electricsheepafrica/africa-ilo-emp-nifl-sex-age-eco-rt-informal-employment-rate-by-sex-age-and-economic-a

--- license: cc-by-4.0 language: - en task_categories: - tabular-classification - tabular-regression - time-series-forecasting multilinguality: monolingual size_categories: - 100K<n<1M tags: - tabular - africa - ilostat - informal-economy - ilo - labour - employment pretty_name: "Informal employment rate by sex, age and economic activity (%) | Africa (ILOSTAT)" --- # Informal employment rate by sex, age and economic activity (%) | Africa (ILOSTAT) 🌍 **172,996 observations** · **45 Africa countries** · **1999–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **172,996 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_NIFL_SEX_AGE_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=EMP_NIFL_SEX_AGE_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 45 Africa countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `ZAF` | 23,222 | 2000 | 2024 | | `EGY` | 11,886 | 2008 | 2024 | | `MUS` | 11,319 | 2012 | 2024 | | `AGO` | 8,965 | 2004 | 2025 | | `MLI` | 8,780 | 2013 | 2024 | | `RWA` | 8,446 | 2017 | 2025 | | `SEN` | 6,853 | 2011 | 2024 | | `ZMB` | 6,685 | 2017 | 2024 | | `ZWE` | 6,657 | 2011 | 2024 | | `BWA` | 6,269 | 2006 | 2024 | | `UGA` | 5,866 | 2010 | 2021 | | `CIV` | 5,192 | 2012 | 2022 | | `NAM` | 4,567 | 2012 | 2018 | | `GMB` | 3,674 | 2012 | 2025 | | `NER` | 3,248 | 2011 | 2022 | | ... | _30 more countries_ | | | ## Indicators (sample) - `EMP_NIFL_SEX_AGE_ECO_RT` — Informal employment rate by sex, age and economic activity (%) ## 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_NIFL_SEX_AGE_ECO_RT` | | `indicator.label` | `string` | Indicator name in English | `Informal employment rate by sex, age …` | | `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 | `ECO_SECTOR_TOTAL` | | `classif2.label` | `string` | — | `Economic activity (Broad sector): Total` | | `time` | `int64` | Observation year | `2025` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `94.488` | | `obs_status` | `string` | Observation status flag (e.g. provisional, unreliable) | `U` | | `obs_status.label` | `string` | — | `Unreliable` | | `note_classif` | `string` | — | `C5:1869` | | `note_classif.label` | `string` | — | `Nonstandard economic activity: Includ…` | | `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-age-eco-rt-informal-employment-rate-by-sex-age-and-economic-a") 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_AGE_ECO_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_NIFL_SEX_AGE_ECO_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_NIFL_SEX_AGE_ECO_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_emp_nifl_sex_age_eco_rt_informal_employment_rate_by_sex_age_and_economic_a_2025, title = {Informal employment rate by sex, age and economic activity (%) | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_NIFL_SEX_AGE_ECO_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-emp-nifl-sex-age-eco-rt-informal-employment-rate-by-sex-age-and-economic-a}} } ``` ## 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_AGE_ECO_RT_This dataset contains informal employment rate data for 45 African countries from 1999 to 2025, disaggregated by sex, age, and economic activity. It includes 172,996 observations covering one core indicator: EMP_NIFL_SEX_AGE_ECO_RT (Informal employment rate by sex, age and economic activity (%)). The data is sourced from the International Labour Organization (ILO) ILOSTAT database, retrieved via API and filtered to African country codes, with fields such as country code, year, observed value, data source, classification variables (e.g., sex and age groups), and quality notes. The dataset is suitable for tabular classification, regression, and time-series forecasting tasks, designed to provide machine learning-ready data for labor market research in Africa.

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）的中央统计数据库ILOSTAT，由Electric Sheep Africa进行标准化重包装而成。原始数据涵盖1999年至2025年间45个非洲国家的非正规就业统计数据，共计172,996条观测记录，围绕性别、年龄和经济活动部门等维度系统编码非正规就业率指标。Electric Sheep Africa在保留原始统计口径的基础上，统一了元数据描述、变量标签和文件格式，以Parquet列式存储发布，并补充了溯源说明、缺失值处理提示及面向分析者的使用指南，从而在原始统计资料与机器学习工作流之间建立起可复现的桥梁。

特点

数据集在时空覆盖上兼具广度与纵深，横跨四分之一个世纪的年度观测，覆盖非洲大陆45个国家，为比较不同经济体非正规就业结构的演变提供了充足样本。其核心特色在于多维度的交叉分类体系，将性别、年龄组与经济activity类别同时纳入统计框架，使研究者得以剖析非正规就业在人口群体与产业部门间的异质性分布。数据以表格与文本双模态呈现，文件采用Parquet格式，便于高效读取与类型保持。元数据标注完整，附有明确的知识共享署名许可，且保留了缺失值以供研究者依据具体分析目标自行裁定插补策略。

使用方法

研究者可通过Hugging Face的datasets库以一行代码加载该数据集，随后利用内置的特征查看功能检视各变量类型与结构，并借助to_pandas方法将表格数据转换为数据框以适配常规分析流程。在建模之前，建议先行探查各变量的缺失模式与分布特征，结合显式的国家、年份及指标字段进行子集筛选或跨数据集联结。使用时应参照README中的溯源说明与质量提示，在数据文件中核实变量定义与计量单位，避免仅凭标签推断政策含义。所有基于该数据集的分析成果宜同时引用原始ILOSTAT来源与Electric Sheep Africa的Hugging Face仓库，以确保研究过程的可追溯性。

背景与挑战

背景概述

非正式就业作为发展中国家劳动力市场的普遍特征，其规模与结构直接关乎生计质量与社会保护覆盖面。国际劳工组织（ILO）长期致力于劳动统计标准化，ILOSTAT数据库即为全球劳动统计的权威枢纽。在此背景下，Electric Sheep Africa于2026年前后对ILOSTAT中非洲区域的非正式就业率数据进行系统性重包装，形成涵盖45个非洲国家、1999至2025年、共计172,996条观测的专题数据集。该数据集按性别、年龄与经济活动部门细分非正式就业率，填补了非洲非正式经济跨时空可比数据的空白，为劳动经济学、发展研究与政策评估提供了关键数据基座。

当前挑战

该数据集所应对的领域问题在于非洲非正式就业的测度与跨时空可比性。非正式就业定义在不同国家统计实践中长期存在口径分歧，非农自雇、家庭帮工与无薪学徒等类别的识别标准不一，导致跨国比较面临概念等价性难题。构建过程中，数据团队需协调ILOSTAT原始指标与Electric Sheep Africa元数据标准之间的映射，处理部分国家年份缺失、性别与年龄分组不完整、经济部门分类粗糙等结构性缺陷，同时避免在不明确单位与定义的情况下对缺失值进行过度插补，确保下游分析的可重复性与统计推断的严谨性。

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，非正规就业率的测度与比较长期依赖跨国可比数据，该数据集凭借国际劳工组织标准化统计框架，覆盖四十五个非洲国家、一百七十二万九千九百六十六个观测值，横跨一九九九至二零二五年，成为刻画非洲劳动力市场非正规化结构性特征的经典面板数据源。研究者通常以性别、年龄组与经济活跃部门为分层维度，构建非正规就业率的时空分布图谱，进而识别青年群体、女性劳动者及特定产业部门中非正规就业的集聚模式与演化轨迹。

衍生相关工作

围绕该数据集已衍生出一系列具有影响力的研究工作，涵盖非正规就业与贫困脆弱性的关联分析、非正规经济对社会保障覆盖率的抑制效应评估、以及非洲区域一体化背景下劳动力市场制度趋同的跨国比较。部分研究进一步将该数据与世界银行发展指标、非洲晴雨表调查等异源数据链接，构建多层级模型，拓展了非正规就业研究的方法论边界，并推动非洲劳动统计数据的开放共享与可复现分析范式。

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治理成为全球发展议题焦点的背景下，该数据集依托ILOSTAT权威统计框架，涵盖45个非洲国家、1999至2025年间逾17万条按性别、年龄及经济活动分类的非正规就业率观测记录，为探究非正规就业的性别差异、年龄分层与行业异质性提供了高粒度面板数据支撑。当前前沿研究正将该数据集与非洲区域一体化、数字经济渗透及社会保障扩展等热点议题相衔接，用于识别非正规就业的时空演变规律与结构性驱动因素，进而为包容性劳动力政策设计、女性经济赋权及非正规部门正规化路径提供实证依据，对推动非洲可持续就业目标与减贫战略具有重要的学术价值与政策意义。