electricsheepafrica/africa-ilo-pop-xwap-sex-age-cbr-nb-working-age-population-by-sex-age-and-place-of-bir

--- 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: "Working-age population by sex, age and place of birth (thousands) | Africa (ILOSTAT)" --- # Working-age population by sex, age and place of birth (thousands) | Africa (ILOSTAT) 🌍 **12,047 observations** · **37 Africa countries** · **1991–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **12,047 observations** of `International migrant stock` data across **37 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=POP_XWAP_SEX_AGE_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=POP_XWAP_SEX_AGE_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 37 Africa countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `GHA` | 954 | 1991 | 2024 | | `ZMB` | 705 | 2017 | 2024 | | `UGA` | 701 | 2010 | 2024 | | `AGO` | 666 | 2009 | 2025 | | `RWA` | 648 | 2014 | 2025 | | `MWI` | 594 | 2005 | 2024 | | `ZWE` | 513 | 2014 | 2024 | | `TZA` | 510 | 2008 | 2024 | | `LBR` | 508 | 2010 | 2020 | | `CPV` | 469 | 2009 | 2022 | | `BFA` | 419 | 2015 | 2024 | | `BEN` | 375 | 2011 | 2024 | | `EGY` | 372 | 2008 | 2011 | | `NER` | 361 | 2011 | 2017 | | `GMB` | 349 | 2012 | 2025 | | ... | _22 more countries_ | | | ## Indicators (sample) - `POP_XWAP_SEX_AGE_CBR_NB` — Working-age population by sex, age 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 | `POP_XWAP_SEX_AGE_CBR_NB` | | `indicator.label` | `string` | Indicator name in English | `Working-age population by sex, age an…` | | `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.) | `AGE_YTHADULT_YGE15` | | `classif1.label` | `string` | — | `Age (Youth, adults): 15+` | | `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) | `20993.124` | | `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-pop-xwap-sex-age-cbr-nb-working-age-population-by-sex-age-and-place-of-bir") 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"] == "POP_XWAP_SEX_AGE_CBR_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="POP_XWAP_SEX_AGE_CBR_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "POP_XWAP_SEX_AGE_CBR_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_pop_xwap_sex_age_cbr_nb_working_age_population_by_sex_age_and_place_of_bir_2025, title = {Working-age population by sex, age and place of birth (thousands) | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=POP_XWAP_SEX_AGE_CBR_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-pop-xwap-sex-age-cbr-nb-working-age-population-by-sex-age-and-place-of-bir}} } ``` ## 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=POP_XWAP_SEX_AGE_CBR_NB_This dataset contains working-age population statistics for 37 African countries from 1991 to 2025, disaggregated by sex, age, and place of birth, in thousands. It includes 12,047 observations, sourced from the International Labour Organization (ILO) ILOSTAT database, focusing on the indicator POP_XWAP_SEX_AGE_CBR_NB for analyzing international migrant stock in the working-age population. Repackaged by Electric Sheep Africa, it is provided in tabular format and supports machine learning tasks such as classification and regression.

搜集汇总

数据集介绍

构建方式

该数据集由Electric Sheep Africa团队在Hugging Face平台工程化发布，其原始数据源自国际劳工组织（ILO）的ILOSTAT中央统计数据库。构建过程以标准化元数据为框架，对ILOSTAT中有关非洲地区工作年龄人口按性别、年龄及出生地分类的统计数据进行系统化提取和重新封装。数据集涵盖1991年至2025年间37个非洲国家的12047条观测记录，以Parquet格式存储，并附带完整的来源注释、加载指南及分析情境说明，旨在为非洲数据发现提供可复现的机器学习就绪资源。

使用方法

使用者可通过Hugging Face datasets库以一行代码加载数据集，并直接访问其内置的split划分与特征结构。对于表格分析，可借助to_pandas()方法将数据无缝转换为Pandas DataFrame，进而执行缺失值探查、变量分布概览及地理与时间维度的分组统计。建议在建模前仔细核对ILOSTAT原始变量定义与计量单位，避免仅凭标签推断政策含义。该数据集亦可与Electric Sheep Africa目录中其他非洲数据集通过国家、年份及指标字段进行联接，以构建更丰富的可复现分析工作流。

背景与挑战

背景概述

国际劳工组织（ILO）自二十世纪中叶起持续构建全球劳动统计体系，其ILOSTAT数据库已成为衡量各国劳动力市场结构与移民就业状况的权威基准。在此脉络下，Electric Sheep Africa于2026年对ILOSTAT中非洲区域劳动年龄人口数据进行了标准化整编，形成涵盖1991至2025年、37个非洲国家、逾1.2万条观测的专题数据集，以性别、年龄与出生地为核心分层维度。该数据集回应了非洲跨境人口流动与劳动供给结构研究中对长时序、跨国可比微观证据的迫切需求，为分析移民劳动参与、人口红利及区域经济一体化提供了可复现的数据基础，在非洲劳动经济学与移民研究中具有显著的索引价值。

当前挑战

该数据集所应对的领域问题在于揭示非洲各国劳动年龄人口按出生地分异的构成特征，其核心挑战源于跨国移民存量统计口径不一、出生地信息在部分国家长期缺失以及非正式跨境流动难以捕捉等结构性困难。构建过程中，整编者面临原始ILOSTAT元数据字段不完整、国家编码与地理标签未系统声明的局限，同时需在保留缺失值与避免误导性插补之间取得平衡。此外，年龄分组与性别维度的时序一致性、单位换算的可追溯性以及跨国可比性维护，均构成持续的方法论挑战，要求下游分析者在建模前审慎核验变量定义与来源方法。

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，针对非洲区域劳动力市场结构性特征的研究长期受制于细粒度人口数据的匮乏。该数据集依托国际劳工组织统计数据库（ILOSTAT）的权威框架，提供了1991至2025年间37个非洲国家按性别、年龄组及出生地划分的劳动年龄人口规模数据，共涵盖12,047条观测记录。其最经典的运用场景在于构建多维度面板数据模型，用以刻画非洲各国劳动年龄人口在性别与年龄结构上的异质性分布，并借助出生地变量揭示国际移民存量对目的地劳动力供给结构的潜在塑造效应。研究者可据此开展跨国比较分析，检验人口转变理论在非洲语境下的适用性，或评估跨境人口流动对区域劳动力市场均衡的扰动机制。

解决学术问题

该数据集在学术研究层面回应了若干长期悬而未决的议题。非洲大陆的国际移民与劳动力市场互动关系，因缺乏系统性的分性别、分年龄及分出生地统计而难以精确量化，既有文献多依赖抽样调查或间接估算，结论的稳健性备受质疑。该数据集以标准化元数据与可复现的Parquet格式，为检验移民存量与劳动年龄人口结构之间的因果关联提供了可靠的经验基础，亦有助于厘清性别维度上移民参与率的差异化模式。其意义在于将非洲劳动力市场研究从宏观总量叙事推进至结构化、细粒度的实证分析阶段，为后续计量建模与政策评估奠定了可追溯的数据基石。

实际应用

在实际应用层面，该数据集为国际组织、区域政策制定机构及发展金融机构提供了可操作的分析工具。非洲联盟及次区域经济共同体在制定劳动力流动协议与移民融合政策时，可借助该数据识别劳动年龄人口中移民占比偏高的国家与年龄区间，从而优化资源配置与社会保障安排。跨国企业在评估非洲市场的劳动力供给潜力与用工成本时，亦可依据分性别、分年龄的人口结构数据进行区位选择与人力规划。此外，该数据集支持与世界银行、联合国人口司等其他公开数据源的整合，便于构建多维度的国别风险画像与人口红利评估模型。

数据集最近研究

最新研究方向

在非洲劳动力市场与人口迁移研究的交汇地带，该数据集以国际劳工组织ILOSTAT为基石，聚焦37个非洲国家1991至2025年间按性别、年龄及出生地划分的适龄劳动人口规模（千人），为探究跨境迁移与本土劳动力供给的耦合机制提供了稀缺的长时段跨国证据。近期前沿研究借此数据切入移民存量与就业结构的动态关联，回应非洲大陆自由贸易区背景下劳动力流动壁垒与人口红利释放等热点议题。其价值在于以标准化元数据支撑可复现的跨国比较分析，助力识别性别与年龄异质性下的迁移就业效应，为区域一体化政策与劳动力市场治理提供实证依据。