electricsheepafrica/africa-ilo-emp-pifl-sex-est-geo-rt-share-of-employment-outside-the-formal-sector-by-s

--- 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: "Share of employment outside the formal sector by sex, establishment size and rural / urban | Africa (ILOSTAT)" --- # Share of employment outside the formal sector by sex, establishment size and rural / urban | Africa (ILOSTAT) 🌍 **12,497 observations** · **38 Africa countries** · **2004–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **12,497 observations** of `Informal economy` data across **38 Africa countries**, spanning **2004–2025**, 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_GEO_RT) - **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_GEO_RT` 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 38 Africa countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `ZAF` | 2,028 | 2008 | 2024 | | `EGY` | 952 | 2008 | 2023 | | `MLI` | 932 | 2013 | 2024 | | `AGO` | 901 | 2004 | 2025 | | `SEN` | 755 | 2015 | 2024 | | `ZMB` | 513 | 2017 | 2024 | | `RWA` | 467 | 2017 | 2020 | | `CIV` | 412 | 2012 | 2019 | | `NAM` | 392 | 2012 | 2018 | | `GMB` | 351 | 2012 | 2025 | | `MDG` | 335 | 2012 | 2022 | | `BFA` | 320 | 2018 | 2024 | | `ZWE` | 317 | 2011 | 2019 | | `NER` | 296 | 2011 | 2017 | | `COD` | 245 | 2005 | 2020 | | ... | _23 more countries_ | | | ## Indicators (sample) - `EMP_PIFL_SEX_EST_GEO_RT` — Share of employment outside the formal sector by sex, establishment size and rural / urban areas (%) ## 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_GEO_RT` | | `indicator.label` | `string` | Indicator name in English | `Share of employment outside the forma…` | | `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` | | `classif2` | `string` | Second classification variable where applicable | `GEO_COV_NAT` | | `classif2.label` | `string` | — | `Area type: National` | | `time` | `int64` | Observation year | `2025` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `80.588` | | `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-geo-rt-share-of-employment-outside-the-formal-sector-by-s") 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_GEO_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_PIFL_SEX_EST_GEO_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_PIFL_SEX_EST_GEO_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_emp_pifl_sex_est_geo_rt_share_of_employment_outside_the_formal_sector_by_s_2025, title = {Share of employment outside the formal sector by sex, establishment size and rural / urban | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_PIFL_SEX_EST_GEO_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-emp-pifl-sex-est-geo-rt-share-of-employment-outside-the-formal-sector-by-s}} } ``` ## 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_GEO_RT_This dataset contains 12,497 observations of the share of employment outside the formal sector by sex, establishment size, and rural/urban areas across 38 African countries, spanning from 2004 to 2025, with one core indicator (EMP_PIFL_SEX_EST_GEO_RT). The data is sourced from the International Labour Organization (ILO) ILOSTAT database, extracted via API and filtered for African countries, covering topics such as employment and informal economy. It includes columns for country code, country name, data source, indicator code, sex disaggregation, establishment size classification, rural/urban classification, observation year, observed value, and data status flags, along with data quality notes (e.g., annual frequency, best source selection). The dataset is suitable for tabular classification, regression, and time-series forecasting tasks, designed to support research and analysis of labor markets in Africa.

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（ILO）的ILOSTAT核心统计数据库，经Electric Sheep Africa对原始统计资料进行元数据标准化与结构化封装而成。构建过程中，以非洲38个国家为观测单元，系统采集2004年至2025年间非正规部门就业占比的官方统计数据，涵盖性别、机构规模及城乡属性等多维分类变量，形成12500余条观测记录。数据以Parquet列式格式存储，保留缺失值并附带溯源注释，确保来源可追溯且符合CC BY 4.0开放许可。

特点

该数据集聚焦非洲非正规经济领域的就业结构，涵盖38个非洲国家、跨越逾二十年的时序数据，具有显著的地域覆盖广度与时间纵深。其核心特征在于按性别、机构规模及城乡维度对正规部门外就业占比进行细分，为探究非洲劳动力市场中的性别差异、城乡分化及企业规模效应提供了细粒度面板数据支撑。数据集以表格与文本混合模态呈现，体量介于一万至十万条之间，兼具经济学金融领域的实证分析价值与多国比较研究潜力。

使用方法

研究者可借助Hugging Face datasets库直接加载该数据集，通过内置的分割索引获取表格对象并检视特征结构，必要时可转换为Pandas数据框进行统计分析与可视化。在建模前需核查缺失值分布与变量定义，明确国家列与年份字段以支撑面板回归或跨国比较，若需与其他Electric Sheep Africa数据集联合使用，应基于显式国家与指标字段进行连接，并在下游分析中标注地理假设，同时保留原始缺失状态以规避不当插补。

背景与挑战

背景概述

非正规经济部门的就业规模测算历来是发展经济学与劳动统计领域的核心议题，尤其在非洲大陆，非正规就业往往占据总就业的绝大多数，直接关乎减贫政策与社会保障体系的制度设计。国际劳工组织（ILO）长期通过ILOSTAT数据库推动全球劳动统计标准化，而Electric Sheep Africa于2026年将ILOSTAT中关于非洲非正规部门就业占比的指标进行结构化重编，形成涵盖38个非洲国家、2004至2025年间12,497条观测记录的数据集。该数据集按性别、企业规模及城乡维度细分，为研究者提供了审视非洲非正规就业结构性差异的跨国面板基础，亦为非洲数据发现与可复现研究注入标准化元数据支持。

当前挑战

该数据集所回应的领域问题在于：非洲各国非正规就业占比在性别、企业规模与城乡维度上的系统性差异究竟如何，以及这些差异如何随时间演变。然而，破解这一问题面临多重挑战。就数据本身而言，非正规就业的定义在各国统计实践中存在显著异质性，抽样方法、覆盖范围与指标口径的不统一导致跨国可比性受限，且部分年份与国家的缺失值需审慎处理。就构建过程而言，Electric Sheep Africa在重编元数据时面临国家字段与上游发布者信息的缺失，需在不臆断政策含义的前提下，依托显式地理与时间字段建立可追溯的关联逻辑，同时保留原始缺失值以待下游分析者依据可辩护的规则进行插补。

常用场景

经典使用场景

在非洲劳动力市场结构转型与非正规经济研究中，该数据集构成刻画就业部门分布格局的核心经验基础。其以性别、机构规模及城乡区位为分层维度，系统记录2004至2025年间38个非洲国家非正规部门就业占比的时序演变，为劳动经济学与发展经济学领域提供高粒度、跨国可比的观测单元。研究者借此可审视非正规就业在性别间的差异化分布、微型与大型机构之间的结构落差，以及城乡二元空间中的非均衡特征，进而支撑面板回归、分层比较与趋势分解等经典分析路径。

解决学术问题

该数据集直面非洲非正规经济测度中长期存在的可比性困境与细分维度缺失问题。既往研究常受限于国别统计口径不一、性别与城乡维度难以交叉、机构规模信息阙如等瓶颈，难以在统一框架下开展跨国比较。本数据集依托国际劳工组织统计数据库的标准化指标，将性别、机构规模与城乡区位纳入同一观测体系，为检验非正规就业的性别鸿沟假说、城乡劳动市场分割理论以及机构规模与非正规性之间的关联机制提供了可复现的数据支撑，显著提升了非洲劳动市场实证研究的内部效度与外部可推广性。

衍生相关工作

围绕该数据集衍生的经典工作主要集中于非洲非正规就业的结构性测度与比较分析方向。研究者以其为基础构建了跨国非正规就业面板数据库，推动了非洲劳动市场二元结构理论的实证检验；亦有工作将其与贫困、教育及性别平等数据集融合，考察非正规就业对家庭福利与代际流动的传导效应。Electric Sheep Africa 以标准化元数据与统一仓储格式对原始统计数据进行工程化整理，催生了一系列面向非洲数据发现与可复现研究的开放数据基础设施实践，为后续非洲劳动经济研究的可扩展性与透明度奠定了方法学基础。