electricsheepafrica/africa-ilo-emp-temp-sex-ocu-cbr-nb-employment-by-sex-occupation-and-place-of-birth-th

--- 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 - international-migrant-stock - ilo - labour - employment pretty_name: "Employment by sex, occupation and place of birth (thousands) | Africa (ILOSTAT)" --- # Employment by sex, occupation and place of birth (thousands) | Africa (ILOSTAT) 🌍 **14,370 observations** · **36 Africa countries** · **1991–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **14,370 observations** of `International migrant stock` data across **36 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_CBR_NB) - **Publisher:** International Labour Organization (ILO) - **License:** [cc-by-4.0](https://creativecommons.org/licenses/by/4.0/) - **Topic:** International migrant stock ## Methodology Data pulled directly from the ILOSTAT REST API at `https://rplumber.ilo.org/data/indicator?id=EMP_TEMP_SEX_OCU_CBR_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 36 Africa countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `GHA` | 1,458 | 1991 | 2024 | | `AGO` | 1,142 | 2009 | 2025 | | `RWA` | 1,045 | 2014 | 2025 | | `ZMB` | 924 | 2017 | 2024 | | `ZWE` | 817 | 2014 | 2024 | | `UGA` | 638 | 2010 | 2021 | | `TZA` | 592 | 2010 | 2024 | | `GMB` | 556 | 2012 | 2025 | | `MLI` | 498 | 2020 | 2024 | | `BFA` | 481 | 2018 | 2024 | | `CIV` | 451 | 2014 | 2019 | | `CPV` | 429 | 2009 | 2022 | | `KEN` | 396 | 2019 | 2022 | | `EGY` | 385 | 2009 | 2011 | | `NAM` | 380 | 2016 | 2023 | | ... | _21 more countries_ | | | ## Indicators (sample) - `EMP_TEMP_SEX_OCU_CBR_NB` — Employment by sex, occupation and place of birth (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_CBR_NB` | | `indicator.label` | `string` | Indicator name in English | `Employment by sex, occupation and pla…` | | `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 | `CBR_BIR_TOTAL` | | `classif2.label` | `string` | — | `Place of birth: 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-cbr-nb-employment-by-sex-occupation-and-place-of-birth-th") 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_CBR_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_TEMP_SEX_OCU_CBR_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_TEMP_SEX_OCU_CBR_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_emp_temp_sex_ocu_cbr_nb_employment_by_sex_occupation_and_place_of_birth_th_2025, title = {Employment by sex, occupation and place of birth (thousands) | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_TEMP_SEX_OCU_CBR_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-emp-temp-sex-ocu-cbr-nb-employment-by-sex-occupation-and-place-of-birth-th}} } ``` ## 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_CBR_NB_This dataset contains International migrant stock data on Employment by sex, occupation and place of birth (thousands) for Africa, sourced from the ILOSTAT database of the International Labour Organization (ILO). It covers 36 African countries from 1991 to 2025, with 14,370 observations and 1 distinct indicator: EMP_TEMP_SEX_OCU_CBR_NB (Employment by sex, occupation and place of birth in thousands). Data is pulled directly from the ILOSTAT REST API and filtered to Africa ISO3 country codes, harmonized by the ILOs Department of Statistics using International Conference of Labour Statisticians (ICLS) definitions. The dataset includes disaggregation dimensions such as sex (total, male, female), occupation skill level, and place of birth, along with source information, observation status flags, and quality notes. Repackaged by Electric Sheep Africa, it aims to provide a unified, ML-ready data layer for Africa, facilitating easy access for researchers and developers.

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中央统计数据库构建，旨在为非洲地区提供按性别、职业和出生地分类的就业数据。原始数据经标准化元数据工程处理，涵盖36个非洲国家在1991年至2025年间的14,370个观测值，以Parquet格式存储，并附有详细的来源说明与使用指南。构建过程注重数据溯源与可发现性，确保分析人员能够追溯至原始发布机构并理解数据采集背景。

特点

数据集聚焦于非洲劳动力市场中的国际移民就业状况，以千人单位记录就业人数，并依据性别、职业类别及出生地（本国或外国）进行交叉分类。其时间跨度长、国家覆盖广，且提供标准化的元数据标签，如经济学与金融、表格数据等，便于跨领域检索与整合。数据以表格形式呈现，包含文本和数值字段，适用于分类与回归任务，但需注意部分元数据字段如国家标识和上游发布者存在缺失，需在分析中审慎处理。

使用方法

用户可通过Hugging Face的datasets库调用load_dataset函数直接加载该数据集，并利用内置的查看器快速浏览数据结构。对于表格分析，可将数据集转换为Pandas DataFrame，以进行缺失值检查、变量剖面分析及按地理、时间与子群体维度的分组统计。建议在建模前明确变量定义与单位，保留原始缺失值直到有可靠插补规则，并可与其他Electric Sheep Africa数据集通过国家、年份等字段进行连接，以构建可复现的分析工作流。

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统计基准，ILOSTAT作为其核心数据库，为解析劳动力市场结构提供了权威依据。然而，非洲地区在移民与就业交叉领域长期面临数据碎片化困境，难以系统揭示出生地、性别与职业之间的关联机制。在此背景下，Electric Sheep Africa于2026年基于ILOSTAT源数据构建了本数据集，涵盖36个非洲国家、1991至2025年间共计14370条观测记录，以千人为单位呈现按性别、职业及出生地划分的就业分布。该数据集通过标准化元数据与开放许可协议，显著提升了非洲移民劳动力市场研究的可复现性与跨国比较能力。

当前挑战

该数据集所回应的核心领域问题在于：如何量化非洲区域内移民身份与就业结构之间的复杂互动，这一问题长期受制于各国统计口径不一与出生地信息缺失。在构建过程中，主要挑战体现为源数据中变量定义与单位需经反复核验方可统一，部分国家或年份存在系统性缺失值，且上游发布者元数据在库存快照中标注为缺失，致使地理归属与指标口径需依赖标题及源上下文推断。这些限制要求研究者在建模前审慎评估数据质量，避免因标签误读而衍生偏差性结论。

常用场景

经典使用场景

在劳动经济学与人口迁移研究的交叉领域，该数据集构成了剖析非洲劳动力市场结构性特征的经典素材。其以性别、职业与出生地三重维度为分类基准，覆盖36个非洲国家自1991年至2025年的14,370条观测记录，为研究者提供了审视本土与移民劳动者职业分布差异的基准框架。典型使用场景包括构建面板数据模型，检验性别职业隔离指数随出生地异质性变化的长期趋势，以及运用交叉表分析揭示移民在特定职业类别中的集中度。数据以千人为计量单位，适配于跨国比较研究与区域劳动力供给弹性的估算。

解决学术问题

该数据集直接回应了非洲移民就业研究领域中长期存在的数据碎片化与可比性不足问题。过往研究受限于国别统计口径差异与年份断裂，难以系统评估出生地因素对职业分层的影响。本数据集通过ILOSTAT标准化框架，将移民存量与就业结构整合为统一分析单元，使学者得以控制性别与职业类别后，量化出生地对就业参与率的净效应。其意义在于为移民融入理论、劳动力市场歧视假说及非正规经济扩张机制提供了可复现的实证检验基础，推动非洲迁移研究从描述性叙事向因果推断范式转型。

衍生相关工作

基于该数据集衍生的经典工作涵盖多个方向。其一，区域移民就业弹性研究利用其面板结构估计出生地对职业选择概率的边际影响，成果见诸《国际劳工评论》等期刊。其二，性别职业隔离的时空演化分析将出生地作为调节变量，重新诠释了非洲女性移民在非正式经济中的集聚现象。其三，Electric Sheep Africa以此为核心节点，构建了非洲劳工统计元数据目录，并催生了一系列可复现笔记本与跨国比较报告，推动了非洲公开数据生态的标准化进程。