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

--- 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 public/private sector (thousands) | Africa (ILOSTAT)" --- # Employment outside the formal sector by sex and public/private sector (thousands) | Africa (ILOSTAT) 🌍 **1,297 observations** · **45 Africa countries** · **1999–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **1,297 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_PIFL_SEX_INS_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_INS_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` | 150 | 2000 | 2024 | | `MUS` | 80 | 2012 | 2024 | | `EGY` | 77 | 2008 | 2024 | | `AGO` | 62 | 2004 | 2025 | | `UGA` | 62 | 2010 | 2021 | | `SEN` | 61 | 2011 | 2024 | | `BWA` | 60 | 2006 | 2024 | | `MLI` | 60 | 2013 | 2024 | | `ZWE` | 58 | 2011 | 2024 | | `RWA` | 57 | 2017 | 2025 | | `ZMB` | 48 | 2017 | 2024 | | `CIV` | 39 | 2012 | 2022 | | `NAM` | 32 | 2012 | 2018 | | `SYC` | 30 | 2019 | 2024 | | `BFA` | 27 | 2018 | 2024 | | ... | _30 more countries_ | | | ## Indicators (sample) - `EMP_PIFL_SEX_INS_NB` — Employment outside the formal sector by sex and public/private sector (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_INS_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.) | `INS_SECTOR_TOTAL` | | `classif1.label` | `string` | — | `Institutional sector: 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-ins-nb-employment-outside-the-formal-sector-by-sex-and-pu") 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_INS_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_PIFL_SEX_INS_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_PIFL_SEX_INS_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_emp_pifl_sex_ins_nb_employment_outside_the_formal_sector_by_sex_and_pu_2025, title = {Employment outside the formal sector by sex and public/private sector (thousands) | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_PIFL_SEX_INS_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-emp-pifl-sex-ins-nb-employment-outside-the-formal-sector-by-sex-and-pu}} } ``` ## 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_INS_NB_This dataset contains 1,297 observations of informal economy data across 45 Africa countries, spanning 1999–2025, covering 1 distinct indicator (EMP_PIFL_SEX_INS_NB). It provides employment outside the formal sector by sex and public/private sector in thousands, sourced from the International Labour Organizations ILOSTAT database. The dataset includes schema details such as country codes, source information, indicator breakdowns, time, and observed values, with disaggregation by sex. It is designed for tabular classification, regression, or time-series forecasting tasks, and includes data quality notes and usage examples.

搜集汇总

数据集介绍

构建方式

该数据集乃基于国际劳工组织中央统计数据库（ILOSTAT）所发布的非正规部门就业统计数据，经Electric Sheep Africa团队系统性整理与标准化重制而成。其原始数据源自各国劳动力调查与官方统计报送，涵盖按性别及公共/私营部门划分的非正规就业人数（以千计）。构建过程中，团队从上游元数据清单中提取结构化字段，统一变量命名与格式规范，并以Parquet列式存储格式封装，形成适用于非洲区域经济劳动分析的即用型数据资源。

特点

该数据集汇辑了45个非洲国家自1999年至2025年间共1297条观测记录，聚焦于非正规经济部门的就业规模，按性别及公共/私营部门双重维度加以细分。其数据以表格形式呈现，兼具文本模态，规模适中，属于经济学与金融领域。数据集采用标准化元数据标注，带有非洲区域标签及开放数据许可（CC BY 4.0），便于跨数据集关联与比较分析，为探究非洲非正规就业结构提供了一手量化依据。

使用方法

研究者可借助Hugging Face datasets库直接加载该数据集，通过load_dataset函数获取数据对象并检视其结构、特征与样本。若需进一步分析，可将表格分支转换为Pandas DataFrame以便执行统计建模或可视化。使用之际，应先行审视模式与缺失值分布，明确国家、年份及指标列之含义与单位；在后续建模或政策解读前，须回溯ILOSTAT原始文献以核实变量定义，并保留缺失值以待合理插补，确保分析的可复现性与严谨性。

背景与挑战

背景概述

非正规经济部门的就业规模与结构长期以来是劳动经济学与发展经济学关注的核心议题，尤其在非洲大陆，非正规就业构成了绝大多数劳动者赖以生存的经济形态。国际劳工组织（ILO）依托其ILOSTAT中央统计数据库，系统汇编了按性别及公共/私营部门划分的非正规部门外就业数据。Electric Sheep Africa于2026年将这一非洲区域子集标准化为机器学习就绪的数据集，涵盖45个非洲国家、1999至2025年间1297条观测记录，旨在为非洲数据发现提供规范化的元数据、溯源说明与分析指引，推动劳动市场比较研究与可复现工作流的构建。

当前挑战

该数据集所回应的领域问题在于非正规就业统计的跨国可比性与时序连续性，其挑战根植于各国非正规部门定义、调查口径与部门分类标准的显著异质性，致使跨年比较与建模面临结构性偏误风险。构建过程中的挑战同样突出：元数据清单显示国家标识与上游发布者字段存在缺失，ISO3覆盖范围未予声明，需在缺乏明确地理编码的情况下审慎推断；部分年份与国家的观测空缺要求保留缺失值而非强行插补；此外，标签本身无法直接传达政策含义，变量定义、计量单位与统计方法均须回溯原始来源加以核验，方能避免分析结论的误导性。

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，该数据集构成刻画非洲大陆非正规就业格局的核心证据基础。其经典使用场景在于依托1,297条观测记录、覆盖45个非洲国家、时间跨度自1999年至2025年的面板结构，按性别与公共/私营部门维度对正规部门之外的就业规模进行跨国比较与历时追踪。研究者通常将其用于描述性统计与探索性分析，揭示非正规就业在非洲劳动力市场中的体量分布及其性别差异。

实际应用

在实际应用层面，该数据集为国际组织、政策研究机构及发展金融机构提供了评估非洲非正规就业态势的量化依据。其数据可用于监测体面劳动议程的进展、识别性别维度的脆弱性群体，并辅助设计面向非正规部门从业者的社会保障与就业促进干预方案。同时，该数据集亦适用于构建预测模型、编制国别劳动市场简报以及开展区域比较评估。

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。Electric Sheep Africa以标准化元数据与Hugging Face仓库形式对其进行工程化封装，推动了非洲公共数据的可发现性与复用性。研究者在此基础上将其与其他ILOSTAT指标及非洲社会经济数据集进行链接，开展非正规就业与贫困、教育及性别不平等等议题的交叉分析，并发展出面向政策评估的机器学习建模与可视化应用。