从就业质量到社会保护的综合政策分析，服务于体面劳动议程的落地。

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与非正规经济持续扩张的背景下，该数据集以性别、职业与残疾状况为三重分层维度，刻画了2005至2025年间31个非洲国家正规部门外就业占比的演变轨迹。近期研究前沿聚焦于将残疾状况纳入非正规就业脆弱性分析框架，弥补传统劳动统计中残障群体可见度不足的缺陷，并借助职业分类细粒度识别非正规就业的行业集聚特征。该数据集与ILOSTAT宏观劳动力调查体系相衔接，为探究结构性不平等、性别就业鸿沟及包容性增长政策提供了可复现的实证基础，对撒哈拉以南非洲社会保障扩展与体面劳动议程的监测评估具有重要参考价值。