electricsheepafrica/africa-ilo-emp-pifl-sex-edu-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, education and rural / urban areas (% | Africa (ILOSTAT)" --- # Share of employment outside the formal sector by sex, education and rural / urban areas (% | Africa (ILOSTAT) 🌍 **18,773 observations** · **41 Africa countries** · **1999–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **18,773 observations** of `Informal economy` data across **41 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_EDU_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_EDU_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 41 Africa countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `ZAF` | 2,436 | 2008 | 2024 | | `EGY` | 1,424 | 2008 | 2024 | | `MLI` | 1,133 | 2013 | 2024 | | `AGO` | 1,100 | 2004 | 2025 | | `RWA` | 1,030 | 2017 | 2025 | | `ZMB` | 971 | 2017 | 2024 | | `UGA` | 931 | 2010 | 2021 | | `SEN` | 902 | 2011 | 2024 | | `ZWE` | 871 | 2011 | 2024 | | `CIV` | 701 | 2012 | 2022 | | `NAM` | 583 | 2012 | 2018 | | `GMB` | 538 | 2012 | 2025 | | `BFA` | 449 | 2018 | 2024 | | `MRT` | 361 | 2012 | 2019 | | `COD` | 289 | 2005 | 2020 | | ... | _26 more countries_ | | | ## Indicators (sample) - `EMP_PIFL_SEX_EDU_GEO_RT` — Share of employment outside the formal sector by sex, education 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_EDU_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.) | `EDU_AGGREGATE_TOTAL` | | `classif1.label` | `string` | — | `Education (Aggregate levels): 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_classif` | `string` | — | `C3:2620` | | `note_classif.label` | `string` | — | `Nonstandard education level: Includin…` | | `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-edu-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_EDU_GEO_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_PIFL_SEX_EDU_GEO_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_PIFL_SEX_EDU_GEO_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_emp_pifl_sex_edu_geo_rt_share_of_employment_outside_the_formal_sector_by_s_2025, title = {Share of employment outside the formal sector by sex, education and rural / urban areas (% | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_PIFL_SEX_EDU_GEO_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-emp-pifl-sex-edu-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_EDU_GEO_RT_This dataset contains informal economy employment data from the International Labour Organization (ILO) ILOSTAT database, specifically the indicator Share of employment outside the formal sector by sex, education and rural / urban areas (%). It covers 41 African countries from 1999 to 2025, with 18,773 observations. Data is sourced via the ILOSTAT REST API and filtered to African ISO3 country codes. The dataset includes detailed columns such as country code, source information, indicator code, sex disaggregation (total, male, female), education and area type classifications, observation year, observed value (percentage), and data quality flags (e.g., unreliable). Data is published at annual frequency and is suitable for tasks like tabular classification, regression, and time-series forecasting. Repackaged by Electric Sheep Africa as part of a unified, ML-ready data layer for 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统计数据库（ILOSTAT）的原始记录，经Electric Sheep Africa团队进行标准化元数据整理与格式重构而成。构建过程以非洲地区非正规经济部门就业状况为核心指标，提取1999年至2025年间41个非洲国家的18,773条观测数据，按照性别、教育程度及城乡区域三个维度进行系统编码与分类聚合，最终以Parquet列式存储格式发布，并配备统一的元数据说明与加载指引，确保数据在Hugging Face平台上的可发现性与可复现性。

使用方法

研究者可通过Hugging Face datasets库以Python接口直接加载该数据集，获取默认分割后的表格对象并检视其特征结构。针对表格化分析需求，可借助to_pandas方法转换为数据框以衔接后续统计建模与可视化流程。使用前应核查仓储文件中的变量定义、单位与缺失值分布，并在涉及地理推断时明确国家编码依据。该数据集可与其他Electric Sheep Africa系列数据集通过国家、年份及指标字段进行连接，以构建更丰富的非洲社会经济分析框架。

背景与挑战

背景概述

非正规经济部门的就业规模是理解发展中国家劳动力市场结构与脆弱性的核心指标。国际劳工组织（ILO）长期致力于标准化全球劳动力统计，其ILOSTAT数据库汇集了各国就业、非正规经济等关键数据。Electric Sheep Africa在此基础上，于2026年构建并发布了涵盖41个非洲国家、1999至2025年间18,773条观测值的非正规部门就业占比数据集，按性别、教育程度及城乡区域进行细分。该数据集为非洲非正规经济研究提供了跨国、跨时间的可比证据，对劳动经济学、发展经济学及社会政策评估具有重要参考价值。

当前挑战

该数据集所应对的领域问题在于：非正规就业的界定与测量在跨国比较中缺乏统一标准，各国统计口径、调查方法和覆盖范围差异显著，导致数据可比性受限。构建过程中面临的挑战包括：原始数据源自多国劳动力调查，存在指标定义不一致、缺失值普遍、时间序列不连续等问题；部分国家缺乏按教育程度或城乡划分的细分数据；元数据中地理与上游发布者字段缺失，增加溯源难度；此外，非正规就业本身具有高度异质性和动态性，准确捕捉其变化趋势对数据质量提出更高要求。

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汇地带，该数据集最经典的使用场景在于刻画非洲各国非正规部门就业的性别、教育程度与城乡分布图景。研究者借助其覆盖41个非洲国家、逾18000条观测记录的纵向面板，可系统描绘1999至2025年间非正规就业占比的时空演变轨迹，并围绕性别差异、教育回报与城乡二元结构展开跨国比较分析。该数据集常被用于构建非正规就业指数，进而检验结构性转型理论在非洲语境下的适用性。

解决学术问题

该数据集有效回应了长期困扰发展经济学的若干核心议题，包括非正规经济规模的准确测度、性别与教育维度下劳动市场分割的成因，以及城乡差异对就业正规化进程的制约机制。通过提供标准化、可复现的跨国比较框架，它缓解了以往研究中数据口径不一、覆盖范围零散的方法论困境，为验证二元经济模型与制度经济学假说提供了坚实的经验基础，其意义在于推动非洲劳动市场研究从碎片化描述走向系统性实证。

实际应用

在政策实践层面，该数据集为国际组织与各国劳动部门评估非正规就业治理成效提供了量化依据。社会保障机构可据此识别教育程度较低与农村地区劳动者的保障缺口，就业促进项目可借助性别维度的分布特征设计针对性干预措施。此外，发展金融机构在制定普惠金融与技能培训方案时，亦可参照该数据集揭示的结构性差异进行资源精准配置，从而提升政策干预的靶向性与实效性。

数据集最近研究

最新研究方向

在非洲劳动力市场结构性转型的宏大叙事中，非正规部门就业份额的性别与教育异质性正成为发展经济学与国际组织政策研判的前沿焦点。该数据集依托ILOSTAT权威统计框架，覆盖41个非洲国家、1999至2025年长达二十六载的18773条观测，为刻画城乡二元结构下非正规就业的动态演化提供了罕见的长时序面板基础。当前研究前沿已从总量测度转向多维分解，重点探究女性在非正规经济中的过度代表现象与教育梯度如何交互塑造劳动脆弱性，并借助机器学习方法识别非正规就业的聚类模式。此数据集不仅支撑包容性增长与体面劳动议程的实证检验，更为非洲大陆自由贸易区背景下的劳动力流动政策设计提供了可复现的数据基础设施，其开放共享的元数据标准亦推动了非洲经济研究的可重复性转向。