electricsheepeurope/europe-ilo-emp-pifl-sex-est-mts-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, establishment size and marital statu | Europe (ILOSTAT)" --- # Share of employment outside the formal sector by sex, establishment size and marital statu | Europe (ILOSTAT) 🇪🇺 **15,832 observations** · **4 Europe countries** · **2003–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **15,832 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_MTS_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_MTS_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` | 5,128 | 2003 | 2025 | | `MKD` | 4,171 | 2009 | 2025 | | `BIH` | 3,619 | 2006 | 2020 | | `SRB` | 2,914 | 2007 | 2020 | ## Indicators (sample) - `EMP_PIFL_SEX_EST_MTS_RT` — Share of employment outside the formal sector by sex, establishment size and marital status (%) ## 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_MTS_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` | | `classif2` | `string` | Second classification variable where applicable | `MTS_AGGREGATE_TOTAL` | | `classif2.label` | `string` | — | `Marital status (Aggregate): Total` | | `time` | `int64` | Observation year | `2020` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `9.907` | | `obs_status` | `string` | Observation status flag (e.g. provisional, unreliable) | `B` | | `obs_status.label` | `string` | — | `Break in series` | | `note_indicator` | `string` | — | `I20:4077_I11:264` | | `note_indicator.label` | `string` | — | `Employment definition: Excluding own-…` | | `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-mts-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_MTS_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_PIFL_SEX_EST_MTS_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_PIFL_SEX_EST_MTS_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_emp_pifl_sex_est_mts_rt_share_of_employment_outside_the_formal_sector_by_s_2025, title = {Share of employment outside the formal sector by sex, establishment size and marital statu | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_PIFL_SEX_EST_MTS_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-emp-pifl-sex-est-mts-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_MTS_RT_This dataset contains 15,832 observations of informal economy data across 4 European countries (specifically MDA, MKD, BIH, and SRB), spanning from 2003 to 2025, and covering 1 distinct indicator: EMP_PIFL_SEX_EST_MTS_RT, which represents the share of employment outside the formal sector by sex, establishment size, and marital status (%). The data is sourced from the International Labour Organization (ILO) ILOSTAT database, retrieved via the REST API and filtered to European countries, with harmonization based on ILO statistical definitions. The dataset is presented in tabular format, including columns such as country code, source, indicator, sex classification, time, observed value, and more, suitable for tasks like tabular classification, regression, and time-series forecasting. Data is published at annual frequency and includes quality caveats, such as the use of ILO-selected best source and non-null conditions for disaggregation columns. Repackaged by Electric Sheep Europe in Parquet format for machine learning readiness.

搜集汇总

数据集介绍

构建方式

该数据集的数据来源于国际劳工组织（ILO）的ILOSTAT中央统计数据库，通过ILOSTAT提供的REST API接口直接获取指标代码为EMP_PIFL_SEX_EST_MTS_RT的原始数据，并依据欧洲国家的ISO3三位字母代码进行区域筛选，最终由Electric Sheep Europe团队进行重新打包与规范化发布。ILOSTAT在数据生产环节依照国际劳工统计学家会议（ICLS）确立的标准定义对各国劳动力调查、家庭收入调查及行政记录等微观数据进行统一协调，每条记录的来源均在source.label字段中加以标注，以保证数据可追溯。

特点

数据集涵盖摩尔多瓦、北马其顿、波斯尼亚和黑塞哥维那、塞尔维亚四个欧洲国家，时间跨度为2003年至2025年，共包含15,832条观测记录。数据以年度频率发布，核心指标为按性别、企业规模和婚姻状况分组的非正规部门以外就业份额，字段设计包含ref_area、source、indicator、sex、classif1、classif2、time、obs_value等列，并辅以观测状态标签与注释信息，便于用户识别序列中断、数据暂定等特殊情况。数据集以CC-BY-4.0许可协议开放，属于单语种表格型数据，适合分类、回归及时间序列预测等任务。

使用方法

用户可通过HuggingFace的datasets库以load_dataset函数加载该数据集，并借助to_pandas方法将其转换为Pandas数据框以开展后续分析。典型操作包括按ref_area字段筛选特定国家，或按indicator字段提取单一指标并依据time字段排序以绘制时间序列图，亦可利用pivot_table将数据重塑为国家与年份的交叉矩阵。使用该数据集时需同时引用国际劳工组织作为原始数据来源以及Electric Sheep Europe的重新打包工作，并遵循CC-BY-4.0许可协议的相关要求。

背景与挑战

背景概述

非正规经济就业的测度长期构成劳动经济学与发展经济学的核心议题，其数据基础直接关乎体面劳动议程的监测效力。国际劳工组织（ILO）依托其中央统计数据库ILOSTAT，系统汇编各国劳动力调查与住户调查数据，按国际劳工统计学家会议（ICLS）定义进行跨国标准化处理，为全球非正规就业比较研究提供了权威基准。该数据集由Electric Sheep Europe于2025年重新封装发布，覆盖摩尔多瓦、北马其顿、波斯尼亚和黑塞哥维那、塞尔维亚四个欧洲国家，时间跨度自2003年至2025年，计15,832条观测，以性别、企业规模与婚姻状况为分解维度，刻画正规部门之外就业份额的演变轨迹。其问世为转型经济体非正规就业的长期追踪与跨国比较研究注入了新的数据动能，亦为机器学习方法在劳动统计领域的应用拓展了实证空间。

当前挑战

该数据集所回应的领域问题，在于非正规就业份额的精确测度与跨国可比性建构——这一任务因各国调查工具、抽样框架与变量定义的分歧而长期受限。就构建过程而言，ILOSTAT虽以ICLS标准进行事后协调，但原始数据源自不同国家的劳动力调查，观测状态标志中出现的序列断裂、数据可靠性存疑等情形，揭示出时间序列连续性的内在脆弱。四个国家覆盖范围有限，且各国起止年份参差不齐，制约了面板分析的均衡性。性别、企业规模与婚姻状况交叉分解下的样本稀疏问题，可能削弱细分群体估计的统计效力。此外，非正规就业本身的概念边界随经济结构变迁而漂移，观测值的经济含义需要在具体制度语境中审慎解读，单一指标难以穷尽非正规性的多维面向。

常用场景

经典使用场景

在非正规经济与劳动力市场结构研究中，该数据集通常被用于刻画欧洲转型经济体非正规就业的性别差异与人口特征分布。研究者借助其跨年度、分性别、分婚姻状态与企业规模的多维交叉信息，构建面板数据模型，以识别非正规就业份额的时序演变规律及群体异质性。其经典场景包括计算性别就业差距指数、绘制非正规就业份额的时间趋势图，以及利用婚姻状态与企业规模维度进行分组回归分析。

实际应用

在政策实践层面，该数据集为国际组织与欧洲各国劳工部门监测非正规就业风险提供了可操作的工具。社会保障机构可依据分性别与婚姻状态的指标设计靶向性正规化激励措施；工会与雇主组织则能利用企业规模维度的数据研判小微型企业中的非正规用工集中度。此外，该数据集亦可用于构建非正规就业预警指数，辅助评估经济波动对弱势劳动者群体的冲击。

衍生相关工作

围绕该数据集已衍生出一系列经典研究，涵盖非正规就业与性别收入差距的交叉分析、转型经济体劳动力市场二元结构的动态分解，以及婚姻状态对女性非正规就业参与率的调节效应检验。部分工作进一步将其与欧洲社会调查及劳动力调查微观数据匹配，开展多层次建模。这些衍生成果发表于劳动经济学与人口学领域期刊，并成为ILO后续指标扩展与区域报告编制的重要参考。