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

--- 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, public/private sector and disability status ( | Africa (ILOSTAT)" --- # Employment outside the formal sector by sex, public/private sector and disability status ( | Africa (ILOSTAT) 🌍 **1,662 observations** · **31 Africa countries** · **2005–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **1,662 observations** of `Informal economy` data across **31 Africa countries**, spanning **2005–2025**, 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_DSB_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_DSB_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 31 Africa countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `RWA` | 171 | 2017 | 2025 | | `SEN` | 152 | 2015 | 2024 | | `ZWE` | 152 | 2014 | 2024 | | `BWA` | 144 | 2019 | 2024 | | `ZMB` | 126 | 2018 | 2024 | | `UGA` | 105 | 2010 | 2021 | | `SYC` | 64 | 2019 | 2024 | | `TZA` | 60 | 2014 | 2024 | | `SWZ` | 54 | 2016 | 2023 | | `CIV` | 54 | 2016 | 2022 | | `GMB` | 54 | 2018 | 2025 | | `EGY` | 45 | 2023 | 2024 | | `BFA` | 45 | 2022 | 2024 | | `ETH` | 42 | 2005 | 2021 | | `LSO` | 40 | 2019 | 2024 | | ... | _16 more countries_ | | | ## Indicators (sample) - `EMP_PIFL_SEX_INS_DSB_NB` — Employment outside the formal sector by sex, public/private sector and disability status (thousands) ## Schema | Column | Type | Description | Example | |--------|------|-------------|---------| | `ref_area` | `string` | ISO 3166-1 alpha-3 country code | `BEN` | | `ref_area.label` | `string` | Country name in English | `Benin` | | `source` | `string` | ILOSTAT source code (e.g. labour force survey) | `BB:426` | | `source.label` | `string` | Source name in English | `HIES - Monitoring Survey of the Modul…` | | `indicator` | `string` | ILOSTAT indicator code | `EMP_PIFL_SEX_INS_DSB_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` | | `classif2` | `string` | Second classification variable where applicable | `DSB_STATUS_TOTAL` | | `classif2.label` | `string` | — | `Disability status: Total` | | `time` | `int64` | Observation year | `2022` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `5209.906` | | `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-dsb-nb-employment-outside-the-formal-sector-by-sex-public") 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_DSB_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_PIFL_SEX_INS_DSB_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_PIFL_SEX_INS_DSB_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_emp_pifl_sex_ins_dsb_nb_employment_outside_the_formal_sector_by_sex_public_2025, title = {Employment outside the formal sector by sex, public/private sector and disability status ( | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_PIFL_SEX_INS_DSB_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-emp-pifl-sex-ins-dsb-nb-employment-outside-the-formal-sector-by-sex-public}} } ``` ## 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_DSB_NB_This dataset contains statistics on employment outside the formal sector in Africa from the International Labour Organization (ILO) ILOSTAT database, specifically the indicator Employment outside the formal sector by sex, public/private sector and disability status (thousands). It covers 31 African countries from 2005 to 2025, with 1,662 observations. The data is organized in tabular format, including country codes, country names, data sources, indicator codes, indicator names, sex disaggregation (total, male, female), first classification variable (e.g., institutional sector), second classification variable (e.g., disability status), observation year, observed value, observation status flags, and related notes. The data is harmonized by ILO using ICLS definitions and filtered to African countries. It is suitable for tasks such as tabular classification, regression, and time-series forecasting, and is repackaged by Electric Sheep Africa for machine learning readiness.

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统计数据库（ILOSTAT）的原始记录，由Electric Sheep Africa团队进行系统化整理与再封装，最终以Parquet格式发布。其构建过程聚焦于非洲区域非正规经济部门的就业状况，通过标准化元数据框架整合来自31个非洲国家的观测数据，时间跨度自2005年至2025年，共收录1662条记录。数据字段涵盖性别、公共或私营部门归属以及残疾状况等维度，旨在为劳动经济学与包容性发展研究提供结构清晰的实证基础。

特点

该数据集以非洲非正规部门就业为核心主题，兼具时间序列与横截面双重属性，覆盖范围广且维度划分细致。其显著特征在于同时纳入性别、部门属性与残疾状态等分组变量，便于剖析劳动力市场中的结构性差异。数据以表格与文本混合模态呈现，规模处于1K至10K区间，遵循CC BY 4.0开放许可，并附有标准化的来源说明与加载指引，提升了跨研究场景的可复用性。

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。当数据以表格形式存储时，可将其转换为Pandas数据框以便开展统计分析。使用过程中需优先核查各变量的定义、单位及缺失值分布，避免依据标签字面含义作出推断。在涉及地理维度时，应显式引用国家字段或在分析中注明对地理范围的假设，并可依据国家、年份与指标字段与其他非洲数据集进行关联整合。

背景与挑战

背景概述

非正规部门就业长期以来是发展经济学与劳动市场研究的核心议题，尤其在非洲大陆，非正规经济吸纳了绝大多数劳动力，其规模与结构直接关系到社会保障覆盖、税收基础与贫困减缓。国际劳工组织（ILO）通过ILOSTAT数据库持续汇编各国劳动力调查数据，为跨国比较提供了权威基准。在此背景下，Electric Sheep Africa于2026年前后对ILOSTAT原始数据进行标准化整理与元数据增强，构建了该数据集，收录31个非洲国家2005至2025年间共1662条观测记录，按性别、公共/私营部门及残疾状况细分非正规部门外就业指标。该数据集的核心贡献在于将碎片化的非洲劳动力市场证据整合为机器学习可读的表格格式，降低了比较研究与可复现分析的门槛。

当前挑战

该数据集所回应的领域问题是非正规就业的跨国可比测量——这一议题因各国非正规部门定义差异、调查工具不统一及残疾状况分类标准分歧而长期面临严峻挑战。在构建过程中，数据整理者需应对多重困难：原始ILOSTAT数据的元数据不完整，国家标识与上游发布机构字段存在缺失，迫使分析师在缺乏明确地理编码的情况下依赖标题或来源元数据作出判断；同时，残疾状况与公共/私营部门的交叉分类在多数非洲国家的劳动力调查中并非标准模块，导致该维度数据存在大量结构性与随机性缺失。此外，2005至2025年的长时段跨度内，各国调查频率与指标定义可能发生变更，若不经严格审核便直接建模，容易将制度性断裂误读为实质性趋势。如何在保留缺失信息的前提下构建可辩护的插补规则，并确保变量定义与单位在源材料中得到确证，构成了该数据集应用中的持续性挑战。

常用场景

经典使用场景

在非正规经济劳动力市场研究中，该数据集以非正规部门就业人口为核心观测对象，围绕性别、公共或私营部门归属以及残疾状况三个维度构建细分统计口径，覆盖31个非洲国家、2005至2025年的时间跨度，为刻画非洲非正规就业的结构性特征与演化轨迹提供了基础性的量化支撑。研究者常将其用于按国别与年份进行横截面与面板描述性分析，考察不同性别与残疾群体在非正规部门中的分布差异及其时序变动趋势。

衍生相关工作

该数据集作为Electric Sheep Africa非洲公开数据目录的组成部分，衍生出一系列围绕国际劳工组织统计数据库的再加工与标准化实践，包括跨国劳动力指标的元数据编目、可复现分析笔记本的构建以及与其他非洲社会经济数据集的联合分析工作。这些衍生工作进一步拓展了非正规经济数据在机器学习就绪型数据管道中的应用边界，促进了非洲数据发现与共享生态的完善。

数据集最近研究

最新研究方向

在非洲劳动力市场深度转型的背景下，非正规经济部门就业的性别差异与残疾状况交织形成的结构性不平等，正成为发展经济学与劳动计量学交叉领域的前沿议题。该数据集覆盖2005至2025年间31个非洲国家1,662项观测，以性别、公私部门归属及残疾状态为分层维度，为刻画非正规就业的脆弱性分布提供了稀缺的纵向证据。当前研究热点聚焦于残疾群体在非正规部门中的系统性边缘化、女性在公私部门间的就业隔离，以及后疫情时代非正规就业的韧性重构。相关分析有助于检验包容性增长政策的实效，并为实现非盟《2063年议程》与联合国可持续发展目标中关于体面工作的承诺提供可复现的经验基础。