electricsheepeurope/europe-ilo-emp-pifl-sex-eco-est-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, economic activity and establishment | Europe (ILOSTAT)" --- # Share of employment outside the formal sector by sex, economic activity and establishment | Europe (ILOSTAT) 🇪🇺 **26,877 observations** · **4 Europe countries** · **2003–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **26,877 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_ECO_EST_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_ECO_EST_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` | 7,415 | 2003 | 2025 | | `BIH` | 7,350 | 2006 | 2024 | | `MKD` | 7,068 | 2009 | 2025 | | `SRB` | 5,044 | 2007 | 2020 | ## Indicators (sample) - `EMP_PIFL_SEX_ECO_EST_RT` — Share of employment outside the formal sector by sex, economic activity and establishment size (%) ## 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_ECO_EST_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.) | `ECO_SECTOR_TOTAL` | | `classif1.label` | `string` | — | `Economic activity (Broad sector): 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) | `13.81` | | `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-eco-est-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_ECO_EST_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_PIFL_SEX_ECO_EST_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_PIFL_SEX_ECO_EST_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_emp_pifl_sex_eco_est_rt_share_of_employment_outside_the_formal_sector_by_s_2025, title = {Share of employment outside the formal sector by sex, economic activity and establishment | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_PIFL_SEX_ECO_EST_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-emp-pifl-sex-eco-est-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_ECO_EST_RT_This dataset contains statistical information on the share of employment outside the formal sector for four European countries (Moldova, Bosnia and Herzegovina, North Macedonia, Serbia). It covers annual observations from 2003 to 2025, totaling 26,877 records. The core indicator is EMP_PIFL_SEX_ECO_EST_RT, which represents the share of employment outside the formal sector by sex, economic activity and establishment size (%). The dataset provides detailed multi-dimensional breakdowns, including disaggregation by sex (total, male, female), economic activity (broad sector), and establishment size (total). Data is sourced from the International Labour Organizations (ILO) ILOSTAT statistical database, collected through national statistical methods such as labour force surveys, and harmonized by the ILO. The dataset structure includes columns for country code, country name, data source, indicator code, classification variables, observation year, observed value, and data quality flags, making it suitable for machine learning tasks such as tabular classification, regression, and time-series forecasting.

搜集汇总

数据集介绍

构建方式

该数据集源自国际劳工组织统计数据库（ILOSTAT）的标准化数据流，通过REST API接口直接抓取指标代码为EMP_PIFL_SEX_ECO_EST_RT的原始观测记录，并依据欧洲ISO3国家代码进行空间筛选。原始调查微数据经国际劳工统计学家会议定义体系调和后，保留来源标签以确保可追溯性。由Electric Sheep Europe进行二次封装，将数据统一为Parquet格式并附以一致性数据集卡片，最终形成涵盖四个欧洲国家、时间跨度为2003至2025年的两万六千余条观测。

特点

数据集聚焦于非正规部门就业份额这一非正规经济核心指标，以性别、经济活动门类及机构规模为多维 disaggregation 维度，提供年度频率的面板数据。观测值附有质量状态标志，可识别临时性或不稳定估计，同时标注序列断裂等溯源信息。数据结构采用长表格式，涵盖国家代码、来源、指标、分类变量及观测值等字段，使用户得以灵活重构截面与时间序列分析，兼顾跨国比较与历时演化研究的需要。

使用方法

研究者和开发者可通过HuggingFace datasets库以一行代码加载数据，并直接转换为Pandas数据框进行探索性分析。典型操作包括按国家代码筛选子集、针对单一指标绘制时间序列趋势图，以及透视生成国家与年份交叉矩阵以观察区域差异。数据亦可服务于表格分类、回归及时间序列预测等机器学习任务，其标准化模式降低了多源劳动统计数据的整合门槛，便于快速开展非正规就业的实证研究。

背景与挑战

背景概述

非正规经济就业的测度长期以来构成劳动统计领域的核心议题，其数据质量直接关乎体面劳动议程的监测与政策评估。国际劳工组织自二十世纪中叶以来持续推动非正规部门就业定义的标准化，并于2003年前后通过国际劳工统计学家会议决议，确立了以就业身份、经济活动部门及机构规模为维度的核算框架。在此背景下，ILOSTAT作为全球劳动统计的权威中枢，整合各国劳动力调查与住户收入调查等微观数据，构建了覆盖二百余个经济体的非正规就业指标库。该数据集由Electric Sheep Europe于2025年经ILOSTAT REST API规范化抽取而成，聚焦欧洲四国非正规部门就业占比，时间跨度自2003年至2025年，为劳动经济学与区域发展研究提供了长时段、可复现的微观基础。

当前挑战

该数据集所回应的领域问题在于如何以跨国可比的方式测度非正规就业的规模与结构，其核心挑战源自非正规部门概念本身的多义性以及各国统计口径的异质性。不同国家劳动力调查对非正规就业的操作化定义存在显著差异，部分经济体仅覆盖非农业部门，部分则将农业自雇纳入统计，致使跨国比较面临系统性偏误。构建过程中，研究者需应对多源数据整合的复杂性，包括各国调查问卷设计差异、抽样方法不一致以及数据缺失与序列中断等情形。此外，非正规就业对经济周期与制度变迁高度敏感，观测值常伴随质量标记，部分年份数据被标注为不可靠或临时性，进一步增加了时序建模与因果推断的难度。

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，该数据集凭借其涵盖性别、经济活动部门及机构规模的多维细分变量，构成了刻画欧洲四国非正规就业结构变迁的核心素材。研究者常将其用于构建面板数据模型，以考察非正规就业份额在性别间的分化态势，以及经济部门异质性对非正规就业分布的影响。时间序列分析则聚焦于2003至2025年间各国非正规就业比重的演变轨迹，揭示转型经济体劳动力市场正规化进程的周期性波动与结构性特征。

解决学术问题

该数据集有效回应了非正规就业测量中跨国可比性不足与细分维度缺失的学术困境。通过ILOSTAT统一采用国际劳工统计学家会议定义对原始调查微观数据进行协调，该数据集使研究者得以在一致的概念框架下开展跨国比较，解决了以往因各国定义差异而导致结论难以横向对照的问题。其性别与经济活动部门的交叉分类，为探究劳动力市场分割理论、非正规就业的性别不平等机制等议题提供了精细化的实证基础，对发展经济学与劳动经济学领域具有显著的推动意义。

衍生相关工作

围绕该数据集衍生的经典工作主要集中于非正规就业的跨国比较研究与劳动力市场转型分析。学者们利用该数据检验了结构转型理论在东南欧转型经济体中的适用性，比较了不同后社会主义国家非正规就业路径的收敛与分化。部分研究将其与ILOSTAT其他指标数据集链接，构建了涵盖就业质量、工资水平与社会保护的综合分析框架。这些工作不仅丰富了非正规经济学的实证文献，也为后续基于机器学习方法的劳动力市场预测模型提供了基准数据源。