electricsheepafrica/africa-ilo-ees-tees-sex-ifl-ec2-nb-employees-by-sex-informal-formal-job-and-economic

--- 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: "Employees by sex, informal/formal job and economic activity - ISIC level 2 (thousands) | Africa (ILOSTAT)" --- # Employees by sex, informal/formal job and economic activity - ISIC level 2 (thousands) | Africa (ILOSTAT) 🌍 **63,759 observations** · **42 Africa countries** · **2000–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **63,759 observations** of `Informal economy` data across **42 Africa countries**, spanning **2000–2025**, 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=EES_TEES_SEX_IFL_EC2_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=EES_TEES_SEX_IFL_EC2_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 42 Africa countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `ZAF` | 12,119 | 2000 | 2024 | | `EGY` | 7,546 | 2008 | 2024 | | `MUS` | 6,083 | 2012 | 2024 | | `RWA` | 3,387 | 2017 | 2025 | | `ZWE` | 2,890 | 2011 | 2024 | | `AGO` | 2,797 | 2019 | 2025 | | `ZMB` | 2,617 | 2017 | 2024 | | `NAM` | 2,234 | 2012 | 2018 | | `MLI` | 2,171 | 2013 | 2024 | | `SEN` | 1,984 | 2011 | 2024 | | `BWA` | 1,755 | 2019 | 2024 | | `SYC` | 1,362 | 2019 | 2024 | | `UGA` | 1,278 | 2010 | 2021 | | `TZA` | 1,263 | 2014 | 2024 | | `CIV` | 1,219 | 2012 | 2019 | | ... | _27 more countries_ | | | ## Indicators (sample) - `EES_TEES_SEX_IFL_EC2_NB` — Employees by sex, informal/formal job and economic activity - ISIC level 2 (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 | `EES_TEES_SEX_IFL_EC2_NB` | | `indicator.label` | `string` | Indicator name in English | `Employees by sex, informal/formal job…` | | `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.) | `IFL_NATURE_TOTAL` | | `classif1.label` | `string` | — | `Nature of job: Total` | | `classif2` | `string` | Second classification variable where applicable | `EC2_ISIC4_TOTAL` | | `classif2.label` | `string` | — | `Economic activity (ISIC-Rev.4), 2 dig…` | | `time` | `int64` | Observation year | `2025` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `3788.156` | | `obs_status` | `string` | Observation status flag (e.g. provisional, unreliable) | `U` | | `obs_status.label` | `string` | — | `Unreliable` | | `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-ees-tees-sex-ifl-ec2-nb-employees-by-sex-informal-formal-job-and-economic") 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"] == "EES_TEES_SEX_IFL_EC2_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EES_TEES_SEX_IFL_EC2_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EES_TEES_SEX_IFL_EC2_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_ees_tees_sex_ifl_ec2_nb_employees_by_sex_informal_formal_job_and_economic_2025, title = {Employees by sex, informal/formal job and economic activity - ISIC level 2 (thousands) | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EES_TEES_SEX_IFL_EC2_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-ees-tees-sex-ifl-ec2-nb-employees-by-sex-informal-formal-job-and-economic}} } ``` ## 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=EES_TEES_SEX_IFL_EC2_NB_This dataset contains informal economy data for 42 African countries from 2000 to 2025, with 63,759 observations covering 1 distinct indicator. The data is sourced from the International Labour Organization (ILO) ILOSTAT database, focusing on the informal economy. The specific indicator is Employees by sex, informal/formal job and economic activity - ISIC level 2 (thousands). The dataset includes fields such as country code, country name, data source, indicator code, indicator name, sex disaggregation (total, male, female), classification variables, observation year, observed value, observation status, and related notes. The data is provided at annual frequency and has been harmonized by the ILO using International Conference of Labour Statisticians (ICLS) definitions. The dataset is suitable for tasks such as tabular classification, tabular regression, and time-series forecasting.

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ILOSTAT中央统计数据库，由Electric Sheep Africa进行元数据标准化与结构化封装。构建过程以国际标准行业分类第二级（ISIC level 2）为经济活动划分依据，系统收录非洲42个国家2000至2025年间按性别、非正规/正规就业身份及经济活动门类交叉分类的雇员人数统计，共计63,759条观测记录。数据以千人为计量单位，经标准化元数据标注、来源溯源说明及面向分析者的上下文注释后，最终以Parquet格式发布，形成可供非洲数据发现与研究复用的表格化资源。

特点

该数据集聚焦非洲劳动力市场中非正规经济这一核心议题，具有显著的跨国家、跨时间与多维分组特征。其覆盖范围横跨42个非洲国家，时间序列长达二十余年，能够支撑对性别差异、就业正规化程度与经济部门结构之间交互关系的细致刻画。数据以表格与文本双重模态呈现，包含标准化的主题标签与地理覆盖说明，适配分类与回归任务。作为Electric Sheep Africa开放数据目录的组成部分，该数据集在元数据层面经过统一整理，便于与其他非洲数据集按国家、年份和指标字段进行关联与比较分析。

使用方法

使用者可通过Hugging Face datasets库以一行代码加载该数据集，获取包含数据特征与样本切片的字典对象。当数据结构为表格形态时，可调用to_pandas方法转换为DataFrame，进而开展缺失值检查、变量分布刻画与地理及时间维度的分组统计。在建模或分析前，建议优先查阅仓库中的数据文件以确认变量定义、计量单位与分类编码，并保留缺失值的原始状态直至确立合理的插补规则。若需扩展分析，可依据显式的国家、年份与指标字段与其他Electric Sheep Africa数据集进行连接，并在可复现的笔记本中标注原始来源与仓库引用信息。

背景与挑战

背景概述

国际劳工组织长期致力于监测全球劳动力市场结构变迁，其中非正规经济就业的性别差异与行业分布是发展经济学关注的核心议题。该数据集由Electric Sheep Africa于2026年基于ILOSTAT中央统计数据库整理发布，覆盖42个非洲国家、2000至2025年间共63,759条观测记录，以ISIC第二级经济活动分类为框架，系统刻画了不同性别在正规与非正规部门中的就业规模。作为非洲劳动力市场结构化证据的集成，该数据集为比较区域就业模式、评估非正规经济规模及其性别维度提供了可复现的数据基础，对劳动经济学与非洲发展研究具有重要参考价值。

当前挑战

该数据集所回应的领域问题在于非洲非正规就业统计长期面临的碎片化与口径不一致困境，各国劳动力调查在非正规部门界定、经济活动分类精度及性别变量编码上存在显著异质性，导致跨国比较与趋势分析面临系统性偏差。构建过程中，原始ILOSTAT数据需经过指标对齐、缺失值处理与元数据标准化，而部分国家年份数据稀疏、行业分类粒度参差，加之Inventory中country与upstream_publisher字段缺失，进一步增加了溯源验证与面板构建的难度。如何在保留原始统计语义的前提下实现跨国家、跨时间的可比性，是该数据集持续面临的核心挑战。

常用场景

经典使用场景

在劳动经济学与非正规经济研究的交叠地带，该数据集凭借国际劳工组织（ILO）统计数据库（ILOSTAT）的权威背书，成为刻画非洲劳动力市场结构性特征的经典素材。其最典型的使用方式，是以性别与非正规／正规就业身份为双重切分维度，结合ISIC二级经济活动分类，对42个非洲国家2000至2025年间的就业人数展开面板分析。研究者常借此考察非正规就业在各国经济部门中的分布密度、性别差异及其随时间的演化轨迹，并与跨国宏观指标形成对照，从而构建可复现的描述性与解释性工作流。

解决学术问题

该数据集有效回应了非洲非正规经济研究中长期存在的证据碎片化难题。以往关于非正规就业规模与性别差异的讨论，常受制于国别统计口径不一、时间序列断裂与部门分类粗糙等局限，难以支撑可靠的跨国比较与趋势推断。此数据集以统一化的ISIC二级部门框架与性别维度重新组织ILOSTAT观测值，使研究者得以检验非正规／正规就业结构变迁、性别就业分化以及经济部门异质性等命题，为非洲劳动力市场制度分析提供了可验证的量化基础，其意义在于将分散的官方统计转化为可供学术推演的一致证据层。

衍生相关工作

围绕该数据集及其所属的Electric Sheep Africa元数据目录，已衍生出一系列面向非洲开放数据的工程与研究工作。Electric Sheep Africa将其纳入标准化元数据清单，推动了跨数据集以国家、年份与指标字段的联接分析，进而催生了非洲劳动力市场比较研究、非正规经济指标整合以及元数据驱动的数据发现实践。此类工作亦为后续结合ILOSTAT其他指标开展多变量建模、构建非洲就业结构知识图谱等探索提供了可复用的数据底座与引用规范。