Journal of Policy Modeling最新文献_第4页

Can inclusive growth reduce multidimensional poverty in Sub-Saharan Africa? 包容性增长能否减少撒哈拉以南非洲的多维贫困？

IF 3.1 2区经济学

Journal of Policy Modeling Pub Date : 2026-03-01 Epub Date: 2025-12-24 DOI: 10.1016/j.jpolmod.2025.11.002

Adama Sawadogo , Noël Thiombiano , Relwendé Sawadogo

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The end of globalization? Evidence and policy evaluation 全球化的终结？证据和政策评估

IF 3.1 2区经济学

Journal of Policy Modeling Pub Date : 2026-03-01 Epub Date: 2025-12-04 DOI: 10.1016/j.jpolmod.2025.12.001

Mario Arturo Ruiz Estrada , Evangelos Koutronas

引用次数: 0

Regional inflation spillovers and monetary policy design 区域通胀溢出与货币政策设计

IF 3.1 2区经济学

Journal of Policy Modeling Pub Date : 2026-03-01 Epub Date: 2025-11-29 DOI: 10.1016/j.jpolmod.2025.10.003

José Aguilar , Ricardo Quineche

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The counterfactual challenge: How machine learning can enhance policy evaluation 反事实挑战：机器学习如何增强政策评估

IF 3.1 2区经济学

Journal of Policy Modeling Pub Date : 2026-03-01 Epub Date: 2025-07-12 DOI: 10.1016/j.jpolmod.2025.05.007

F. Fantechi, A. Cusimano

{"title":"The counterfactual challenge: How machine learning can enhance policy evaluation","authors":"F. Fantechi, A. Cusimano","doi":"10.1016/j.jpolmod.2025.05.007","DOIUrl":"10.1016/j.jpolmod.2025.05.007","url":null,"abstract":"<div><div>Understanding the impact of public policies and programmed actions is a complex and challenging task that deserves attention and resources. Even if the economic literature on counterfactual methods and identification is well established, conventional methodologies still struggles with holding up the <em>unconfoundedness assumption</em> in complex socio-economic and policy contexts. This paper sets up a ‘counterfactual challenge’, testing the ability of conventional matching versus a novel application of Supervised Machine Learning classification process to identify suitable counterfactuals. Working with high-dimensional data in complex socio-economic and policy contexts, results show that Machine Learning algorithms are better equipped to effectively balance treatment and control groups across a wide range of covariates compared to conventional matching methods. In the context of decision making and policy planning, we show the potential of Machine Learning to drastically improve the reliability and precision of information supporting policymakers in their choices. This is argued to have a positive impact on the effective use of public resources especially in complex and underdeveloped areas and contexts. Improvements in the precision of impact evaluation could result in significant gains in resource efficiency, both by generating realistic expectations of policy outputs, and improving scarce resource allocation we show that the use of Machine Learning algorithms for counterfactual identification consistently provides more precise results and supports policymakers in navigating complex contexts.</div></div>","PeriodicalId":48015,"journal":{"name":"Journal of Policy Modeling","volume":"48 2","pages":"Pages 343-355"},"PeriodicalIF":3.1,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147552054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

From tweets to trends: Modeling public opinion for policy insights on emerging technologies 从推特到趋势：为新兴技术的政策见解建模公众舆论

IF 3.1 2区经济学

Journal of Policy Modeling Pub Date : 2026-03-01 Epub Date: 2025-08-05 DOI: 10.1016/j.jpolmod.2025.06.024

Surabhi Singh , Shiwangi Singh , Sanjay Dhir

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Covid-19 delayed and exacerbated the impending food price spikes Covid-19推迟并加剧了即将到来的粮食价格飙升

IF 3.1 2区经济学

Journal of Policy Modeling Pub Date : 2026-03-01 Epub Date: 2026-02-10 DOI: 10.1016/j.jpolmod.2026.02.001

Jane Du , Cheng King

引用次数: 0

Insuring crops against climate risks: A most effective agricultural practice in India 为农作物投保气候风险：印度最有效的农业实践

IF 3.1 2区经济学

Journal of Policy Modeling Pub Date : 2026-03-01 Epub Date: 2025-06-28 DOI: 10.1016/j.jpolmod.2025.06.021

Dinamani Biswal , Chandra Sekhar Bahinipati

{"title":"Insuring crops against climate risks: A most effective agricultural practice in India","authors":"Dinamani Biswal , Chandra Sekhar Bahinipati","doi":"10.1016/j.jpolmod.2025.06.021","DOIUrl":"10.1016/j.jpolmod.2025.06.021","url":null,"abstract":"<div><div>Several studies have enquired about the reason for the low adoption of crop insurance in India and noticed determinants associated with demographic, socio-economic, landholding, institutional characteristics, etc. However, limited studies consider nationally representative sample data for analysis, and further, there is a dearth of understanding in the intensification of crop insurance adoption. A few studies reveal that determinants are different for adopting crop insurance and the number of crops insured. Thus, this study aims to identify factors influencing crop insurance adoption and the number of crops insured in India. The National Sample Survey Office 70th and 77th rounds related to the Situation Assessment Survey of Agricultural Households conducted in the 2012–13 and 2018–19 Kharif season (July - December) data are taken, and the double hurdle model is employed. Like other empirical studies in India, this study finds that socio-economic variables like caste, education, and economic status influence farmers’ behaviour towards crop insurance adoption. Large farmers are more likely to opt for crop insurance than other farmers. In particular, most variables related to access to various agricultural institutions are positive and significant for adoption but not for intensification of crop insurance. A separate analysis for landholding categories highlights the importance of these variables, particularly for marginal farmers. This needs special attention in the policy as it is imperative from the ongoing agricultural crisis in India to enhance adoption coverage so more farmers opt for crop insurance and purchase insurance for multiple crops. Such analysis is imperative as crop insurance is considered one of the most effective climate-smart agriculture practices.</div></div>","PeriodicalId":48015,"journal":{"name":"Journal of Policy Modeling","volume":"48 2","pages":"Pages 308-326"},"PeriodicalIF":3.1,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147552104","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Beyond sanctions: Unpacking growth generators for Iranian small business 制裁之外：拆解伊朗小企业的增长引擎

IF 3.1 2区经济学

Journal of Policy Modeling Pub Date : 2026-03-01 Epub Date: 2025-08-05 DOI: 10.1016/j.jpolmod.2025.07.004

Iman Cheratian, Saleh Goltabar

引用次数: 0

Social policy gone bad educationally: Unintended peer effects from transferred students 社会政策在教育上变坏：来自转学学生的意外同伴效应

IF 3.1 2区经济学

Journal of Policy Modeling Pub Date : 2026-03-01 Epub Date: 2026-02-12 DOI: 10.1016/j.jpolmod.2026.01.003

Christos Genakos , Eleni Kyrkopoulou

引用次数: 0

The economic costs of oligopoly in the markets of rare earth minerals 稀土矿物市场寡头垄断的经济成本

IF 3.1 2区经济学

Journal of Policy Modeling Pub Date : 2026-03-01 Epub Date: 2026-01-12 DOI: 10.1016/j.jpolmod.2025.11.004

Giuseppe Di Vita , Paolo Lorenzo Ferrara , Alessandra Patti

{"title":"The economic costs of oligopoly in the markets of rare earth minerals","authors":"Giuseppe Di Vita , Paolo Lorenzo Ferrara , Alessandra Patti","doi":"10.1016/j.jpolmod.2025.11.004","DOIUrl":"10.1016/j.jpolmod.2025.11.004","url":null,"abstract":"<div><div>In this research, the costs of rare earth elements (REEs) due to the oligopolistic market structure are estimated. On the supply side, China exercises a dominant position in the international REEs market because it controls more than 40 % of deposits and more than 60 % of REEs traded. Other countries assume an ancillary role in international markets for these minerals, which will increasingly play a crucial role in economic growth in the coming decades, since REEs are considered the minerals of technology and the green transition. Using the fringe oligopoly model, the costs due to this market structure for economies less endowed with deposits of these minerals are estimated. To perform this analysis, the Herfindahl-Hirschman Index was calculated, and a new index of dependence on REEs, as a proxy for a country's economic vulnerability, was constructed. Our database covers fifty years and sixty-two countries. Most of the data used comes from the World Bank database. Using quantile econometric models, we can calculate the economic costs of countries with fewer REEs caused by the market power exerted by the leading country in this market and estimate the loss for each country involved in the international trade of these minerals. Finally, technological advancements play a pivotal role in aiding countries reliant on REEs to lower their costs. This can be achieved through policies aimed at minimizing the utilization of REEs during manufacturing, harnessing the recycling of minerals from used products, and discovering alternative materials to replace REEs.</div></div>","PeriodicalId":48015,"journal":{"name":"Journal of Policy Modeling","volume":"48 2","pages":"Pages 255-288"},"PeriodicalIF":3.1,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147552058","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0