使用美国行政数据估计运输和仓储场所的决定因素

IF 0.7 Q2 AREA STUDIES
C. Carpenter, R. Dudensing, Anders Van Sandt
{"title":"使用美国行政数据估计运输和仓储场所的决定因素","authors":"C. Carpenter, R. Dudensing, Anders Van Sandt","doi":"10.18335/region.v9i1.366","DOIUrl":null,"url":null,"abstract":"Interactions between transportation and warehousing and other industry clusters are not widely explored and the determinants of logistics locational determinants is limited in the U.S. context. These gaps in the literature, along with the U.S. transportation and warehousing sector's decentralization from urban areas and concentration in regions, highlight the importance of understanding the effects of place-based factors and inter-industry clusters on the locations and employment of transportation and warehousing industries. The analysis uses restricted-access U.S. Census Bureau data aggregated to the county level, along with secondary data sources, to estimate the locational determinants of transportation and warehousing (TW) industries based on transportation infrastructure as well as sociodemographic and institutional variables. The analysis takes a cross-sectional (non-causal) approach to focus on time-invariant location factors while testing and implementing zero-inflated count data distributions to model the data generation processes more accurately. Results indicate that subsectors are affected differently by infrastructure, sociodemographic, and institutional variables. Additionally, different factors are associated with industry presence versus size. Finally, we show that data using aggregated industries obscures locational factors' importance for individual sub-sectors and, further, that industrial aggregation obscures TW sectors' relationships to other clusters.","PeriodicalId":43257,"journal":{"name":"Baltic Region","volume":null,"pages":null},"PeriodicalIF":0.7000,"publicationDate":"2022-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Estimating Determinants of Transportation and Warehousing Establishment Locations Using U.S. Administrative Data\",\"authors\":\"C. Carpenter, R. Dudensing, Anders Van Sandt\",\"doi\":\"10.18335/region.v9i1.366\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Interactions between transportation and warehousing and other industry clusters are not widely explored and the determinants of logistics locational determinants is limited in the U.S. context. These gaps in the literature, along with the U.S. transportation and warehousing sector's decentralization from urban areas and concentration in regions, highlight the importance of understanding the effects of place-based factors and inter-industry clusters on the locations and employment of transportation and warehousing industries. The analysis uses restricted-access U.S. Census Bureau data aggregated to the county level, along with secondary data sources, to estimate the locational determinants of transportation and warehousing (TW) industries based on transportation infrastructure as well as sociodemographic and institutional variables. The analysis takes a cross-sectional (non-causal) approach to focus on time-invariant location factors while testing and implementing zero-inflated count data distributions to model the data generation processes more accurately. Results indicate that subsectors are affected differently by infrastructure, sociodemographic, and institutional variables. Additionally, different factors are associated with industry presence versus size. Finally, we show that data using aggregated industries obscures locational factors' importance for individual sub-sectors and, further, that industrial aggregation obscures TW sectors' relationships to other clusters.\",\"PeriodicalId\":43257,\"journal\":{\"name\":\"Baltic Region\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.7000,\"publicationDate\":\"2022-01-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Baltic Region\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.18335/region.v9i1.366\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"AREA STUDIES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Baltic Region","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.18335/region.v9i1.366","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"AREA STUDIES","Score":null,"Total":0}
引用次数: 1

摘要

运输和仓储与其他产业集群之间的相互作用没有得到广泛的探索,物流区位决定因素的决定因素在美国的背景下是有限的。文献中的这些差距,以及美国运输和仓储业从城市地区的分散化和区域集中,突出了理解基于地方的因素和产业间集群对运输和仓储业的位置和就业的影响的重要性。该分析使用限制访问的美国人口普查局汇总到县一级的数据,以及二级数据源,根据交通基础设施以及社会人口和制度变量估计运输和仓储(TW)行业的位置决定因素。该分析采用横截面(非因果)方法,在测试和实现零膨胀计数数据分布以更准确地建模数据生成过程的同时,关注定常位置因素。结果表明,基础设施、社会人口和制度变量对子行业的影响不同。此外,与行业存在和规模相关的因素也不尽相同。最后,我们表明,使用聚集产业的数据模糊了位置因素对单个子行业的重要性,进一步,产业聚集模糊了两个行业与其他集群的关系。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Estimating Determinants of Transportation and Warehousing Establishment Locations Using U.S. Administrative Data
Interactions between transportation and warehousing and other industry clusters are not widely explored and the determinants of logistics locational determinants is limited in the U.S. context. These gaps in the literature, along with the U.S. transportation and warehousing sector's decentralization from urban areas and concentration in regions, highlight the importance of understanding the effects of place-based factors and inter-industry clusters on the locations and employment of transportation and warehousing industries. The analysis uses restricted-access U.S. Census Bureau data aggregated to the county level, along with secondary data sources, to estimate the locational determinants of transportation and warehousing (TW) industries based on transportation infrastructure as well as sociodemographic and institutional variables. The analysis takes a cross-sectional (non-causal) approach to focus on time-invariant location factors while testing and implementing zero-inflated count data distributions to model the data generation processes more accurately. Results indicate that subsectors are affected differently by infrastructure, sociodemographic, and institutional variables. Additionally, different factors are associated with industry presence versus size. Finally, we show that data using aggregated industries obscures locational factors' importance for individual sub-sectors and, further, that industrial aggregation obscures TW sectors' relationships to other clusters.
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
Baltic Region
Baltic Region AREA STUDIES-
CiteScore
1.60
自引率
37.50%
发文量
11
审稿时长
24 weeks
×
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:481959085
Book学术官方微信