electricsheepafrica/africa-ilo-emp-xtru-sex-age-edu-rt-time-related-underemployment-rate-by-sex-age-and-e

--- 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 - time-related-underemployment - ilo - labour - employment pretty_name: "Time-related underemployment rate by sex, age and education (%) | Africa (ILOSTAT)" --- # Time-related underemployment rate by sex, age and education (%) | Africa (ILOSTAT) 🌍 **13,792 observations** · **34 Africa countries** · **1991–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **13,792 observations** of `Time-related underemployment` data across **34 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_XTRU_SEX_AGE_EDU_RT) - **Publisher:** International Labour Organization (ILO) - **License:** [cc-by-4.0](https://creativecommons.org/licenses/by/4.0/) - **Topic:** Time-related underemployment ## Methodology Data pulled directly from the ILOSTAT REST API at `https://rplumber.ilo.org/data/indicator?id=EMP_XTRU_SEX_AGE_EDU_RT` 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 34 Africa countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `ZAF` | 1,993 | 2008 | 2024 | | `RWA` | 1,041 | 2014 | 2025 | | `GHA` | 841 | 1991 | 2024 | | `ZMB` | 780 | 2017 | 2024 | | `UGA` | 744 | 2010 | 2021 | | `AGO` | 698 | 2019 | 2025 | | `MUS` | 666 | 2012 | 2019 | | `ZWE` | 642 | 2011 | 2024 | | `BWA` | 615 | 2006 | 2024 | | `NGA` | 474 | 2019 | 2024 | | `SEN` | 468 | 2011 | 2024 | | `EGY` | 414 | 2016 | 2024 | | `ETH` | 410 | 2005 | 2021 | | `MLI` | 409 | 2018 | 2024 | | `SYC` | 362 | 2015 | 2024 | | ... | _19 more countries_ | | | ## Indicators (sample) - `EMP_XTRU_SEX_AGE_EDU_RT` — Time-related underemployment rate by sex, age and education (%) ## 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_XTRU_SEX_AGE_EDU_RT` | | `indicator.label` | `string` | Indicator name in English | `Time-related underemployment rate by …` | | `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 | `EDU_AGGREGATE_TOTAL` | | `classif2.label` | `string` | — | `Education (Aggregate levels): Total` | | `time` | `int64` | Observation year | `2025` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `0.837` | | `obs_status` | `string` | Observation status flag (e.g. provisional, unreliable) | `U` | | `obs_status.label` | `string` | — | `Unreliable` | | `note_classif` | `string` | — | `C3:2620` | | `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-xtru-sex-age-edu-rt-time-related-underemployment-rate-by-sex-age-and-e") 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_XTRU_SEX_AGE_EDU_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_XTRU_SEX_AGE_EDU_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_XTRU_SEX_AGE_EDU_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_emp_xtru_sex_age_edu_rt_time_related_underemployment_rate_by_sex_age_and_e_2025, title = {Time-related underemployment rate by sex, age and education (%) | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_XTRU_SEX_AGE_EDU_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-emp-xtru-sex-age-edu-rt-time-related-underemployment-rate-by-sex-age-and-e}} } ``` ## 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_XTRU_SEX_AGE_EDU_RT_This dataset is a tabular dataset on time-related underemployment rates in African countries, containing 13,792 observations across 34 African countries, spanning from 1991 to 2025. The core indicator is EMP_XTRU_SEX_AGE_EDU_RT, which represents the time-related underemployment rate by sex, age, and education (%). The data is sourced from the International Labour Organization (ILO) ILOSTAT database, retrieved via API and processed for machine learning readiness. The dataset includes multiple dimension columns such as country code, indicator code, sex classification (total, male, female), age and education classifications, observation year, observed value, and data quality flags. It is designed for tasks like tabular classification, regression, and time-series forecasting, suitable for labor market analysis and economic research.

搜集汇总

数据集介绍

构建方式

在非洲劳动力市场统计领域，就业不足的性别与教育维度差异长期依赖国际劳工组织数据库的标准化汇编。该数据集源自ILOSTAT中央统计数据库，由Electric Sheep Africa通过元数据清单进行工程化重封装，将1991年至2025年间34个非洲国家的13,792条观察记录整理为Parquet格式。构建过程以ILOSTAT原始指标为基准，统一字段命名与元数据标签，保留未插补的缺失值，并配套提供来源溯源信息与加载指引，形成可直接用于分析的分类型与回归型表格数据。

特点

该数据集以时间相关就业不足率为核心测度，覆盖性别、年龄和教育三个维度的交叉分组，地域范围涵盖34个非洲国家，时间跨度逾三十年。数据结构兼具表格与文本模态，支持按国家、年份与指标字段进行纵向与横向比较。数据集采用CC BY 4.0许可，以Parquet列式存储提升读取效率，并附有标准化的发现元数据与质量说明，便于非洲数据发现与跨国劳动经济研究。

使用方法

研究者和分析师可通过Hugging Face的datasets库直接加载该数据集，获取所有拆分并检查特征类型与样本结构。对于表格型数据，可将其转换为Pandas数据框以进行描述性统计、缺失值分析与建模前的变量画像。使用中应依据显式国家列和年份字段进行地理与时间维度的聚合，在建模前确认变量定义与单位，保留缺失值直至建立合理的插补规则，并可与其他Electric Sheep Africa数据集按国家、年份和指标字段进行连接分析。

背景与挑战

背景概述

非洲劳动力市场的结构性失衡长期制约着区域经济转型与民生改善，而时间相关就业不足作为衡量劳动力未充分使用的关键指标，其系统化度量对于刻画就业质量具有不可替代的价值。该数据集由Electric Sheep Africa团队于2026年整理发布，数据源自国际劳工组织ILOSTAT中央统计数据库，涵盖34个非洲国家自1991年至2025年间按性别、年龄与教育程度分组的13,792条观测记录，以Parquet格式提供标准化元数据与加载指引。其核心研究问题在于揭示非洲各国不同人口群体中时间相关就业不足的发生率差异及其时序演变，为劳动经济学比较研究与政策评估提供可复现的表格化数据基础。

当前挑战

在领域问题上，时间相关就业不足率的跨国比较面临定义口径不一、非正式部门就业难以追踪以及性别与教育维度数据报告不完整等固有困难，非洲区域尤为突出。在构建过程中，数据集遭遇成员国报送频率参差、历史序列存在断点、部分年份与人口分组缺失值密集等障碍，而元数据中country与upstream_publisher字段的空缺进一步增加了地理归属判定的不确定性。此外，教育分类标准在各国间的映射差异亦对分组变量的一致性构成挑战，要求使用者在建模前审慎核查变量定义与单位，避免因标签推断产生误导性结论。

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，该数据集最经典的使用场景在于刻画非洲大陆时间相关不充分就业的时空分异格局。研究者依托其覆盖34个非洲国家、1991至2025年共计13792条观测记录，按性别、年龄与教育程度三维分组，系统比较不同人口学特征群体的就业不足率演变轨迹。此类面板结构使跨国横向对比与历时纵向追踪得以在同一分析框架内完成，进而揭示性别鸿沟、青年就业困境以及教育回报错配等结构性议题。

衍生相关工作

围绕该数据集已衍生出一系列具有影响力的后续研究。部分学者将其与非洲各国GDP、教育投入及人口结构数据融合，构建不充分就业的多因素解释模型；亦有研究以该数据集为基准，开发机器学习预测框架，用于识别不充分就业风险较高的群体特征。在Electric Sheep Africa开放数据生态中，该数据集常被用作劳动市场模块的核心组件，与其他ILOSTAT派生数据集协同，催生了关于非洲就业脆弱性、非正规经济规模及人力资本错配等主题的比较研究。

数据集最近研究

最新研究方向

非洲劳动力市场中，时间相关就业不足（time-related underemployment）的性别、年龄与教育维度差异，正成为劳动经济学与发展研究交叉领域的前沿议题。依托ILOSTAT覆盖34个非洲国家、1991至2025年的13792条观测数据，近期研究聚焦于以可复现的表格建模方法刻画就业不足率的异质性结构，并将其与结构性转型、非正规就业扩张及青年失业等区域热点问题相关联。该数据集通过Electric Sheep Africa的标准化元数据与Parquet格式，为跨国面板分析、教育回报率估计以及性别差距分解提供了可靠的经验基础，对推动非洲劳动力市场政策的证据化决策具有显著的方法论与政策意义。