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

--- license: cc-by-4.0 language: - en task_categories: - tabular-classification - tabular-regression - time-series-forecasting multilinguality: monolingual size_categories: - n<1K tags: - tabular - europe - ilostat - informal-economy - ilo - labour - employment pretty_name: "Employment outside the formal sector by sex and public/private sector (thousands) | Europe (ILOSTAT)" --- # Employment outside the formal sector by sex and public/private sector (thousands) | Europe (ILOSTAT) 🇪🇺 **438 observations** · **4 Europe countries** · **2003–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **438 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_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_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` | 138 | 2003 | 2025 | | `BIH` | 114 | 2006 | 2024 | | `MKD` | 102 | 2009 | 2025 | | `SRB` | 84 | 2007 | 2020 | ## Indicators (sample) - `EMP_PIFL_SEX_INS_NB` — Employment outside the formal sector by sex and public/private sector (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_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` | | `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) | `B` | | `obs_status.label` | `string` | — | `Break in series` | | `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-ins-nb-employment-outside-the-formal-sector-by-sex-and-pu") 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_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_PIFL_SEX_INS_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_PIFL_SEX_INS_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_emp_pifl_sex_ins_nb_employment_outside_the_formal_sector_by_sex_and_pu_2025, title = {Employment outside the formal sector by sex and public/private sector (thousands) | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_PIFL_SEX_INS_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-emp-pifl-sex-ins-nb-employment-outside-the-formal-sector-by-sex-and-pu}} } ``` ## 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_NB_This dataset contains 438 observations of informal economy data across 4 Europe countries (MDA, BIH, MKD, SRB), spanning 2003–2025, covering 1 distinct indicator: Employment outside the formal sector by sex and public/private sector (thousands). The data is sourced from the International Labour Organization (ILO)s ILOSTAT database, pulled via REST API and filtered to Europe ISO3 country codes. It provides a detailed schema including columns such as country code, country name, data source, indicator code, sex disaggregation, observation year, observed value, etc., for analyzing employment in the informal economy sector in Europe. The data is published at annual frequency and includes data quality notes and caveats.

搜集汇总

数据集介绍

构建方式

该数据集由Electric Sheep Europe团队从国际劳工组织统计数据库（ILOSTAT）的REST API接口直接抓取原始数据，并依据欧洲ISO3国家代码进行筛选与重新打包。ILOSTAT本身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、家庭收入调查等微观数据进行统一调和，数据集保留了来源标签列以便追溯。整个构建过程遵循可复现的管道化流程，最终以Parquet格式发布，确保数据从权威源头到机器学习可用形态的完整传递。

特点

数据集涵盖四个欧洲国家（摩尔多瓦、波黑、北马其顿、塞尔维亚）在2003至2025年间的438条观测记录，聚焦于非正规部门就业的性别与公私部门分布（单位：千人）。数据以年度频率呈现，包含国家代码、来源、指标、性别分类、年份及观测值等字段，并提供观测状态标志与注释信息以标识序列中断或方法修订。该数据集规模精简（n<1K），结构规整，专为表格分类、回归及时间序列预测任务而设计，兼具权威性与即用性。

使用方法

研究者可通过HuggingFace datasets库以单行代码加载数据集，并转换为Pandas数据框进行后续分析。典型操作包括按国家代码筛选特定国家子集、提取单一指标的时间序列并排序绘图，以及利用透视表将数据重塑为国家×年份矩阵。这些操作支持快速探索性分析与建模准备，同时数据集的字段语义清晰，便于与其它ILOSTAT指标或欧洲社会经济数据联合使用。

背景与挑战

背景概述

非正规部门就业长期构成全球劳动力市场监测的核心议题，亦是国际劳工组织体面劳动议程的关键关切。该数据集由国际劳工组织统计局依托ILOSTAT平台构建，经Electric Sheep Europe于2025年重新封装发布，覆盖摩尔多瓦、波黑、北马其顿与塞尔维亚四国2003至2025年间按性别及公私部门划分的非正规部门外就业观测数据，共438条记录。其核心研究问题在于揭示转型经济体非正规就业的性别差异与部门分布特征，为劳动力市场政策评估提供跨国可比的数据支撑，对欧洲非正规经济研究与可持续发展目标监测具有重要的实证价值。

当前挑战

该数据集所回应的领域问题在于非正规就业测度的跨国可比性：各国劳动力量调查在非正规部门界定、抽样框架与统计口径上存在显著异质性，且非正规经济活动本身具有隐蔽性，易出现系统性漏报。构建过程中的挑战主要体现在数据整合层面：ILOSTAT需依据国际劳工统计学家会议定义对原始调查微观数据进行统一调和，当同一国家年份存在多源数据时须甄别最优来源，而部分观测值因方法论修订而带有序列断裂或可靠性不足等质量标记，进一步加剧了跨国比较与时间序列分析的难度。

常用场景

经典使用场景

在非正规经济部门就业的量化研究中，该数据集构成了一项关键的跨国比较基础。聚焦于欧洲四国（摩尔多瓦、波斯尼亚和黑塞哥维那、北马其顿与塞尔维亚）自2003年至2025年的年度观测，研究者可依据性别及公共/私营部门分类，系统刻画非正规就业的规模与结构变迁。时间序列分析与面板数据建模成为经典路径，用以揭示不同性别人群在非正规部门中的分布差异及其随经济转型的演变轨迹。

实际应用

在政策评估与劳动力市场监测领域，该数据集可服务于国际组织与各国统计部门对非正规就业趋势的追踪。通过性别与部门维度的细分，实务工作者能够识别脆弱就业群体，设计有针对性的社会保障扩展方案。时间序列预测与异常检测等任务亦可借助该数据支撑，为就业政策调整提供前瞻性依据。

衍生相关工作

基于该数据集的再包装与开放获取，衍生出一系列与ILOSTAT指标相关的比较劳动研究及机器学习基准任务。Electric Sheep Europe的标准化处理促进了跨国面板数据的可复现分析，后续研究常将其与其它劳动统计指标结合，用于非正规经济规模估算、性别就业差距分解以及转型经济体劳动力市场制度评估等经典议题。