electricsheepafrica/africa-ilo-emp-temp-sex-est-mts-nb-employment-by-sex-establishment-size-and-marital-s

--- 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 - employment - ilo - labour pretty_name: "Employment by sex, establishment size and marital status (thousands) | Africa (ILOSTAT)" --- # Employment by sex, establishment size and marital status (thousands) | Africa (ILOSTAT) 🌍 **47,022 observations** · **41 Africa countries** · **1991–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **47,022 observations** of `Employment` data across **41 Africa countries**, spanning **1991–2025**, 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_TEMP_SEX_EST_MTS_NB) - **Publisher:** International Labour Organization (ILO) - **License:** [cc-by-4.0](https://creativecommons.org/licenses/by/4.0/) - **Topic:** Employment ## Methodology Data pulled directly from the ILOSTAT REST API at `https://rplumber.ilo.org/data/indicator?id=EMP_TEMP_SEX_EST_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 41 Africa countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `ZAF` | 8,713 | 2000 | 2024 | | `EGY` | 3,394 | 2008 | 2023 | | `MLI` | 3,090 | 2013 | 2024 | | `AGO` | 2,882 | 2004 | 2025 | | `SEN` | 2,112 | 2015 | 2024 | | `BWA` | 2,045 | 2006 | 2024 | | `RWA` | 1,649 | 2014 | 2020 | | `TZA` | 1,557 | 2008 | 2024 | | `ZMB` | 1,454 | 2017 | 2024 | | `NAM` | 1,323 | 2012 | 2018 | | `GHA` | 1,300 | 1991 | 2015 | | `SYC` | 1,258 | 2019 | 2024 | | `BFA` | 1,183 | 2014 | 2024 | | `CIV` | 1,073 | 2012 | 2019 | | `KEN` | 1,036 | 2019 | 2022 | | ... | _26 more countries_ | | | ## Indicators (sample) - `EMP_TEMP_SEX_EST_MTS_NB` — Employment by sex, establishment size 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_TEMP_SEX_EST_MTS_NB` | | `indicator.label` | `string` | Indicator name in English | `Employment by sex, establishment size…` | | `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.) | `EST_AGGREGATE_TOTAL` | | `classif1.label` | `string` | — | `Establishment size (Aggregate): Total` | | `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) | `13984.984` | | `obs_status` | `string` | Observation status flag (e.g. provisional, unreliable) | `U` | | `obs_status.label` | `string` | — | `Unreliable` | | `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-temp-sex-est-mts-nb-employment-by-sex-establishment-size-and-marital-s") 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_TEMP_SEX_EST_MTS_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_TEMP_SEX_EST_MTS_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_TEMP_SEX_EST_MTS_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_emp_temp_sex_est_mts_nb_employment_by_sex_establishment_size_and_marital_s_2025, title = {Employment by sex, establishment size and marital status (thousands) | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_TEMP_SEX_EST_MTS_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-emp-temp-sex-est-mts-nb-employment-by-sex-establishment-size-and-marital-s}} } ``` ## 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 2026-05-26 via the Electric Sheep pipeline. Source URL: https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_TEMP_SEX_EST_MTS_NB_This dataset contains 47,022 observations of employment data across 41 African countries, spanning from 1991 to 2025, covering one specific indicator: Employment by sex, establishment size and marital status (thousands). The data is sourced from the ILOSTAT database of the International Labour Organization (ILO), retrieved via API and filtered to African country codes. It includes fields such as country code, country name, data source, indicator code, indicator name, sex disaggregation, classification variables, observation year, observed value, observation status, and related notes. The data is published at an annual frequency, harmonized by ILO, and includes caveats on data quality, such as the use of best source and limitations on disaggregation columns. The dataset is repackaged by Electric Sheep Africa as part of a unified, ML-ready data layer for Africa.

搜集汇总

数据集介绍

构建方式

该数据集由Electric Sheep Africa团队基于国际劳工组织（ILO）的ILOSTAT数据库进行工程化重构而成。原始数据源自ILOSTAT这一全球劳动统计的权威汇编，涵盖1991年至2025年间41个非洲国家的就业观测记录。构建过程中，团队对源数据进行了标准化元数据标注、格式转换与文档补充，将其封装为Hugging Face平台上的Parquet格式数据集，包含47,022条观测，覆盖按性别、机构规模及婚姻状况分类的就业指标，同时保留了原始发布者的许可与出处信息，确保数据可追溯且符合CC BY 4.0协议。

使用方法

研究者可通过Hugging Face datasets库便捷加载该数据集，使用load_dataset函数指定仓库路径即可获取数据对象，进而查看特征结构、划分信息及样本预览。对于表格分析任务，可将指定划分转换为Pandas DataFrame，以便进行缺失值检查、变量分布描述及跨国跨期比较。在建模前应优先审视数据模式与缺失情况，明确国家、年份、指标等字段的定义，并依据研究设计进行必要的合并或筛选。引用时需同时注明原始ILOSTAT来源与Electric Sheep Africa的仓库信息，确保分析过程可复现且遵循知识共享许可。

背景与挑战

背景概述

国际劳工组织（ILO）长期致力于构建全球劳动力市场统计体系，ILOSTAT作为其核心数据库，为就业结构、性别差异等议题提供权威数据支撑。在此背景下，Electric Sheep Africa于2026年对ILOSTAT中非洲区域数据进行系统化整理，形成涵盖41个非洲国家、1991至2025年间47,022条观测值的专项数据集，聚焦于按性别、机构规模与婚姻状况分类的就业人数（千人）。该数据集旨在填补非洲劳动力市场细分维度数据的可发现性与可复用性缺口，为比较劳动经济学与性别研究提供结构化基础，其元数据标准化实践亦提升了非洲公开数据的机器可读水平。

当前挑战

该数据集所应对的领域问题在于非洲劳动力市场细分维度统计的稀缺性与碎片化，传统上按机构规模与婚姻状况交叉分类的就业数据在非洲区域覆盖极为有限，且跨国家、跨年份的可比性面临定义与采集方法的异质性。构建过程中，源数据来自多国劳动力调查与统计年鉴，指标口径、缺失模式及报告标准不一，整合为统一表格需处理国家代码缺失、单位隐含及元数据不全等障碍；同时，婚姻状况与机构规模等敏感维度在部分国家存在报告偏差或覆盖缺口，对下游分析的效度构成持续挑战。

常用场景

经典使用场景

在劳动经济学与非洲发展研究的交汇领域，该数据集以性别、机构规模与婚姻状况为三重维度，系统刻画了1991至2025年间41个非洲国家的就业分布格局。研究者通常将其用于剖析非洲劳动力市场的结构性异质性，例如比较不同规模企业中已婚与未婚劳动者的性别就业差距，或追踪随时间推移的就业构成演变。其经典场景在于为跨国面板分析提供精细化分组依据，从而揭示非正式部门扩张、家庭劳动分工与就业脆弱性之间的关联，成为非洲就业研究领域不可或缺的基准数据资源。

解决学术问题

该数据集直面非洲劳动统计中长期存在的数据碎片化与维度缺失难题。既往研究常因缺乏按婚姻状况和机构规模交叉分类的就业数据，而难以精确检验家庭结构如何调节性别就业差异，亦无法有效评估小型与大型企业在吸纳不同婚姻状态劳动力方面的角色分化。通过提供统一口径、长时段、多国家的观测值，它使学者得以控制国家与年份固定效应，识别婚姻状况对女性就业参与的非线性影响，并检验机构规模在其中的中介机制，从而推动非洲就业理论从宏观总量描述转向微观异质性解释。

实际应用

在政策实践层面，该数据集为非洲各国劳工部门、国际发展机构及社会组织提供了可操作的证据基础。政策制定者可依据不同婚姻状况与机构规模下的性别就业分布，设计更具靶向性的职业培训、 childcare 支持及小微企业扶持计划，以缓解已婚女性面临的就业约束。国际组织亦可利用其监测可持续发展目标中关于充分就业与体面工作的进展，并在跨国比较中定位优先干预群体。此外，咨询机构与金融机构可借助该数据评估非洲不同规模企业的劳动力构成风险，优化投资与信贷决策。

数据集最近研究

最新研究方向

在非洲劳动力市场结构性转型与非正规经济持续扩张的背景下，该数据集以性别、企业规模与婚姻状况的三维交叉视角，为解析就业分层机制提供了稀缺的国别面板证据。当前前沿研究正依托此类微观分组数据，探究婚姻状态如何通过照料负担与议价能力差异，调节女性在微型与小型企业中的就业参与轨迹，进而揭示性别不平等在职业层级中的再生产逻辑。与此同时，国际劳工组织体面劳动议程与非盟2063年议程对包容性增长的持续关注，使该数据成为评估结构性转型政策成效的关键经验基础，其价值在于将个体人口特征与宏观经济变迁相联结，为非洲就业脆弱性研究开辟了精细化路径。