Non-Traditional Data for Macroeconomic Estimation Unemployment in Jordan as an Application.

IF 5.8 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

Scientific Data Pub Date : 2025-03-18 DOI:10.1038/s41597-025-04721-6

Osama Abdelhay, Taghreed Altamimi

{"title":"Non-Traditional Data for Macroeconomic Estimation Unemployment in Jordan as an Application.","authors":"Osama Abdelhay, Taghreed Altamimi","doi":"10.1038/s41597-025-04721-6","DOIUrl":null,"url":null,"abstract":"<p><p>This dataset comprises quarterly unemployment rates in Jordan from 2010 to 2024, alongside Google Trends search interest data for 88 unemployment-related keywords in Arabic and English. The unemployment rates, sourced from the Jordanian Department of Statistics, provide official figures over 14 years. The Google Trends data reflects public search behaviour related to unemployment and job seeking in Jordan. Keywords were selected through consultations with experts from governmental agencies, NGOs, and private job portals to include terms relevant to local dialects and current job market trends. The search data was aggregated using Mean Aggregation, Exponentially Weighted Moving Average, and Seasonally Adjusted Weighted Average to align with the quarterly unemployment rates. By integrating official statistics with enriched search data, this dataset offers a valuable resource for researchers and policymakers exploring the relationship between economic indicators and online search behaviour. It supports economics, social sciences, and data science. The dataset can aid in developing predictive models, analysing economic sentiment, and informing policy decisions in Jordan and similar contexts.</p>","PeriodicalId":21597,"journal":{"name":"Scientific Data","volume":"12 1","pages":"449"},"PeriodicalIF":5.8000,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11920033/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Scientific Data","FirstCategoryId":"103","ListUrlMain":"https://doi.org/10.1038/s41597-025-04721-6","RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}

引用次数: 0

Abstract

This dataset comprises quarterly unemployment rates in Jordan from 2010 to 2024, alongside Google Trends search interest data for 88 unemployment-related keywords in Arabic and English. The unemployment rates, sourced from the Jordanian Department of Statistics, provide official figures over 14 years. The Google Trends data reflects public search behaviour related to unemployment and job seeking in Jordan. Keywords were selected through consultations with experts from governmental agencies, NGOs, and private job portals to include terms relevant to local dialects and current job market trends. The search data was aggregated using Mean Aggregation, Exponentially Weighted Moving Average, and Seasonally Adjusted Weighted Average to align with the quarterly unemployment rates. By integrating official statistics with enriched search data, this dataset offers a valuable resource for researchers and policymakers exploring the relationship between economic indicators and online search behaviour. It supports economics, social sciences, and data science. The dataset can aid in developing predictive models, analysing economic sentiment, and informing policy decisions in Jordan and similar contexts.

查看原文本刊更多论文

用于宏观经济估算的非传统数据--约旦失业率的应用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Scientific Data Social Sciences-Education

CiteScore

11.20

自引率

4.10%

发文量

689

审稿时长

16 weeks

期刊介绍： Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.