electricsheepafrica/africa-ilo-emp-nifl-sex-age-dsb-nb-informal-employment-by-sex-age-and-disability-stat

--- 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: "Informal employment by sex, age and disability status (thousands) | Africa (ILOSTAT)" --- # Informal employment by sex, age and disability status (thousands) | Africa (ILOSTAT) 🌍 **6,333 observations** · **31 Africa countries** · **2007–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **6,333 observations** of `Informal economy` data across **31 Africa countries**, spanning **2007–2025**, 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_NIFL_SEX_AGE_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_NIFL_SEX_AGE_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` | 726 | 2017 | 2025 | | `SEN` | 559 | 2015 | 2024 | | `ZMB` | 553 | 2018 | 2024 | | `ZWE` | 486 | 2014 | 2024 | | `BWA` | 436 | 2019 | 2024 | | `UGA` | 324 | 2010 | 2021 | | `GMB` | 318 | 2012 | 2025 | | `TZA` | 243 | 2014 | 2024 | | `CIV` | 243 | 2016 | 2022 | | `SYC` | 234 | 2019 | 2024 | | `SWZ` | 221 | 2016 | 2023 | | `LSO` | 169 | 2019 | 2024 | | `BFA` | 162 | 2022 | 2024 | | `LBR` | 160 | 2010 | 2017 | | `EGY` | 158 | 2023 | 2024 | | ... | _16 more countries_ | | | ## Indicators (sample) - `EMP_NIFL_SEX_AGE_DSB_NB` — Informal employment by sex, age 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_NIFL_SEX_AGE_DSB_NB` | | `indicator.label` | `string` | Indicator name in English | `Informal employment by sex, age and d…` | | `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.) | `AGE_YTHADULT_YGE15` | | `classif1.label` | `string` | — | `Age (Youth, adults): 15+` | | `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) | `5246.107` | | `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-nifl-sex-age-dsb-nb-informal-employment-by-sex-age-and-disability-stat") 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_NIFL_SEX_AGE_DSB_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_NIFL_SEX_AGE_DSB_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_NIFL_SEX_AGE_DSB_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_emp_nifl_sex_age_dsb_nb_informal_employment_by_sex_age_and_disability_stat_2025, title = {Informal employment by sex, age and disability status (thousands) | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_NIFL_SEX_AGE_DSB_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-emp-nifl-sex-age-dsb-nb-informal-employment-by-sex-age-and-disability-stat}} } ``` ## 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 2026-05-25 via the Electric Sheep pipeline. Source URL: https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_NIFL_SEX_AGE_DSB_NB_This dataset contains 6,333 observations of Informal economy data across 31 Africa countries, spanning 2007–2025, covering 1 distinct indicators. It focuses on informal employment by sex, age and disability status (in thousands), sourced from the ILOSTAT database of the International Labour Organization (ILO) and repackaged by Electric Sheep Africa. The data is provided in tabular format with columns such as country code, year, indicator value, data source, and disaggregation dimensions, suitable for tasks like tabular classification, regression, and time-series forecasting.

搜集汇总

数据集介绍

构建方式

该数据集由Electric Sheep Africa基于国际劳工组织劳工统计数据库（ILOSTAT）的公开数据重新封装而成，属于非洲非正规经济统计数据目录的组成部分。原始数据涵盖2007年至2025年间31个非洲国家的非正规就业观测记录，按性别、年龄组及残疾状况进行交叉分类，共计6,333条观测。构建过程中，编写团队对原始元数据实施标准化处理，统一了指标定义、单位说明与来源注释，并以Parquet格式加以存储，从而在保留ILOSTAT原始统计口径的同时，提升了数据的可发现性与复用效率。

特点

该数据集聚焦于非洲非正规就业这一劳动经济学核心议题，其突出特征在于同时纳入性别、年龄与残疾状况三个维度的分层变量，为考察劳动力市场中的多重弱势群体提供了细粒度证据。数据覆盖31个非洲国家并跨越近二十年时间跨度，兼具截面比较与历时分析的双重潜力；以表格与文本模态并存的Parquet格式分发，规模处于1K至10K区间，体量适中且便于加载。标准化的元数据标签体系进一步增强了其在非洲数据发现场景中的互操作性。

使用方法

使用者可借助Hugging Face datasets库以load_dataset函数直接加载该数据集，并通过数据集查看器检视各分片结构与特征类型。对于表格型数据，可将对应分片转换为Pandas DataFrame，以便开展缺失值诊断、按地理与时间维度的变量画像以及分组统计。在建模之前，应先行核实变量定义、计量单位与来源方法，保留原始缺失值直至确立可辩护的插补规则。该数据集亦可与Electric Sheep Africa目录下的其他数据集通过国家、年份与指标字段进行连接，支撑可复现的比较研究。

背景与挑战

背景概述

非正规就业构成众多发展中国家劳动力市场的核心特征，其规模与结构的精准刻画直接关系到体面劳动议程的推进与社会保护政策的制定。国际劳工组织依托其中央统计数据库ILOSTAT，长期致力于整合各国劳动力调查数据，以生成跨国家、跨时期可比的非正规就业统计。在此背景下，Electric Sheep Africa对ILOSTAT原始数据进行了系统性整理与标准化封装，于2026年发布该数据集，覆盖31个非洲国家自2007年至2025年间的6333条观测记录，按性别、年龄组与残疾状况细分非正规就业人口规模。该数据集填补了非洲区域残疾状况维度就业统计的空白，为劳动经济学、社会政策评估及包容性发展研究提供了可复现的数据基础。

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的跨国可比性困境：各国劳动力调查在非正规部门定义、就业身份分类及残疾状况测量工具上存在显著异质性，致使非洲区域层面的汇总与比较分析面临口径不一致的根本障碍。构建过程中的具体挑战涵盖多个层面：原始ILOSTAT数据在性别、年龄与残疾状况交叉维度上的大量缺失值需在不引入偏误的前提下审慎处理；残疾状况的界定标准在不同国家调查中差异悬殊，影响数据的可汇总性；非正规就业以千人为单位的计数数据缺乏对应的抽样误差信息，限制了统计推断的可靠性；元数据清单中上游发布者与国别字段的缺失亦增加了数据溯源与质量核验的难度。上述挑战共同构成了该数据集在分析与建模中的核心约束。

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处，该数据集凭借其性别、年龄与残疾状况的三维分层结构，成为刻画非洲非正规就业异质性格局的经典工具。研究者通常借助其时间序列特性，对2007至2025年间31个非洲国家的非正规就业规模进行趋势拟合与结构分解，从而揭示不同人口群体在非正规部门中的参与率差异。其表格化存储格式与标准化元数据设计，使得跨国比较与面板回归分析得以在统一框架下高效开展。

实际应用

在政策实践层面，该数据集为国际组织与非洲各国劳工部门监测非正规就业动态、评估社会保障覆盖缺口提供了可操作的数据支撑。发展机构可据此识别残疾群体与青年女性等高脆弱人群的就业分布特征，进而设计针对性的技能培训、社会保障扩展及就业促进项目。同时，该数据集亦适用于构建预警指标，辅助研判经济波动对非正规部门就业的传导效应。

衍生相关工作

围绕该数据集衍生的经典工作包括基于ILOSTAT框架的非洲非正规经济面板分析、残疾与就业不平等测度研究，以及跨国劳动统计可比性方法探讨。Electric Sheep Africa的元数据标准化实践进一步催生了面向非洲开放数据的机器学习就绪型数据集系列，推动了数据发现、溯源与再利用的基础设施建设。相关研究亦常将其与国际劳工组织的其他指标数据集进行链接，以丰富多维分析视角。