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

--- 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: "Employment outside the formal sector by sex, occupation and establishment size (thousands) | Europe (ILOSTAT)" --- # Employment outside the formal sector by sex, occupation and establishment size (thousands) | Europe (ILOSTAT) 🇪🇺 **13,138 observations** · **4 Europe countries** · **2003–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **13,138 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_OCU_EST_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_OCU_EST_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 4 Europe countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `BIH` | 3,755 | 2006 | 2024 | | `MDA` | 3,676 | 2003 | 2025 | | `MKD` | 3,344 | 2009 | 2025 | | `SRB` | 2,363 | 2007 | 2020 | ## Indicators (sample) - `EMP_PIFL_SEX_OCU_EST_NB` — Employment outside the formal sector by sex, occupation and establishment size (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_OCU_EST_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.) | `OCU_SKILL_TOTAL` | | `classif1.label` | `string` | — | `Occupation (Skill level): Total` | | `classif2` | `string` | Second classification variable where applicable | `EST_AGGREGATE_TOTAL` | | `classif2.label` | `string` | — | `Establishment size (Aggregate): Total` | | `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_classif` | `string` | — | `—` | | `note_classif.label` | `string` | — | `—` | | `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-ocu-est-nb-employment-outside-the-formal-sector-by-sex-occupa") 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_OCU_EST_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_PIFL_SEX_OCU_EST_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_PIFL_SEX_OCU_EST_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_emp_pifl_sex_ocu_est_nb_employment_outside_the_formal_sector_by_sex_occupa_2025, title = {Employment outside the formal sector by sex, occupation and establishment size (thousands) | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_PIFL_SEX_OCU_EST_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-emp-pifl-sex-ocu-est-nb-employment-outside-the-formal-sector-by-sex-occupa}} } ``` ## 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_OCU_EST_NB_This dataset contains informal economy data from the International Labour Organizations (ILO) ILOSTAT database, focusing on 4 European countries (e.g., Bosnia and Herzegovina, Moldova, North Macedonia, Serbia) from 2003 to 2025, with 13,138 observations. The indicator is Employment outside the formal sector by sex, occupation and establishment size (thousands), covering disaggregation dimensions such as sex, occupation, and establishment size. Data is sourced directly from the ILOSTAT REST API and filtered to European ISO3 country codes, harmonized using ILO definitions. It is structured in tabular format with columns for country codes, year, observed values, data sources, and quality flags, suitable for tasks like tabular classification, regression, and time-series forecasting. The dataset is repackaged by Electric Sheep Europe for machine learning and research purposes.

搜集汇总

数据集介绍

构建方式

该数据集依托国际劳工组织（ILO）的ILOSTAT中央统计数据库构建，通过ILOSTAT REST API直接抽取指标EMP_PIFL_SEX_OCU_EST_NB的原始观测记录，并依据ISO3国家代码筛选出欧洲区域样本。ILOSTAT对源自国家劳动力调查、家庭收入调查及行政记录等原始微观数据进行统一协调，采用国际劳工统计学家会议（ICLS）标准定义加以规范化处理，并为每条记录标注来源标签以保障可追溯性。数据经Electric Sheep Europe重新打包为Parquet格式并发布至HuggingFace平台，最终形成覆盖四个欧洲国家的非正规经济部门就业统计数据集。

特点

数据集涵盖2003年至2025年期间共计13,138条观测记录，地理范围涉及波斯尼亚和黑塞哥维那、摩尔多瓦、北马其顿和塞尔维亚四个欧洲国家，时间跨度逾二十年。数据以性别为主要 disaggregation 维度，区分总计、男性与女性三类取值，同时包含职业分类与机构规模分类等辅助维度，便于开展多层次的交叉分析。指标单位为千人，观测值附带状态标记，可识别临时性或可靠性存疑的数据点。年度频率的表格结构支持分类、回归与时间序列预测等多种建模任务，数据规模处于万级区间，兼顾统计充分性与计算便捷性。

使用方法

研究者可通过HuggingFace datasets库以load_dataset函数直接加载该数据集，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，按indicator字段提取目标指标并依时间排序以绘制时间序列趋势图，或利用pivot_table方法将数据重塑为国家与年份的二维矩阵以便横向比较。数据可服务于非正规就业水平的国别差异评估、性别维度下的就业结构分析以及跨年度的趋势预测等研究目标。使用时应留意数据质量声明中关于年度频率、最佳来源选择及分类字段非空条件的说明，并在发表成果时同时引用ILO原始来源与Electric Sheep Europe的再打包工作。

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统计领域的核心议题，其数据的可得性与可比性直接影响体面劳动议程的监测效能。国际劳工组织（ILO）依托国际劳工统计学家会议（ICLS）确立的界定标准，经由ILOSTAT平台系统汇编各国劳动力调查等微观数据，构建了覆盖二百余个经济体的劳动统计体系。本数据集由Electric Sheep Europe于2026年自ILOSTAT接口规范化重打包发布，聚焦欧洲四国（波黑、摩尔多瓦、北马其顿、塞尔维亚）2003至2025年间非正规部门就业的性别、职业与机构规模分布，含13,138条观测，为转型经济体非正规就业的结构性研究提供了跨国可比的数据基础。

当前挑战

非正规就业统计面临界定标准与调查口径异质性所致的跨国可比性难题，非正规部门与非正规就业的概念边界在不同国家的劳动力调查中执行尺度不一，且非农自我就业与家庭帮工等类别常存在覆盖盲区。就本数据集而言，四国均属西巴尔干及周边转型经济体，其劳动力调查体系在观测期内经历多次方法学修订，数据中标记的序列断点和不可靠观测即反映了此类扰动；部分国家年份覆盖不完整，机构规模与职业维度的交叉分类存在缺失，加之非正规就业本身具有高度季节性与隐蔽性，抽样调查对其规模的捕捉能力受限，这些因素共同构成对时序建模与跨国面板分析的有效约束。

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，该数据集最经典的使用场景在于刻画欧洲非正规部门就业的性别差异与职业结构特征。研究者依托国际劳工组织标准化的性别、职业与机构规模三重分类维度，按年度构建跨国比较面板，用以衡量波黑、摩尔多瓦、北马其顿与塞尔维亚四国非正规就业的规模演变与结构异质性。此类分析常借助时间序列建模与横截面回归，揭示非正规就业在性别与技能层级之间的分布规律，为区域劳动力市场监测提供定量基础。

衍生相关工作

以该数据集为基础，衍生研究主要沿着两条路径拓展。其一为跨国比较研究，将欧洲四国经验纳入全球非正规就业格局，检验制度环境与经济发展水平对非正规就业规模的条件效应；其二为方法创新，部分工作将其与世界银行、经合组织等微观数据库进行链接，构建多源异构面板，推动非正规就业测量从简单统计向机器学习预测与因果推断演进。这些工作共同提升了非正规经济领域经验研究的透明度与可复现性。

数据集最近研究

最新研究方向

伴随全球对非正规经济测度标准化的持续关切，该数据集所承载的就业非正规性多维分解议题正跻身劳动经济学前沿。研究者依托其性别、职业与机构规模的三重交叉维度，运用面板回归与机器学习方法，精密辨识欧洲转型经济体非正规就业的结构性异质，尤其聚焦女性在低技能职业与微型机构中的过度集聚现象。近期围绕国际劳工组织第204号建议书的政策辩论，以及后疫情时代非正规部门韧性重估，赋予该数据以实证标尺意义，推动包容性劳动市场指标的跨国可比性研究迈向纵深。