electricsheepeurope/europe-ilo-emp-pifl-sex-ins-dsb-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: - n<1K tags: - tabular - europe - ilostat - informal-economy - ilo - labour - employment pretty_name: "Employment outside the formal sector by sex, public/private sector and disability status ( | Europe (ILOSTAT)" --- # Employment outside the formal sector by sex, public/private sector and disability status ( | 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_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_DSB_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 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_NB` — Employment outside the formal sector by sex, public/private sector and disability status (thousands) ## 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_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 | `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) | `749.447` | | `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-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_DSB_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_PIFL_SEX_INS_DSB_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_PIFL_SEX_INS_DSB_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_emp_pifl_sex_ins_dsb_nb_employment_outside_the_formal_sector_by_sex_public_2025, title = {Employment outside the formal sector by sex, public/private sector and disability status ( | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_PIFL_SEX_INS_DSB_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-emp-pifl-sex-ins-dsb-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_DSB_NB_This dataset contains statistical data on employment outside the formal sector in Europe, disaggregated by sex, public/private sector, and disability status. The data is sourced from the ILOSTAT database of the International Labour Organization (ILO), covering 2 European countries (MDA and SRB) from 2007 to 2025, with 162 observations. It includes one main indicator: EMP_PIFL_SEX_INS_DSB_NB, which represents employment outside the formal sector by sex, public/private sector, and disability status (in thousands). The datasets structure comprises columns such as country code, country name, data source, indicator code, sex classification, classification variables, observation year, observed value, and more, along with data quality notes and usage examples. Repackaged by Electric Sheep Europe, the dataset aims to provide a unified, ML-ready data layer for Europe.

搜集汇总

数据集介绍

构建方式

该数据集以国际劳工组织（ILO）的ILOSTAT中央统计数据库为数据源，通过其REST API接口直接获取指标“EMP_PIFL_SEX_INS_DSB_NB”的原始观测值，并依据ISO3国家代码筛选出欧洲地区数据，最终由Electric Sheep Europe重新打包发布。ILOSTAT依据国际劳工统计学家会议（ICLS）定义对各国劳动力调查等微观数据进行标准化处理，来源信息记录于source.label字段以保障可追溯性。

使用方法

研究人员可通过Hugging Face的datasets库以一行代码加载数据集，并转换为Pandas数据框进行灵活操作。典型用法包括按国家代码筛选特定国家数据、针对单一指标提取时间序列并进行可视化，或通过透视表将数据重塑为国家与年份的矩阵形式，便于比较分析。

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，其规模与结构直接关乎社会保障覆盖、体面劳动监测及可持续发展目标的评估。国际劳工组织（ILO）依托国际劳工统计学家会议（ICLS）确立的定义框架，通过ILOSTAT数据库系统汇编各国劳动力调查与住户调查数据，构建了跨区域可比的非正规就业指标。该数据集由Electric Sheep Europe于2025年前后自ILOSTAT REST API抽取并重新封装，涵盖摩尔多瓦共和国与塞尔维亚两个欧洲国家，时间跨度为2007年至2025年，共162条观测记录，围绕正规部门以外就业按性别、公私部门及残疾状况的分布展开，为转型经济体非正规就业的性别差异与制度部门异质性研究提供了微观聚合层面的数据基础。

当前挑战

该数据集所回应的领域问题在于非正规部门就业的精确识别与跨制度可比性，传统劳动力调查对非正规就业的界定常因部门分类与残疾状况等维度缺失而难以支撑结构性分析。构建过程中的核心挑战体现于数据来源的异质性：摩尔多瓦共和国与塞尔维亚的观测年份分布极不均衡，前者观测集中于2018至2025年而后者仅覆盖2007年，导致时序分析的可比性受限；同时，性别、制度部门与残疾状况等多重分类维度的非空值仅在指标发布对应细分时方可获得，缺失机制复杂，且部分观测带有暂定或不可靠状态标志，对建模与推断构成实质性制约。

常用场景

经典使用场景

在非正规经济就业研究领域，该数据集经典地用于按性别、公共/私营部门及残疾状况细分，剖析欧洲国家脱离正规部门的就业规模与结构。研究者常以时间序列分析刻画2007至2025年间摩尔多瓦与塞尔维亚非正规就业的演变轨迹，或通过横截面比较揭示性别与残疾身份交织下的就业脆弱性。其162条观测虽精炼，却为探究转型经济体劳动力市场边缘化提供了关键量化依据。

解决学术问题

该数据集直击非正规就业测量中分类维度粗疏、性别与残疾交叉性数据稀缺的学术困境。通过提供性别、机构部门与残疾状况三重分类的就业人数，它使研究者得以检验残疾劳动者是否更易被排挤出正规部门，以及公共与私营部门在吸纳弱势群体上的差异。其意义在于推动非正规经济研究从宏观总量走向精细化分层，为包容性劳动力市场政策提供实证基础。

实际应用

在国际组织与政策制定层面，该数据集支撑对欧洲非正规就业的监测与国别评估。国际劳工组织及各国劳工部门可依据其识别残疾或女性群体在非正规部门中的集中度，从而设计针对性社会保障扩展方案。同时，数据可嵌入劳动力市场信息系统，辅助非政府组织评估职业康复与就业援助项目的成效，促进体面劳动议程落地。

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，该数据集以其对性别、公共/私营部门与残疾状况三重维度的交叉刻画，为劳动经济学与社会政策分析的精细化提供了关键素材。当前前沿研究尤为关注非正规就业中的结构性不平等，特别是残障群体在正规部门边缘化与非正规经济中的过度代表现象，以及性别与部门属性如何叠加放大这一脆弱性。摩尔多瓦与塞尔维亚的纵向观测数据为转型经济体劳动市场制度变迁的因果推断与政策评估提供了实证基础，亦契合国际劳工组织关于体面劳动与SDG8的监测需求，推动包容性就业政策从总量统计向交叉性精准识别转型。