electricsheepeurope/europe-ilo-emp-pifl-sex-ins-dsb-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: - n<1K tags: - tabular - europe - ilostat - informal-economy - ilo - labour - employment pretty_name: "Share of employment outside the formal sector by sex, public/private sector and disability | Europe (ILOSTAT)" --- # Share of employment outside the formal sector by sex, public/private sector and disability | Europe (ILOSTAT) 🇪🇺 **162 observations** · **2 Europe countries** · **2007–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **162 observations** of `Informal economy` data across **2 Europe countries**, spanning **2007–2025**, 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_DSB_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_INS_DSB_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 2 Europe countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `MDA` | 144 | 2018 | 2025 | | `SRB` | 18 | 2007 | 2007 | ## Indicators (sample) - `EMP_PIFL_SEX_INS_DSB_RT` — Share of employment outside the formal sector by sex, public/private sector and disability status (%) ## Schema | Column | Type | Description | Example | |--------|------|-------------|---------| | `ref_area` | `string` | ISO 3166-1 alpha-3 country code | `MDA` | | `ref_area.label` | `string` | Country name in English | `Republic of Moldova` | | `source` | `string` | ILOSTAT source code (e.g. labour force survey) | `BA:571` | | `source.label` | `string` | Source name in English | `LFS - Labour Force Survey` | | `indicator` | `string` | ILOSTAT indicator code | `EMP_PIFL_SEX_INS_DSB_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.) | `INS_SECTOR_TOTAL` | | `classif1.label` | `string` | — | `Institutional sector: Total` | | `classif2` | `string` | Second classification variable where applicable | `DSB_STATUS_TOTAL` | | `classif2.label` | `string` | — | `Disability status: Total` | | `time` | `int64` | Observation year | `2025` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `53.154` | | `obs_status` | `string` | Observation status flag (e.g. provisional, unreliable) | `—` | | `obs_status.label` | `string` | — | `—` | | `note_indicator` | `string` | — | `—` | | `note_indicator.label` | `string` | — | `—` | | `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-dsb-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_INS_DSB_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_PIFL_SEX_INS_DSB_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_PIFL_SEX_INS_DSB_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_emp_pifl_sex_ins_dsb_rt_share_of_employment_outside_the_formal_sector_by_s_2025, title = {Share of employment outside the formal sector by sex, public/private sector and disability | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_PIFL_SEX_INS_DSB_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-emp-pifl-sex-ins-dsb-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_INS_DSB_RT_This dataset contains 162 observations of informal economy data across 2 Europe countries, spanning 2007–2025, covering 1 distinct indicator. Specifically, it records the share of employment outside the formal sector by sex, public/private sector and disability status (%). The data is sourced from the International Labour Organization (ILO)s ILOSTAT database, retrieved via its REST API and filtered to European countries. The dataset includes multiple fields such as country code, country name, data source, indicator code, sex disaggregation, classification variables, observation year, observed value, observation status, etc., providing a detailed statistical perspective on informal economy employment.

搜集汇总

数据集介绍

构建方式

在国际劳工组织统计司对全球劳动力市场指标进行系统协调与标准化的背景下，该数据集源自ILOSTAT中央统计数据库的官方接口，经由Electric Sheep Europe机构重新封装而成。数据获取通过ILOSTAT的REST API直接拉取指标EMP_PIFL_SEX_INS_DSB_RT的原始记录，随后依据ISO3国家代码筛选出欧洲区域样本，最终形成覆盖摩尔多瓦与塞尔维亚两个国家、时间跨度为2007至2025年的162条观测。原始调查微观数据已由国际劳工组织按照国际劳工统计学家会议的定义进行统一协调，来源信息在source.label列中予以标注，以确保数据可追溯。

特点

该数据集聚焦于非正规部门就业份额这一非正规经济领域的核心议题，具有鲜明的时间序列与截面双重属性。其突出特点在于多维度分解能力，涵盖性别、公共或私营部门以及残疾状况三重分类变量，为剖析劳动力市场边缘群体的就业结构提供了精细的观测视角。数据以年度频率发布，仅收录国际劳工组织筛选的最佳来源记录，且分类维度列在指标未发布细分数据时保持空缺，体现了对数据质量的严格把控。

使用方法

研究者可借助HuggingFace数据集加载库便捷地获取数据，通过load_dataset函数载入后转换为Pandas数据框进行灵活操作。典型用法包括依据ref_area字段筛选特定国家子集，针对单一指标按时间排序绘制趋势折线图，或利用透视表将数据重塑为国家与年份的二维矩阵以便于横向比较。该数据集适用于非正规就业的时序预测、跨国比较分析以及分类与回归等表格型机器学习任务，为劳动经济学与社会科学研究提供结构化支撑。

背景与挑战

背景概述

非正规就业的量化测度长期构成劳动经济学与发展经济学的核心议题，国际劳工组织（ILO）依托国际劳工统计学家会议（ICLS）框架，持续推动跨国可比数据的标准化生产。该数据集由ILO统计部门编纂，经Electric Sheep Europe于2025年重新封装发布，覆盖摩尔多瓦与塞尔维亚两国2007至2025年间162条观测记录，以性别、公私部门及残疾状况为分类维度，刻画正规部门之外就业份额的演变轨迹。其核心研究问题在于揭示转型经济体劳动力市场非正规化的结构性特征，为体面劳动监测与SDG目标评估提供实证基础，对比较劳动制度分析与弱势群体就业政策研究具有重要参考价值。

当前挑战

非正规就业统计面临概念界定与操作化测量的固有张力，ICLS定义在不同国家统计体系中的落地程度参差不齐，导致跨国可比性受限。该数据集仅涵盖两个欧洲国家，样本容量偏小，且摩尔多瓦与塞尔维亚数据年份分布严重失衡，时序连续性不足，制约了面板计量与趋势推断的稳健性。分类维度中公私部门与残疾状况的交叉细分在多数年份呈现缺失，削弱了交叉异质性分析的可行性。构建过程中，原始调查微数据经ILO协调后存在来源更替与观测状态标注不一致等问题，部分年份数据标注为临时性或可靠性存疑，进一步增加了数据清洗与建模的不确定性。

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，该数据集最经典的应用场景在于构建跨国别的非正规就业性别差异面板分析。研究者依托国际劳工组织标准化的劳动力调查数据，将就业部门、性别与残疾状态等维度纳入统一框架，用以刻画欧洲转型经济体在非正规部门就业中的结构性特征。摩尔多瓦与塞尔维亚两国跨越近二十年的观测记录，为时间序列建模、性别分层比较以及公私部门异质性检验提供了稀缺的微观聚合样本。借助表格分类与回归任务，学者能够评估非正规就业比例的演变趋势及其与制度转型的关联。

衍生相关工作

围绕该数据集的衍生产出主要体现于非正规经济测度方法的比较研究以及转型经济体劳动力市场分割的实证文献。部分学者将其与欧洲社会调查、世界银行企业调查等数据源链接，检验非正规就业指标在不同调查工具间的收敛效度。亦有研究以其为基准，发展出针对小样本国家的时空插补与预测模型，推动劳动统计中缺失数据处理方法论的进步。这些工作共同拓展了国际劳工组织数据体系在计量经济学与政策评估中的二次分析价值。

数据集最近研究

最新研究方向

在全球非正规经济持续引发学界与政策界关注的背景下，该数据集凭借ILOSTAT基于国际劳工统计学家会议标准统一协调的微观调查数据，为刻画欧洲非正规就业的性别差异、公共/私营部门分布及残障状态异质性提供了精细的观测基础。当前前沿研究趋向于利用此类面板数据，结合时间序列预测与因果推断方法，探讨非正规就业份额的动态演变及其与结构性不平等、社会保障覆盖之间的复杂关联。伴随联合国可持续发展目标中体面劳动议程的推进，该数据集亦成为监测欧洲边缘群体劳动力市场融入进程、评估福利政策干预成效的关键证据来源，对推动包容性增长与减贫战略具有重要的实证参考意义。