electricsheepafrica/africa-ilo-sdg-1041-noc-rt-sdg-indicator-10-4-1-labour-income-share-as-a-perc

--- 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 - income-and-pay-inequality - ilo - labour - employment pretty_name: "SDG indicator 10.4.1 - Labour income share as a percent of GDP (%) | Africa (ILOSTAT)" --- # SDG indicator 10.4.1 - Labour income share as a percent of GDP (%) | Africa (ILOSTAT) 🌍 **1,212 observations** · **53 Africa countries** · **2004–2026** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **1,212 observations** of `Income and pay inequality` data across **53 Africa countries**, spanning **2004–2026**, 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=SDG_1041_NOC_RT) - **Publisher:** International Labour Organization (ILO) - **License:** [cc-by-4.0](https://creativecommons.org/licenses/by/4.0/) - **Topic:** Income and pay inequality ## Methodology Data pulled directly from the ILOSTAT REST API at `https://rplumber.ilo.org/data/indicator?id=SDG_1041_NOC_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 53 Africa countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `AGO` | 23 | 2004 | 2026 | | `BDI` | 23 | 2004 | 2026 | | `BEN` | 23 | 2004 | 2026 | | `BFA` | 23 | 2004 | 2026 | | `BWA` | 23 | 2004 | 2026 | | `CAF` | 23 | 2004 | 2026 | | `CIV` | 23 | 2004 | 2026 | | `CMR` | 23 | 2004 | 2026 | | `COD` | 23 | 2004 | 2026 | | `COG` | 23 | 2004 | 2026 | | `COM` | 23 | 2004 | 2026 | | `CPV` | 23 | 2004 | 2026 | | `DJI` | 23 | 2004 | 2026 | | `DZA` | 23 | 2004 | 2026 | | `EGY` | 23 | 2004 | 2026 | | ... | _38 more countries_ | | | ## Indicators (sample) - `SDG_1041_NOC_RT` — SDG indicator 10.4.1 - Labour income share as a percent of GDP (%) ## 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) | `XA:2010` | | `source.label` | `string` | Source name in English | `ILO - Modelled Estimates` | | `indicator` | `string` | ILOSTAT indicator code | `SDG_1041_NOC_RT` | | `indicator.label` | `string` | Indicator name in English | `SDG indicator 10.4.1 - Labour income …` | | `time` | `int64` | Observation year | `2026` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `43.973` | | `obs_status` | `string` | Observation status flag (e.g. provisional, unreliable) | `I` | | `obs_status.label` | `string` | — | `Imputation` | ## 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-sdg-1041-noc-rt-sdg-indicator-10-4-1-labour-income-share-as-a-perc") 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"] == "SDG_1041_NOC_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="SDG_1041_NOC_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "SDG_1041_NOC_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_sdg_1041_noc_rt_sdg_indicator_10_4_1_labour_income_share_as_a_perc_2026, title = {SDG indicator 10.4.1 - Labour income share as a percent of GDP (%) | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2026}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=SDG_1041_NOC_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-sdg-1041-noc-rt-sdg-indicator-10-4-1-labour-income-share-as-a-perc}} } ``` ## 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=SDG_1041_NOC_RT_This dataset contains data on SDG indicator 10.4.1 - Labour income share as a percent of GDP (%) for Africa, sourced from ILOSTAT (ILOs statistics database). It covers 53 African countries, spanning the years 2004 to 2026, with 1,212 observations. The indicator is SDG_1041_NOC_RT, which measures the share of labour income in GDP as a percentage. The data is organized in tabular format, including columns such as country code, country name, data source, indicator code, year, observed value, and status. The dataset is designed to support research and analysis on income and pay inequality and Sustainable Development Goal (SDG) targets, suitable for tasks like tabular classification, regression, and time-series forecasting. It has been repackaged by Electric Sheep Africa to provide a machine learning-ready format.

搜集汇总

数据集介绍

构建方式

该数据集源自国际劳工组织（ILO）的ILOSTAT中央统计数据库，并整合了联合国可持续发展目标（SDG）指标10.4.1的官方数据。Electric Sheep Africa对原始数据进行了标准化元数据封装与格式转换，以Parquet格式重新发布，涵盖2004至2026年间53个非洲国家的1212条观测记录。数据集的构建遵循开放数据原则，通过统一的元数据描述、来源标注和加载指引，确保数据的可发现性与可复用性，同时保留原始发布者的版权归属。

使用方法

用户可通过Hugging Face的datasets库以一行代码加载数据集，并查看数据分割、特征与样本。对于表格数据，可借助to_pandas()方法转换为Pandas DataFrame，便于后续统计分析与建模。使用前应检查数据文件的模式、缺失值及单位定义，避免仅凭标签推断政策含义。建议结合显式的国家、年份等字段进行分组分析，或与其他Electric Sheep Africa数据集进行连接，同时保留缺失值直至确立合理的插补规则。

背景与挑战

背景概述

在可持续发展目标（SDGs）的全球监测框架下，劳动力收入份额作为衡量经济增长包容性的关键指标，被纳入目标10.4.1，用以追踪收入不平等状况。该数据集由Electric Sheep Africa于2026年整理发布，源自国际劳工组织（ILOSTAT）和联合国SDG数据库，涵盖53个非洲国家自2004年至2026年的1212条观测记录。其核心研究问题在于揭示非洲地区劳动收入占GDP比重的时空演变，为分析经济增长与收入分配之间的关系提供标准化面板数据。该数据集不仅填补了非洲劳动力市场数据的整合空白，也为政策制定者和研究者评估减贫与不平等政策效果提供了实证基础。

当前挑战

该数据集所应对的领域问题在于量化劳动力收入份额的跨国差异与动态变化，这直接关联到收入不平等和体面劳动等复杂社会经济议题。在构建过程中，主要挑战包括：原始数据来源于多个国际机构，指标定义和统计方法可能存在不一致，需进行标准化处理；部分国家和年份数据缺失，需谨慎处理缺失值以避免偏差；非洲国家统计能力参差不齐，数据质量和可比性面临考验；此外，数据集需明确地理和变量单位，确保下游分析的准确性。这些挑战要求研究者在建模前充分理解数据来源和局限性。

常用场景

经典使用场景

在劳动力市场与收入分配研究领域，劳动收入份额作为衡量劳动者在国民收入中所得比例的核心指标，长期被用于评估经济增长的包容性与公平性。该数据集以非洲53个国家2004至2026年间1212条观测记录为基础，构建了覆盖广泛时空维度的面板数据，成为探究非洲地区劳动收入份额演变趋势的经典数据来源。研究者常以此数据集为依托，开展跨国比较分析与时间序列建模，检验劳动收入份额与人均GDP、就业结构及部门构成之间的动态关联。其标准化表格格式与元数据标注亦便于纳入机器学习流程，用于分类与回归任务的基准测试。

解决学术问题

该数据集有效回应了非洲劳动收入份额研究中长期存在的数据稀缺与可比性不足问题。此前，针对非洲国家的劳动收入份额分析往往受限于零散的国别报告与不一致的统计口径，难以支撑严谨的跨区域比较。本数据集依托国际劳工组织统计数据库与联合国可持续发展目标监测框架，统一了指标定义、覆盖范围与时间跨度，为检验结构性转型理论、评估全球化对劳动者议价能力的影响以及分析收入不平等驱动机制提供了可信的实证基础。其开放许可与可复现格式亦推动了非洲经济研究的透明度与累积性知识生产。

实际应用

在政策制定与国际发展实践中，该数据集为监测联合国可持续发展目标10.4.1的进展提供了关键量化依据。各国政府与区域组织可借助其国别面板数据，识别劳动收入份额偏离预期轨道的时段与情境，进而调整最低工资、集体谈判及社会保障等劳动力市场制度。国际机构亦可将该数据集与贫困、就业及GDP数据融合，构建预警指标或政策模拟模型。此外，数据科学团队可利用其表格结构开展特征工程与预测建模，为非洲经济发展路径的定量评估提供可操作的分析素材。

数据集最近研究

最新研究方向

围绕可持续发展目标10.4.1的劳工收入份额监测，近期研究聚焦于非洲53国2004至2026年间收入与薪酬不平等的时间序列演进，借助ILOSTAT与联合国SDG数据的结构化面板，探索劳动报酬占GDP比重与经济增长、就业结构转型之间的动态关联。伴随全球对包容性增长与体面劳动议程的持续关注，该数据集为刻画非洲大陆劳动收入份额的区域异质性提供了可复现的实证基础，其标准化元数据与Parquet格式亦便利了机器学习方法在收入分配预测与政策情景模拟中的应用，对缩小数据鸿沟、推动非洲经济治理的循证决策具有实质意义。