electricsheepafrica/africa-ilo-emp-nifl-sex-edu-nb-informal-employment-by-sex-and-education-thousands

--- 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 and education (thousands) | Africa (ILOSTAT)" --- # Informal employment by sex and education (thousands) | Africa (ILOSTAT) 🌍 **8,259 observations** · **45 Africa countries** · **1999–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **8,259 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_NIFL_SEX_EDU_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_EDU_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` | 1,175 | 2000 | 2024 | | `MUS` | 547 | 2012 | 2024 | | `EGY` | 486 | 2008 | 2024 | | `MLI` | 429 | 2013 | 2024 | | `AGO` | 387 | 2004 | 2025 | | `RWA` | 364 | 2017 | 2025 | | `SEN` | 351 | 2011 | 2024 | | `ZMB` | 348 | 2017 | 2024 | | `UGA` | 319 | 2010 | 2021 | | `ZWE` | 318 | 2011 | 2024 | | `BWA` | 294 | 2006 | 2024 | | `CIV` | 249 | 2012 | 2022 | | `NAM` | 216 | 2012 | 2018 | | `GMB` | 188 | 2012 | 2025 | | `BFA` | 173 | 2018 | 2024 | | ... | _30 more countries_ | | | ## Indicators (sample) - `EMP_NIFL_SEX_EDU_NB` — Informal employment by sex and education (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_EDU_NB` | | `indicator.label` | `string` | Indicator name in English | `Informal employment by sex and educat…` | | `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.) | `EDU_AGGREGATE_TOTAL` | | `classif1.label` | `string` | — | `Education (Aggregate levels): 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` | `string` | — | `C3:2620` | | `note_classif.label` | `string` | — | `Nonstandard education level: Includin…` | | `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-edu-nb-informal-employment-by-sex-and-education-thousands") 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_EDU_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_NIFL_SEX_EDU_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_NIFL_SEX_EDU_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_emp_nifl_sex_edu_nb_informal_employment_by_sex_and_education_thousands_2025, title = {Informal employment by sex and education (thousands) | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_NIFL_SEX_EDU_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-emp-nifl-sex-edu-nb-informal-employment-by-sex-and-education-thousands}} } ``` ## 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_EDU_NB_This dataset contains informal employment data for 45 African countries from 1999 to 2025, disaggregated by sex and education (in thousands). It comprises 8,259 observations, with the core indicator EMP_NIFL_SEX_EDU_NB representing informal employment by sex and education (thousands). The data is sourced from the International Labour Organization (ILO)s ILOSTAT database, retrieved via API and filtered to African country codes, harmonized using International Conference of Labour Statisticians (ICLS) definitions. The schema includes columns such as country code (ref_area), source (source), indicator (indicator), sex (sex), education classification (classif1), year (time), observed value (obs_value), along with data quality flags and notes. Organized in tabular format, it is suitable for tabular classification, regression, and time-series forecasting tasks, supporting machine learning applications. Released under the CC-BY-4.0 license, the dataset is repackaged by Electric Sheep Africa as part of a unified, ML-ready data layer for Africa on HuggingFace.

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数据库ILOSTAT所汇集的正规就业统计原始记录，经Electric Sheep Africa团队系统化整理与重新架构，以Parquet格式封装发布于Hugging Face平台。数据覆盖非洲45个国家，时间跨度自1999年延伸至2025年，共计8259条观测，围绕非正规就业按性别与受教育程度分列的指标（以千人为计量单位）进行组织。构建过程中保留了原始发布方的指标定义与单位体系，并附带标准化元数据清单、来源溯源说明及数据分析导向的上下文信息，使得非洲非正规经济领域的分散统计得以整合为可复现、可机读的结构化资源。

特点

数据集以表格与文本双模态呈现，体量处于1K至10K之间，隶属经济金融领域，采用CC BY 4.0开放许可。其核心特征在于多维分组结构，将性别与受教育程度同时纳入非正规就业的观测维度，为剖析非洲劳动力市场中性别差异与教育分层的交互作用提供了细粒度依据。元数据经过标准化处理，附带主题标签、国别覆盖范围与数据质量提示，并明确标识了国家与上游发布方等元数据缺口，提醒使用者审慎对待地理归属与变量定义。多语言属性为单语英语，便于国际研究者直接接入。

使用方法

研究者可借助Hugging Face的datasets库以一行代码加载该数据集，随后打印数据集结构、特征列表与样本记录，快速完成初步探查。当需要进一步分析时，可将首个数据分割转换为Pandas数据框，便于开展统计建模与可视化。使用过程中建议先检验模式结构与缺失值分布，再按地理、时间与亚组变量进行剖面分析；在与其他Electric Sheep Africa数据集合并时，应利用显式的国家、年份与指标字段进行连接，并在建模前确认变量定义与计量单位。所有分析应保留缺失值直至确立可辩护的插补规则，并在成果中引用原始来源与仓库出处。

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，其规模与结构直接关系到劳动权益保障与人力资本配置效率。国际劳工组织长期通过ILOSTAT数据库系统采集各国劳动力统计数据，为跨国比较提供权威基准。该数据集由Electric Sheep Africa团队基于ILOSTAT源数据于2026年整理发布，涵盖45个非洲国家自1999年至2025年间按性别与教育程度分列的非正规就业人口估计，共计8259条观测，以千人为计量单位，采用CC BY 4.0许可协议开放获取。其核心研究问题在于揭示非洲地区非正规就业的性别差异与教育梯度，为劳动经济学与发展经济学交叉研究提供结构化证据基础。

当前挑战

非正规就业的统计界定在各国之间存在显著异质性，ILOSTAT虽提供统一指标框架，但源数据依赖各国劳动力调查的覆盖范围与抽样设计，导致跨国可比性受限，部分国家年份缺失与口径调整亦削弱时间序列的连续性。就领域问题而言，如何在教育程度与性别交叉维度上准确刻画非正规就业分布，涉及非正规部门统计中普遍存在的信息偏差与漏报问题。构建过程中，元数据清单缺失国家字段与上游发布者信息，需依赖标题与来源注释推断地理覆盖，且缺失值处理策略需在不损害数据完整性的前提下审慎确定，对下游建模的稳健性构成直接约束。

常用场景

经典使用场景

在国际劳工统计与非洲非正规经济研究的交叉领域，该数据集以按性别与受教育程度分列的就业人数（千人）为核心变量，构筑起覆盖45个非洲国家、时间跨度自1999年至2025年的面板观测体系，总计8,259条记录。经典使用场景集中于对非洲各国非正规就业结构的性别差异与教育梯度进行横向比较与纵向追踪，研究者可借助其标准化表格结构与帕凯格式，直接开展分组描述统计、缺失模式探查以及与国家宏观经济指标的时间序列对齐，从而在统一口径下审视非正规部门中劳动力配置的演化轨迹。

解决学术问题

长期以来，非洲非正规就业研究受制于跨国可比数据匮乏、性别与教育维度割裂以及时间序列断裂等瓶颈，难以在统一框架下检验结构性假说。该数据集通过整合ILOSTAT的权威统计口径，将性别与受教育程度同时纳入非正规就业人数的测量体系，使研究者得以探讨教育扩张是否伴随非正规就业的女性化、不同教育层级劳动力的部门吸纳差异等经典议题。其意义在于为劳动经济学与发展经济学的跨国实证提供可复现的数据基础，并推动对非正规经济统计边界与方法论争议的持续反思。

衍生相关工作

围绕该数据集及其所属的Electric Sheep Africa目录，已衍生出若干具有代表性的研究方向与工程实践：一方面，研究者将其与ILOSTAT其他指标数据集进行跨国面板合并，开展非正规就业决定因素的面板回归与聚类分析；另一方面，该目录推动了一系列标准化元数据卡片、可复现笔记本与数据发现工具的构建，形成了面向非洲公开数据的工程化整理范式。这些工作共同拓展了非正规经济数据的可及性与互操作性，并为后续机器学习建模与因果推断研究奠定了数据治理层面的基础。