electricsheepafrica/africa-ilo-sdg-b831-sex-eco-rt-sdg-indicator-8-3-1-proportion-of-informal-employm

--- 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: "SDG indicator 8.3.1 - Proportion of informal employment in total employment by sex and eco | Africa (ILOSTAT)" --- # SDG indicator 8.3.1 - Proportion of informal employment in total employment by sex and eco | Africa (ILOSTAT) 🌍 **2,375 observations** · **23 Africa countries** · **2016–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **2,375 observations** of `Informal economy` data across **23 Africa countries**, spanning **2016–2025**, 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=SDG_B831_SEX_ECO_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=SDG_B831_SEX_ECO_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 23 Africa countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `RWA` | 324 | 2017 | 2025 | | `ZMB` | 285 | 2017 | 2024 | | `AGO` | 264 | 2019 | 2025 | | `BWA` | 246 | 2019 | 2024 | | `ZWE` | 202 | 2019 | 2024 | | `UGA` | 118 | 2017 | 2021 | | `GMB` | 112 | 2018 | 2025 | | `SWZ` | 80 | 2021 | 2023 | | `TZA` | 80 | 2020 | 2024 | | `SYC` | 76 | 2023 | 2024 | | `CIV` | 76 | 2016 | 2019 | | `LSO` | 72 | 2019 | 2024 | | `MDG` | 42 | 2022 | 2022 | | `COM` | 42 | 2021 | 2021 | | `EGY` | 42 | 2024 | 2024 | | ... | _8 more countries_ | | | ## Indicators (sample) - `SDG_B831_SEX_ECO_RT` — SDG indicator 8.3.1 - Proportion of informal employment in total employment by sex and economic activity -- 19th ICLS (%) ## 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 | `SDG_B831_SEX_ECO_RT` | | `indicator.label` | `string` | Indicator name in English | `SDG indicator 8.3.1 - Proportion of i…` | | `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.) | `ECO_SECTOR_TOTAL` | | `classif1.label` | `string` | — | `Economic activity (Broad sector): Total` | | `time` | `int64` | Observation year | `2025` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `94.09` | | `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-sdg-b831-sex-eco-rt-sdg-indicator-8-3-1-proportion-of-informal-employm") 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"] == "SDG_B831_SEX_ECO_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="SDG_B831_SEX_ECO_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "SDG_B831_SEX_ECO_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_sdg_b831_sex_eco_rt_sdg_indicator_8_3_1_proportion_of_informal_employm_2025, title = {SDG indicator 8.3.1 - Proportion of informal employment in total employment by sex and eco | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=SDG_B831_SEX_ECO_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-sdg-b831-sex-eco-rt-sdg-indicator-8-3-1-proportion-of-informal-employm}} } ``` ## 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=SDG_B831_SEX_ECO_RT_This dataset contains informal employment data for 23 African countries from 2016 to 2025, specifically focusing on the United Nations Sustainable Development Goal (SDG) indicator 8.3.1, which measures the proportion of informal employment in total employment by sex and economic activity. It includes 2,375 observations covering one core indicator (SDG_B831_SEX_ECO_RT). The data is sourced from the International Labour Organization (ILO) ILOSTAT database, retrieved via its REST API and filtered to include only African countries. The dataset is organized in tabular format with columns such as country code (ref_area), country name (ref_area.label), data source (source.label), indicator code (indicator), sex disaggregation (sex), year (time), observed value (obs_value), and others, enabling detailed analysis by country, year, and sex. The data is harmonized by ILO using International Conference of Labour Statisticians (ICLS) definitions and includes quality flags (e.g., unreliable data). It is suitable for machine learning tasks like tabular classification, regression, and time-series forecasting, providing a structured and accessible resource for studying Africas labor market and informal economy.

搜集汇总

数据集介绍

构建方式

该数据集源于国际劳工组织（ILO）的ILOSTAT中央统计数据库，并依托联合国可持续发展目标（SDG）指标8.3.1的框架，聚焦于非洲地区非正规就业在总就业中的比例。Electric Sheep Africa对原始数据进行了系统性重封装，形成包含2375个观测值的结构化数据文件，覆盖23个非洲国家，时间跨度为2016年至2025年。数据以parquet格式存储，在保留原始指标定义与分类维度的基础上，整合了标准化元数据与来源说明，使得数据集既符合SDG监测的规范要求，又适配机器学习工作流的便捷调用。

使用方法

研究者可通过Hugging Face的datasets库以一行代码加载数据集，并借助内置查看器快速浏览数据结构与特征类型。对于表格分析，可将数据集转换为Pandas数据框以便进行统计描述、缺失值探查及可视化。在使用过程中，建议先核对变量定义与单位，谨慎处理缺失值，避免未经论证的插补；当需要与其他非洲数据集联合分析时，应基于明确的国家、年份和指标字段进行连接，并在分析文档中注明对地理范围的假设，以确保结论的稳健性与可追溯性。

背景与挑战

背景概述

非正规就业的规模与结构长期构成发展中国家劳动力市场治理的核心议题，亦是联合国可持续发展目标8.3.1的监测对象。该数据集由Electric Sheep Africa基于国际劳工组织ILOSTAT与联合国SDG数据库整编而成，收录2016至2025年间23个非洲国家共2375条观测记录，按性别与经济活动类别细分非正规就业占总就业的比例。作为非洲公共数据目录的组成部分，其以标准化元数据与开放许可发布，为劳动经济学、性别研究与政策评估提供了可比对的实证基础，并推动了非洲非正规经济研究的可复现性。

当前挑战

该数据集所应对的领域问题在于非正规就业的界定与测度：各国调查口径、参考期与经济活动分类标准存在异质性，跨境比较面临概念一致性难题，而性别与经济类别的交叉分层又使部分单元格样本稀疏。构建过程中，上游元数据存在国家与发布机构字段缺失，地理范围仅由标题或来源隐含推定，时间跨度内指标定义可能发生修订。如何在保留缺失值的前提下厘清变量单位与方法差异，并以透明规则衔接多源数据，构成使用该数据集进行建模与政策推断时不容回避的挑战。

常用场景

经典使用场景

在劳动经济学与非正规经济研究中，该数据集构成刻画非洲非正规就业结构的基础性证据来源。其经典使用场景聚焦于按性别与经济部门维度解构非正规就业占总就业的比重，覆盖二十三个非洲国家自二零一六年至二零二五年的观测记录。研究者借助这一面板结构，可开展跨国比较与时间序列趋势分析，识别非正规就业在性别间的分布差异及其随经济部门变动的演化轨迹，进而为可持续发展目标第八项第三款的具体监测提供可复现的数据支撑。

解决学术问题

非正规就业长期面临界定模糊、跨国可比性不足的测量困境，制约了劳动市场结构研究的深化。该数据集依托国际劳工组织统一口径与联合国可持续发展目标框架，将分散于各国劳动力调查的指标整合为标准化表格，缓解了统计口径异质带来的偏误。其意义在于为检验非正规就业与贫困、性别不平等及社会保障覆盖之间的理论假说提供一致化经验基础，并为非洲区域劳动治理的比较制度分析奠定数据根基。

实际应用

在政策实践层面，该数据集服务于劳动监察、社会保障扩面与就业促进项目的循证设计。国际组织与各国劳工部门可依据分性别与分部门的非正规就业比例，定位脆弱群体集中的经济部门，评估非正规就业随时间的变动是否与政策干预相呼应。研究者亦可将该表与非洲其他公开数据集按国家、年份及指标字段进行联结，构建多维分析管道，用以监测体面劳动议程在区域内的推进状况。

数据集最近研究

最新研究方向

围绕联合国可持续发展目标8.3.1的非正规就业测度，当前研究前沿正从静态比例描述转向性别与行业异质性的动态追踪，并借助ILOSTAT与联合国SDG数据的跨国面板展开比较分析。非洲23国2016至2025年的观测记录为探究非正规经济规模演变、性别就业差距及经济周期敏感性提供了实证基础。相关研究热点包括非正规就业与数字经济扩张、结构性转型及社会保障覆盖之间的关联机制，以及在高频数据稀缺背景下如何利用标准化元数据提升跨国可比性。该数据集的经济金融属性使其成为劳动经济学与发展政策评估中连接微观证据与宏观监测的重要纽带，对优化非洲劳动力市场治理和推动包容性增长具有参考价值。