electricsheepafrica/africa-ilo-emp-nifl-sex-age-mts-nb-informal-employment-by-sex-age-and-marital-status

--- 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: "Informal employment by sex, age and marital status (thousands) | Africa (ILOSTAT)" --- # Informal employment by sex, age and marital status (thousands) | Africa (ILOSTAT) 🌍 **73,680 observations** · **45 Africa countries** · **2000–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **73,680 observations** of `Informal economy` data across **45 Africa countries**, spanning **2000–2025**, 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_MTS_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_MTS_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` | 10,330 | 2000 | 2024 | | `EGY` | 4,917 | 2008 | 2024 | | `MUS` | 4,374 | 2012 | 2024 | | `MLI` | 4,004 | 2013 | 2024 | | `RWA` | 3,753 | 2017 | 2025 | | `AGO` | 3,286 | 2004 | 2025 | | `SEN` | 3,139 | 2011 | 2024 | | `ZMB` | 3,099 | 2017 | 2024 | | `ZWE` | 2,931 | 2011 | 2024 | | `CIV` | 2,438 | 2012 | 2022 | | `UGA` | 2,434 | 2010 | 2021 | | `BWA` | 2,327 | 2019 | 2024 | | `NAM` | 2,186 | 2012 | 2018 | | `BFA` | 1,590 | 2018 | 2024 | | `GMB` | 1,446 | 2012 | 2025 | | ... | _30 more countries_ | | | ## Indicators (sample) - `EMP_NIFL_SEX_AGE_MTS_NB` — Informal employment by sex, age and marital status (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_NIFL_SEX_AGE_MTS_NB` | | `indicator.label` | `string` | Indicator name in English | `Informal employment by sex, age and m…` | | `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 | `MTS_AGGREGATE_TOTAL` | | `classif2.label` | `string` | — | `Marital status (Aggregate): Total` | | `time` | `int64` | Observation year | `2025` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `13214.118` | | `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-mts-nb-informal-employment-by-sex-age-and-marital-status") 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_MTS_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_NIFL_SEX_AGE_MTS_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_NIFL_SEX_AGE_MTS_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_emp_nifl_sex_age_mts_nb_informal_employment_by_sex_age_and_marital_status_2025, title = {Informal employment by sex, age and marital status (thousands) | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_NIFL_SEX_AGE_MTS_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-emp-nifl-sex-age-mts-nb-informal-employment-by-sex-age-and-marital-status}} } ``` ## 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_MTS_NB_This dataset contains 73,680 observations of informal employment data across 45 African countries, spanning 2000–2025, with a focus on informal employment disaggregated by sex, age, and marital status (in thousands). Sourced from the International Labour Organizations ILOSTAT database via API and filtered to African ISO3 country codes, it includes one core indicator (EMP_NIFL_SEX_AGE_MTS_NB) and provides detailed fields such as country, data source, sex, age classification, marital status, observation year, observed value, and data status. The data is harmonized by the ILO using International Conference of Labour Statisticians (ICLS) definitions and is suitable for tabular classification, regression, and time-series forecasting tasks.

搜集汇总

数据集介绍

构建方式

该数据集脱胎于国际劳工组织旗舰劳动力统计数据库ILOSTAT，由Electric Sheep Africa团队在Hugging Face平台完成工程化重构。构建过程以ILOSTAT公开的非正规就业统计记录为原始素材，经标准化清洗与结构映射后封装为parquet格式，形成覆盖45个非洲国家、时间跨度自2000年至2025年的面板数据。原始指标以千人为计量单位，按性别、年龄与婚姻状况交叉分类，最终汇为73,680条观测。元数据体系同步标注了来源归属、许可协议与变量语义，使公开统计资料转化为可复用的机器学习就绪型表格资源。

特点

数据集在结构上呈现高维分组面板特征，性别、年龄与婚姻状况三重维度交织，使非正规就业的异质性得以细致刻画。时间维度横跨四分之一世纪，为趋势识别与跨国比较提供充足纵深。地域覆盖45个非洲国家，兼顾区域代表性与跨国可比性。数据以规范的表格与文本双模态存储，缺失值保留原始形态，便于研究者依据分析目标自行制定插补策略。标签体系与经济学金融领域分类对齐，并附有人口、年份、指标等显式字段，支持与Electric Sheep Africa目录内其他数据集进行链接与融合。

使用方法

研究者可借助datasets库以一行代码加载该数据集，先通过打印数据集对象与特征结构快速把握整体轮廓，再使用切片操作预览样本记录。对于表格分析场景，可将指定划分转换为pandas数据框，进而开展描述统计、缺失模式探查与分组画像。建模之前应核验变量定义与单位，明确地理字段的取值方式，并在文档中记录对地理或来源缺口的推断假设。若需跨域比较，可利用显式的国家、年份与指标字段同其他非洲公开数据集进行连接，同时以可复现笔记本形式记录来源出处与分析流程。

背景与挑战

背景概述

非正规就业历来是发展经济学与劳动经济学关注的核心议题，尤其在非洲大陆，非正规经济吸纳了绝大多数劳动力，其规模、结构与动态直接关系到贫困减缓、社会保障扩展与性别平等。该数据集由Electric Sheep Africa基于国际劳工组织ILOSTAT数据库整理发布，覆盖45个非洲国家、2000至2025年间73680条观测记录，按性别、年龄与婚姻状况细分非正规就业人数。ILOSTAT作为全球劳动统计的权威来源，为跨国比较与趋势分析提供了标准化框架。该数据集的发布回应了非洲劳动市场微观异质性数据稀缺的困境，为研究者审视非正规就业的性别分化、生命周期效应与婚姻状态关联提供了可复用的结构化证据，对制定包容性劳动政策具有基础性支撑价值。

当前挑战

该数据集所回应的领域问题在于非正规就业测度的跨国可比性与细分维度下的统计稳健性，非正规就业本身界定模糊、各国调查口径不一，导致跨国汇总易受测量误差与覆盖偏差的干扰。构建过程中，原始ILOSTAT数据存在指标定义随年份调整、部分国家缺失严重、年龄与婚姻状况交叉分类后单元格稀疏等难题，加之元数据清单中明确标注的国家与上游发布者字段缺失，进一步增加了来源追溯与地理归类的复杂度。此外，非正规就业人数以千人为单位呈现，未提供抽样误差或置信区间，使得基于该数据的推断需格外审慎，避免将标签的统计分类误读为政策含义。

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，该数据集最经典的使用场景在于构建非洲地区非正规就业规模的结构化面板数据，进而支撑跨国家、跨性别、跨年龄组与婚姻状态的比较分析。研究者通常以性别和年龄作为核心分层维度，考察非正规就业在青年群体、女性劳动者以及不同婚姻状态人群中的分布差异，并借助时间序列观察2000至2025年间非洲45国的演变轨迹，从而刻画非正规经济部门的性别分化与代际特征。

实际应用

在政策评估与项目设计层面，该数据集可服务于国际组织、区域机构与各国劳动部门对非正规就业脆弱群体的精准识别。实务工作者可依据性别、年龄与婚姻状态的交叉统计，定位最易陷入低质量就业的子群体，进而设计有针对性的技能培训、社会保险扩展与女性就业支持方案。同时，该数据可用于监测非洲区域就业目标的进展，为劳动力市场干预措施的优先排序提供量化依据。

衍生相关工作

围绕该数据集，Electric Sheep Africa系列已衍生出多个面向非洲劳动市场的可加载数据集与元数据目录，支持研究者将非正规就业指标与就业结构、工资、工时等主题进行连接分析。在学术层面，相关衍生工作集中于构建非洲非正规经济跨国面板、检验性别与年龄对就业正规化的影响，以及开发可复现的笔记本工作流。这些工作共同扩展了ILOSTAT原始来源在非洲情境下的机器学习就绪程度与跨域复用能力。