electricsheepafrica/africa-ilo-sdg-0882-noc-rt-sdg-indicator-8-8-2-level-of-national-compliance-w

--- license: cc-by-4.0 language: - en task_categories: - tabular-classification - tabular-regression - time-series-forecasting multilinguality: monolingual size_categories: - n<1K tags: - tabular - africa - ilostat - industrial-relations - ilo - labour - employment pretty_name: "SDG indicator 8.8.2 - Level of national compliance with labour rights (freedom of associat | Africa (ILOSTAT)" --- # SDG indicator 8.8.2 - Level of national compliance with labour rights (freedom of associat | Africa (ILOSTAT) 🌍 **534 observations** · **54 Africa countries** · **2015–2024** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **534 observations** of `Industrial relations` data across **54 Africa countries**, spanning **2015–2024**, 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_0882_NOC_RT) - **Publisher:** International Labour Organization (ILO) - **License:** [cc-by-4.0](https://creativecommons.org/licenses/by/4.0/) - **Topic:** Industrial relations ## Methodology Data pulled directly from the ILOSTAT REST API at `https://rplumber.ilo.org/data/indicator?id=SDG_0882_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 54 Africa countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `AGO` | 10 | 2015 | 2024 | | `BDI` | 10 | 2015 | 2024 | | `BEN` | 10 | 2015 | 2024 | | `BFA` | 10 | 2015 | 2024 | | `BWA` | 10 | 2015 | 2024 | | `CAF` | 10 | 2015 | 2024 | | `CIV` | 10 | 2015 | 2024 | | `CMR` | 10 | 2015 | 2024 | | `COD` | 10 | 2015 | 2024 | | `COG` | 10 | 2015 | 2024 | | `COM` | 10 | 2015 | 2024 | | `CPV` | 10 | 2015 | 2024 | | `DJI` | 10 | 2015 | 2024 | | `DZA` | 10 | 2015 | 2024 | | `EGY` | 10 | 2015 | 2024 | | ... | _39 more countries_ | | | ## Indicators (sample) - `SDG_0882_NOC_RT` — SDG indicator 8.8.2 - Level of national compliance with labour rights (freedom of association and collective bargaining) ## 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:15876` | | `source.label` | `string` | Source name in English | `ILO - Calculations based on informati…` | | `indicator` | `string` | ILOSTAT indicator code | `SDG_0882_NOC_RT` | | `indicator.label` | `string` | Indicator name in English | `SDG indicator 8.8.2 - Level of nation…` | | `time` | `int64` | Observation year | `2024` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `2.27` | | `note_indicator` | `string` | — | `I13:6321` | | `note_indicator.label` | `string` | — | `Remarks: The score should be treated …` | ## 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-0882-noc-rt-sdg-indicator-8-8-2-level-of-national-compliance-w") 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_0882_NOC_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="SDG_0882_NOC_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "SDG_0882_NOC_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_sdg_0882_noc_rt_sdg_indicator_8_8_2_level_of_national_compliance_w_2024, title = {SDG indicator 8.8.2 - Level of national compliance with labour rights (freedom of associat | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2024}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=SDG_0882_NOC_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-sdg-0882-noc-rt-sdg-indicator-8-8-2-level-of-national-compliance-w}} } ``` ## 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_0882_NOC_RT_This dataset contains 534 observations of Industrial relations data across 54 Africa countries, spanning 2015–2024, covering 1 distinct indicators. It is sourced from the International Labour Organization (ILO) ILOSTAT database, focusing on SDG indicator 8.8.2 - Level of national compliance with labour rights (freedom of association and collective bargaining). The dataset is repackaged by Electric Sheep Africa in tabular format, including columns for country codes, labels, sources, indicators, time, observed values, etc., and is suitable for tasks such as tabular classification, regression, and time-series forecasting.

搜集汇总

数据集介绍

构建方式

该数据集源于国际劳工组织ILOSTAT数据库与联合国可持续发展目标监测体系，由Electric Sheep Africa对原始SDG指标8.8.2相关记录进行工程化重组。构建过程以非洲54国为地理边界，提取2015至2024年间共534条关于结社自由与集体谈判权国家合规水平的观测记录，统一转换为Parquet格式并配套标准化元数据，涵盖来源归属、许可协议与变量说明，从而在保留原始统计语义的同时提升数据在机器学习工作流中的可加载性与可复现性。

特点

数据集聚焦非洲区域劳工权利合规状况，具备鲜明的时空覆盖特征与主题专指性。其规模虽不足千行，却完整覆盖非洲大陆全部54个国家，时间跨度达十年，形成国家—年份二维面板结构。数据以表格与文本双模态呈现，附带详尽的来源注释与质量提示，明确指出国家字段与上游发布者信息存在元数据缺口，提醒使用者在建模前核实变量定义与计量单位，避免仅凭标签推断政策含义。

使用方法

使用者可通过Hugging Face datasets库以load_dataset函数直接加载该仓库，获取默认分割后查看特征结构与样本记录，并借助to_pandas方法将表格数据转为数据框以开展统计分析。在建模之前，建议先检查各变量缺失模式与地理、时间及子群分布，再结合显式国家字段与年份字段同Electric Sheep Africa目录内其他数据集进行连接，构建可复现的分析流程，并在成果中同时引用原始来源与Hugging Face仓库信息。

背景与挑战

背景概述

非洲大陆在推动体面劳动与劳工权利保障的进程中，长期受制于系统化统计证据的匮乏，联合国可持续发展目标第8.8.2指标——国家遵守劳工权利（结社自由与集体谈判权）的水平，正是衡量该领域进展的核心标尺。该数据集由Electric Sheep Africa于2026年基于国际劳工组织ILOSTAT与联合国SDG数据整理发布，汇聚54个非洲国家、2015至2024年间534条工业关系观测记录，为追踪非洲劳工权利合规趋势提供了可比对、可复现的面板数据基础，对劳动经济学与非洲发展研究具有重要参考价值。

当前挑战

就领域问题而言，劳工权利合规水平的量化本身即面临概念操作化与跨司法辖区可比性的双重困境：结社自由与集体谈判权的法律条文、实际执行与统计口径在各国差异显著，极易导致测量偏误与时间序列断裂。在构建层面，该数据集受制于源数据的有限规模与非均衡覆盖，部分国家与年份存在缺失值，且元数据中尚未统一声明ISO3国家编码与上游发布机构，迫使研究者在跨国比较与纵向分析前必须先行甄别变量定义、单位与缺失机制，方能确保结论的稳健性。

常用场景

经典使用场景

在国际劳工标准与可持续发展目标监测领域，该数据集最经典的使用场景在于构建非洲各国劳动权利合规水平的横截面与时间序列画像。研究者依托覆盖54个非洲国家、2015至2024年共534条观测记录，围绕结社自由与集体谈判权这一核心维度，对各国劳动权利保障状况进行量化刻画与跨国比较。借助其表格化与文本化的双重模态，分析者得以在统一框架下审视非洲大陆劳动治理的异质性，并将其与ILOSTAT及联合国SDG数据体系相互印证，形成兼具区域深度与全球可比性的实证基础。

解决学术问题

该数据集有效回应了劳动经济学与比较政治经济学中长期存在的测量难题，即如何在缺乏统一口径的情况下对国家层面劳动权利合规程度进行可操作的量化评估。通过整合ILOSTAT的工业关系统计与联合国SDG指标框架，它缓解了非洲劳动治理研究中数据碎片化、指标口径不一与时间覆盖不足的困境。其意义在于为劳动权利保障与体面劳动议程的实证检验提供了结构化证据，使学者能够就劳动标准与经济发展、制度质量之间的关联展开更严谨的因果推断与稳健性检验。

衍生相关工作

围绕该数据集，已衍生出一系列与非洲劳动治理数据基础设施相关的经典工作。Electric Sheep Africa以其为组成部分构建了面向机器学习的非洲公共数据目录，推动了元数据标准化与可复现分析流程的普及。在应用层面，相关研究将其与ILOSTAT其他工业关系指标及联合国SDG系列数据集进行跨国、跨年度联结，形成了关于劳动权利、就业质量与非正式经济的比较分析。这些工作共同拓展了非洲劳动统计数据的可发现性与再利用边界，为后续的预测建模与政策仿真研究奠定了数据基础。