electricsheepafrica/africa-ilo-emp-nifl-sex-eco-nb-informal-employment-by-sex-and-economic-activity-t

--- 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 and economic activity (thousands) | Africa (ILOSTAT)" --- # Informal employment by sex and economic activity (thousands) | Africa (ILOSTAT) 🌍 **18,219 observations** · **45 Africa countries** · **1999–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **18,219 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_ECO_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_ECO_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 45 Africa countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `ZAF` | 2,257 | 2000 | 2024 | | `EGY` | 1,226 | 2008 | 2024 | | `MUS` | 1,224 | 2012 | 2024 | | `MLI` | 925 | 2013 | 2024 | | `AGO` | 918 | 2004 | 2025 | | `RWA` | 888 | 2017 | 2025 | | `SEN` | 742 | 2011 | 2024 | | `ZMB` | 740 | 2017 | 2024 | | `ZWE` | 713 | 2011 | 2024 | | `BWA` | 709 | 2006 | 2024 | | `UGA` | 638 | 2010 | 2021 | | `CIV` | 543 | 2012 | 2022 | | `NAM` | 509 | 2012 | 2018 | | `GMB` | 391 | 2012 | 2025 | | `NER` | 347 | 2011 | 2022 | | ... | _30 more countries_ | | | ## Indicators (sample) - `EMP_NIFL_SEX_ECO_NB` — Informal employment by sex and economic activity (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_ECO_NB` | | `indicator.label` | `string` | Indicator name in English | `Informal employment by sex and econom…` | | `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) | `13214.118` | | `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-eco-nb-informal-employment-by-sex-and-economic-activity-t") 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_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_NIFL_SEX_ECO_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_NIFL_SEX_ECO_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_emp_nifl_sex_eco_nb_informal_employment_by_sex_and_economic_activity_t_2025, title = {Informal employment by sex and economic activity (thousands) | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_NIFL_SEX_ECO_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-emp-nifl-sex-eco-nb-informal-employment-by-sex-and-economic-activity-t}} } ``` ## 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_NB_This dataset is named Informal employment by sex and economic activity (thousands) | Africa (ILOSTAT) and is a tabular dataset on informal employment in Africa. It contains 18,219 observations across 45 African countries, spanning the years 1999 to 2025, with the core indicator being EMP_NIFL_SEX_ECO_NB—informal employment by sex and economic activity in thousands. The data is sourced from the International Labour Organization (ILO) ILOSTAT database, retrieved via its REST API and filtered to African countries. The dataset includes detailed columns: country code (ref_area), country name (ref_area.label), source code and label (source, source.label), indicator code and label (indicator, indicator.label), sex disaggregation (sex, sex.label, including total, male, female), first classification variable for economic activity (classif1, classif1.label), observation year (time), observed value (obs_value), observation status flag (obs_status, obs_status.label), and various note columns. Data is published at annual frequency, with ILO harmonizing raw survey microdata using International Conference of Labour Statisticians (ICLS) definitions for comparability. It is suitable for tasks such as tabular classification, regression, and time-series forecasting, primarily used for research in labor markets, informal economy, and development economics.

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统计数据库（ILOSTAT）所发布的非正规就业统计数据，由Electric Sheep Africa以CC BY 4.0许可协议重新封装为Hugging Face平台上的Parquet格式数据集。其构建过程以非洲区域为地理边界，抽取45个非洲国家在1999至2025年间按性别与经济activity分类的非正规就业观测值，共计18,219条记录，并保留原始指标定义与单位，未施加额外的插补或变换处理。

特点

在领域层面，非正规就业构成发展中国家劳动力市场分析的核心议题，本数据集以性别与经济activity为双重分类维度，覆盖非洲大陆近二十七年时间跨度，为研究者提供跨国、跨时间的比较基础。数据以表格与文本双模态存储，体量介于一万至十万条之间，具备中等规模特征，适合作为计量分析与机器学习建模的输入源。

使用方法

研究者可经由Hugging Face datasets库以两行代码加载该数据集，获取默认划分后，使用features属性检视字段结构，或调用to_pandas方法将之转换为数据框以便统计计算。在建模之前，应当核验各变量的定义与计量单位，保持缺失值原状，并依据国家、年份与指标字段与其他Electric Sheep Africa数据集进行连接，以支持可复现的分析流程。

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、性别结构与行业分布直接关乎生计脆弱性与社会保障覆盖。国际劳工组织旗舰数据库ILOSTAT虽持续汇编相关指标，但原始数据在非洲语境下的可发现性与可复用性受限。2026年，Electric Sheep Africa将ILOSTAT中按性别与经济活动划分的非正规就业数据整理发布为标准化数据集，覆盖45个非洲国家、18219条观测，时间跨度自1999年至2025年。该数据集以开放元数据与Parquet格式降低了非洲劳动经济研究的准入门槛，为非正规经济测度、性别就业差距及结构转型分析提供了可比较的基础证据。

当前挑战

该数据集所应对的领域问题，在于非正规就业统计长期面临定义纷繁、跨国口径不一与时间序列断裂的困境，跨国比较与趋势识别因此困难重重。构建过程中的挑战同样显著：原始ILOSTAT数据经多轮标准化与元数据重述，国别与上游发布者字段存在缺失，地理仅由标题或来源隐含，需依赖显式国家、年份与指标字段进行校验；缺失值分布与抽样方法未充分披露，直接建模可能引入偏差。如何在保持源数据忠实性的同时，确保变量定义、单位与分类体系在跨年度、跨经济体分析中的一致性，构成该数据集持续面临的方法论考验。

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，该数据集堪称洞察非洲劳动力市场结构性特征的经典资源。其最为经典的使用场景在于按性别与经济 activity 对非正规就业规模进行系统性制表与比较分析。研究者借助该数据集可考察 1999 至 2025 年间 45 个非洲国家不同经济部门中非正规就业的性别分布差异，从而刻画非正规经济活动的行业集聚模式及其时序演变轨迹。这一场景充分体现了数据集在劳动统计标准化框架下的基础性价值，为后续计量建模提供了坚实的数据基座。

实际应用

在实际应用层面，该数据集为政策制定者、国际发展机构及社会企业提供了评估非洲非正规就业脆弱性的操作化工具。劳工部门可据此识别非正规就业高度集中的经济部门与性别群体，进而设计更具靶向性的社会保障与技能培训干预措施。发展金融机构则可将其纳入国别风险评估模型，用以研判非正规经济波动对宏观就业稳定的传导效应。数据集的公开许可与 parquet 格式亦降低了分析门槛，便于一线从业者快速构建监测仪表盘。

衍生相关工作

围绕该数据集已衍生出一系列具有代表性的后续工作，涵盖非正规就业的性别分层研究、非洲结构转型进程中的劳动再配置分析以及非正规经济与贫困脆弱性的关联探索。部分研究将其与 Electric Sheep Africa 目录中的其他劳动统计数据集进行跨国面板拼接，拓展了比较劳动经济学的研究边界。这些衍生工作不仅验证了数据集在元数据标准化方面的工程价值，也促进了非洲劳动统计数据的开放科学实践与可复现研究文化的发展。