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X-WR-CALDESC:Events for California Center for Population Research
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DTSTART;TZID=America/Los_Angeles:20210512T120000
DTEND;TZID=America/Los_Angeles:20210512T133000
DTSTAMP:20260719T071404
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UID:10000579-1620820800-1620826200@ccpr.ucla.edu
SUMMARY:"A Signal to End Child Marriage: Theory and Experimental Evidence from Bangladesh\," Erica Field\, Duke University
DESCRIPTION:Erica Field\, Duke University\nSeminar Co-Sponsor: Luskin Public Policy \nAbstract: Child marriage remains common in many settings even where schooling and\nlabor market opportunities have grown considerably. To explain this phenomenon\,\nwe introduce a marriage market signaling model in which bride type is not per-\nfectly observed but preferred brides have lower benefits of delaying marriage.\nThis gives preferred brides an incentive to reveal their type by marrying young\,\nshifting the market towards early marriage even when everyone benefits from\ndelay. In this setting\, a small incentive that shifts preferred brides towards later\nmarriage can delay marriage of all types through spillovers. We test this predic-\ntion by evaluating the impact of a financial incentive to delay marriage among\n15\,576 adolescent girls in Bangladesh. Consistent with the theory\, girls eligible\nfor the incentive for two years were 21% less likely to marry before 18\, and\ngirls who were ineligible for the incentive but lived near treatment communities\nalso delayed marriage. \nA recording of Dr. Field’s presentation may be accessed here. \n 
URL:https://ccpr.ucla.edu/event/erica-field-duke-university/
CATEGORIES:CCPR Seminar,Divisional Publish
ATTACH;FMTTYPE=image/jpeg:https://ccpr.ucla.edu/wp-content/uploads/2020/08/efield.jpg
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DTSTART;TZID=America/Los_Angeles:20210519T120000
DTEND;TZID=America/Los_Angeles:20210519T133000
DTSTAMP:20260719T071404
CREATED:20200831T231401Z
LAST-MODIFIED:20210520T201839Z
UID:10000581-1621425600-1621431000@ccpr.ucla.edu
SUMMARY:"Prevalences\, Penalties\, and the Small Impact of Single Motherhood on America's High Child Poverty\," David Brady\, UC Riverside
DESCRIPTION:David Brady\, UC Riverside\nAbstract: Many claim high U.S. child poverty results from a high prevalence of single motherhood\, and reducing single motherhood would substantially reduce child poverty. To scrutinize these arguments\, we apply the “prevalences and penalties” framework to Luxembourg Income Study data across 30 rich democracies and over time within the U.S. 1979–2016. The descriptive patterns fail to support these claims. Although the U.S. has a moderately high prevalence of single motherhood\, a fairly high prevalence is typical cross-nationally and in recent U.S. history. Single motherhood is the most common risk of the four major risks in the U.S.\, but this is because low education\, young headship\, and unemployment have declined. The U.S. has the highest penalty for single motherhood\, however single motherhood has the smallest penalty of the four major risks in the U.S. and cross-nationally. A wide variety of counterfactual simulations demonstrate that reducing single motherhood would not substantially reduce child poverty. Even with zero single motherhood\, U.S. child poverty would only move from the third to fourth highest among 30 rich democracies (from 21.3% to 18.8%). Ultimately\, we demonstrate that the U.S. has systemically high child poverty for all family structures\, and extremely high child poverty for racial/ethnic minorities regardless of single motherhood. Reducing the penalty attached to single motherhood and reducing America’s systemically high child poverty across all families would be far more effective than reducing the prevalence of single motherhood. \nBiography: David Brady is a Professor in the School of Public Policy\, and Director of the Blum Initiative on Global and Regional Poverty at the University of California\, Riverside. At UCR\, he teaches classes on poverty\, public policy analysis\, and statistics. \nMore on Prof. Brady \nYou can find a recording of Dr. Brady’s presentation here.
URL:https://ccpr.ucla.edu/event/david-brady-uc-riverside/
CATEGORIES:CCPR Seminar,Divisional Publish
ATTACH;FMTTYPE=image/jpeg:https://ccpr.ucla.edu/wp-content/uploads/2020/08/DavidBrady-1.jpg
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DTSTART;TZID=America/Los_Angeles:20210526T120000
DTEND;TZID=America/Los_Angeles:20210526T133000
DTSTAMP:20260719T071404
CREATED:20210208T224804Z
LAST-MODIFIED:20210528T231616Z
UID:10000720-1622030400-1622035800@ccpr.ucla.edu
SUMMARY:"Demographic perspectives on COVID-19: one year later\," Jennifer Beam Dowd\, University of Oxford
DESCRIPTION:Jennifer Beam Dowd\, University of Oxford\nAbstract: While epidemiology is certainly having a moment\, demography has been key to understanding COVID-19 data since the early days of the pandemic. This talk will take stock of demographic insights into COVID a year on ranging from the intersection of population age structure and mortality to estimates of excess mortality and optimal vaccination priority strategies. Dr. Dowd will also discuss the role of academics in science communication during this time and her experiences with the COVID-19 science communication effort Dear Pandemic. \nBiography: Dr. Jenn Dowd is currently Associate Professor of Demography and Population Health and Deputy Director of the Leverhulme Centre for Demographic Science\,  Department of Sociology\, University of Oxford\, and Associate Member\, Nuffield College.  Dr. Dowd is a quantitative health and social scientist with interdisciplinary training in demography\, epidemiology\, economics\, and infectious disease. Her research focuses on how social and biological processes interact over the life course and specifically how social factors “get under the skin” to impact health. She has examined the social determinants of infections and immune function and links between infections and chronic disease. \nMore on Prof. Beam Dowd \nDr. Beam Dowd’s presentation may be accessed here.
URL:https://ccpr.ucla.edu/event/jennifer-beam-dowd-university-of-oxford/
CATEGORIES:CCPR Seminar
ATTACH;FMTTYPE=image/jpeg:https://ccpr.ucla.edu/wp-content/uploads/2021/02/img-5488_1.jpg
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