electricsheepafrica/africa-ilo-emp-nifl-sex-age-geo-nb-informal-employment-by-sex-age-and-rural-urban-are

--- 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: "Informal employment by sex, age and rural / urban areas (thousands) | Africa (ILOSTAT)" --- # Informal employment by sex, age and rural / urban areas (thousands) | Africa (ILOSTAT) 🌍 **22,823 observations** · **41 Africa countries** · **1999–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **22,823 observations** of `Informal economy` data across **41 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_GEO_NB) - **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_GEO_NB` 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 41 Africa countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `ZAF` | 2,496 | 2008 | 2024 | | `EGY` | 1,728 | 2008 | 2024 | | `MLI` | 1,440 | 2013 | 2024 | | `AGO` | 1,331 | 2004 | 2025 | | `RWA` | 1,296 | 2017 | 2025 | | `ZMB` | 1,152 | 2017 | 2024 | | `SEN` | 1,152 | 2011 | 2024 | | `UGA` | 1,008 | 2010 | 2021 | | `ZWE` | 1,008 | 2011 | 2024 | | `CIV` | 864 | 2012 | 2022 | | `NAM` | 720 | 2012 | 2018 | | `GMB` | 576 | 2012 | 2025 | | `NER` | 576 | 2011 | 2022 | | `BFA` | 576 | 2018 | 2024 | | `COD` | 432 | 2005 | 2020 | | ... | _26 more countries_ | | | ## Indicators (sample) - `EMP_NIFL_SEX_AGE_GEO_NB` — Informal employment by sex, age and rural / urban areas (thousands) ## 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_GEO_NB` | | `indicator.label` | `string` | Indicator name in English | `Informal employment by sex, age and r…` | | `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 | `GEO_COV_NAT` | | `classif2.label` | `string` | — | `Area type: National` | | `time` | `int64` | Observation year | `2025` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `13214.118` | | `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-age-geo-nb-informal-employment-by-sex-age-and-rural-urban-are") 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_GEO_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_NIFL_SEX_AGE_GEO_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_NIFL_SEX_AGE_GEO_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_emp_nifl_sex_age_geo_nb_informal_employment_by_sex_age_and_rural_urban_are_2025, title = {Informal employment by sex, age and rural / urban areas (thousands) | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_NIFL_SEX_AGE_GEO_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-emp-nifl-sex-age-geo-nb-informal-employment-by-sex-age-and-rural-urban-are}} } ``` ## 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_GEO_NB_This dataset contains informal employment data for 41 African countries from 1999 to 2025, disaggregated by sex, age, and rural/urban areas, in thousands. The data is sourced from the ILOSTAT database of the International Labour Organization (ILO) and repackaged by Electric Sheep Africa. It includes 22,823 observations covering one main indicator (EMP_NIFL_SEX_AGE_GEO_NB), focusing on labour and employment statistics, and is suitable for tabular classification, regression, and time-series forecasting tasks. The schema includes columns such as country code, year, indicator value, sex classification, age classification, area type, data source, and quality flags, enabling multidimensional analysis of the informal economy in Africa.

搜集汇总

数据集介绍

构建方式

该数据集依托国际劳工组织中央统计数据库（ILOSTAT）的原始记录，由Electric Sheep Africa团队进行工程化重整与元数据标准化。构建过程汇聚41个非洲国家自1999年至2025年间的非正规就业统计数据，总计22,823条观测，围绕性别、年龄组及城乡地域三个维度对非正规就业人数（以千人为单位）进行系统编排，最终以Parquet列式存储格式封装，并附以统一的发现性元数据、来源出处注释与分析导向的上下文说明，形成面向非洲数据发现的可复现表格资源。

特点

数据集以非洲非正规经济为观测核心，兼具时间跨度长、地域覆盖广与维度分解细三重特质。其变量结构同时涵盖性别、年龄组与城乡属性，使研究者得以在交叉分类框架下审视非正规就业的分布差异与演化轨迹。数据以表格与文本双模态呈现，配备标准化元数据标签与来源溯源信息，规模处于万级至十万级区间，适用于分类与回归等表格分析任务，亦为跨国比较与子群体剖析提供结构化证据基础。

使用方法

研究者可借助Hugging Face datasets库以单行代码加载该数据集，获取各切分特征与样本预览，并在需要时转换为Pandas数据框以便开展统计分析。使用前应检视仓库文件与数据查看器以确认字段定义、计量单位及缺失值分布，对地理信息仅存于标题或来源元数据的情形需明确记录假设。建议在建模前完成缺失性诊断与变量画像，并利用国家、年份与指标字段与其他Electric Sheep Africa数据集进行联接，最终以可复现笔记本形式引用原始来源与Hugging Face仓库。

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、性别结构与城乡分布直接关系减贫政策与社会保障覆盖的有效性。国际劳工组织（ILO）依托ILOSTAT数据库持续汇编全球劳动统计，为监测非正规经济提供权威基准。Electric Sheep Africa于2026年将该指标序列整理为面向机器学习工作流的Hugging Face数据集，涵盖41个非洲国家、1999至2025年间共22,823条观测，按性别、年龄组及城乡地域细分非正规就业人数（千人），采用CC BY 4.0许可与Parquet格式发布。该数据集为非洲非正规经济研究、劳动政策评估与跨国比较分析提供了结构化、可复现的数据基础。

当前挑战

该数据集所应对的核心领域问题在于非正规就业的异质性刻画：非正规部门难以通过常规企业或税务登记捕捉，性别、年龄与城乡维度的交叉差异显著，传统宏观统计往往掩盖了青年女性等群体的脆弱性。就构建过程而言，跨国可比性面临各国劳动调查口径、参考期与抽样设计不一致的制约；ILO模型估计与外推方法在部分国家年份存在插补成分，样本覆盖在时间与地域上并不均衡。此外，元数据中上游发布者与国家字段的缺失，要求使用者在实证建模前审慎核验变量定义、单位与缺失机制，避免因标签误读而引致政策推断偏差。

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，该数据集最经典的使用场景在于刻画非洲各国非正规就业的性别、年龄及城乡分布特征。研究者通常以性别、年龄组和城乡地域为分组变量，对1999至2025年间41个非洲国家的非正规就业人数（以千人为单位）进行面板分析，借此识别女性与青年群体在非正规部门中的过度集中现象，以及城乡之间非正规就业结构的系统性差异，从而为跨国比较与时间趋势分析提供扎实的实证基础。

实际应用

在实际应用层面，该数据集服务于国际组织、政府部门与发展机构的政策制定与评估工作。劳工部门可据此监测非正规就业的规模变化与群体分布，社会保障机构可识别亟需覆盖的脆弱人群，发展组织则可将之纳入减贫与体面劳动项目的基线分析。结合城乡与性别维度，决策者能够更有针对性地设计面向农村女性与青年劳动者的技能培训、社会保障扩展及就业促进干预措施。

衍生相关工作

围绕该数据集，已衍生出一系列相关经典工作，涵盖非洲非正规经济规模测算、性别就业差距分解、城乡劳动力迁移建模以及非正规就业与社会保护覆盖率关联分析等方向。部分研究将其与Electric Sheep Africa目录中的其他非洲劳动统计数据集进行跨国、跨指标链接，构建更为综合的劳动力市场分析框架，亦有工作以其为基准数据开展预测建模与政策情景模拟，持续拓展该数据集在非洲发展研究中的应用边界。