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

--- 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: "Employment outside the formal sector by sex and economic activity (thousands) | Africa (ILOSTAT)" --- # Employment outside the formal sector by sex and economic activity (thousands) | Africa (ILOSTAT) 🌍 **17,177 observations** · **45 Africa countries** · **1999–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **17,177 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_ECO_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_ECO_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` | 2,163 | 2000 | 2024 | | `EGY` | 1,208 | 2008 | 2024 | | `MUS` | 1,173 | 2012 | 2024 | | `MLI` | 877 | 2013 | 2024 | | `AGO` | 864 | 2004 | 2025 | | `RWA` | 823 | 2017 | 2025 | | `SEN` | 693 | 2011 | 2024 | | `ZMB` | 662 | 2017 | 2024 | | `ZWE` | 645 | 2011 | 2024 | | `UGA` | 607 | 2010 | 2021 | | `BWA` | 607 | 2006 | 2024 | | `CIV` | 528 | 2012 | 2022 | | `NAM` | 497 | 2012 | 2018 | | `GMB` | 368 | 2012 | 2025 | | `NER` | 318 | 2011 | 2022 | | ... | _30 more countries_ | | | ## Indicators (sample) - `EMP_PIFL_SEX_ECO_NB` — Employment outside the formal sector by sex and economic activity (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_ECO_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.) | `ECO_SECTOR_TOTAL` | | `classif1.label` | `string` | — | `Economic activity (Broad 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_classif` | `string` | — | `C5:1869` | | `note_classif.label` | `string` | — | `Nonstandard economic activity: Includ…` | | `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-eco-nb-employment-outside-the-formal-sector-by-sex-and-ec") 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_ECO_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_PIFL_SEX_ECO_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_PIFL_SEX_ECO_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_emp_pifl_sex_eco_nb_employment_outside_the_formal_sector_by_sex_and_ec_2025, title = {Employment outside the formal sector by sex and economic activity (thousands) | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_PIFL_SEX_ECO_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-emp-pifl-sex-eco-nb-employment-outside-the-formal-sector-by-sex-and-ec}} } ``` ## 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_ECO_NB_This dataset contains informal economy employment statistics for 45 African countries from 1999 to 2025, with the specific indicator Employment outside the formal sector by sex and economic activity (thousands). It includes 17,177 observations, sourced from the International Labour Organizations ILOSTAT database, retrieved directly via the ILOSTAT REST API and filtered for African countries. Data fields include country code, country name, data source, indicator code, indicator label, sex disaggregation (total, male, female), economic activity and classifications, observation year, observed value, observation status, and related notes. The dataset is suitable for tabular classification, regression, and time-series forecasting tasks, aiming to provide machine learning-ready data for African labor market research.

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）所发布的非正规部门就业统计，由Electric Sheep Africa团队以元数据驱动的方式加以标准化重构。构建过程以非洲区域为范围，系统汇集45个非洲国家自1999年至2025年间的观测记录，共形成17177条数据，聚焦非正规经济中按性别与经济activity分类的就业人数（以千人为单位）。团队对原始指标进行统一编码与元数据封装，生成便于直接加载的Parquet格式数据，同时保留来源追溯信息，使数据集在结构一致性与来源可信度之间取得平衡。

特点

该数据集以表格与文本双模态呈现，覆盖非洲大陆广泛地域，时间跨度逾二十年，兼具截面与时间序列分析价值。其核心特点在于以性别和经济activity为双重维度刻画非正规部门就业规模，为劳动经济学与性别研究提供细分视角。数据以Parquet格式存储，规模适中，便于高效读取与处理。元数据标签体系完备，涵盖非正规经济、劳动力、就业等主题，并附带来源说明与使用指引，显著降低了非洲数据发现与再利用的门槛。

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函数获取数据实例，并查看其特征结构与样本内容。对于表格型数据，可利用to_pandas方法转换为DataFrame，进而开展缺失值检查、按地理与时间维度的变量画像以及子群比较等分析。使用时应以显式国家与年份字段进行数据连接与合并，避免仅凭标题推断地理归属；在建模前需核实变量定义与计量单位，保留缺失值直至确立合理的插补规则，并规范引用原始来源与Electric Sheep Africa仓库。

背景与挑战

背景概述

非正规经济部门就业统计历来是劳动经济学与发展经济学交汇处的关键议题，其数据稀缺长期制约着非洲劳动力市场研究的纵深推进。该数据集由Electric Sheep Africa团队于2026年整合发布，源自国际劳工组织ILOSTAT中央统计数据库，涵盖45个非洲国家、1999至2025年间共17,177条观测记录，按性别与经济活动门类系统记录了正规部门以外就业人口的规模（以千人为单位）。数据集以标准化元数据与CC BY 4.0许可开放获取，填补了非洲非正规就业跨国、跨时段可比数据的空白，为追踪性别差异与经济结构转型提供了实证基础，对非洲劳动力市场政策评估和包容性增长研究具有重要支撑意义。

当前挑战

该数据集所应对的核心领域问题在于非正规就业的界定与度量本身存在跨国口径分歧，各国对非正规部门的统计标准、抽样方法与覆盖范围不尽一致，致使跨时跨域比较面临系统性偏差风险。构建过程中亦遭遇多重障碍：源数据中部分国家与年份存在缺失，元数据中country与upstream_publisher字段未能完整声明，地理信息仅由标题或来源语境隐含，ISO3覆盖范围未予明确标注。此外，以千人为单位的计数指标是否经过估算或调整未在卡片中充分说明，分析者在建模前须回溯原始文献核验定义与单位，否则易从标签本身推演出缺乏依据的政策结论。

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织地带，该数据集以按性别和经济活动分列的非洲非正规部门就业人数为核心变量，构筑了跨国别、长时序的宏观劳动市场观测框架。研究者通常将其用于刻画非正规就业的性别结构与行业分布特征，通过面板数据模型检视不同经济活动门类中非正规就业的规模演变，并借助分位数回归或聚类分析识别非洲各国非正规劳动市场的异质性模式，从而为比较制度分析提供量化基础。

衍生相关工作

以该数据集为经验基础，后续研究相继拓展出多条分析脉络：其一，围绕非正规就业与贫困脆弱性的关联机制展开的跨国计量研究；其二，针对非洲女性劳动力参与率与非正规部门性别隔离的专题探讨；其三，将本数据与非洲各国宏观经济增长、贸易开放度等指标匹配而形成的多源面板分析。这些工作共同丰富了非洲非正规劳动市场的知识图谱，并推动了Electric Sheep Africa系列数据产品在发展经济学界的传播与应用。

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的研究领域，该数据集提供了1999至2025年间45个非洲国家按性别和经济活动分类的非正规就业规模估计，成为探究性别分层与劳动市场脆弱性的关键实证基础。近年来，伴随国际劳工组织推动非正规经济统计标准化以及非洲大陆自由贸易区对劳动力流动的关注，研究者愈发聚焦于非正规就业的性别差异与经济周期、产业结构转型的交互效应。该数据集支持构建面板模型以检验性别隔离如何随经济活动类型变化，并可与贫困、教育等数据集链接，揭示非正规就业对减贫政策的异质性影响。其高覆盖度和较长时序为非洲劳动经济学前沿议题提供了可复现的数据支撑，对推动区域包容性增长和性别平等政策具有显著意义。