RAPID: Collaborative Research: The Diffusion of State Policy Responses to the 2019 Novel Coronavirus

RAPID:合作研究:国家对 2019 年新型冠状病毒的政策反应的扩散

基本信息

  • 批准号:
    2028675
  • 负责人:
  • 金额:
    $ 1.64万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2020
  • 资助国家:
    美国
  • 起止时间:
    2020-05-15 至 2022-04-30
  • 项目状态:
    已结题

项目摘要

When the 2019 novel coronavirus arrived in the United States in February and March of 2020, state governments quickly began enacting policies intended to contain and mitigate its spread. Understanding the timing and sequence of these policy choices, and those policies’ eventual consequences, is critical for assessing how governments can be most effective during pandemics. This project collects data on state and local governments’ responses to COVID-19, including policies related to closing schools, canceling travel, banning public gatherings, closing restaurants and bars, delaying rent payments, and rules on medical licenses. This data allows researchers to examine the factors that influence states’ policy choices, whether those factors differ from the ways in which states enact policies during normal times, which policies are effective in slowing the spread and morbidity of the virus, and how states roll back policies in a manner that allows economic activity to resume while maintaining preparedness to avoid and mitigated waves of the virus. This project collects data on state government responses to COVID-19 by scraping government websites daily, focusing on sites dedicated to COVID-19 and those associated with the executive branch, state legislatures, and state departments of public health. It also collects data on the number of diagnosed cases, fatalities, recoveries in the states, and mobility data that tracks geographic movements from mobile phones. The policy recommendations or decisions recorded from state government pages include decisions related to closing schools, canceling travel, banning public gatherings (and their size), closing restaurants and bars, travel quarantines, postponing elections, safe shelter orders, limiting elective medical procedures, as well as when states modify these policies; additional data is collected from official state Twitter accounts. This data allows researchers to examine the factors that influence states’ policy choices, whether those factors differ from the ways in which states enact policies during normal times, which policies are effective in slowing the spread and morbidity of the virus, and how states roll back policies in a manner that allows economic activity to resume while maintaining preparedness to avoid and mitigated waves of the virus. This project is jointly funded by the Accountable Institutions and Behavior Program and the Established Program to Stimulate Competitive Research (EPSCoR).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
当2019年新型冠状病毒于2020年2月和3月抵达美国时,各州政府迅速开始制定旨在遏制和减缓其传播的政策。了解这些政策选择的时机和顺序,以及这些政策的最终后果,对于评估政府如何在大流行期间发挥最大效力至关重要。该项目收集有关州和地方政府应对COVID-19的数据,包括与关闭学校、取消旅行、禁止公共集会、关闭餐馆和酒吧、延迟支付租金以及医疗许可证规定有关的政策。 这些数据使研究人员能够研究影响国家政策选择的因素,这些因素是否与国家在正常时期制定政策的方式不同,哪些政策可以有效减缓病毒的传播和发病率,以及国家如何以允许经济活动恢复的方式回滚政策,同时保持准备以避免和减轻病毒的浪潮。该项目通过每天抓取政府网站来收集州政府对COVID-19的反应数据,重点关注专门针对COVID-19的网站以及与行政分支、州立法机构和州公共卫生部门相关的网站。 它还收集有关各州诊断病例、死亡人数、康复情况的数据,以及通过移动的手机跟踪地理移动的移动数据。从州政府页面记录的政策建议或决定包括与关闭学校,取消旅行,禁止公共集会(及其规模),关闭餐馆和酒吧,旅行禁令,推迟选举,安全庇护令,限制选择性医疗程序以及各州修改这些政策有关的决定;其他数据从官方国家Twitter帐户收集。这些数据使研究人员能够研究影响国家政策选择的因素,这些因素是否与国家在正常时期制定政策的方式不同,哪些政策可以有效减缓病毒的传播和发病率,以及国家如何以允许经济活动恢复的方式回滚政策,同时保持准备以避免和减轻病毒的浪潮。该项目由问责机构和行为计划以及刺激竞争研究的既定计划(EPSCoR)共同资助。该奖项反映了NSF的法定使命,并通过使用基金会的知识价值和更广泛的影响审查标准进行评估,被认为值得支持。

项目成果

期刊论文数量(2)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Attention to the COVID‐19 Pandemic on Twitter: Partisan Differences Among U.S. State Legislators
Twitter 上对 COVID-19 大流行的关注:美国各州立法者之间的党派分歧
  • DOI:
    10.1111/lsq.12367
  • 发表时间:
    2021
  • 期刊:
  • 影响因子:
    1.5
  • 作者:
    Kim, Taegyoon;Nakka, Nitheesha;Gopal, Ishita;Desmarais, Bruce A.;Mancinelli, Abigail;Harden, Jeffrey J.;Ko, Hyein;Boehmke, Frederick J.
  • 通讯作者:
    Boehmke, Frederick J.
Government websites as data: a methodological pipeline with application to the websites of municipalities in the United States
政府网站作为数据:适用于美国市政当局网站的方法管道
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Bruce Desmarais其他文献

Legislative support for environmental policy innovation: an experimental test for diffusion through a cross-state policy network
  • DOI:
    10.1007/s41109-024-00677-5
  • 发表时间:
    2024-12-23
  • 期刊:
  • 影响因子:
    1.500
  • 作者:
    Ishita Gopal;Bruce Desmarais
  • 通讯作者:
    Bruce Desmarais

Bruce Desmarais的其他文献

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{{ truncateString('Bruce Desmarais', 18)}}的其他基金

Collaborative Research: HNDS-I: Digitally Accountable Public Representation
合作研究:HNDS-I:数字化负责任的公共代表
  • 批准号:
    2318460
  • 财政年份:
    2023
  • 资助金额:
    $ 1.64万
  • 项目类别:
    Standard Grant
Collaborative Research: Patterns, Context, and Secondary Impacts of State Policy Responses to the Pandemic
合作研究:国家应对疫情政策的模式、背景和次要影响
  • 批准号:
    2148215
  • 财政年份:
    2022
  • 资助金额:
    $ 1.64万
  • 项目类别:
    Standard Grant
RIDIR: Collaborative Research: DAPPR: Diffusion Analytics for Public Policy Research
RIDIR:协作研究:DAPPR:公共政策研究的扩散分析
  • 批准号:
    1637089
  • 财政年份:
    2016
  • 资助金额:
    $ 1.64万
  • 项目类别:
    Standard Grant
Collaborative Research: An Expanded Framework for Inferring Public Policy Diffusion Networks
合作研究:推断公共政策扩散网络的扩展框架
  • 批准号:
    1558661
  • 财政年份:
    2016
  • 资助金额:
    $ 1.64万
  • 项目类别:
    Standard Grant
Scientific Evidence in Regulation and Governance
监管和治理的科学证据
  • 批准号:
    1641047
  • 财政年份:
    2015
  • 资助金额:
    $ 1.64万
  • 项目类别:
    Standard Grant
Collaborative Research: Specification and Estimation of Exponential Family Random Graph Models for Weighted Networks
合作研究:加权网络指数族随机图模型的规范和估计
  • 批准号:
    1619644
  • 财政年份:
    2015
  • 资助金额:
    $ 1.64万
  • 项目类别:
    Standard Grant
Scientific Evidence in Regulation and Governance
监管和治理的科学证据
  • 批准号:
    1360104
  • 财政年份:
    2014
  • 资助金额:
    $ 1.64万
  • 项目类别:
    Standard Grant
Collaborative Research: Specification and Estimation of Exponential Family Random Graph Models for Weighted Networks
合作研究:加权网络指数族随机图模型的规范和估计
  • 批准号:
    1357606
  • 财政年份:
    2014
  • 资助金额:
    $ 1.64万
  • 项目类别:
    Standard Grant

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