从数据到决策:通过衡量不可衡量的经济数字来减轻贫困,促进发展

Emmanuel A. Onsay , Jomar F. Rabajante
{"title":"从数据到决策:通过衡量不可衡量的经济数字来减轻贫困,促进发展","authors":"Emmanuel A. Onsay ,&nbsp;Jomar F. Rabajante","doi":"10.1016/j.socimp.2025.100138","DOIUrl":null,"url":null,"abstract":"<div><div>This work has been carried out and is currently being conducted in the poorest region of Luzon, Philippines. Since poverty is multifaceted and considered unmeasurable in social science, it is notoriously difficult to measure. The methods currently used to measure poverty require a significant amount of time, money, and labor. This poses challenges for policymakers in implementing poverty-reduction policies. Indigenous communities are among the most disadvantaged, vulnerable, and neglected populations in society, facing complex and diverse socioeconomic situations. Poverty stands as one of the oldest and most challenging social problems to have ever existed. Research on indigenous peoples typically takes a qualitative approach, whereas studies on poverty tend to be broad, making them susceptible to significant sampling errors and primarily intended for national policy-making. Using community-based monitoring system (CBMS) data, we achieved a prediction accuracy of 92.60–98.00 % using Random Forest classification and reduced traditional survey and data processing costs by up to 70 %. The proposed model incorporates 27 socioeconomic variables and enables localized policy targeting. Therefore, it is crucial to assess multifaceted poverty and simulate socioeconomic conditions for each tribe to foster economic development. By training and testing datasets, this work proposes new metrics and illustrates the effectiveness of machine learning in predicting poverty. Lastly, the results provide various localities with customized policy targeting tools for poverty alleviation. These techniques can be replicated, adapted, or repurposed by other researchers to assist impoverished populations in improving their well-being.</div></div>","PeriodicalId":101167,"journal":{"name":"Societal Impacts","volume":"6 ","pages":"Article 100138"},"PeriodicalIF":0.0000,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"From data to decision: Alleviating poverty and promoting development through measuring the unmeasurable economic numbers\",\"authors\":\"Emmanuel A. Onsay ,&nbsp;Jomar F. Rabajante\",\"doi\":\"10.1016/j.socimp.2025.100138\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>This work has been carried out and is currently being conducted in the poorest region of Luzon, Philippines. Since poverty is multifaceted and considered unmeasurable in social science, it is notoriously difficult to measure. The methods currently used to measure poverty require a significant amount of time, money, and labor. This poses challenges for policymakers in implementing poverty-reduction policies. Indigenous communities are among the most disadvantaged, vulnerable, and neglected populations in society, facing complex and diverse socioeconomic situations. Poverty stands as one of the oldest and most challenging social problems to have ever existed. Research on indigenous peoples typically takes a qualitative approach, whereas studies on poverty tend to be broad, making them susceptible to significant sampling errors and primarily intended for national policy-making. Using community-based monitoring system (CBMS) data, we achieved a prediction accuracy of 92.60–98.00 % using Random Forest classification and reduced traditional survey and data processing costs by up to 70 %. The proposed model incorporates 27 socioeconomic variables and enables localized policy targeting. Therefore, it is crucial to assess multifaceted poverty and simulate socioeconomic conditions for each tribe to foster economic development. By training and testing datasets, this work proposes new metrics and illustrates the effectiveness of machine learning in predicting poverty. Lastly, the results provide various localities with customized policy targeting tools for poverty alleviation. These techniques can be replicated, adapted, or repurposed by other researchers to assist impoverished populations in improving their well-being.</div></div>\",\"PeriodicalId\":101167,\"journal\":{\"name\":\"Societal Impacts\",\"volume\":\"6 \",\"pages\":\"Article 100138\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-08-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Societal Impacts\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2949697725000372\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Societal Impacts","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2949697725000372","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

这项工作已经在菲律宾最贫穷的吕宋岛地区进行,目前正在进行中。由于贫困是多方面的,而且在社会科学中被认为是无法衡量的,因此它是出了名的难以衡量的。目前用来衡量贫困的方法需要大量的时间、金钱和劳动力。这给政策制定者实施减贫政策带来了挑战。土著社区是社会中处境最不利、最脆弱和最被忽视的群体之一,面临着复杂多样的社会经济状况。贫困是有史以来最古老、最具挑战性的社会问题之一。对土著人民的研究通常采取定性方法,而对贫穷的研究往往涉猎广泛,容易出现重大的抽样误差,而且主要是为了国家决策。利用社区监测系统(CBMS)数据,采用随机森林分类,预测精度达到92.60-98.00 %,将传统调查和数据处理成本降低了70% %。该模型包含27个社会经济变量,能够实现局部政策目标。因此,评估多方面的贫困并模拟每个部落的社会经济条件以促进经济发展至关重要。通过训练和测试数据集,这项工作提出了新的衡量标准,并说明了机器学习在预测贫困方面的有效性。最后,研究结果为各地扶贫提供了针对性的政策工具。这些技术可以被其他研究人员复制、改编或重新利用,以帮助贫困人口改善他们的福祉。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
From data to decision: Alleviating poverty and promoting development through measuring the unmeasurable economic numbers
This work has been carried out and is currently being conducted in the poorest region of Luzon, Philippines. Since poverty is multifaceted and considered unmeasurable in social science, it is notoriously difficult to measure. The methods currently used to measure poverty require a significant amount of time, money, and labor. This poses challenges for policymakers in implementing poverty-reduction policies. Indigenous communities are among the most disadvantaged, vulnerable, and neglected populations in society, facing complex and diverse socioeconomic situations. Poverty stands as one of the oldest and most challenging social problems to have ever existed. Research on indigenous peoples typically takes a qualitative approach, whereas studies on poverty tend to be broad, making them susceptible to significant sampling errors and primarily intended for national policy-making. Using community-based monitoring system (CBMS) data, we achieved a prediction accuracy of 92.60–98.00 % using Random Forest classification and reduced traditional survey and data processing costs by up to 70 %. The proposed model incorporates 27 socioeconomic variables and enables localized policy targeting. Therefore, it is crucial to assess multifaceted poverty and simulate socioeconomic conditions for each tribe to foster economic development. By training and testing datasets, this work proposes new metrics and illustrates the effectiveness of machine learning in predicting poverty. Lastly, the results provide various localities with customized policy targeting tools for poverty alleviation. These techniques can be replicated, adapted, or repurposed by other researchers to assist impoverished populations in improving their well-being.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信