electricsheepafrica/africa-ilo-emp-temp-sex-ocu-ins-nb-employment-by-sex-occupation-and-public-private-se

--- 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, occupation and public/private sector (thousands) | Africa (ILOSTAT)" --- # Employment by sex, occupation and public/private sector (thousands) | Africa (ILOSTAT) 🌍 **34,259 observations** · **47 Africa countries** · **1991–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **34,259 observations** of `Employment` data across **47 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_OCU_INS_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_OCU_INS_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 47 Africa countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `ZAF` | 3,739 | 2000 | 2024 | | `MUS` | 2,895 | 2003 | 2024 | | `EGY` | 2,094 | 2009 | 2024 | | `AGO` | 1,611 | 2004 | 2025 | | `GHA` | 1,454 | 1991 | 2024 | | `SYC` | 1,390 | 2014 | 2024 | | `ZMB` | 1,309 | 2015 | 2024 | | `MLI` | 1,297 | 2013 | 2024 | | `RWA` | 1,259 | 2014 | 2025 | | `ZWE` | 1,173 | 2011 | 2024 | | `UGA` | 1,060 | 2010 | 2021 | | `TZA` | 1,015 | 2001 | 2024 | | `BWA` | 981 | 2006 | 2024 | | `SEN` | 885 | 2015 | 2024 | | `NAM` | 742 | 2012 | 2018 | | ... | _32 more countries_ | | | ## Indicators (sample) - `EMP_TEMP_SEX_OCU_INS_NB` — Employment by sex, occupation and public/private sector (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_OCU_INS_NB` | | `indicator.label` | `string` | Indicator name in English | `Employment by sex, occupation and pub…` | | `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` | | `classif2` | `string` | Second classification variable where applicable | `INS_SECTOR_TOTAL` | | `classif2.label` | `string` | — | `Institutional sector: 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` | `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-temp-sex-ocu-ins-nb-employment-by-sex-occupation-and-public-private-se") 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_OCU_INS_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_TEMP_SEX_OCU_INS_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_TEMP_SEX_OCU_INS_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_emp_temp_sex_ocu_ins_nb_employment_by_sex_occupation_and_public_private_se_2025, title = {Employment by sex, occupation and public/private sector (thousands) | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_TEMP_SEX_OCU_INS_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-emp-temp-sex-ocu-ins-nb-employment-by-sex-occupation-and-public-private-se}} } ``` ## 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_OCU_INS_NB_This dataset contains 34,259 observations of employment data across 47 Africa countries, spanning 1991 to 2025, covering 1 distinct indicator: Employment by sex, occupation and public/private sector (thousands). It is sourced from the ILOSTAT database of the International Labour Organization (ILO), processed and filtered for Africa, and suitable for tabular classification, regression, and time-series forecasting tasks.

搜集汇总

数据集介绍

构建方式

在国际劳工统计领域，非洲地区就业结构的精细化测度长期依赖跨国可比数据源。该数据集由Electric Sheep Africa基于国际劳工组织ILOSTAT中央统计数据库进行标准化重包装，覆盖47个非洲国家1991至2025年间按性别、职业及公共/私营部门分类的就业观测记录，总量达34,259条。构建过程以元数据驱动的方式整编原始指标，将分散的统计表转换为Parquet格式，并补充发现层级的文档说明与来源注释，从而形成可直接用于分析工作流的次级数据集。

使用方法

使用该数据集时，可借助Hugging Face datasets库以load_dataset函数直接加载，并通过查看特征结构与样本记录快速把握数据模式。由于数据为表格形态，可经to_pandas方法转换为数据框以衔接常规统计分析流程。在建模之前，建议先行检查各变量的缺失情况与单位定义，保留缺失值直至确立可辩护的插补规则，并利用显式的国家、年份与指标字段与其他非洲数据集进行连接，以构建可复现的分析笔记。

背景与挑战

背景概述

国际劳工组织（ILO）自1919年成立以来始终致力于全球劳动统计标准的制定与数据编纂，其ILOSTAT数据库是劳动经济学领域最具权威性的跨国比较数据源之一。在此背景下，Electric Sheep Africa于2026年对ILOSTAT中非洲区域就业数据进行了系统性再包装，构建了涵盖47个非洲国家、1991至2025年间34,259条观测记录的就业数据集，按性别、职业及公共/私营部门维度提供千人规模的就业估计。该数据集的核心研究问题在于揭示非洲劳动力市场结构性差异，为性别就业差距、职业隔离及公私部门就业分布等议题提供可复现的实证基础，其开放许可与标准化元数据架构显著降低了非洲劳动经济研究的准入门槛。

当前挑战

该数据集所涉领域面临的核心挑战在于非洲各国劳动统计体系发育不均，非正规经济部门庞大的现实使得正规就业指标难以完整映射实际劳动参与状况，跨国可比性受限于各国统计口径与调查方法的历时性差异。构建过程中，源数据在职业分类与公私部门界定上存在国别报告不一致，部分年份与指标存在系统性缺失值，需审慎处理以避免插补引入偏误；元数据清单中上游出版者与国别字段的缺失亦增加了溯源难度。此外，2025年数据可能包含估计值而非实测值，使用者须在建模前确认变量定义、单位及估算方法，以免将统计推断误作观测事实。

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叉领域中，该数据集凭借其覆盖47个非洲国家、横跨1991至2025年的34,259条观测记录，成为刻画性别、职业与公私部门就业结构的经典面板数据资源。研究者通常将其用于分析非洲劳动力市场的性别隔离程度、职业分布差异以及公共部门与私营部门在吸纳就业方面的角色分化，亦可用于构建时间序列模型以追踪就业结构的长期演变趋势。

解决学术问题

该数据集有效回应了非洲劳动统计领域长期存在的数据碎片化与可比性不足问题。通过ILOSTAT标准化框架整合多国就业数据，研究者得以在统一口径下开展跨国比较研究，检验性别平等、职业分层与部门分割等理论假说，为非洲劳动力市场制度变迁的实证分析提供了可靠的数据基础，推动了区域比较劳动经济学的方法论进展。

实际应用

在实际应用层面，该数据集为国际组织、政策研究机构与非洲各国劳工部门提供了就业监测与政策评估的量化依据。其可用于识别性别就业差距的重点行业与部门，评估公共就业政策的实施效果，辅助制定针对性的职业培训与就业促进方案，亦可嵌入数据驱动的决策支持系统，服务于非洲区域一体化与包容性增长的政策议程。

数据集最近研究

最新研究方向

伴随国际劳工组织统计数据库对非洲劳动力市场结构化数据的持续释放，围绕性别、职业与公私部门交叉维度的就业分布研究正成为劳动经济学与发展经济学的交汇前沿。该数据集覆盖47个非洲国家、1991至2025年间逾三万观测，为刻画非洲非正规经济中女性就业的边缘化格局、公共部门就业的性别隔离以及职业结构变迁提供了长时段面板基础。当前研究热点聚焦于运用该数据检验结构性转型假说，探讨公私部门就业配置如何映射殖民遗产与后调整时代政策效应，并借助机器学习方法识别性别职业隔离的跨国异质性。其意义在于为非洲包容性增长政策、性别平等干预及劳动力市场制度比较提供可复现的证据支撑，推动全球南方数据主权与开放科学实践。