electricsheepafrica/africa-ilo-emp-pifl-sex-eco-dsb-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: - 1K<n<10K tags: - tabular - africa - ilostat - informal-economy - ilo - labour - employment pretty_name: "Employment outside the formal sector by sex, economic activity and disability status (thou | Africa (ILOSTAT)" --- # Employment outside the formal sector by sex, economic activity and disability status (thou | Africa (ILOSTAT) 🌍 **8,416 observations** · **31 Africa countries** · **2005–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **8,416 observations** of `Informal economy` data across **31 Africa countries**, spanning **2005–2025**, 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_DSB_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_DSB_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 31 Africa countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `RWA` | 916 | 2017 | 2025 | | `SEN` | 687 | 2015 | 2024 | | `ZWE` | 677 | 2014 | 2024 | | `ZMB` | 650 | 2018 | 2024 | | `BWA` | 608 | 2019 | 2024 | | `UGA` | 424 | 2010 | 2021 | | `GMB` | 422 | 2012 | 2025 | | `TZA` | 333 | 2014 | 2024 | | `CIV` | 329 | 2016 | 2022 | | `SWZ` | 311 | 2016 | 2023 | | `SYC` | 302 | 2019 | 2024 | | `EGY` | 237 | 2023 | 2024 | | `LBR` | 224 | 2010 | 2017 | | `ETH` | 223 | 2005 | 2021 | | `GHA` | 223 | 2013 | 2015 | | ... | _16 more countries_ | | | ## Indicators (sample) - `EMP_PIFL_SEX_ECO_DSB_NB` — Employment outside the formal sector by sex, economic activity and disability status (thousands) ## Schema | Column | Type | Description | Example | |--------|------|-------------|---------| | `ref_area` | `string` | ISO 3166-1 alpha-3 country code | `BEN` | | `ref_area.label` | `string` | Country name in English | `Benin` | | `source` | `string` | ILOSTAT source code (e.g. labour force survey) | `BB:426` | | `source.label` | `string` | Source name in English | `HIES - Monitoring Survey of the Modul…` | | `indicator` | `string` | ILOSTAT indicator code | `EMP_PIFL_SEX_ECO_DSB_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 | `DSB_STATUS_TOTAL` | | `classif2.label` | `string` | — | `Disability status: Total` | | `time` | `int64` | Observation year | `2022` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `5209.906` | | `obs_status` | `string` | Observation status flag (e.g. provisional, unreliable) | `U` | | `obs_status.label` | `string` | — | `Unreliable` | | `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-eco-dsb-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_DSB_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_PIFL_SEX_ECO_DSB_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_PIFL_SEX_ECO_DSB_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_emp_pifl_sex_eco_dsb_nb_employment_outside_the_formal_sector_by_sex_econom_2025, title = {Employment outside the formal sector by sex, economic activity and disability status (thou | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_PIFL_SEX_ECO_DSB_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-emp-pifl-sex-eco-dsb-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_DSB_NB_This dataset contains 8,416 observations of informal economy employment data across 31 African countries, spanning from 2005 to 2025, covering 1 distinct indicator: Employment outside the formal sector by sex, economic activity and disability status (thousands). The data is sourced from the ILOSTAT database of the International Labour Organization (ILO), retrieved via the REST API and filtered to African ISO3 country codes, harmonized using ILO standards such as the International Conference of Labour Statisticians definitions. It provides disaggregated data by sex, economic activity, and disability status, suitable for tasks like tabular classification, regression, and time-series forecasting.

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）关于非正规部门就业的公开数据，经Electric Sheep Africa团队以标准化元数据流程重新封装而成。构建过程覆盖31个非洲国家，时间跨度为2005年至2025年，汇集8416条观察记录，涉及一项核心指标，即按性别、经济活动类型与残疾状况分类的非正规部门外就业人数。原始数据经格式转换后以Parquet格式发布，并配套生成元数据清单，为非洲数据发现提供可复用的标准化入口。

特点

数据集聚焦非洲非正规经济中的就业结构，具备鲜明的区域与主题双重属性。其时间跨度长达二十年，兼具性别、经济活动及残疾状况等多维分类变量，能够支撑交叉分析与异质性研究。数据规模属于千级至万级区间，适合中等粒度的统计建模与政策评估。发布格式为Parquet，便于高效读取与列式存储，且附带完整的来源追溯与许可说明，体现了开放数据在可发现性与可复用性上的工程化追求。

使用方法

研究者可通过Hugging Face的datasets库以一行代码加载该数据集，进而获取数据集的划分结构与特征信息。在确认列名、单位与缺失值分布后，可将目标划分转换为Pandas数据框，以支持探索性分析与建模。使用时应优先依据显式的国家与年份字段进行筛选或连接，对于仅由标题或元数据隐含的地理信息，须在下游分析中明确记录相关假设。缺失值宜保留至形成可辩护的插补规则后再行处理，以确保分析过程的可复现性。

背景与挑战

背景概述

非正规经济就业的测度长期以来构成发展中国家劳动力市场统计的核心难题，非洲地区尤甚。国际劳工组织（ILO）统计数据库（ILOSTAT）作为全球劳动统计的权威来源，持续汇集各国劳动力调查数据。Electric Sheep Africa于2026年将其整理为面向机器学习工作流的标准化数据集，覆盖31个非洲国家、2005至2025年间8416条观测记录，按性别、经济活动门类与残疾状况细分正规部门以外的就业规模。该数据集为比较非洲非正规就业的性别差异与结构性特征提供了系统化证据基础，亦服务于可持续发展目标中体面劳动议程的监测需求。

当前挑战

该数据集所回应的领域问题在于非正规就业统计本身的固有复杂性：非正规部门边界模糊、各国调查口径与参照期不一致、残疾状况等敏感变量的采集覆盖率有限，致使跨国可比性长期受限。构建过程中，元数据清单显示国家字段与上游出版机构信息存在缺失，ISO3编码未予声明，部分记录的地理归属仅能从标题或来源元数据推断，增加了实体对齐与数据溯源的不确定性。此外，缺失值的处理策略、指标单位与定义的核验，均要求使用者在建模前结合原始来源材料审慎判断，以免从标签本身过度推断政策含义。

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使用场景在于刻画非洲各国非正规部门就业的性别差异与残障群体参与状况。研究者通常以国家、年份、经济活动部门与性别为分组维度，对2005至2025年间31个非洲国家的非正规就业人数进行面板分析，借此观察非正规经济规模随时间的演变轨迹。此类分析往往结合残障状态变量，探讨边缘群体在非正规劳动市场中的分布特征，为跨国比较研究提供统一的观测框架。

实际应用

在政策实践层面，该数据集可服务于非洲各国劳动部门与国际发展机构的就业监测与干预评估。借助分性别与分经济活动部门的非正规就业数据，政策制定者能够识别非正规经济中性别鸿沟显著的行业，并据此设计针对性的技能培训、社会保障扩展与创业扶持方案。对于残障群体的就业统计，亦有助于社会福利部门评估包容性就业政策的覆盖成效，为非正规劳动市场的治理提供量化依据。

衍生相关工作

围绕该数据集，衍生出一系列与非洲劳动市场相关的数据工程与实证研究。Electric Sheep Africa以统一元数据标准将其编入非洲公开数据目录，促进其与同源国际劳工组织指标数据集进行跨国、跨年度的联合分析。后续工作多聚焦于非正规就业与贫困、教育、性别平等议题的关联建模，亦有研究将其作为机器学习任务的基准数据，用于表格分类与回归模型的训练与评估，拓展了该数据集在数据科学与社会科学交叉领域的应用边界。