electricsheepeurope/europe-ilo-emp-pifl-sex-ins-mts-nb-employment-outside-the-formal-sector-by-sex-public

--- 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, public/private sector and marital status (tho | Europe (ILOSTAT)" --- # Employment outside the formal sector by sex, public/private sector and marital status (tho | Europe (ILOSTAT) 🇪🇺 **3,630 observations** · **4 Europe countries** · **2003–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **3,630 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_INS_MTS_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_INS_MTS_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 | |---------|-----:|-----------:|----------:| | `MDA` | 1,194 | 2003 | 2025 | | `MKD` | 892 | 2009 | 2025 | | `BIH` | 866 | 2006 | 2020 | | `SRB` | 678 | 2007 | 2020 | ## Indicators (sample) - `EMP_PIFL_SEX_INS_MTS_NB` — Employment outside the formal sector by sex, public/private sector and marital status (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_INS_MTS_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.) | `INS_SECTOR_TOTAL` | | `classif1.label` | `string` | — | `Institutional sector: 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) | `116.214` | | `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-ins-mts-nb-employment-outside-the-formal-sector-by-sex-public") 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_INS_MTS_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_PIFL_SEX_INS_MTS_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_PIFL_SEX_INS_MTS_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_emp_pifl_sex_ins_mts_nb_employment_outside_the_formal_sector_by_sex_public_2025, title = {Employment outside the formal sector by sex, public/private sector and marital status (tho | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_PIFL_SEX_INS_MTS_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-emp-pifl-sex-ins-mts-nb-employment-outside-the-formal-sector-by-sex-public}} } ``` ## 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_INS_MTS_NB_This dataset contains statistics on employment outside the formal sector in Europe, specifically focusing on employment by sex, public/private sector, and marital status (in thousands). The data is sourced from the International Labour Organization (ILO) ILOSTAT database, covering 4 European countries (Moldova, North Macedonia, Bosnia and Herzegovina, Serbia) from 2003 to 2025, with 3,630 observations. It includes only one indicator: EMP_PIFL_SEX_INS_MTS_NB, which provides detailed employment data disaggregated by dimensions such as sex, institutional sector, and marital status. The data is published at an annual frequency and harmonized by ILO for consistency and comparability. Organized in tabular format, the dataset includes columns for country codes, data sources, indicator codes, classification variables, observation years, observed values, and more, making it suitable for tasks like tabular classification, regression, and time-series forecasting. Repackaged by Electric Sheep Europe for ease of use in machine learning research and development.

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，原始数据经由ILOSTAT REST API接口直接提取，并依据欧洲ISO3国家代码进行筛选。ILOSTAT采用国际劳工统计学家会议（ICLS）标准定义，对各国劳动力调查、住户收入调查等微观数据进行协调与标准化处理，以确保跨国可比性；源数据来源在source.label列中予以标注，以保障可追溯性。

使用方法

研究人员可通过HuggingFace的datasets库以load_dataset函数加载数据，并转换为Pandas数据框进行后续分析。用户可依据国家代码筛选特定国家子集，或针对单一指标构建时间序列图，亦可利用透视表生成国家与年份的矩阵视图，从而支持跨国比较与趋势分析。

背景与挑战

背景概述

非正规部门就业的测度长期构成劳动统计领域的核心议题。国际劳工组织（ILO）依托国际劳工统计学家会议（ICLS）确立的界定标准，整合各国劳动力调查等微观数据，构建了覆盖200余经济体的ILOSTAT数据库。本数据集由Electric Sheep Europe于2025年从ILOSTAT REST API抓取并重新封装，聚焦欧洲四国（摩尔多瓦、北马其顿、波黑、塞尔维亚），时间跨度为2003至2025年，共3,630条观测，按性别、公私部门及婚姻状况等维度细分，为解析转型经济体非正规就业的结构性差异提供了标准化、可直接加载的时序面板，对非正规经济核算与体面劳动监测具有基础性支撑价值。

当前挑战

该数据集所回应的领域难题在于，非正规就业的定义边界随ICLS决议演进，各国调查口径、参考期与抽样设计参差不齐，跨境可比性脆弱。构建过程中的具体挑战包括：源头ILOSTAT仅提供少量欧洲国家且时间断点频现，序列中标注了“Break in series”等状态旗标，需谨慎处理结构性突变；多个分类维度并非所有国家×年份组合均完整发布，稀疏与缺失并存；原始API返回的代码标签需映射为可读语义，而多源合并时ILO仅保留单一“最佳来源”，可能掩盖平行调查间的口径分歧。上述因素共同制约了长时序建模与跨国稳健推断。

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转型经济体非正规就业的性别差异与部门分布特征。研究者常以性别、公共/私营部门及婚姻状况为分组维度，构建面板数据模型，考察2003至2025年间摩尔多瓦、北马其顿、波黑与塞尔维亚四国非正规就业的规模变动与结构演化。借助时间序列分析与跨国比较，该数据集能够揭示非正规就业在性别与婚姻状态间的异质性，为理解后社会主义转型期劳动力市场碎片化提供实证基础。

解决学术问题

该数据集有效回应了非正规就业测度中性别盲视与部门划分粗疏的学术难题。传统劳动统计往往忽略婚姻状况对女性非正规就业的调节效应，而本数据集通过交叉分类性别、公私部门与婚姻状态，使研究者得以辨析不同家庭角色下男女劳动者进入非正规部门的差异化路径。其意义在于推动非正规经济研究从总量估算转向结构化解释，为检验性别分层理论与劳动力市场分割假说提供跨国可比证据，并助力国际劳工组织体面劳动议程的量化监测。

实际应用

在实际应用层面，该数据集为政策制定者与开发机构提供了识别非正规就业脆弱群体的操作化工具。社会保障部门可依据性别与婚姻状况的交叉分布，精准定位需要扩展社会保护覆盖的目标人群；就业服务机构能借助公私部门分类，设计针对非正规经济从业者的技能培训与创业扶持项目。欧洲邻国政策框架下的劳动力市场改革亦可援引该数据，评估非正规就业正规化措施的性别敏感性与部门针对性，从而优化资源配置与干预时序。

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，该数据集聚焦欧洲四国（摩尔多瓦、北马其顿、波黑、塞尔维亚）2003至2025年间非正规部门就业的性别差异、公私部门分布及婚姻状况影响，为转型经济体劳动力市场结构变迁提供了稀缺的纵向观测窗口。当前前沿研究正借助此类数据探讨非正规就业的性别分层机制与制度转型的交互效应，尤其关注婚姻状况如何调节女性在非正规经济中的参与路径。该数据集亦呼应国际劳工组织关于体面劳动与SDG8的监测议程，为评估后社会主义国家福利制度碎片化对劳动非正规化的驱动作用提供了实证基础，对理解欧洲边缘地带劳动力市场二元结构及其性别后果具有重要学术价值与政策意义。