electricsheepafrica/africa-ilo-emp-pifl-sex-est-nb-employment-outside-the-formal-sector-by-sex-and-es

--- 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: "Employment outside the formal sector by sex and establishment size (thousands) | Africa (ILOSTAT)" --- # Employment outside the formal sector by sex and establishment size (thousands) | Africa (ILOSTAT) 🌍 **4,988 observations** · **40 Africa countries** · **2000–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **4,988 observations** of `Informal economy` data across **40 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=EMP_PIFL_SEX_EST_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_PIFL_SEX_EST_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 40 Africa countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `ZAF` | 975 | 2000 | 2024 | | `MLI` | 322 | 2013 | 2024 | | `EGY` | 320 | 2008 | 2023 | | `AGO` | 300 | 2004 | 2025 | | `SEN` | 262 | 2015 | 2024 | | `BWA` | 245 | 2006 | 2024 | | `ZMB` | 174 | 2017 | 2024 | | `RWA` | 156 | 2017 | 2020 | | `SYC` | 142 | 2019 | 2024 | | `CIV` | 138 | 2012 | 2019 | | `NAM` | 132 | 2012 | 2018 | | `GMB` | 117 | 2012 | 2025 | | `MDG` | 116 | 2012 | 2022 | | `ZWE` | 110 | 2011 | 2019 | | `BFA` | 108 | 2018 | 2024 | | ... | _25 more countries_ | | | ## Indicators (sample) - `EMP_PIFL_SEX_EST_NB` — Employment outside the formal sector by sex and establishment size (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_PIFL_SEX_EST_NB` | | `indicator.label` | `string` | Indicator name in English | `Employment outside the formal sector …` | | `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.) | `EST_AGGREGATE_TOTAL` | | `classif1.label` | `string` | — | `Establishment size (Aggregate): Total` | | `time` | `int64` | Observation year | `2025` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `11270.18` | | `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-emp-pifl-sex-est-nb-employment-outside-the-formal-sector-by-sex-and-es") 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_PIFL_SEX_EST_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_PIFL_SEX_EST_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_PIFL_SEX_EST_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_emp_pifl_sex_est_nb_employment_outside_the_formal_sector_by_sex_and_es_2025, title = {Employment outside the formal sector by sex and establishment size (thousands) | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_PIFL_SEX_EST_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-emp-pifl-sex-est-nb-employment-outside-the-formal-sector-by-sex-and-es}} } ``` ## 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=EMP_PIFL_SEX_EST_NB_This dataset contains informal economy employment data for Africa from the International Labour Organization (ILO) ILOSTAT database, specifically the indicator Employment outside the formal sector by sex and establishment size (thousands). It covers 40 African countries, spans the years 2000 to 2025, and includes 4,988 observations. Data is sourced via the ILOSTAT REST API, harmonized, and includes fields such as country code, year, sex disaggregation, establishment size classification, observed values, and data sources, suitable for tabular classification, regression, or time-series forecasting tasks.

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），系全球劳动统计领域的权威数据源。Electric Sheep Africa对原始数据进行了标准化封装与元数据补全，经由Hugging Face平台以Parquet格式重新发布。数据集覆盖40个非洲国家，时间跨度自2000年至2025年，共计4,988条观测记录，聚焦于非正规部门就业的性别与机构规模维度。构建过程中保留了原始统计口径，遵循CC BY 4.0开放许可协议。

特点

数据集以表格与文本双模态呈现，规模处于1K至10K区间，属于经济学与金融领域。其核心特征在于以性别和机构规模为分类维度，系统刻画非洲各国非正规部门就业的分布格局。数据经Electric Sheep Africa标准化元数据管理，附有溯源说明与分析指引，语言为英语。缺失值处理需依赖研究者自行判定，未提供预设填补方案。

使用方法

研究者可通过Hugging Face datasets库调用load_dataset函数直接加载数据集，并利用to_pandas方法转换为数据框以开展分析。建议在建模前先行检视数据模式与缺失情况，明确国家列及年份字段后再进行跨区域或时序比较。若需与其他Electric Sheep Africa数据集联合使用，应基于显式国家、年份与指标字段进行匹配。

背景与挑战

背景概述

非正规经济就业的测度长期构成发展经济学与劳动经济学的核心议题。国际劳工组织（ILO）依托ILOSTAT数据库，系统采集全球劳动力市场统计指标，为跨国比较提供基准。非洲地区非正规部门吸纳了绝大多数劳动力，其规模与结构的精确刻画对减贫政策、社会保障扩展及性别平等评估具有关键意义。Electric Sheep Africa于2026年将ILOSTAT中按性别与机构规模分列的非正规部门外就业数据（以千人为单位）标准化为Hugging Face数据集，覆盖40个非洲国家、2000至2025年间共4988条观测记录。该数据集以CC BY 4.0许可发布，为非洲劳动市场研究提供了可复现的表格化数据基础，并借助标准化元数据增强了数据发现与跨源整合能力。

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的固有复杂性：非正规部门界定标准在不同国家与时期存在差异，机构规模的分类口径亦未完全统一，加之非洲各国劳动力调查频率与覆盖范围参差不齐，导致跨国比较与时间序列分析面临测量误差与缺失值挑战。在构建过程中，元数据标准化需处理来源字段缺失问题（如国家与上游发布机构信息未完整声明），并需在保留原始数值与提供可分析格式之间取得平衡。此外，数据以千人为单位聚合呈现，限制了对个体层面异质性的推断，分析者须在建模前明确单位定义与缺失机制，避免因标签含义不清而产生误导性政策解读。

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叉领域，该数据集凭借其覆盖四十个非洲国家、横跨四分之一世纪的四千余条观测，成为刻画非正规部门就业性别结构与经营规模分布的基准性资源。研究者常以其为基础，开展按性别与机构规模分层的非正规就业存量测算，并借助面板回归、分组比较与趋势分解等方法，揭示不同规模经营单元中男女劳动者参与程度的时序演变与国别差异。

衍生相关工作

围绕该数据集，已衍生出一系列面向非洲劳动力市场的分析与建模工作。研究者将其与ILOSTAT其他就业指标及Electric Sheep Africa目录中的非正规经济数据集进行跨国别、跨年度联结，构建更完整的非正规就业画像；也有工作以其为基准，对非正规就业统计口径进行敏感性检验，或训练表格回归模型以预测缺失年份与国别的就业水平，推动非洲劳动统计数据的可计算化与可复用化。

数据集最近研究

最新研究方向

在非洲非正规经济部门就业结构研究中，该数据集凭借其覆盖40个非洲国家、时间跨度达2000至2025年的4988条观测记录，正成为探究性别差异与经营规模对非正规就业影响的关键实证基础。当前前沿研究聚焦于利用此类长期面板数据，结合ILOSTAT的标准化指标，分析非正规就业中的性别隔离现象及其与企业规模分布的内在关联，进而评估结构转型政策对弱势群体就业质量的差异化效应。该数据集亦推动了非洲劳动力市场元数据标准化与可复现分析框架的构建，为跨国比较研究提供了可靠的数据底座，对制定包容性就业政策具有重要的参考意义。