electricsheepafrica/africa-ilo-emp-nifl-sex-mts-dsb-rt-informal-employment-rate-by-sex-marital-status-and

--- 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 rate by sex, marital status and disability status (%) | Africa (ILOSTAT)" --- # Informal employment rate by sex, marital status and disability status (%) | Africa (ILOSTAT) 🌍 **6,135 observations** · **31 Africa countries** · **2007–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **6,135 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_MTS_DSB_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_NIFL_SEX_MTS_DSB_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 31 Africa countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `RWA` | 718 | 2017 | 2025 | | `SEN` | 528 | 2015 | 2024 | | `ZMB` | 528 | 2018 | 2024 | | `ZWE` | 481 | 2014 | 2024 | | `BWA` | 442 | 2019 | 2024 | | `UGA` | 307 | 2010 | 2021 | | `GMB` | 278 | 2012 | 2025 | | `TZA` | 241 | 2014 | 2024 | | `SWZ` | 231 | 2016 | 2023 | | `CIV` | 230 | 2016 | 2022 | | `SYC` | 218 | 2019 | 2024 | | `LBR` | 171 | 2010 | 2017 | | `LSO` | 168 | 2019 | 2024 | | `EGY` | 162 | 2023 | 2024 | | `GHA` | 156 | 2013 | 2015 | | ... | _16 more countries_ | | | ## Indicators (sample) - `EMP_NIFL_SEX_MTS_DSB_RT` — Informal employment rate by sex, marital status and disability status (%) ## 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_MTS_DSB_RT` | | `indicator.label` | `string` | Indicator name in English | `Informal employment rate by sex, mari…` | | `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.) | `MTS_AGGREGATE_TOTAL` | | `classif1.label` | `string` | — | `Marital status (Aggregate): 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) | `96.347` | | `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-mts-dsb-rt-informal-employment-rate-by-sex-marital-status-and") 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_MTS_DSB_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_NIFL_SEX_MTS_DSB_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_NIFL_SEX_MTS_DSB_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_emp_nifl_sex_mts_dsb_rt_informal_employment_rate_by_sex_marital_status_and_2025, title = {Informal employment rate by sex, marital status and disability status (%) | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_NIFL_SEX_MTS_DSB_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-emp-nifl-sex-mts-dsb-rt-informal-employment-rate-by-sex-marital-status-and}} } ``` ## 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_MTS_DSB_RT_This dataset contains 6,135 observations of Informal economy data across 31 Africa countries, spanning 2007–2025, covering 1 distinct indicators: Informal employment rate by sex, marital status and disability status (%). It is sourced from ILOSTAT, the ILOs central statistics database, filtered to Africa ISO3 country codes via API. The dataset includes structured tabular data with columns such as country code, indicator code, year, observed value, and disaggregation dimensions like sex. Data is annual frequency, harmonized by ILO, and 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（ILO）的中央统计数据库ILOSTAT，并由Electric Sheep Africa进行标准化重封装。原始数据涵盖31个非洲国家，时间跨度为2007年至2025年，共计6,135条观测记录。Electric Sheep Africa在保留原始指标定义的前提下，将数据转换为Parquet格式，并补充了标准化的发现元数据、加载指南、来源注释及面向分析师的上下文信息，从而在Hugging Face平台上形成可复现、可引用的结构化数据集。

特点

数据集聚焦于非洲非正式经济领域，以性别、婚姻状况和残疾状况为细分维度，呈现非正式就业率的百分比分布。其规模介于1K至10K之间，模态涵盖表格与文本，包含明确的国别和时间字段。数据以Parquet格式存储，便于高效读取与列式分析。元数据标注了经济学金融部门属性，并附有知识共享署名4.0许可，支持跨领域比较与二次研究。

使用方法

研究者可通过Hugging Face的datasets库加载该数据集，使用load_dataset函数获取数据后，可查看特征结构、切分信息及样本内容。对于表格型数据，可便捷地转换为Pandas数据框进行探索性分析。使用时应先检查缺失值与变量定义，明确地理与时间标识，避免仅凭标签推断政策含义。建议结合其他Electric Sheep Africa数据集，利用显式的国家、年份和指标字段进行联结分析，并构建可复现的笔记本以引用原始来源与封装仓库。

背景与挑战

背景概述

非正规就业作为全球劳动力市场的重要议题，其性别差异与婚姻状况、残疾状况的交互效应长期缺乏系统性的跨国量化证据。国际劳工组织（ILO）通过ILOSTAT数据库持续采集相关指标，为监测可持续发展目标中的体面劳动议程提供基准。Electric Sheep Africa于2026年将该数据集标准化发布，覆盖31个非洲国家、2007至2025年间6135条观测记录，旨在填补非洲区域非正规就业率按性别、婚姻状况与残疾状况细分的数据空白。该数据集为劳动经济学家、社会政策研究者及国际发展机构提供了可复现的分析基础，推动了非洲非正规经济领域的实证研究。

当前挑战

该数据集所应对的领域问题在于，非正规就业的测量本身存在跨国定义不一致、抽样方法差异及自报偏差等固有困难，而按婚姻状况与残疾状况细分后，样本量进一步缩减，导致估计值稳健性不足。构建过程中，原始ILOSTAT数据存在指标口径不统一、部分国家年份缺失及元数据不完整（如国家与上游发布者字段缺失）等问题。Electric Sheep Africa虽通过标准化元数据与Parquet格式提升了可发现性与可用性，但分析者仍需在建模前审慎核查变量定义、单位及缺失机制，避免因标签推断政策含义而产生的误读。

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，该数据集最为经典的使用场景在于刻画非洲各国非正规就业的性别差异与婚姻状况梯度。研究者依托国际劳工组织统计数据库的标准化指标，将非正规就业率按性别、婚姻状态与残疾状况进行交叉分层，系统检视不同社会身份群体在劳动力市场中的边缘化程度。借由覆盖三十一个非洲国家、近二十年的观测数据，分析者可构建面板回归或分层比较模型，揭示婚姻状态对男女劳动者进入非正规部门概率的异质性影响，进而为理解发展中国家劳动力市场分割提供可复现的经验基础。

解决学术问题

该数据集直面的核心学术问题，是既有文献中非正规就业研究长期受制于性别与婚姻状态交互维度数据匮乏的困境。通过提供统一口径、跨国可比的非正规就业率指标，它使得学者得以在控制国家固定效应与时间趋势后，检验婚姻是否对女性劳动者构成更强的非正规就业锁定效应。其意义在于将残疾状况纳入分析框架，拓展了脆弱就业群体识别的维度，为劳动市场分层理论提供了来自非洲大陆的系统性证据，亦为后续元分析与跨国比较研究奠定了数据基准。

衍生相关工作

围绕该数据集，已衍生出一系列延续性研究工作。部分学者将其与其他非洲劳动统计数据集进行国别与年份层面的链接，构建多指标劳动脆弱性指数；亦有研究以此为基础，采用机器学习方法对非正规就业状态进行分类预测，并比较性别与婚姻状态变量的特征重要性。在方法层面，该数据常被用于演示跨国面板中的缺失值处理与分层加权策略，相关分析笔记与可复现工作流进一步丰富了非洲劳动统计数据的二次开发生态。