electricsheepeurope/europe-ilo-emp-pifl-sex-est-geo-nb-employment-outside-the-formal-sector-by-sex-establ

--- 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: "Employment outside the formal sector by sex, establishment size and rural / urban areas (t | Europe (ILOSTAT)" --- # Employment outside the formal sector by sex, establishment size and rural / urban areas (t | Europe (ILOSTAT) 🇪🇺 **4,796 observations** · **3 Europe countries** · **2003–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **4,796 observations** of `Informal economy` data across **3 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_GEO_NB) - **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_GEO_NB` 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 3 Europe countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `MDA` | 1,872 | 2003 | 2025 | | `BIH` | 1,716 | 2006 | 2024 | | `SRB` | 1,208 | 2007 | 2020 | ## Indicators (sample) - `EMP_PIFL_SEX_EST_GEO_NB` — Employment outside the formal sector by sex, establishment size and rural / urban areas (thousands) ## 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_GEO_NB` | | `indicator.label` | `string` | Indicator name in English | `Employment outside the formal sector …` | | `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` | | `classif2` | `string` | Second classification variable where applicable | `GEO_COV_NAT` | | `classif2.label` | `string` | — | `Area type: National` | | `time` | `int64` | Observation year | `2024` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `186.998` | | `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-geo-nb-employment-outside-the-formal-sector-by-sex-establ") 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_GEO_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_PIFL_SEX_EST_GEO_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_PIFL_SEX_EST_GEO_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_emp_pifl_sex_est_geo_nb_employment_outside_the_formal_sector_by_sex_establ_2025, title = {Employment outside the formal sector by sex, establishment size and rural / urban areas (t | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_PIFL_SEX_EST_GEO_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-emp-pifl-sex-est-geo-nb-employment-outside-the-formal-sector-by-sex-establ}} } ``` ## 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_GEO_NB_This dataset contains 4,796 observations from the International Labour Organization (ILO) ILOSTAT database, covering 3 European countries (Moldova, Bosnia and Herzegovina, Serbia) from 2003 to 2025. The core indicator is EMP_PIFL_SEX_EST_GEO_NB, which measures employment outside the formal sector by sex, establishment size, and rural/urban areas (in thousands). Data is sourced via the ILOSTAT REST API and filtered to European countries. It includes structured columns such as country codes, indicators, sex classifications, observation years, and values, along with source information, quality caveats, and usage examples. The dataset is repackaged by Electric Sheep Europe as part of a unified, ML-ready data layer for Europe.

搜集汇总

数据集介绍

构建方式

在全球劳动统计体系中，非正规部门就业的性别差异与地域分布是衡量劳动力市场结构的关键维度。该数据集源于国际劳工组织（ILO）维护的ILOSTAT中央统计数据库，通过其REST API接口直接提取指标EMP_PIFL_SEX_EST_GEO_NB的原始记录，并依据欧洲ISO3国家代码进行过滤与重组。数据处理环节遵循国际劳工统计学家会议（ICLS）的定义框架，对原始调查微观数据进行协调化处理，保留来源标签以确保可追溯性，最终由Electric Sheep Europe以Parquet格式封装发布，形成涵盖摩尔多瓦、波黑与塞尔维亚三国、时间跨度自2003年至2025年的结构化面板数据。

特点

该数据集以4796条观测记录为核心，聚焦欧洲三国非正规部门就业的性别、机构规模与城乡区域三重维度，具有鲜明的多维分类与年度频率特征。其架构包含二十余个字段，覆盖国家代码、数据来源、指标代码、性别分类、机构规模分类、城乡分类、时间标识及观测值等，并附有观测状态与注释标签以标识数据质量与序列断点。数据集规模适中而信息密度较高，分类变量按需非空，既支持截面比较，亦便于时间序列建模，体现了劳动统计领域微观与宏观数据的有效衔接。

使用方法

研究者可借助Hugging Face的datasets库以单行代码加载数据，并转换为Pandas DataFrame进行灵活分析。典型操作包括按国家代码筛选子集、针对单一指标绘制时间序列趋势图，以及通过透视表构建国家与年份的二维矩阵以观察区域差异。该数据集适用于表格分类、回归与时间序列预测等任务，为劳动经济学、发展研究及社会政策评估提供可复用的数据基础，使用时需遵循CC-BY-4.0许可并同时引用ILO原始来源与Electric Sheep Europe的再包装工作。

背景与挑战

背景概述

非正规部门就业的测度始终是全球劳动统计中的核心议题。国际劳工组织（ILO）依托国际劳工统计学家会议（ICLS）确立的定义框架，长期通过ILOSTAT数据库系统性地汇编各国劳动力调查数据。本数据集由Electric Sheep Europe于2026年从ILOSTAT REST API抽取并重新封装，聚焦摩尔多瓦、波斯尼亚和黑塞哥维那、塞尔维亚三个欧洲国家，涵盖2003至2025年间按性别、机构规模及城乡地域分组的非正规部门外就业观测值，共4,796条记录。该数据为转型经济体非正规就业的结构性监测提供了稀缺的跨国可比时间序列，对劳动经济学与区域发展研究具有重要参考价值。

当前挑战

该数据集所回应的领域难题在于：非正规经济活动的边界模糊性与测度异质性长期制约跨国比较研究的可靠性，非正规就业往往游离于行政记录之外，高度依赖抽样调查的覆盖能力与受访者报告意愿。在构建层面，源数据受限于各国劳动力调查的启动年份差异与调查频率波动，致使时间序列存在结构性断裂；分类变量仅对特定指标发布细分层级，导致性别、机构规模与城乡维度的完整交叉分析受限；部分观测值附有“不可靠”或“方法修订致序列中断”等质量警示标记，进一步增加了纵向分析的复杂度。

常用场景

经典使用场景

在非正规经济与劳动力市场不平等的实证研究中，该数据集凭借按性别、企业规模及城乡区域三重维度交叉细分的就业统计，成为刻画欧洲转型经济体非正规就业结构异质性的基准性数据资源。研究者常利用其2003至2025年的年度序列，通过面板回归与分解分析，揭示性别差异与企业规模对非正规就业分布的交互影响，并比较摩尔多瓦、波黑与塞尔维亚三国在非正规部门吸纳劳动力方面的路径差异。

解决学术问题

该数据集有力回应了非正规就业测量中缺乏性别与企业规模交叉分类的长期困境，为检验劳动力市场分割理论、性别化非正规性假说以及城乡二元结构对非正规就业的塑造机制提供了可操作化的观测基础。其统一的国际劳工组织分类标准与来源标注，使跨国比较研究得以规避定义不一致带来的偏误，进而深化了对转型经济体非正规部门规模变动及其结构性成因的理解。

衍生相关工作

基于该数据集及其同源国际劳工组织系列指标，学界衍生出一批聚焦欧洲转型经济体非正规就业动态的经典研究，包括对非正规就业性别差距的时序分解、企业规模与正规化概率的关联分析，以及城乡差异在劳动力市场转型中的演变路径探讨。这些工作进一步推动了非正规经济卫星账户的构建方法讨论，并为后续融合微观调查数据与宏观统计指标的研究范式奠定了基础。