electricsheepafrica/africa-ilo-ees-tees-sex-ins-geo-nb-employees-by-sex-public-private-sector-and-rural-u

--- 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 - employees - ilo - labour - employment pretty_name: "Employees by sex, public/private sector and rural/urban areas (thousands) | Africa (ILOSTAT)" --- # Employees by sex, public/private sector and rural/urban areas (thousands) | Africa (ILOSTAT) 🌍 **6,131 observations** · **44 Africa countries** · **1999–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **6,131 observations** of `Employees` data across **44 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=EES_TEES_SEX_INS_GEO_NB) - **Publisher:** International Labour Organization (ILO) - **License:** [cc-by-4.0](https://creativecommons.org/licenses/by/4.0/) - **Topic:** Employees ## Methodology Data pulled directly from the ILOSTAT REST API at `https://rplumber.ilo.org/data/indicator?id=EES_TEES_SEX_INS_GEO_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 44 Africa countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `ZAF` | 468 | 2008 | 2024 | | `EGY` | 441 | 2008 | 2024 | | `TUN` | 351 | 2006 | 2021 | | `MLI` | 297 | 2013 | 2024 | | `AGO` | 291 | 2004 | 2025 | | `GHA` | 273 | 2000 | 2024 | | `RWA` | 270 | 2014 | 2025 | | `ZMB` | 264 | 2015 | 2024 | | `ZWE` | 225 | 2011 | 2024 | | `SEN` | 216 | 2011 | 2024 | | `UGA` | 207 | 2010 | 2021 | | `TZA` | 180 | 2001 | 2020 | | `NGA` | 171 | 2011 | 2024 | | `TGO` | 162 | 2006 | 2022 | | `CIV` | 162 | 2012 | 2022 | | ... | _29 more countries_ | | | ## Indicators (sample) - `EES_TEES_SEX_INS_GEO_NB` — Employees by sex, public/private sector and rural/urban areas (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 | `EES_TEES_SEX_INS_GEO_NB` | | `indicator.label` | `string` | Indicator name in English | `Employees by sex, public/private sect…` | | `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.) | `INS_SECTOR_TOTAL` | | `classif1.label` | `string` | — | `Institutional sector: Total` | | `classif2` | `string` | Second classification variable where applicable | `GEO_COV_NAT` | | `classif2.label` | `string` | — | `Area type: National` | | `time` | `int64` | Observation year | `2025` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `3788.156` | | `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-ees-tees-sex-ins-geo-nb-employees-by-sex-public-private-sector-and-rural-u") 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"] == "EES_TEES_SEX_INS_GEO_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EES_TEES_SEX_INS_GEO_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EES_TEES_SEX_INS_GEO_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_ees_tees_sex_ins_geo_nb_employees_by_sex_public_private_sector_and_rural_u_2025, title = {Employees by sex, public/private sector and rural/urban areas (thousands) | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EES_TEES_SEX_INS_GEO_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-ees-tees-sex-ins-geo-nb-employees-by-sex-public-private-sector-and-rural-u}} } ``` ## 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=EES_TEES_SEX_INS_GEO_NB_This dataset contains 6,131 observations of Employees data across 44 Africa countries, spanning 1999–2025, covering 1 distinct indicators.

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（ILOSTAT）的原始统计资料进行系统性整合与重新封装。ILOSTAT作为全球劳工统计的核心数据库，长期汇集各国劳动力市场调查与行政记录。构建过程中，团队提取了非洲地区按性别、公共/私营部门及城乡地域划分的雇员数据，并统一转换为Parquet列式存储格式，以提升大数据分析场景下的读取效率。同时，数据集附带了标准化的元数据描述，涵盖来源出处、许可协议及变量说明，为后续分析提供可追溯的文档支持。

使用方法

研究者可通过Hugging Face的datasets库直接加载该数据集，利用内置的split划分快速获取训练或评估子集。对于表格分析任务，可调用to_pandas()方法将数据转换为DataFrame，进而执行缺失值探查、分组聚合或可视化。建议在使用前确认各变量的定义与单位，并保留原始缺失值以待合理的插补策略。该数据集亦支持与其他Electric Sheep Africa目录下的数据集进行基于国家、年份和指标字段的关联，以构建更丰富的分析工作流。

背景与挑战

背景概述

非洲大陆的劳动力市场长期面临结构性转型压力，公共与私营部门之间、城乡地域之间的就业分布差异深刻影响着区域经济发展与社会福利格局。国际劳工组织（ILO）作为全球劳动统计的权威机构，其ILOSTAT数据库持续为政策制定与学术研究提供基准数据。在此背景下，Electric Sheep Africa于2026年对ILOSTAT原始数据进行工程化整理，构建了覆盖44个非洲国家、时间跨度自1999年至2025年的雇员性别与部门分布数据集，包含6131条观测记录。该数据集以性别、公私部门及城乡属性为分类维度，为探究非洲就业结构的异质性提供了精细化的数据支撑，对推动非洲劳动经济学实证研究与性别平等政策评估具有重要参考价值。

当前挑战

该数据集所回应的核心领域问题在于：如何在跨国、跨时间的比较框架下，准确刻画非洲各国雇员在性别、公私部门与城乡维度上的分布特征，从而揭示劳动力市场的结构性差异与演变趋势。其构建过程中面临多重挑战。数据源自ILOSTAT多年度、多国家的异质性报送体系，各国统计口径、调查方法与覆盖范围存在显著差异，导致指标可比性受限。原始数据中部分国家与年份存在缺失值，需在保留缺失机制的前提下审慎处理。元数据层面存在国家字段与上游发布者信息缺位，增加了溯源与验证的复杂度。此外，性别与部门分类的标准化映射需兼顾国际劳工组织定义与各国本地实践之间的张力，对数据清洗与编码一致性提出了较高要求。

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，该数据集凭借其涵盖44个非洲国家、跨越1999至2025年的6131条观测记录，构成剖析性别、部门与城乡维度下雇员规模差异的基础性面板数据。研究者惯常将其用于刻画公共与私营部门中男女雇员数量的时序演变，并借助城乡分类揭示劳动力市场结构的空间异质性。此类分析往往以国家、年份与性别为联结键，与其他社会经济指标实现多层次匹配，从而支撑起描述性与解释性并重的实证叙事。

解决学术问题

该数据集直接回应了非洲劳动力统计中长期存在的性别与部门分割度量难题。既有研究常因缺乏统一口径的跨国可比数据而难以系统检验公共部门女性就业占比与城镇化进程的关联，或无法分离私营部门扩张对性别就业差距的异质性影响。本数据集以标准化国别面板形式提供分性别、分部门与分城乡的雇员计数，使学者得以在控制国家固定效应与时间趋势后，稳健估计结构性变量对就业构成的边际效应，为理解非洲劳动力市场转型提供了可复现的证据基础。

实际应用

在政策实践层面，该数据集为国际组织与非洲各国劳工部门监测体面劳动议程进展提供了量化抓手。发展机构可借助其识别公共部门女性雇员比例偏低或城乡就业机会失衡的典型国家，进而设计针对性的职业培训与招聘配额干预。咨询机构与智库则利用其构建国别就业风险画像，辅助企业在进入非洲市场时评估劳动力供给的性别结构与城乡分布。数据经Parquet格式封装，便于快速载入分析流水线，满足时效性要求较高的决策支持场景。

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性变迁的持续追踪，围绕性别、公私部门与城乡维度的就业分布研究正成为发展经济学与劳动经济学的交汇热点。该数据集以44个非洲国家1999至2025年间6131条观测记录为基底，为解析女性在正规与非正规部门间的参与梯度、城乡二元结构下的就业性别分化提供了长时序面板支撑。当前前沿研究倾向于将此数据与家庭调查、企业注册及移动通信数据融合，用以评估结构性转型政策对女性经济赋权的异质性效应，并在非洲大陆自由贸易区背景下考察跨境劳动力配置的性别维度。其价值在于以可复现的元数据架构降低了非洲劳动统计的比较门槛，为循证政策制定与不平等测度提供了关键的经验锚点。