electricsheepafrica/africa-world-bank-social-protection-and-labor-indicators-for-cameroon
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--- annotations_creators: - no-annotation language_creators: - found language: - en license: cc-by-4.0 multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - other task_ids: [] tags: - africa - humanitarian - hdx - electric-sheep-africa - indicators - socioeconomics - cmr pretty_name: "Cameroon - Social Protection and Labor" dataset_info: splits: - name: train num_examples: 3610 - name: test num_examples: 902 --- # Cameroon - Social Protection and Labor **Publisher:** World Bank Group · **Source:** [HDX](https://data.humdata.org/dataset/world-bank-social-protection-and-labor-indicators-for-cameroon) · **License:** `cc-by` · **Updated:** 2026-03-27 --- ## Abstract Contains data from the World Bank's [data portal](http://data.worldbank.org/). There is also a [consolidated country dataset](https://data.humdata.org/dataset/world-bank-combined-indicators-for-cameroon) on HDX. The supply of labor available in an economy includes people who are employed, those who are unemployed but seeking work, and first-time job-seekers. Not everyone who works is included: unpaid workers, family workers, and students are often omitted, while some countries do not count members of the armed forces. Data on labor and employment are compiled by the International Labour Organization (ILO) from labor force surveys, censuses, establishment censuses and surveys, and administrative records such as employment exchange registers and unemployment insurance schemes. Each row in this dataset represents country-level aggregates. Data was last updated on HDX on 2026-03-27. Geographic scope: **CMR**. *Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).* --- ## Dataset Characteristics | | | |---|---| | **Domain** | Humanitarian and development data | | **Unit of observation** | Country-level aggregates | | **Rows (total)** | 4,513 | | **Columns** | 8 (2 numeric, 6 categorical, 0 datetime) | | **Train split** | 3,610 rows | | **Test split** | 902 rows | | **Geographic scope** | CMR | | **Publisher** | World Bank Group | | **HDX last updated** | 2026-03-27 | --- ## Variables **Geographic** — `country_name` (Cameroon), `country_iso3` (CMR), `year` (range 1976.0–2025.0). **Outcome / Measurement** — `value` (range -0.5102–11466010.0). **Identifier / Metadata** — `indicator_name` (Labor force participation rate, male (% of male population ages 15-64) (modeled ILO estimate), Ratio of female to male labor force participation rate (%) (modeled ILO estimate), Labor force, female (% of total labor force)), `indicator_code` (SL.TLF.ACTI.MA.ZS, SL.TLF.CACT.FM.ZS, SL.TLF.TOTL.FE.ZS), `esa_source` (HDX), `esa_processed` (2026-04-15). --- ## Quick Start ```python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-world-bank-social-protection-and-labor-indicators-for-cameroon") train = ds["train"].to_pandas() test = ds["test"].to_pandas() print(train.shape) train.head() ``` --- ## Schema | Column | Type | Null % | Range / Sample Values | |---|---|---|---| | `country_name` | object | 0.0% | Cameroon | | `country_iso3` | object | 0.0% | CMR | | `year` | int64 | 0.0% | 1976.0 – 2025.0 (mean 2010.9909) | | `indicator_name` | object | 0.0% | Labor force participation rate, male (% of male population ages 15-64) (modeled ILO estimate), Ratio of female to male labor force participation rate (%) (modeled ILO estimate), Labor force, female (% of total labor force) | | `indicator_code` | object | 0.0% | SL.TLF.ACTI.MA.ZS, SL.TLF.CACT.FM.ZS, SL.TLF.TOTL.FE.ZS | | `value` | float64 | 0.0% | -0.5102 – 11466010.0 (mean 129190.1736) | | `esa_source` | object | 0.0% | HDX | | `esa_processed` | object | 0.0% | 2026-04-15 | --- ## Numeric Summary | Column | Min | Max | Mean | Median | |---|---|---|---|---| | `year` | 1976.0 | 2025.0 | 2010.9909 | 2014.0 | | `value` | -0.5102 | 11466010.0 | 129190.1736 | 14.7164 | --- ## Curation Raw data was downloaded from HDX via the CKAN API and converted to Parquet. Column names were lowercased and standardised to snake_case. Common missing-value markers (`N/A`, `null`, `none`, `-`, `unknown`, `no data`, `#N/A`) were unified to `NaN`. The dataset was split 80/20 into train and test partitions using a fixed random seed (42) and saved as Snappy-compressed Parquet. --- ## Limitations - Data originates from World Bank Group and has not been independently validated by ESA. - Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection. - Refer to the [original HDX dataset page](https://data.humdata.org/dataset/world-bank-social-protection-and-labor-indicators-for-cameroon) for the publisher's own methodology notes and caveats. --- ## Citation ```bibtex @dataset{hdx_africa_world_bank_social_protection_and_labor_indicators_for_cameroon, title = {Cameroon - Social Protection and Labor}, author = {World Bank Group}, year = {2026}, url = {https://data.humdata.org/dataset/world-bank-social-protection-and-labor-indicators-for-cameroon}, note = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)} } ``` --- *[Electric Sheep Africa](https://huggingface.co/electricsheepafrica) — Africa's ML dataset infrastructure. Lagos, Nigeria.*
--- 注释创建者： - 无注释 语言创建者： - 抓取所得 语言： - 英语 许可证：CC-BY-4.0 多语言属性： - 单语言 数据规模分类： - 1000 < 数据条数 < 10000 源数据集： - 原创数据集 任务类别： - 其他 任务子类别： - 无 标签： - 非洲 - 人道主义 - HDX - Electric Sheep Africa - 指标 - 社会经济学 - CMR 数据集简称："喀麦隆——社会保护与劳动" 数据集信息： 数据划分： - 名称：train（训练集），样本数：3610 - 名称：test（测试集），样本数：902 --- # 喀麦隆——社会保护与劳动 **发布方**：世界银行集团 · **数据来源**：[HDX](https://data.humdata.org/dataset/world-bank-social-protection-and-labor-indicators-for-cameroon) · **许可证**：`cc-by` · **最后更新时间**：2026-03-27 --- ## 摘要 本数据集包含来自世界银行[数据门户（World Bank Data Portal）](http://data.worldbank.org/)的相关数据。HDX平台上另有一份[喀麦隆综合国家数据集](https://data.humdata.org/dataset/world-bank-combined-indicators-for-cameroon)。 经济体中可用的劳动力供给涵盖就业人员、正在求职的失业人员以及首次求职者。并非所有参与劳动的人员都会被纳入统计：无报酬劳动者、家庭帮工与学生常被排除在外，部分国家还未将军人纳入统计范畴。劳动力与就业相关数据由国际劳工组织（International Labour Organization, ILO）通过劳动力调查、人口普查、企业普查与调查，以及就业登记册、失业保险计划等行政记录整理汇编而成。 本数据集的每一行均代表国家级汇总数据。本数据集在HDX平台的最后更新时间为2026-03-27。地理覆盖范围：**CMR**。 *本数据集已由[Electric Sheep Africa](https://huggingface.co/electricsheepafrica)整理为适配机器学习的Parquet格式。* --- ## 数据集特征 | | | |---|---| | **数据领域** | 人道主义与发展数据 | | **观测单元** | 国家级汇总数据 | | **总数据行数** | 4,513 | | **字段总数** | 8个（2个数值型、6个分类型、0个日期时间型） | | **训练集划分** | 3,610条 | | **测试集划分** | 902条 | | **地理覆盖范围** | CMR | | **发布方** | 世界银行集团 | | **HDX平台最后更新时间** | 2026-03-27 | --- ## 字段分类 **地理类字段** — `country_name`（国家名称：喀麦隆）、`country_iso3`（国家ISO3代码：CMR）、`year`（年份范围：1976.0–2025.0）。 **结果/测量类字段** — `value`（数值范围：-0.5102–11466010.0）。 **标识符/元数据类字段** — `indicator_name`（指标名称：男性劳动力参与率（占15-64岁男性人口的百分比，国际劳工组织模型估算值）、男女劳动力参与率之比（%，国际劳工组织模型估算值）、女性劳动力占总劳动力的百分比）、`indicator_code`（指标代码：SL.TLF.ACTI.MA.ZS, SL.TLF.CACT.FM.ZS, SL.TLF.TOTL.FE.ZS）、`esa_source`（数据来源：HDX）、`esa_processed`（数据处理时间：2026-04-15）。 --- ## 快速上手 python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-world-bank-social-protection-and-labor-indicators-for-cameroon") train = ds["train"].to_pandas() test = ds["test"].to_pandas() print(train.shape) train.head() --- ## 数据结构 | 字段名 | 数据类型 | 空值占比 | 取值范围/示例值 | |---|---|---|---| | `country_name` | 对象型（object） | 0.0% | 喀麦隆 | | `country_iso3` | 对象型（object） | 0.0% | CMR | | `year` | 64位整型（int64） | 0.0% | 1976.0 – 2025.0（均值：2010.9909） | | `indicator_name` | 对象型（object） | 0.0% | 男性劳动力参与率（占15-64岁男性人口的百分比，国际劳工组织模型估算值）、男女劳动力参与率之比（%，国际劳工组织模型估算值）、女性劳动力占总劳动力的百分比 | | `indicator_code` | 对象型（object） | 0.0% | SL.TLF.ACTI.MA.ZS, SL.TLF.CACT.FM.ZS, SL.TLF.TOTL.FE.ZS | | `value` | 64位浮点型（float64） | 0.0% | -0.5102 – 11466010.0（均值：129190.1736） | | `esa_source` | 对象型（object） | 0.0% | HDX | | `esa_processed` | 对象型（object） | 0.0% | 2026-04-15 | --- ## 数值统计摘要 | 字段名 | 最小值 | 最大值 | 均值 | 中位数 | |---|---|---|---|---| | `year` | 1976.0 | 2025.0 | 2010.9909 | 2014.0 | | `value` | -0.5102 | 11466010.0 | 129190.1736 | 14.7164 | --- ## 数据整理流程 原始数据通过CKAN API从HDX平台下载，并转换为Parquet格式。字段名称均转为小写并标准化为蛇形命名法（snake_case）。常见的缺失值标记（`N/A`, `null`, `none`, `-`, `unknown`, `no data`, `#N/A`）被统一替换为`NaN`。本数据集以固定随机种子（42）按80/20的比例划分为训练集与测试集，并以Snappy压缩的Parquet格式存储。 --- ## 数据集局限性 - 本数据集源自世界银行集团，未由Electric Sheep Africa（ESA）进行独立验证。 - 自动化数据清洗无法修正原始数据收集中的错报值、定义不一致问题或抽样偏差。 - 如需查看发布方提供的方法说明与注意事项，请参阅[原始HDX数据集页面](https://data.humdata.org/dataset/world-bank-social-protection-and-labor-indicators-for-cameroon)。 --- ## 引用格式 bibtex @dataset{hdx_africa_world_bank_social_protection_and_labor_indicators_for_cameroon, title = {Cameroon - Social Protection and Labor}, author = {World Bank Group}, year = {2026}, url = {https://data.humdata.org/dataset/world-bank-social-protection-and-labor-indicators-for-cameroon}, note = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)} } --- *[Electric Sheep Africa](https://huggingface.co/electricsheepafrica) — 非洲机器学习数据集基础设施。尼日利亚拉各斯。*
