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

--- 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 weekly hours actually worked (thousands) | Africa (ILOSTAT)" --- # Employment outside the formal sector by sex and weekly hours actually worked (thousands) | Africa (ILOSTAT) 🌍 **4,229 observations** · **39 Africa countries** · **2000–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **4,229 observations** of `Informal economy` data across **39 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_HOW_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_HOW_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 39 Africa countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `ZAF` | 633 | 2000 | 2024 | | `MUS` | 310 | 2012 | 2024 | | `EGY` | 276 | 2008 | 2024 | | `RWA` | 243 | 2017 | 2025 | | `MLI` | 217 | 2013 | 2024 | | `ZMB` | 216 | 2017 | 2024 | | `AGO` | 189 | 2019 | 2025 | | `SEN` | 189 | 2015 | 2024 | | `UGA` | 182 | 2010 | 2021 | | `BWA` | 168 | 2006 | 2024 | | `ZWE` | 159 | 2014 | 2024 | | `NAM` | 126 | 2012 | 2018 | | `CIV` | 120 | 2012 | 2019 | | `GMB` | 108 | 2012 | 2025 | | `SYC` | 94 | 2019 | 2024 | | ... | _24 more countries_ | | | ## Indicators (sample) - `EMP_PIFL_SEX_HOW_NB` — Employment outside the formal sector by sex and weekly hours actually worked (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_HOW_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.) | `HOW_BANDS_TOTAL` | | `classif1.label` | `string` | — | `Hour bands: 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) | `B` | | `obs_status.label` | `string` | — | `Break in series` | | `note_classif` | `float64` | — | `—` | | `note_classif.label` | `float64` | — | `—` | | `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-how-nb-employment-outside-the-formal-sector-by-sex-and-we") 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_HOW_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_PIFL_SEX_HOW_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_PIFL_SEX_HOW_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_emp_pifl_sex_how_nb_employment_outside_the_formal_sector_by_sex_and_we_2025, title = {Employment outside the formal sector by sex and weekly hours actually worked (thousands) | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_PIFL_SEX_HOW_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-emp-pifl-sex-how-nb-employment-outside-the-formal-sector-by-sex-and-we}} } ``` ## 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_HOW_NB_This dataset contains statistical data on employment outside the formal sector in Africa, with the core indicator being Employment outside the formal sector by sex and weekly hours actually worked (thousands). It includes 4,229 observations across 39 African countries, spanning the years 2000 to 2025. The data is sourced from the International Labour Organization (ILO) ILOSTAT database, retrieved via API and filtered to African countries. The dataset is in tabular format, with columns such as country code, country name, indicator code, sex classification (total, male, female), year, observed value, etc. Data is disaggregated by sex for analyzing employment trends in Africas informal economy. Repackaged by Electric Sheep Africa to provide a standardized, ML-ready data layer for Africa. Licensed under cc-by-4.0.

搜集汇总

数据集介绍

构建方式

该数据集根植于国际劳工组织（ILO）对全球非正规经济部门的长期统计监测，其原始数据源自ILOSTAT这一劳工统计中央数据库。Electric Sheep Africa在此基础上进行了系统性的元数据工程处理，将覆盖三十九个非洲国家、时间跨度为2000至2025年的四千二百二十九条观测记录重新打包为Parquet格式，并附加标准化的发现层元数据。整个过程遵循可复现的开放数据工程规范，在保留原始统计口径的同时，为每一变量补充了来源注释、加载指引与分析情境说明，使分散的劳工统计数据转化为机器学习就绪的结构化资源。

特点

数据集的核心特征在于其聚焦非洲非正规部门就业的性别维度与工时结构，以千人计数的周实际工作小时数为量化基准，同时区分男性与女性的就业分布。空间上覆盖三十九个非洲国家，时间上纵贯四分之一世纪，兼具跨截面比较与纵向追踪的双重分析潜力。数据以表格与文本双模态呈现，体量处于一千至一万行区间，适合中等规模的计算实验。标签体系融合了劳工统计、非正规经济与非洲区域研究等多重主题，并遵循CC BY 4.0开放许可，便于学术传播与二次开发。

使用方法

研究者可借助Hugging Face的datasets库以单行代码加载全部分片，随后通过特征检查与样本预览快速把握数据结构。若需统计建模，可将目标分片转换为Pandas数据框以衔接主流分析流程。在正式建模前，建议先审查各变量的缺失模式与量纲定义，对地理标识仅存于标题或元数据的情况应在分析文档中明确假设。该数据集亦适合与Electric Sheep Africa目录下的其他非洲数据集通过国家、年份与指标字段进行联结，从而构建更具解释力的多源分析框架。

背景与挑战

背景概述

国际劳工组织长期致力于全球劳动力市场统计体系的建设，其核心数据库ILOSTAT为衡量各国就业结构与非正规经济规模提供了权威基准。在此背景下，Electric Sheep Africa于2026年将ILOSTAT中关于非洲非正规部门就业的细分数据整理发布，覆盖39个非洲国家、2000至2025年间逾四千条观测记录，按性别与每周实际工作小时数对正规部门以外就业人口进行分层刻画。该数据集回应了非洲非正规经济长期缺乏可比跨国面板数据的困境，为劳动经济学、发展经济学及社会政策研究提供了可复现的量化基础。

当前挑战

非正规就业统计本身即构成劳动经济测量中最棘手的领域之一，其边界模糊、口径不一，各国调查方法与覆盖范围差异显著，致使跨国比较面临系统性偏误风险。数据构建过程中，原始ILOSTAT指标虽经标准化处理，但部分国家的缺失值与性别维度的报告不完整仍需审慎对待；同时，工作小时数的分组口径在时间序列上可能存在调整，影响纵向一致性。如何在保留缺失信息的前提下建立可辩护的插补规则，并在跨国异质性中提取稳健结论，构成该数据集应用的核心挑战。

常用场景

经典使用场景

在劳动经济学与非正规经济研究领域，该数据集最为经典的运用场景在于刻画非洲各国非正规部门就业的性别结构与工时分布特征。依托国际劳工组织（ILOSTAT）所建立的统计框架，研究者得以按性别与每周实际工作小时数这两个关键维度，对2000年至2025年间39个非洲国家非正规就业的规模与强度进行跨国比较。此类分析往往服务于非正规经济规模测算、劳动力市场性别差距评估以及工时贫困监测等议题，为理解非洲劳动力市场结构性特征提供了不可替代的量化基础。

衍生相关工作

围绕该数据集，Electric Sheep Africa已构建起一个持续扩展的非洲公共数据目录，将ILOSTAT来源的非正规就业指标与其他劳动、经济与社会领域的数据集进行关联整合。衍生的相关工作涵盖跨国非正规经济比较研究、性别与劳动力市场参与率的联合分析，以及基于统一元数据规范的可复现分析笔记本与数据发现工具，逐步形成了面向非洲数据科学研究的开放数据生态。

数据集最近研究

最新研究方向

在非洲非正规经济占劳动力市场主导地位的背景下，该数据集以性别和周实际工作小时数为核心维度，量化了39个非洲国家2000至2025年间正规部门之外的就业规模，为劳动经济学与性别研究交叉领域提供了稀缺的跨国面板证据。当前前沿研究愈发关注非正规就业中的性别差异与工时分配如何共同塑造收入脆弱性，该数据集使学者能够检验非正规部门女性从业者是否在更长工时下仍面临更高贫困风险等假设，并与ILOSTAT其他指标链接以构建多维分析框架。其标准化元数据与开放许可亦推动了非洲劳动统计的可复现研究，为政策制定者设计针对性社会保障干预、监测可持续发展目标中的体面劳动进展提供了实证基础。