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X-WR-CALNAME:California Center for Population Research
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X-WR-CALDESC:Events for California Center for Population Research
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DTSTART;TZID=America/Los_Angeles:20230503T120000
DTEND;TZID=America/Los_Angeles:20230503T132000
DTSTAMP:20260504T194341
CREATED:20220826T063415Z
LAST-MODIFIED:20230428T001624Z
UID:10000805-1683115200-1683120000@ccpr.ucla.edu
SUMMARY:Zack Almquist\, University of Washington
DESCRIPTION:Biography: Zack W. Almquist is currently an Associate Professor in the Department of Sociology\, Adjunct Associate Professor of Statistics\, and Senior Data Science Fellow at the eScience Institute at the University of Washington. Before coming to UW in 2020\, Prof. Almquist held positions as a Research Scientist at Facebook\, Inc and as an Assistant Professor of Sociology and Statistics at the University of Minnesota. Dr. Almquist is a recipient of the American Sociological Association’s Section on Methodology’s Leo Goodman Award. He is also a recipient of the NSF’s CAREER Award and the ARO’s Young Investigator Program Award. He is currently the Editor-in-Chief of the Journal of Mathematical Sociology. His research centers on the development and application of mathematical\, computational and statistical methodology to problems and theory of social networks\, demography\, homelessness\, and environmental action and governance. \nA Qualitative and Quantitative PIT Count using Respondent Driven Sampling (RDS): Understanding and Counting Unsheltered Homelessness in King County \nAbstract: Traditionally\, unsheltered Point in Time (PIT) Counts are the result of volunteers conducting an in-person head-count of individuals experiencing homelessness on a single night. This resource-intensive method is widely understood to be an undercount. It also fails to capture essential qualitative data about what people living unsheltered experience and need. \nThis past spring\, the King County Regional Homelessness Authority (RHA)\, in coordination with Professor Zack W. Almquist (University of Washington) and Lived Experience Coalition (LEC)\, took a novel approach to the PIT. The RHA conducted the 2022 unsheltered PIT count as a combined qualitative interview process and quantitative survey over the course of a month. The respondent selection for both the qualitative and quantitative surveys followed a Respondent Driven Sampling (RDS) protocol. RDS provides a sampling strategy for estimating size and percentages of hard-to-reach populations that lack an administrative sampling frame. \nDuring this seminar\, I will provide an overview of the RHA partnership effort\, and how we executed this novel approach to the PIT. I will review the history of RDS as a means of sampling vulnerable populations\, and I will cover the implementation of the sampling  and estimation strategies based on the RHA RDS sample. Finally\, I will review the demographics provided to HUD\, and what we learned from conducting the RDS sample for the PIT count\, and how it can and should affect future PIT counts going forwards. \nYou may access the seminar using this link.
URL:https://ccpr.ucla.edu/event/zack_almquist_washington/
LOCATION:4240A Public Affairs Bldg
CATEGORIES:CCPR Seminar
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230504T110000
DTEND;TZID=America/Los_Angeles:20230505T170000
DTSTAMP:20260504T194341
CREATED:20221028T181522Z
LAST-MODIFIED:20230315T210501Z
UID:10000666-1683198000-1683306000@ccpr.ucla.edu
SUMMARY:All-UC Demography Conference 2023
DESCRIPTION:All-UC Demography Conference 2023 – Save the date  \n\n\nUCI’s Center for Population\, Inequality and Policy will invite submissions to present at the inaugural All-UC Demography Conference. This meeting will highlight current demographic research happening within the UC system and provide a venue for making connections across UC campuses\, with a keynote talk by Ron Lee\, Distinguished Professor and founding director of UC Berkeley Center for the Economics and Demography of Aging. \n\n\nThe conference will be held Thursday\, May 4\, and Friday\, May 5 in person at University of California\, Irvine. It will begin at 9am on the 4th and end at noon\, May 5th. We are planning for the keynote talk and faculty paper sessions with discussants. We will also host a graduate poster session and a reception at the end of the Thursday sessions. Lunch will be served on both days\, with boxed lunch available at the conclusion of the Friday morning session. More details are forthcoming. \n\n\nTo receive meeting updates and our call for papers\, please register for our mailing list. \n\n\nConference website: https://www.cpip.uci.edu/conference.php \n\n\nOrganizing Committee: Brittney Morey\, Jade Jenkins\, Paul Hanselman\, Greg Duncan\, Timothy Bruckner \n\n\nThe Center for Population\, Inequality\, and Policy (CPIP) was founded at UCI in 2020 to centralize the efforts of several population-related centers at UCI that were internally funded and operating independently. CPIP is now the sole recipient of university-level resources to support a center for the study of population sciences at UCI. To learn more\, please visit us at https://www.cpip.uci.edu/.
URL:https://ccpr.ucla.edu/event/all-uc-demography-conference-2023/
CATEGORIES:CCPR Conference,CCPR Seminar,Other Conferences
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230510T120000
DTEND;TZID=America/Los_Angeles:20230510T132000
DTSTAMP:20260504T194341
CREATED:20230501T163218Z
LAST-MODIFIED:20230508T162707Z
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SUMMARY:Itzik Fadlon\, University of California San Diego
DESCRIPTION:Biography: Itzik Fadlon is an Associate Professor of Economics at the University of California\, San Diego and a Research Associate in the programs on Aging and Public Economics at the National Bureau of Economic Research. His primary fields of interest are public finance\, health economics\, and labor/family economics. His work studies household behavior and the effects of government policies\, as well as how these impacts on households’ behavior translate to the optimal design of social policies. His work has been published in leading journals such as American Economic Journal: Applied Economics\, American Economic Review\, Journal of Health Economics\, Journal of Public Economics\, and Review of Economics and Statistics. Itzik received his Ph.D. in economics from Harvard University in May 2015. Before joining UCSD in 2016 as an Assistant Professor\, Itzik spent a year as a Postdoctoral Fellow in Disability Policy Research at the National Bureau of Economic Research\, and he spent the academic year of 2019-2020 as a Visiting Scholar in Aging and Health Research at the NBER. \nTitle: “Causal Effects of Early Career Sorting on Labor and Marriage Market Choices: A Foundation for Gender Disparities and Norms” \nAbstract: We study whether and how early labor market choices determine longer-run career versus family outcomes differentially for male and female professionals. We analyze the physician labor market by exploiting a randomized lottery that determines the sorting of Danish physicians into internships across local labor markets. Using administrative data spanning ten years after physicians’ graduations\, we find causal effects of early-career sorting on a range of life cycle outcomes that cascade from labor market choices\, including human capital accumulation and occupational choice\, to marriage market choices\, including matching and fertility. The persistent effects are entirely concentrated among women\, whereas men experience only temporary career disruptions. The evidence points to differential family-career tradeoffs and the mentorship employers provide as channels underlying this gender divergence. Our findings have implications for policies aimed at gender equality in outcomes\, as they reveal how persistent gaps can arise even in institutionally gender-neutral settings with early-stage equality of opportunity. \n 
URL:https://ccpr.ucla.edu/event/itzik-fadlon-university-of-california-san-diego/
LOCATION:4240A Public Affairs Bldg
CATEGORIES:CCPR Seminar
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230517T120000
DTEND;TZID=America/Los_Angeles:20230517T132000
DTSTAMP:20260504T194341
CREATED:20220728T230831Z
LAST-MODIFIED:20230520T003328Z
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SUMMARY:Steven Stillman\, Free University of Bozen-Bolzano
DESCRIPTION:Biography: Steven Stillman received his PhD in Economics from the University of Washington in 2000. Prior to moving to Italy in 2016\, he was a Professor of Economics at the University of Otago in New Zealand. His research focuses on empirical labour economics\, specialising in the behaviour of individuals and households\, and the interplay between government policy and human behavior. He has done extensive work examining the impact of migration on immigrants and their families exploiting a lottery used to allocate migrant quota slots. In recent work\, he has also examined voting behavior along a number of dimensions and how individuals have responded to changes in a number of policies\, including minimum wages\, the drinking age and doctor’s fees. \nLearning About Leave: Peer Influences in Maternal Leave Decisions \nAbstract: We examine how the parental leave decisions of mothers are influenced by parental leave decisions made by their work colleagues (peers) using income tax data on the universe of women who gave birth in New Zealand between 2002 and 2018. Maternal leave in New Zealand replaces 100% of mothers’ income up to a fairly low maximum. We use this maximum threshold to implement a regression kink design\, estimating the causal impact of peer leave decisions on mothers’ own leave decisions. We find that for every week that peers shorten their maternity leave in response to this threshold\, mothers reduce their own leave by 0.5 to 0.6 weeks. This effect is larger in smaller firms and in situations where the peer is more likely to influence the decision of the study mother. \nA recording of Dr. Stillman’s presentation may be accessed here.
URL:https://ccpr.ucla.edu/event/steven-stillman-free-university-of-bozen-bolzano/
LOCATION:4240A Public Affairs Bldg
CATEGORIES:CCPR Seminar
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230524T120000
DTEND;TZID=America/Los_Angeles:20230524T133000
DTSTAMP:20260504T194341
CREATED:20220728T231644Z
LAST-MODIFIED:20230526T000913Z
UID:10000798-1684929600-1684935000@ccpr.ucla.edu
SUMMARY:Difference-In-Difference Panel Discussion and Mini Conference
DESCRIPTION:Differences-in-differences Mini-conference \nMay 24\, 2023  \nUCLA\, California Center for Population Research \n9-11:30am Speakers hold for meetings \n12-1:30pm [CCPR seminar slot] Panel discussion: What’s new with differences-in-differences?  \nAndrew Goodman-Bacon (Minneapolis Federal Reserve Bank)\, Alyssa Bilinski (Brown)\, Jon Roth (Brown)\, Pedro Sant’Anna (Vanderbilt)\, Jeff Wooldridge (MSU)  \nSHORT LUNCH BREAK & ROOM SET UP \n2:15-3:00pm Andrew Goodman-Bacon (Minneapolis Federal Reserve Bank)\, Pedro Sant’Anna (Vanderbilt) \n“Difference-in-Differences with a Continuous Treatments” \nThis paper analyzes difference-in-differences setups with a continuous treatment. We show that treatment effect on the treated-type parameters can be identified under a generalized parallel trends assumption that is similar to the binary treatment setup. However\, interpreting differences in these parameters across different values of the treatment can be particularly challenging due to treatment effect heterogeneity. We discuss alternative\, typically stronger\, assumptions that alleviate these challenges. We also provide a variety of treatment effect decomposition results\, highlighting that parameters associated with popular two-way fixed-effect specifications can be hard to interpret\, even when there are only two time periods. We introduce alternative estimation strategies that do not suffer from these drawbacks. Our results also cover cases where (i) there is no available untreated comparison group and (ii) there are multiple periods and variation in treatment timing\, which are both common in empirical work. \n3:00-3:45pm Alyssa Bilinski (Brown) \n“Parallel Trends in an Unparalleled Pandemic: Difference-in-Differences for Infectious Disease Policy Evaluation”  \nOver the course of the COVID-19 pandemic\, researchers have extensively studied the impact of public health interventions\, such as stay-at-home orders and mask policies\, on disease incidence and mortality.  Many policy evaluations employ a difference-in-differences (DiD) design\, which assumes that treatment and non-experimental comparison groups would have moved in parallel in expectation\, absent the intervention (the “parallel trends assumption”).  While researchers have used different specifications to capture potential non-linearities\, the plausibility of these specifications in the context of dynamic infection transmission is less well-understood.  Our work bridges this gap by formalizing epidemiological assumptions required for different DiD specifications\, positing an underlying susceptible\, infectious\, recovered (SIR) model.  We first explore common DiD specifications\, demonstrating that these often imply strict epidemiological assumptions.  For example\, DiD modeling raw cases or deaths as an outcome will be biased unless treatment and comparison groups have the same initial conditions\, susceptible fraction\, and transmission rate (“force of infection”); using a log transformation allows for different initial conditions\, but requires equal transmission rates and and susceptible fractions among groups.  Furthermore\, even if estimates are unbiased\, both specifications are often highly anti-conservative under standard error assumptions of a stochastic agent-based SIR model.  We then present more robust alternatives\, including modeling log difference as the primary outcome and modeling the causal effect of an intervention on the effective reproduction number\, rather than cases or deaths.  We demonstrate the implications of this work reanalyzing prior work on school mask policies. \n3:45pm Coffee break \n4-4:45pm Jeff Woodridge (MSU) \n“Estimating Distributional Treatment Effects with Staggered Interventions for Panel Data” \nI propose simple\, parametric approaches for estimating distributions of potential outcomes in a staggered difference-in-differences setting. The approach relies on versions of no anticipation and parallel trends assumptions. Estimators include imputation estimators or pooled maximum likelihood estimation. The approach can be applied to discrete\, continuous\, and mixed outcomes. A leading application is estimating quantile treatment effects in staggered DiD settings for a continuous outcome. \n4:45-5:30pm Jonathan Roth (Brown) \n“Log-like? Identified ATEs Defined with Zero-valued Outcomes are (Arbitrarily) Scale-Dependent” \nEconomists frequently estimate average treatment effects (ATEs) for transformations of the outcome that are well-defined at zero but behave like logpyq when y is large (e.g.\, logp1 ` yq\, arcsinhpyq). We show that these ATEs depend arbitrarily on the units of the outcome\, and thus should not be interpreted as percentage effects. In line with this result\, we find that estimated treatment effects for arcsinh-transformed outcomes published in the American Economic Review change substantially when we multiply the units of the outcome by 100 (e.g.\, convert dollars to cents). To help delineate alternative approaches\, we prove that when the outcome can equal zero\, there is no average treatment effect of the form EP rgpY p1q\, Y p0qqs that is point-identified and unit-invariant. We conclude by discussing sensible alternative target parameters for settings with zero-valued outcomes that relax at least one of these requirements. \n  \n 
URL:https://ccpr.ucla.edu/event/workshop-4/
LOCATION:4240A Public Affairs Bldg
CATEGORIES:CCPR Conference,CCPR Seminar,CCPR Workshop
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230531T120000
DTEND;TZID=America/Los_Angeles:20230531T132000
DTSTAMP:20260504T194341
CREATED:20220728T231105Z
LAST-MODIFIED:20230531T165944Z
UID:10000794-1685534400-1685539200@ccpr.ucla.edu
SUMMARY:Robert Mare Student Lecture
DESCRIPTION:Biography: Caitlin is a doctoral candidate in Sociology at UCLA. She is broadly interested in inequality in educational and occupational attainment\, the role of education in social stratification processes\, and policy approaches to addressing poverty and inequality. She is also interested in applications of causal inference using large-scale survey data. Her dissertation explores the ways that initial college enrollment decisions shape the transition to adulthood\, with a focus on the least selective four-year college options. Caitlin is a student affiliate at the California Center for Population Research and a research fellow for the Los Angeles Education Research Institute. In the fall\, she will start a postdoctoral fellowship with Stanford Impact Labs\, where she will be studying the effects of guaranteed income and baby bonds with the Economic Inclusion Project. \nThe Power of a Degree: Examining the Direct and Indirect Effects of Broad-Access College Enrollment on Economic Disadvantage \nAbstract: As the availability of high-quality jobs for less-educated workers has diminished\, college is seen as the surest way to avoid adverse socioeconomic outcomes. Yet\, the benefits to college depend not only on whether an individual enrolls in school but also where. This study uses longitudinal data sources to examine the impact of enrollment in minimally or non-selective four-year colleges on bachelor’s degree completion and economic disadvantage. My findings show positive effects of broad-access four-year college enrollment on bachelor’s degree completion relative to community college enrollment\, but negative effects relative to more-selective college. However\, these differences in degree completion contribute to only modest changes in economic disadvantage in early adulthood. Instead\, not enrolling in any college is associated with greater economic disadvantage than enrolling in a broad-access four-year college. These findings collectively underscore the importance of any college enrollment in preventing economic disadvantage and the pivotal role of bachelor’s degree completion in reaping the economic benefits of attending college. However\, they also highlight the need to address structural inequalities in college access to reduce disparities in educational attainment and emphasize the importance of considering the counterfactual group when evaluating the returns to college selectivity. \nBiography: Pablo Geraldo Bastías is a PhD Candidate in Sociology at UCLA. His research examines how institutions influence inequality in education and the labor market\, with a particular focus in the comparative study of skill formation systems and school-to-work transitions. Methodologically\, he is interested in the intersection of causal inference using graphical models\, machine learning\, and network analysis. After graduating from UCLA\, he will be a Postdoctoral Prize Research Fellowship in Sociology at Nuffield College\, University of Oxford.  \nSecondary effects of tuition-free college on high school trajectories: Evidence from Chile \nAbstract: The discussion about the costs of college education\, who has to pay for it\, and its implication for social inequality and mobility\, is of primary importance for educational stratification scholars and policy makers. While increasing evidence has accumulated on the impact of access to secure funding in reducing enrollment and completion gaps in higher education\, there is less evidence on how large-scale policy changes on college funding would affect students’ decision earlier in their educational trajectories\, in anticipation of benefiting from such policies in the future. In this study I show how the introduction of tuition-free college in Chile (2016) affected the educational trajectories of high school students. Using administrative data analyzed in an event-study framework\, I provide evidence of the positive effect that increasing access to guaranteed funding for higher education had on secondary students\, lowering grade retention and dropout rates\, especially among the most socially disadvantaged students.
URL:https://ccpr.ucla.edu/event/robert-mare-student-lecture-2/
LOCATION:4240A Public Affairs Bldg
CATEGORIES:CCPR Seminar
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