electricsheepafrica/africa-ilo-emp-nifl-sex-ocu-rt-informal-employment-rate-by-sex-and-occupation

--- 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 and occupation (%) | Africa (ILOSTAT)" --- # Informal employment rate by sex and occupation (%) | Africa (ILOSTAT) 🌍 **8,525 observations** · **43 Africa countries** · **1999–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **8,525 observations** of `Informal economy` data across **43 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_OCU_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_OCU_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 43 Africa countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `ZAF` | 1,234 | 2000 | 2024 | | `MUS` | 561 | 2012 | 2024 | | `EGY` | 506 | 2009 | 2024 | | `MLI` | 472 | 2013 | 2024 | | `AGO` | 452 | 2004 | 2025 | | `RWA` | 378 | 2017 | 2025 | | `ZMB` | 369 | 2017 | 2024 | | `UGA` | 327 | 2010 | 2021 | | `ZWE` | 324 | 2011 | 2024 | | `BWA` | 324 | 2006 | 2024 | | `SEN` | 298 | 2015 | 2024 | | `NAM` | 247 | 2012 | 2018 | | `BFA` | 191 | 2018 | 2024 | | `CIV` | 188 | 2016 | 2022 | | `GMB` | 184 | 2012 | 2025 | | ... | _28 more countries_ | | | ## Indicators (sample) - `EMP_NIFL_SEX_OCU_RT` — Informal employment rate by sex and occupation (%) ## 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_OCU_RT` | | `indicator.label` | `string` | Indicator name in English | `Informal employment rate by sex and o…` | | `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.) | `OCU_SKILL_TOTAL` | | `classif1.label` | `string` | — | `Occupation (Skill level): Total` | | `time` | `int64` | Observation year | `2025` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `94.488` | | `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-ocu-rt-informal-employment-rate-by-sex-and-occupation") 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_OCU_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_NIFL_SEX_OCU_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_NIFL_SEX_OCU_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_emp_nifl_sex_ocu_rt_informal_employment_rate_by_sex_and_occupation_2025, title = {Informal employment rate by sex and occupation (%) | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_NIFL_SEX_OCU_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-emp-nifl-sex-ocu-rt-informal-employment-rate-by-sex-and-occupation}} } ``` ## 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_OCU_RT_This dataset contains 8,525 observations of informal economy data across 43 Africa countries, spanning 1999–2025, covering 1 distinct indicator: Informal employment rate by sex and occupation (%). It is sourced from the ILOSTAT database of the International Labour Organization (ILO), repackaged for machine learning readiness.

搜集汇总

数据集介绍

构建方式

该数据集依托国际劳工组织（ILOSTAT）的权威统计体系构建，由Electric Sheep Africa团队对原始劳动力市场调查数据进行标准化重包装。构建过程以非洲区域非正规经济就业状况为核心关切，系统汇集1999年至2025年间43个非洲国家的调查记录，形成8525条观测样本。数据以Parquet列式格式存储，保留性别与职业维度的交叉分类，并通过统一元数据标注实现来源可追溯，确保原始统计口径与单位定义在研究流程中得以完整延续。

特点

数据集聚焦非洲非正规就业领域，以性别和职业为双重分类轴线，呈现非正规就业率的百分比测度。其显著特征在于时间跨度长达二十六年，覆盖43个非洲国家，兼具性别比较与职业分层的分析潜力。数据体量介于一千至一万条之间，采用表格与文本混合模态，附带标准化元数据、来源注释与分析导向说明。变量虽以数值型指标为主，但国家、年份与指标字段为跨数据集关联提供了结构化基础。

使用方法

研究者可通过Hugging Face的datasets库直接加载数据集，调用load_dataset函数获取数据对象并检视其结构。对于表格型分析，可将首个数据分割转换为Pandas数据框，便于开展统计描述与建模。使用前应核查数据文件的变量定义与计量单位，明确国家与年份字段的显式标识，谨慎处理缺失值并保留其原始状态。该数据集可与Electric Sheep Africa目录中其他非洲数据集按国家、年份及指标字段进行连接，适用于非正规就业的时空演变与性别差异分析。

背景与挑战

背景概述

非正规就业作为广大发展中国家劳动力市场的重要构成，长期关乎生计保障与体面劳动的衡量。国际劳工组织（ILO）依托ILOSTAT数据库，系统汇编全球劳动统计指标，为跨国比较提供权威依据。Electric Sheep Africa于2026年发布该数据集，整合ILOSTAT中按性别与职业分类的非正规就业率数据，覆盖43个非洲国家、8525条观测、跨1999至2025年。该数据集以标准化元数据与开放许可发布，服务于非洲劳动市场研究、性别差距分析与经济政策评估，为非洲数据发现与机器学习工作流提供可复现的结构化基础。

当前挑战

非正规就业的界定在不同国家与调查口径间存在差异，职业分类标准的版本更迭与抽样方法的不统一，使跨国、跨时段的横纵可比性成为核心难题。原始数据中缺失值与指标口径不一致普遍存在，要求分析者在建模前谨慎核查变量定义与单位。该数据集的多项元数据尚存缺口，国家字段与上游出版方信息未能完整声明，增加了地理归因与溯源的难度。如何在保留缺失模式的前提下构建可解释的模型，并避免由标签直接推导政策含义，是分析者面临的另一项挑战。

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刻画非洲各国非正规就业率的性别差异与职业分布格局。研究者依托覆盖43个非洲国家、1999至2025年的8525条观测记录，按性别与职业类别对非正规就业比例进行横截面与时序比较，揭示女性在低技能职业中非正规就业集中度偏高的结构性特征，进而支撑关于劳动力市场分割与性别不平等的实证分析。

解决学术问题

该数据集有效回应了非洲非正规经济统计口径不一、跨国可比数据匮乏的学术困境。通过ILOSTAT标准化指标框架，它为检验非正规就业与性别、职业结构之间的关联提供了统一观测基础，缓解了既往研究因样本碎片化而难以开展跨国面板分析的难题。其意义在于推动对非正规部门性别分层机制的系统性理解，并为非正规就业率测算方法的可比性讨论提供经验参照。

衍生相关工作

围绕该数据集，已衍生出若干与非洲劳动力市场相关的数据工程与研究工作。Electric Sheep Africa以此为基础发布标准化元数据目录，推动非洲公共数据在Hugging Face平台的机器学习就绪化整合；后续分析工作借助其国别与指标字段，与ILOSTAT其他就业指标及社会经济数据集进行联接，形成关于非正规经济、性别差距与职业结构的跨域比较研究。