electricsheepeurope/europe-ilo-emp-pifl-sex-age-jbt-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: - 10K<n<100K tags: - tabular - europe - ilostat - informal-economy - ilo - labour - employment pretty_name: "Share of employment outside the formal sector by sex, age and working time arrangement (%) | Europe (ILOSTAT)" --- # Share of employment outside the formal sector by sex, age and working time arrangement (%) | Europe (ILOSTAT) 🇪🇺 **66,901 observations** · **34 Europe countries** · **2003–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **66,901 observations** of `Informal economy` data across **34 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_AGE_JBT_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_AGE_JBT_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 34 Europe countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `MDA` | 3,082 | 2003 | 2025 | | `BIH` | 2,549 | 2006 | 2024 | | `NLD` | 2,452 | 2007 | 2024 | | `POL` | 2,387 | 2007 | 2024 | | `SRB` | 2,376 | 2007 | 2024 | | `ITA` | 2,342 | 2007 | 2024 | | `RUS` | 2,340 | 2010 | 2022 | | `PRT` | 2,336 | 2007 | 2024 | | `MKD` | 2,278 | 2009 | 2025 | | `LVA` | 2,271 | 2007 | 2024 | | `ESP` | 2,259 | 2007 | 2024 | | `FIN` | 2,248 | 2007 | 2024 | | `SWE` | 2,245 | 2007 | 2024 | | `BGR` | 2,173 | 2007 | 2024 | | `FRA` | 2,155 | 2007 | 2024 | | ... | _19 more countries_ | | | ## Indicators (sample) - `EMP_PIFL_SEX_AGE_JBT_RT` — Share of employment outside the formal sector by sex, age and working time arrangement (%) ## Schema | Column | Type | Description | Example | |--------|------|-------------|---------| | `ref_area` | `string` | ISO 3166-1 alpha-3 country code | `AUT` | | `ref_area.label` | `string` | Country name in English | `Austria` | | `source` | `string` | ILOSTAT source code (e.g. labour force survey) | `BB:275` | | `source.label` | `string` | Source name in English | `HIES - EU Statistics on Income and Li…` | | `indicator` | `string` | ILOSTAT indicator code | `EMP_PIFL_SEX_AGE_JBT_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.) | `AGE_YTHADULT_YGE15` | | `classif1.label` | `string` | — | `Age (Youth, adults): 15+` | | `classif2` | `string` | Second classification variable where applicable | `JBT_TIME_TOTAL` | | `classif2.label` | `string` | — | `Working time arrangement: Total` | | `time` | `int64` | Observation year | `2024` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `3.907` | | `obs_status` | `string` | Observation status flag (e.g. provisional, unreliable) | `U` | | `obs_status.label` | `string` | — | `Unreliable` | | `note_classif` | `string` | — | `C6:1058` | | `note_classif.label` | `string` | — | `Nonstandard age group: Excluding age 15` | | `note_indicator` | `string` | — | `I11:264` | | `note_indicator.label` | `string` | — | `Break in series: Methodology revised` | | `note_source` | `string` | — | `R1:3513_T2:85` | | `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-age-jbt-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_AGE_JBT_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_PIFL_SEX_AGE_JBT_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_PIFL_SEX_AGE_JBT_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_emp_pifl_sex_age_jbt_rt_share_of_employment_outside_the_formal_sector_by_s_2025, title = {Share of employment outside the formal sector by sex, age and working time arrangement (%) | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_PIFL_SEX_AGE_JBT_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-emp-pifl-sex-age-jbt-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_AGE_JBT_RT_This dataset contains 66,901 observations of informal economy data across 34 Europe countries, spanning 2003–2025, covering 1 distinct indicator: Share of employment outside the formal sector by sex, age and working time arrangement (%). It is sourced from the ILOSTAT database of the International Labour Organization (ILO), pulled via REST API and filtered to European countries. The dataset includes columns such as country codes, country names, data sources, indicator codes, sex disaggregation, age classification, working time arrangement classification, observation year, and observed values, with notes on data quality and usage examples. It is designed as an ML-ready data layer for studying the informal economy in Europe.

搜集汇总

数据集介绍

构建方式

非正规部门就业占比是衡量劳动力市场结构与非正规经济规模的关键指标。该数据集由Electric Sheep Europe从国际劳工组织统计数据库（ILOSTAT）的REST API直接提取，筛选欧洲34个国家的ISO3代码，构建了涵盖2003至2025年、共计66,901条观测的时序面板。原始数据源自各国劳动力调查、家庭收入调查等，经ILO采用国际劳工统计学家会议定义进行跨国协调，并标注来源以确保可追溯性。

特点

数据集以表格形式组织，包含国家、性别、年龄、工作安排等多维分类变量，覆盖34个欧洲国家，时间跨度逾二十年。变量富含元数据标签，如观测状态、分类注释和来源说明，便于数据质量评估。指标单一而维度丰富，适用于非正规就业的性别与年龄差异分析，且以CC-BY-4.0许可开放，支持学术与政策研究。

使用方法

研究者可通过HuggingFace的datasets库加载数据集，并转换为Pandas数据框进行灵活分析。典型用法包括按国家筛选（如德国），针对单一指标绘制时间序列图，或通过透视表生成国家×年份矩阵以观察趋势。该数据集亦适用于分类、回归及时间序列预测等机器学习任务，代码示例展示了从加载到可视化的完整流程。

背景与挑战

背景概述

非正规部门就业的规模与结构历来是发展经济学与劳动经济学的核心议题。国际劳工组织（ILO）依托其全球劳动统计数据库ILOSTAT，于2025年经Electric Sheep Europe重新封装发布了这一覆盖欧洲34国、时间跨度自2003年至2025年的数据集，旨在刻画正规部门之外就业份额在性别、年龄与工作时间安排维度上的分布。该数据集源自各国劳动力调查与家庭收入调查等微观数据，经国际劳工统计学家会议（ICLS）定义协调后系统汇集，为监测非正规经济、评估体面劳动进展及检验劳动市场二元结构理论提供了高颗粒度的跨国面板证据，对推动欧洲乃至全球非正规就业比较研究具有基础性意义。

当前挑战

从领域问题看，非正规就业份额的跨国比较面临三大固有难题：各国对非正规部门的操作化定义存在系统性差异，观测单元与参照期不一致，且自雇与无酬家庭帮工等边界情形常因调查工具差异而难以对齐。构建过程中，原始调查数据的碎片化与元数据缺失构成显著约束，ILOSTAT虽以'最佳来源'规则进行调和，但不同国家序列间仍存在方法论断裂与观测状态标记不完整的情形，加之部分年份与人口组别存在数据稀疏乃至空缺，使得时间序列的连续性和跨群组可加性受到削弱，为因果推断与长期趋势拟合带来识别上的复杂挑战。

常用场景

经典使用场景

在非正规经济部门的劳动经济学研究中，该数据集通常被用于刻画欧洲各国非正规就业比例的时序演变与结构差异。研究者可依据性别、年龄组及工作时间安排等维度，对不同国家与年份的非正规就业占比进行横截面比较与纵向追踪，进而识别非正规就业在性别与代际之间的分布特征。其面板数据结构亦支撑固定效应模型、双重差分设计以及时间序列预测等定量分析任务，为探究非正规部门规模变动的驱动因素提供了基础数据支撑。

衍生相关工作

基于该数据集及其所属的ILOSTAT非正规经济指标群，学术界与政策研究机构衍生出若干经典工作。其中包括关于非正规就业与经济增长关系的跨国面板研究、非正规部门女性化程度的比较分析，以及非正规就业对社会保障可持续性影响的模拟评估。这些工作常与ILO发布的《世界非正规经济报告》及可持续发展目标中体面劳动指标的监测报告相互印证，形成了从数据到政策分析的完整研究链条。

数据集最近研究

最新研究方向

在全球非正规经济监测与体面劳动议程深化的背景下，该数据集所承载的性别、年龄与工作时间维度信息，正推动劳动经济学前沿研究从总量测度转向结构性异质性分析。近期研究聚焦于非正规就业的性别差距与代际分化，结合欧洲劳动力市场转型、平台经济扩张及后疫情复苏等热点议题，运用面板回归与时间序列预测方法，识别制度变迁对非正规就业份额的动态影响。该数据集的精细分类与长时序覆盖，为评估ILO体面劳动目标进展、检验非正规性与工作贫困的关联机制提供了关键实证基础，对劳动力市场政策设计与国际比较研究具有重要参考价值。