electricsheepafrica/africa-ilo-emp-nifl-sex-eco-dsb-rt-informal-employment-rate-by-sex-economic-activity

--- 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: "Informal employment rate by sex, economic activity and disability status (%) | Africa (ILOSTAT)" --- # Informal employment rate by sex, economic activity and disability status (%) | Africa (ILOSTAT) 🌍 **8,411 observations** · **31 Africa countries** · **2007–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **8,411 observations** of `Informal economy` data across **31 Africa countries**, spanning **2007–2025**, 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_NIFL_SEX_ECO_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_NIFL_SEX_ECO_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` | 928 | 2017 | 2025 | | `SEN` | 690 | 2015 | 2024 | | `ZWE` | 680 | 2014 | 2024 | | `ZMB` | 663 | 2018 | 2024 | | `BWA` | 628 | 2019 | 2024 | | `UGA` | 429 | 2010 | 2021 | | `GMB` | 427 | 2012 | 2025 | | `TZA` | 333 | 2014 | 2024 | | `CIV` | 327 | 2016 | 2022 | | `SYC` | 326 | 2019 | 2024 | | `SWZ` | 318 | 2016 | 2023 | | `EGY` | 237 | 2023 | 2024 | | `LBR` | 227 | 2010 | 2017 | | `GHA` | 223 | 2013 | 2015 | | `LSO` | 212 | 2019 | 2024 | | ... | _16 more countries_ | | | ## Indicators (sample) - `EMP_NIFL_SEX_ECO_DSB_RT` — Informal employment rate by sex, economic activity 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_NIFL_SEX_ECO_DSB_RT` | | `indicator.label` | `string` | Indicator name in English | `Informal employment rate by sex, econ…` | | `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` | | `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) | `96.347` | | `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-nifl-sex-eco-dsb-rt-informal-employment-rate-by-sex-economic-activity") 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_NIFL_SEX_ECO_DSB_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_NIFL_SEX_ECO_DSB_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_NIFL_SEX_ECO_DSB_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_emp_nifl_sex_eco_dsb_rt_informal_employment_rate_by_sex_economic_activity_2025, title = {Informal employment rate by sex, economic activity and disability status (%) | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_NIFL_SEX_ECO_DSB_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-emp-nifl-sex-eco-dsb-rt-informal-employment-rate-by-sex-economic-activity}} } ``` ## 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 2026-05-25 via the Electric Sheep pipeline. Source URL: https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_NIFL_SEX_ECO_DSB_RT_This dataset is a tabular dataset on informal employment rates in Africa, sourced from the International Labour Organizations (ILO) ILOSTAT database and repackaged by Electric Sheep Africa. It contains 8,411 observations across 31 African countries, spanning the years 2007 to 2025, focusing on one core indicator: Informal employment rate by sex, economic activity and disability status (%) (indicator code: EMP_NIFL_SEX_ECO_DSB_RT). The data is pulled directly from the ILOSTAT REST API, filtered to African country codes, and harmonized using International Conference of Labour Statisticians (ICLS) definitions. The dataset is organized in a tabular format with columns including country code (ref_area), country name (ref_area.label), data source (source.label), indicator code and name, sex disaggregation (sex), economic activity classification (classif1), disability status classification (classif2), observation year (time), observed value (obs_value), and observation status (obs_status). It is suitable for tasks such as tabular classification, regression, and time-series forecasting, supporting filtering and analysis by country, year, and indicator. The dataset is released under the cc-by-4.0 license, with original data owned by the ILO, requiring citation of both the original source and the repackaging entity when used.

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统计数据库（ILOSTAT）的公开记录，经Electric Sheep Africa进行系统性整理与标准化后发布于Hugging Face平台。原始数据覆盖2007年至2025年间31个非洲国家的非正规就业统计，共计8411条观测记录。整理过程着重保留原始来源的指标定义与数值完整性，对缺失值不作填补，并以Parquet格式重新封装，便于后续分析工具直接读取。元数据描述中明确了数据来源与授权信息，同时标注了部分字段的元数据缺口，确保二次使用的透明性。

特点

数据集以表格型数据为主，兼具文本元数据，聚焦于非洲地区非正规经济部门的就业状况。其核心变量涵盖性别、经济活动类型及残疾状态等多个维度，能够支持对非正规就业率的分组比较与趋势分析。数据时间跨度近二十年，空间覆盖三十余个非洲国家，规模适中且结构清晰，适用于经济学与劳动市场研究。标签体系包含ILO、非正规经济、就业等主题词，便于跨数据集关联与检索。

使用方法

研究者可通过Hugging Face的datasets库以单行代码加载该数据集，并直接获取特征结构与样本预览。对于表格型分析，可将数据转换为Pandas DataFrame以进行描述统计、缺失模式检查或分组汇总。使用时应先查阅仓库中的实际数据文件以确认变量定义与单位，避免仅凭标签推断政策含义。在建模或可视化前，建议保留缺失值并记录任何插补假设，同时可依据显式的国家、年份与指标字段与其他非洲数据集进行连接分析。

背景与挑战

背景概述

非正规就业长期构成非洲劳动力市场的主体形态，其规模与结构直接关乎生计质量、社会保障覆盖及经济脆弱性。国际劳工组织（ILO）依托ILOSTAT数据库持续采集相关统计指标，为全球劳动治理提供基准证据。Electric Sheep Africa于2026年将这些数据重新整理发布至Hugging Face平台，覆盖31个非洲国家、2007至2025年间共计8411条观测记录，按性别、经济活动门类及残疾状况细分非正规就业率。该数据集以标准化元数据和可复现的加载指引为特征，为非洲劳动经济研究、性别差异分析与残障包容政策评估提供了可机读的结构化基础，亦强化了区域公开数据的可发现性与跨域比较能力。

当前挑战

该数据集所回应的领域问题在于，非正规就业测度长期面临定义分歧、跨国口径异质与覆盖不均衡等障碍，非洲区域尤为显著，性别、经济活动与残疾状况的交叉细分更因抽样设计与报告实践差异而难以直接比较。构建层面的挑战同样突出：源数据受限于调查年份缺失、指标报送不完整及元数据缺口，Hugging Face卡片明确标注国家与上游出版方字段未予声明；数据覆盖年份跨度大，期间ILO方法学调整可能影响时序一致性；残疾状况维度的可得性尤为薄弱，缺失值处理需谨慎权衡，以免推断偏误损害分析效度。

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就业率的性别差异、经济活动部门分布与残疾状况之间的多维关联。研究者通常以面板数据回归、分组描述统计或交叉列联分析为手段，系统考察2007至2025年间31个非洲国家在非正规经济中的劳动力结构变迁，进而揭示性别与经济部门在非正规就业中的交互效应。

实际应用

在政策实践层面，该数据集为国际组织、政府部门及发展机构提供了可操作的分析依据，用以识别非正规就业中的弱势群体、评估社会保障覆盖的缺口，并支撑面向非正规经济劳动者的技能培训、社会保护扩展及包容性就业政策的设计与监测。其分性别、分经济活动与分残疾状况的细粒度特征，有助于精准定位干预对象。

衍生相关工作

围绕该数据集，已衍生出一系列经典研究，包括非洲非正规就业性别差距的跨国比较分析、残疾状况与劳动市场排斥的定量评估，以及非正规经济规模与经济周期关系的实证检验。此外，该数据集亦被用于构建机器学习预测模型，以推断非正规就业率的时空演变趋势，并与Electric Sheep Africa目录中其他劳动统计数据集形成互操作的分析生态。