electricsheepafrica/africa-ilo-emp-temp-sex-edu-mts-nb-employment-by-sex-education-and-marital-status-tho

--- 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, education and marital status (thousands) | Africa (ILOSTAT)" --- # Employment by sex, education and marital status (thousands) | Africa (ILOSTAT) 🌍 **99,750 observations** · **49 Africa countries** · **1982–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **99,750 observations** of `Employment` data across **49 Africa countries**, spanning **1982–2025**, 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_EDU_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_EDU_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 49 Africa countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `ZAF` | 10,449 | 2000 | 2024 | | `MUS` | 7,037 | 2001 | 2024 | | `EGY` | 6,373 | 2008 | 2024 | | `GHA` | 4,694 | 1991 | 2024 | | `MLI` | 4,148 | 2009 | 2024 | | `AGO` | 3,648 | 2004 | 2025 | | `RWA` | 3,494 | 2014 | 2025 | | `ZMB` | 3,209 | 2015 | 2024 | | `TUN` | 2,789 | 2005 | 2023 | | `TZA` | 2,785 | 2001 | 2024 | | `BWA` | 2,760 | 2006 | 2024 | | `TGO` | 2,669 | 2006 | 2022 | | `SEN` | 2,621 | 2011 | 2024 | | `ZWE` | 2,567 | 2011 | 2024 | | `BFA` | 2,412 | 2006 | 2024 | | ... | _34 more countries_ | | | ## Indicators (sample) - `EMP_TEMP_SEX_EDU_MTS_NB` — Employment by sex, education 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_EDU_MTS_NB` | | `indicator.label` | `string` | Indicator name in English | `Employment by sex, education and 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.) | `EDU_AGGREGATE_TOTAL` | | `classif1.label` | `string` | — | `Education (Aggregate levels): 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_classif` | `string` | — | `C3:3710` | | `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-temp-sex-edu-mts-nb-employment-by-sex-education-and-marital-status-tho") 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_EDU_MTS_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_TEMP_SEX_EDU_MTS_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_TEMP_SEX_EDU_MTS_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_emp_temp_sex_edu_mts_nb_employment_by_sex_education_and_marital_status_tho_2025, title = {Employment by sex, education and marital status (thousands) | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_TEMP_SEX_EDU_MTS_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-emp-temp-sex-edu-mts-nb-employment-by-sex-education-and-marital-status-tho}} } ``` ## 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_EDU_MTS_NB_This dataset contains employment data from the International Labour Organization (ILO) ILOSTAT database, specifically focused on Africa. It includes 99,750 observations across 49 African countries spanning the years 1982 to 2025, with the core indicator EMP_TEMP_SEX_EDU_MTS_NB representing employment by sex, education, and marital status (in thousands). The data provides detailed disaggregation dimensions including sex (total, male, female), education (aggregate levels), and marital status (aggregate), structured with columns for country codes, year, observed values, and source annotations. The dataset is suitable for tasks such as tabular classification, regression, and time-series forecasting, aiming to provide machine learning-ready data for African labor market research.

搜集汇总

数据集介绍

构建方式

在全球劳动力市场统计体系中，国际劳工组织（ILO）的ILOSTAT数据库长期作为权威数据源，为就业结构分析提供跨国可比的基础信息。本数据集由Electric Sheep Africa基于ILOSTAT公开数据进行标准化再封装，覆盖非洲49个国家，1982年至2025年期间共99,750条观测记录，以就业人数（千人）为度量单位，按性别、教育程度与婚姻状况交叉分类。数据以Parquet格式存储，附带标准化元数据说明与来源标注，原始发布者信息与许可条款均予以保留，确保数据溯源链条完整可查。

使用方法

研究者可通过Hugging Face的datasets库以load_dataset函数直接加载数据，获取数据集的划分信息与特征结构，并可将表格数据转换为Pandas数据框以进行后续分析。在建模之前，建议先检查数据模式与缺失分布，确认变量定义与计量单位，再按地理、时间及子群维度进行变量画像。若需整合其他Electric Sheep Africa数据集，可利用国家、年份和指标字段进行连接。所有分析流程应保留对原始来源与再封装仓库的引用，以确保研究可复现。

背景与挑战

背景概述

伴随全球劳动力市场结构性变迁，按性别、教育程度与婚姻状况细分就业数据成为洞察社会不平等与人力资本配置的关键。非洲大陆长期面临劳动力统计碎片化困境，国际劳工组织（ILO）虽通过ILOSTAT提供权威指标，但非洲区域数据分散且可及性不足。Electric Sheep Africa于2026年将ILOSTAT中非洲就业数据系统化重编码为HuggingFace数据集，覆盖49国、99,750条观测，时间跨度1982至2025年。该数据集以标准化元数据与机器学习就绪格式，填补了非洲劳动经济学微观异质性分析的空白，为研究者、政策制定者及国际组织提供了可复现的跨国比较基准。

当前挑战

该数据集所回应的领域挑战在于：非洲劳动力市场长期存在性别就业差距、教育回报异质性及婚姻状况对劳动参与的非线性影响，但多国统计能力薄弱导致跨国可比指标稀缺，难以支撑精准的政策评估与因果推断。构建过程中面临的挑战包括：原始ILOSTAT数据中部分国家年份缺失严重，婚姻状况与教育分类编码在各国间存在口径差异，单位千人数导致小国或细分群体观测值精度受损；元数据中country与upstream_publisher字段缺失，需依赖标题与来源上下文重建地理归属，可能引入推断误差；此外，泛化标准化的重编码过程可能模糊原始调查方法差异，增加分析者误读变量定义的风险。

常用场景

经典使用场景

在劳动经济学与人口统计学的交叉领域，就业结构的分层分析始终是理解劳动力市场运行机制的核心议题。该数据集以非洲49个国家为地理范畴，汇集了1982年至2025年间按性别、教育程度与婚姻状况交叉分类的就业人数（以千人为单位）数据，共计近十万条观测记录。其经典用法在于构建多维度面板数据模型，用以刻画不同性别、学历层次与婚姻状态群体在非洲各国就业市场中的分布特征与动态演变趋势，进而支撑劳动参与率差异分解、教育回报率估计以及婚姻状况与就业关联性等议题的实证检验。

解决学术问题

长期以来，非洲劳动力市场研究面临微观就业数据匮乏且跨国可比性不足的困境，尤其缺乏将性别、教育与婚姻状况三重维度同时纳入的标准化统计资料。该数据集依托国际劳工组织ILOSTAT的权威统计框架，系统整合了非洲区域跨度逾四十年的就业观测值，有效缓解了既有文献中因数据碎片化而导致的估计偏误与外部效度局限。其学术意义在于为发展经济学、劳动社会学及性别研究提供了可复现的跨国比较基础，推动了关于教育扩张如何影响不同婚姻状态群体就业参与、以及性别就业差距如何随教育水平变化等理论假说的量化验证。

实际应用

在政策实践层面，该数据集为非洲各国政府、国际发展机构及非政府组织提供了评估劳动力市场政策效果的基础性证据。劳动主管部门可据此识别低学历已婚女性等就业弱势群体的分布特征与变化趋势，从而设计有针对性的职业技能培训与就业促进方案。国际组织在制定减贫与性别平等干预策略时，亦可借助该数据集进行跨国基准比较与目标群体定位。此外，企业人力资源部门与市场研究机构能够利用该数据洞察不同教育层次劳动力的供给结构，辅助区域投资决策与人才战略规划。

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性别平等议题交织的学术语境下，该数据集凭借覆盖49个非洲国家、1982至2025年间近十万条观测的时空纵深，正推动劳动经济学研究从总量就业估算转向婚姻状态、教育分层与性别交互效应的精细化识别。前沿方向聚焦于运用面板数据模型与机器学习方法，揭示已婚女性教育回报率的异质性、非正式就业中的婚姻溢价现象，以及教育扩张对性别就业差距的收敛作用。ILOSTAT作为国际劳工统计权威来源，其数据被广泛用于评估非洲大陆自由贸易区框架下的劳动力配置效率，以及联合国可持续发展目标中体面工作议程的进展监测。Electric Sheep Africa的元数据标准化实践则降低了跨国比较研究的数据清洗成本，为循证政策制定提供了可复现的分析基座。