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

--- 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, economic activity and public/private sector ( | Africa (ILOSTAT)" --- # Employment outside the formal sector by sex, economic activity and public/private sector ( | Africa (ILOSTAT) 🌍 **35,626 observations** · **45 Africa countries** · **1999–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **35,626 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_INS_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_INS_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` | 4,332 | 2000 | 2024 | | `EGY` | 2,475 | 2008 | 2024 | | `MUS` | 2,416 | 2012 | 2024 | | `AGO` | 1,788 | 2004 | 2025 | | `MLI` | 1,754 | 2013 | 2024 | | `RWA` | 1,667 | 2017 | 2025 | | `ZWE` | 1,580 | 2011 | 2024 | | `UGA` | 1,515 | 2010 | 2021 | | `SEN` | 1,465 | 2011 | 2024 | | `BWA` | 1,359 | 2006 | 2024 | | `ZMB` | 1,324 | 2017 | 2024 | | `CIV` | 1,077 | 2012 | 2022 | | `NAM` | 1,005 | 2012 | 2018 | | `SYC` | 678 | 2019 | 2024 | | `NER` | 636 | 2011 | 2022 | | ... | _30 more countries_ | | | ## Indicators (sample) - `EMP_PIFL_SEX_ECO_INS_NB` — Employment outside the formal sector by sex, economic activity and public/private sector (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_INS_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` | | `classif2` | `string` | Second classification variable where applicable | `INS_SECTOR_TOTAL` | | `classif2.label` | `string` | — | `Institutional 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-ins-nb-employment-outside-the-formal-sector-by-sex-econom") 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_INS_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_PIFL_SEX_ECO_INS_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_PIFL_SEX_ECO_INS_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_emp_pifl_sex_eco_ins_nb_employment_outside_the_formal_sector_by_sex_econom_2025, title = {Employment outside the formal sector by sex, economic activity and public/private sector ( | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_PIFL_SEX_ECO_INS_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-emp-pifl-sex-eco-ins-nb-employment-outside-the-formal-sector-by-sex-econom}} } ``` ## 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_INS_NB_This dataset contains informal economy employment data for 45 African countries from the International Labour Organization (ILO) ILOSTAT database, spanning the years 1999 to 2025 with 35,626 observations. The primary indicator is EMP_PIFL_SEX_ECO_INS_NB, which represents employment outside the formal sector by sex, economic activity, and public/private sector (in thousands). Data is pulled directly from the ILOSTAT REST API and filtered to include only African countries. The dataset schema includes columns such as country code, indicator code, sex disaggregation (total, male, female), economic activity and institutional sector classifications, observation year, numerical values, and data quality flags. Sources include labour force surveys, household income surveys, and others, harmonized using International Conference of Labour Statisticians (ICLS) definitions. Repackaged by Electric Sheep Africa, it aims to provide a unified, ML-ready data layer for Africa, suitable for tabular classification, regression, and time-series forecasting tasks.

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）的正式部门外就业统计，经Electric Sheep Africa团队系统化整理与重新封装而成。原始数据由各国劳工统计机构上报，涵盖非正规经济中按性别、经济活动类型及公共/私营部门分类的就业观测值。构建过程遵循元数据标准化流程，对字段命名、格式规范与来源注释进行统一，并以Parquet格式存储于Hugging Face平台，形成覆盖非洲区域的可复现数据集。

使用方法

研究者可通过Hugging Face的datasets库以单行代码加载数据集，利用数据集查看器快速检视结构与样本。加载后可将目标分片转换为Pandas数据框，以便执行缺失值诊断、地理与时间维度的描述性统计，或与其他Electric Sheep Africa数据集按国家、年份及指标字段进行联结。使用时应参照README中的变量定义与单位说明，在建模前确认地理覆盖假设，并保留缺失值直至确立合理的插补规则。

背景与挑战

背景概述

非正规部门就业长期以来是发展中国家劳动力市场研究的核心议题，其规模与结构直接关系到社会保障覆盖、税收基础及经济韧性评估。国际劳工组织（ILO）通过ILOSTAT数据库系统采集全球劳动力市场指标，为跨区域比较分析奠定数据基础。Electric Sheep Africa于2026年发布该数据集，整合了1999至2025年间45个非洲国家35,626条观测记录，按性别、经济活动及公私部门维度刻画正规部门之外的就业分布。该数据集弥补了非洲非正规经济微观证据的碎片化缺陷，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标准化数据资源。

当前挑战

该数据集所应对的领域问题在于非正规就业统计口径的跨国异质性，各国对非正规部门的界定、抽样方法及调查周期存在显著差异，致使跨国比较面临测量误差与可比性风险。构建过程中的挑战同样突出：源数据缺失国家与上游发布者等关键元数据，部分观测的地理归属仅能依据标题或来源信息推断，需在分析中明确假设；缺失值需保留以待合理插补规则，不可轻率填充；同时，指标标签的语义边界须回归原始文献核实，避免因标签直译而产生政策含义的误读。

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，该数据集构成了一类典型的表格数据资源，其经典使用场景聚焦于按性别、经济活动类别与公私部门属性，对非洲各国非正规部门就业规模进行跨截面与时序的比较分析。研究者以之刻画非正规就业的性别结构差异，并借助分类与回归任务对就业形态进行预测建模。

解决学术问题

该数据集回应了非正规就业统计中长期存在的国别口径不一、时序断裂与性别维度缺失等学术难题，通过统一整理国际劳工组织统计口径下的四十五个非洲国家近二十六年观测数据，为跨国比较与面板计量分析提供了可靠基础，推动了非正规经济测度方法论的规范化与实证研究的可复现性。

实际应用

在政策实践层面，该数据集可支撑劳动部门评估非正规就业规模与结构，为社会保障扩面、税收征管优化与女性就业促进等政策制定提供量化依据。国际组织与智库亦可借此监测非洲劳动力市场转型进程，识别非正规部门吸纳就业的行业差异，辅助区域发展战略的精准施策。

数据集最近研究

最新研究方向

伴随国际劳工组织对非正规经济统计边界的持续厘清，围绕非洲劳动力市场非正规部门就业的量化研究正加速从总量测度转向性别与经济活动异质性并重的精细化分析。该数据集整合了1999至2025年间45个非洲国家逾三万五千条观察记录，按性别、经济活动类别及公私部门属性刻画正规部门之外的就业分布，为探究性别就业鸿沟、非正规经济周期敏感性及结构性转型路径提供了长时序面板证据。在非洲大陆自由贸易区推进与后疫情劳动力市场重塑的背景下，此类标准化、可复现的元数据支撑型数据集，正成为劳动经济学、发展经济学与性别研究交叉领域检验制度假设、评估社会保障覆盖缺口的关键基础设施，亦为机器学习驱动的缺失值插补与跨国比较预测研究开辟了新的方法论空间。