electricsheepeurope/europe-ilo-emp-pifl-sex-est-rt-share-of-employment-outside-the-formal-sector-by-s

--- 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 - europe - ilostat - informal-economy - ilo - labour - employment pretty_name: "Share of employment outside the formal sector by sex and establishment size (%) | Europe (ILOSTAT)" --- # Share of employment outside the formal sector by sex and establishment size (%) | Europe (ILOSTAT) 🇪🇺 **2,334 observations** · **4 Europe countries** · **2003–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **2,334 observations** of `Informal economy` data across **4 Europe countries**, spanning **2003–2025**, 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_PIFL_SEX_EST_RT) - **Publisher:** International Labour Organization (ILO) - **License:** [cc-by-4.0](https://creativecommons.org/licenses/by/4.0/) - **Topic:** Informal economy ## Methodology Data pulled directly from the ILOSTAT REST API at `https://rplumber.ilo.org/data/indicator?id=EMP_PIFL_SEX_EST_RT` and filtered to Europe 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 4 Europe countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `MDA` | 664 | 2003 | 2025 | | `MKD` | 629 | 2009 | 2025 | | `BIH` | 609 | 2006 | 2024 | | `SRB` | 432 | 2007 | 2020 | ## Indicators (sample) - `EMP_PIFL_SEX_EST_RT` — Share of employment outside the formal sector by sex and establishment size (%) ## Schema | Column | Type | Description | Example | |--------|------|-------------|---------| | `ref_area` | `string` | ISO 3166-1 alpha-3 country code | `BIH` | | `ref_area.label` | `string` | Country name in English | `Bosnia and Herzegovina` | | `source` | `string` | ILOSTAT source code (e.g. labour force survey) | `BA:493` | | `source.label` | `string` | Source name in English | `LFS - Labour Force Survey` | | `indicator` | `string` | ILOSTAT indicator code | `EMP_PIFL_SEX_EST_RT` | | `indicator.label` | `string` | Indicator name in English | `Share of employment outside the forma…` | | `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.) | `EST_AGGREGATE_TOTAL` | | `classif1.label` | `string` | — | `Establishment size (Aggregate): Total` | | `time` | `int64` | Observation year | `2024` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `13.81` | | `obs_status` | `string` | Observation status flag (e.g. provisional, unreliable) | `U` | | `obs_status.label` | `string` | — | `Unreliable` | | `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("electricsheepeurope/europe-ilo-emp-pifl-sex-est-rt-share-of-employment-outside-the-formal-sector-by-s") df = ds["train"].to_pandas() print(df.head()) ``` ### Filter to one country ```python germany = df[df["ref_area"] == "DEU"] ``` ### Time-series for a single indicator ```python sample = (df[df["indicator"] == "EMP_PIFL_SEX_EST_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_PIFL_SEX_EST_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_PIFL_SEX_EST_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_emp_pifl_sex_est_rt_share_of_employment_outside_the_formal_sector_by_s_2025, title = {Share of employment outside the formal sector by sex and establishment size (%) | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_PIFL_SEX_EST_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-emp-pifl-sex-est-rt-share-of-employment-outside-the-formal-sector-by-s}} } ``` ## 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 Europe repackaging. ## About Electric Sheep Electric Sheep Europe is part of the Electric Sheep mission: a unified, ML-ready data layer for Europe 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/electricsheepeurope](https://huggingface.co/electricsheepeurope) --- _Provenance: ingested 2026-05-26 via the Electric Sheep pipeline. Source URL: https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_PIFL_SEX_EST_RT_This dataset contains informal economy employment data from the International Labour Organization (ILO) ILOSTAT database, specifically the indicator Share of employment outside the formal sector by sex and establishment size (%). It covers 4 European countries (including Bosnia and Herzegovina, North Macedonia, Moldova, Serbia) from 2003 to 2025, with 2,334 observations. The data is disaggregated by sex (total, male, female) and establishment size, updated annually, and includes information on data sources, observation status, and relevant notes. The dataset is designed to provide structured, machine learning-ready time-series data for labor market analysis and informal economy research.

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旗舰数据库ILOSTAT，依托其 REST API 接口（https://rplumber.ilo.org/data/indicator?id=EMP_PIFL_SEX_EST_RT）直接抽取非正规经济部门就业份额指标，并依据 ISO3 国家代码筛选出欧洲地域范围。原始调查微观数据经 ILO 统计局以国际劳工统计学家会议定义进行标准化调和，来源信息以 source.label 字段保留溯源线索，最终由 Electric Sheep Europe 重新封装为 ML-ready 的 Parquet 格式，形成涵盖 2334 条观测、时间跨度自 2003 至 2025 年的结构化数据集。

特点

数据集聚焦于非正规部门就业份额这一劳动经济学核心议题，按性别与机构规模双重维度展开，覆盖摩尔多瓦、北马其顿、波黑与塞尔维亚四个欧洲国家。变量体系包含国家代码、数据来源、指标编码、性别分类、机构规模分类、年份与观测值等字段，并附有观测状态标志及指标注释，可识别临时性、不可靠或序列断点等数据质量信号。年度频率与长时序跨度赋予其时间序列分析潜力，样本量处于 1K 至 10K 区间，兼顾统计稳健性与计算友好性，且以 CC-BY-4.0 协议开放共享。

使用方法

研究者可借助 HuggingFace datasets 库以 load_dataset() 函数直接加载数据，并转换为 pandas 数据框进行后续分析。典型操作包括按 ref_area 字段筛选特定国家子集，或针对单一指标 EMP_PIFL_SEX_EST_RT 按时间排序以绘制时序趋势图。数据亦支持通过 pivot_table 重构为国家×年份矩阵，便于开展跨国比较与面板回归。使用时应留意 obs_status 与 note_indicator 字段所标记的数据质量警示，并遵循 CC-BY-4.0 许可要求，同时引用 ILO 原始来源与 Electric Sheep Europe 的再封装工作。

背景与挑战

背景概述

非正规经济部门的就业规模长期以来是发展经济学与劳动经济学关注的核心议题，其测量精度直接影响社会保障政策设计。国际劳工组织（ILO）自二十世纪中叶起持续推动非正规就业统计的标准化工作，通过国际劳工统计学家会议（ICLS）决议界定相关概念框架。在此背景下，Electric Sheep Europe于2025年对ILOSTAT数据库中的EMP_PIFL_SEX_EST_RT指标进行系统提取与规范化封装，覆盖摩尔多瓦、北马其顿、波黑与塞尔维亚四国2003至2025年的2334条观测记录。该数据集以性别与企业规模双重维度解构非正规就业占比，为转型经济体劳动力市场研究提供了跨国可比的基础数据层。

当前挑战

该数据集所应对的领域问题在于非正规就业的系统性量化——其定义随ICLS决议修订而变动，导致跨国比较面临概念漂移风险；同时非正规部门活动常被常规劳动力调查遗漏，尤其涉及自雇与家庭帮工群体，测量误差难以避免。构建过程中，原始数据源自各国劳动力调查与社会经济数据库，观测频率与统计口径参差不齐，需依赖ILO的调和工作；部分观测值标记为“不可靠”或存在序列断点（如方法学修订），缺失值处理与时序对齐构成实质性障碍；此外，仅覆盖四个欧洲国家，样本时空范围有限，制约了推断的外部效度。

常用场景

经典使用场景

在劳动经济学与非正规经济研究领域，该数据集构成了刻画欧洲非正规就业结构的关键实证基础。其经典使用场景聚焦于以性别与机构规模为双重视角，剖析非正规部门就业份额的时序演化与横截面差异。研究者通常将观测值按年份与国家进行面板重构，进而考察性别分工如何随机构规模变化而重塑非正规就业的分布形态。借助该数据集，可系统识别不同规模机构中男性与女性劳动者在非正规部门的相对集中程度，为理解转型经济体的劳动力市场二元结构提供量化依据。

衍生相关工作

围绕该数据集，衍生出一系列聚焦非正规就业测度与劳动市场分割的经典研究。部分工作将其与ILOSTAT其他指标整合，构建多维非正规就业指数，用以检验制度质量与影子经济规模之间的关联。另一些研究利用其性别与机构规模维度，发展出分解非正规就业性别差距的计量框架，并应用于东南欧转型经济体的比较分析。在方法论层面，该数据集还激发了针对分类变量缺失与观测状态标记的插补与稳健性检验方法，为劳动统计数据的二次分析树立了规范。这些衍生工作共同拓展了非正规经济实证研究的边界。

数据集最近研究

最新研究方向

在全球非正规经济研究持续深化的背景下，该数据集凭借其按性别与机构规模双维度拆分的欧洲四国（摩尔多瓦、北马其顿、波黑、塞尔维亚）2003至2025年长时间序列观测，为探析非正规就业的结构性演进提供了独特视角。当前前沿研究聚焦于运用面板回归与时序预测模型，识别机构规模对非正规就业性别差异的调节效应，并借助ILOSTAT来源标签与系列断点注释，开展数据质量敏感的稳健性检验。该数据亦被用于评估西巴尔干地区劳动力市场正规化政策成效，与联合国可持续发展目标8中体面劳动议程紧密呼应，对理解转型经济体非正规部门动态具有重要实证价值。