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DTSTART;TZID=America/Los_Angeles:20201202T120000
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SUMMARY:"Voting after Shelby: Did pre-clearance matter?" Ariel White\, Massachusetts Institute of Technology
DESCRIPTION:“Voting after Shelby: Did pre-clearance matter?”\nAriel White\, Massachusetts Institute of Technology \n(co-authored with Mayya Komisarchik) \nAbstract: Nearly five decades after the passage of the Voting Rights Act\, the law was dramatically changed by the Supreme Court’s decision in Shelby County v. Holder. The court effectively removed the “preclearance” process that had required places with a history of racial discrimination to get Justice Department approval before changing their voting procedures. Dissenting justices and voting-rights advocates feared that this decision could lead to massive changes to election administration and ultimately to lower rates of voter participation in minority communities. In this paper\, we evaluate the impact of this decision on election practices and on Black and Hispanic voter registration and turnout. We use a combination of administrative data on registration and voting\, survey data on mobilization and local election administration\, and state legislative records to examine different facets of the voting rights landscape after the Court’s decision. \nBio: Prof. White research focuses on voting and voting rights\, race\, the criminal justice system\, and bureaucratic behavior. Prof. White’s work uses large datasets to measure individual-level experiences\, and to shed light on people’s everyday interactions with government. \nMore on Prof. White
URL:https://ccpr.ucla.edu/event/ariel-white-massachusetts-institute-of-technology/
CATEGORIES:CCPR Seminar,Divisional Publish
ATTACH;FMTTYPE=image/jpeg:https://ccpr.ucla.edu/wp-content/uploads/2020/08/ArielWhite.jpg
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DTSTART;TZID=America/Los_Angeles:20201209T120000
DTEND;TZID=America/Los_Angeles:20201209T140000
DTSTAMP:20260721T102922
CREATED:20200928T212259Z
LAST-MODIFIED:20210123T024745Z
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SUMMARY:Population-Based Modeling and Measurement of COVID-19
DESCRIPTION:“Population-Based Modeling and Measurement of COVID-19”\nThe recording of the event is available here. \nPanelists: \nChristina Ramirez\, Prof. of Biostatistics UCLA\nMark Handcock\, Prof. of Statistics UCLA\nPatrick Heuveline\, Prof. of Sociology UCLA\nHiram Beltrán-Sánchez\, Prof. of Community Health Sciences \nFor more information on panelists’ research\, see: \n\nPatrick Heuveline.  Covid-19 will reduce US life expectancy at birth by more than one year in 2020. https://www.medrxiv.org/content/10.1101/2020.12.03.20243717v1 \n  \nMark Handcock and colleagues. Asymptomatic and Presymptomatic Transmission of 2019 Nover Coronavirus (COVID-19) Infection:  An Estimation from a Cluster of Confirmed Cases in Ho Chi Minh City\, Vietnam. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3630119 \n  \nWatson and colleagues. Fusing a Bayesian Case Velocity Model with Random Forest for Predicting COVID-19 in the U.S. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3594606 \n  \nDi Xiong and colleagues. Pseudo-likelihood based logistic regression for estimating COVID-19 infection and case fatality rates by gender\, race\, and age in California.https://www.sciencedirect.com/science/article/pii/S1755436520300396?via%3Dihub 
URL:https://ccpr.ucla.edu/event/population-based-modeling-and-measurement-of-covid-19/
CATEGORIES:CCPR Seminar,Divisional Publish
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