• The Summer Institutes in Computational Social Science (SICSS) 2024

    CCPR Seminar Room 4240 Public Affairs Building, Los Angeles, CA, United States

    From June 24 to July 3, 2024 the University of California, Los Angeles (UCLA) Division of Social Sciences and the California Center for Population Research will sponsor the Summer Institute in Computational Social Science, to be held at the University of California Los Angeles. The Organizing Committee Jennie Brand, Professor, Sociology and Statistics Dora Costa, […]

  • “How to do Differences-in-Differences” Workshop

    CCPR Seminar Room 4240 Public Affairs Building, Los Angeles, CA, United States

    CCPR will host an in-person workshop, "How to do Difference-in-Differences," with Pedro Sant'Anna.  Difference-in-Differences (DiD) is among the most popular strategies to identify causal effects in observational studies. The workshop will update you on this fast-moving literature and best practices with hands-on practice in R and Stata.   For Ph.D. students, post-docs, faculty (or anyone […]

  • Workshop: Homelessness M4H UCLA/VA

    CCPR Seminar Room 4240 Public Affairs Building, Los Angeles, CA, United States

    The goal of this workshop is to share information among a broad group of investigators who are employing mobile technology to study persons who have experienced homelessness. Projects discussed will include studies of homelessness among Veterans and non-Veterans. Presented projects will range from early-stage studies that are in progress to completed studies, including those that […]

  • Summer Institute in Computational Social Science

    CCPR Seminar Room 4240 Public Affairs Building, Los Angeles, CA, United States

    The purpose of the Summer Institute is to bring together graduate students, postdoctoral researchers, and early career faculty interested in computational social science. The Summer Institute is open to both social scientists (broadly conceived) and data scientists (broadly conceived).

  • Pascaline Dupas, Stanford University

    CCPR Seminar Room 4240 Public Affairs Building, Los Angeles, CA, United States

    The Incidence of Public Subsidies to Private Hospitals under Weak Governance: Evidence from India (joint with Radhika Jain)

    Expanding public health insurance and enlisting private agents for service delivery are common policy strategies to meet the goals of universal health coverage, but there is limited evidence from developing countries to inform their design. This paper, joint with Radhika Jain from Harvard School of Public Health, provides quantitative evidence on how insurance design affects program performance and incidence in the context of a government-funded health insurance program that aims to provide free care to 46 million people in Rajasthan, India. We exploit a policy-induced natural experiment, and use administrative claims linked to patient surveys, to provide the first large-scale evidence of private hospital behavior under public health insurance.

  • Jennifer Ahern, UC Berkeley

    CCPR Seminar Room 4240 Public Affairs Building, Los Angeles, CA, United States

    Leveraging big data to assess health effects of changes in physical and social environments, and policy and program implementation

    In the era of big data there are opportunities to answer policy-relevant health questions in ways that are timely and cost-efficient. This talk will describe coordination of health data resources for health monitoring and to address questions about the health effects of policies in California. Examples of health effect assessments, including those related to gun shows and the Mental Health Services Act (MHSA), will be presented.

  • Martha Bailey, University of Michigan

    CCPR Seminar Room 4240 Public Affairs Building, Los Angeles, CA, United States

    Prep School for Poor Kids: The Long-Run Impacts of Head Start on Human Capital and Economic Self-Sufficiency 

    This paper evaluates the long-run effects of Head Start using large-scale, restricted 2000-2013 Census-ACS data linked to date and place of birth in the SSA’s Numident file. Using the county-level rollout of Head Start between 1965 and 1980 and state age-eligibility cutoffs for school entry, we find that participation in Head Start is associated with increases in adult human capital and economic self-sufficiency, including a 0.29-year increase in schooling, a 2.1-percent increase in high-school completion, an 8.7-percent increase in college enrollment, and a 19-percent increase in college completion. These estimates imply sizable, longterm returns to investing in large-scale preschool programs.

  • Fernando Riosmena, CU Boulder

    CCPR Seminar Room 4240 Public Affairs Building, Los Angeles, CA, United States

    Individual and Neighborhood Vulnerability over the Latin American Immigrant Health Experience

    The state of Latino health seemingly defies the way in which the historical disadvantages faced by people of color in the United States get under the skin, and how place matters in reflecting or further reproducing these disparities. Hispanics –especially foreign-born individuals with lower socioeconomic statuses– have more favorable health than other race/ethnic groups –notably, U.S.-born non-Hispanic whites– in a limited but very important set of health outcomes such as cardiovascular function, some cancers and mortality across much of the life course.

    In this talk, I discuss the mechanisms/phenomena driving the Hispanic immigrant health advantage, which is likely tied to processes of self-selection as well as to protection and resilience likely operating particularly well in heavily-concentrated Latino neighborhoods and enclaves. Throughout, I present my empirical research aimed at disentangling self-selection processes from the protection that immigrants might draw from fellow neighborhood and/or community members. I further discuss these findings in the context of how immigrants adapt in the longer term: these advantages and resilience eventually erode as immigrants spend more time in the United States –as well as across immigrant generations– through a series of processes by which immigrant and Latino vulnerability become somatized.

    I conclude by speculating on the likely future of Latino and immigrant health, discussing how the resilience and vulnerability of Latino immigrants might evolve given recent major shifts U.S. immigration and social policies and practices, and due to important changes in the dynamics of migration between Mesoamerica and the United States.

  • Jan Van Bavel, University of Leuven

    CCPR Seminar Room 4240 Public Affairs Building, Los Angeles, CA, United States

    The Reversal of the Gender Gap in Education and Family Dynamics in Europe

    Although men tended to receive more education than women in the past, the gender gap in education has reversed in recent decades in most Western and many non-Western countries. In this talk, I will discuss the main results of a major research project that aimed to investigate the implications of the reversal of the gender gap in advanced education for family life across European countries. I highlight the results about union formation and assortative mating, discuss our findings about union stability as well as about husbands’ and wives’ relative earnings. Finally, I present some first results from research about implications for fertility behavior. To conclude, I will reflect on implications for conventional theories used in family sociology and demography.

  • Peter Bearman, Columbia University

    CCPR Seminar Room 4240 Public Affairs Building, Los Angeles, CA, United States

    Neural mechanisms lie behind the emergence of dyadic affective reciprocity and transitive closure in human groups

    This talk considers a set of findings from socializing cognitive social neuroscience that captures neural and social network data at multiple time points for a group of students who volunteered to organize workers in very difficult social situations on the 50th anniversary of Freedom Summer, in the Summer of Respect project. We identify a neural mechanism for the emergence of affective reciprocity, the building block of social solidarity. We show that we can predict from neural signatures who group members will like five months in the future. We extend this work to a discussion of transitivity, or balance. Time permitting, we discuss how a neural signature of self-enhancement (narcissism) predicts becoming peripheral in small groups, supporting the idea that there is "no I in team".

    Co-sponsored by the Sociology Department

  • Ushma Upadhyay, UC San Francisco

    CCPR Seminar Room 4240 Public Affairs Building, Los Angeles, CA, United States

    Measuring Reproductive Autonomy: Are the questions different for Adolescents and Young Adults?

    Ushma Upadhyay, PhD, MPH will discuss her previous work developing the Reproductive Autonomy Scale, which has been mainly used among adults. Her current work focuses on understanding reproductive empowerment among young people, and the development of a new psychometric measure of Sexual Health and Reproductive Empowerment for Young Adults (The SHREYA Scale).

    Co-sponsored with The Bixby Center 

  • Adrian Raftery, University of Washington

    CCPR Seminar Room 4240 Public Affairs Building, Los Angeles, CA, United States

    Bayesian Population Projections with Migration Uncertainty

    The United Nations recently issued official probabilistic population projections for all countries for the first time, using a Bayesian hierarchical modeling framework developed by our group at the University of Washington. These take account of uncertainty about future fertility and mortality, but not international migration. We propose a Bayesian hierarchical autoregressive model for obtaining joint probabilistic projections of migration rates for all countries, broken down by age and sex. Joint trajectories for all countries are constrained to satisfy the requirement of zero global net migration. We evaluate our model using out-of-sample validation and compare point projections to the projected migration rates from a persistence model similar to the UN's current method for projecting migration, and also to a state of the art gravity model. We also resolve an apparently paradoxical discrepancy between growth trends in the proportion of the world population migrating and the average absolute migration rate across countries. This is joint work with Jonathan Azose and Hana Ševčíková.

    Co-sponsored with the Center for Social Statistics 

  • Introducing the Brazilian Longitudinal Study of Aging (ELSI-Brasil)

    CCPR Seminar Room 4240 Public Affairs Building, Los Angeles, CA, United States

    Fabíola Bof de Andrade, Fundação Oswaldo Cruz, Instituto de Pesquisas René Rachou, Brazil & James Macinko, UCLA This seminar will provide an introduction to the newest study in the Health and Retirement Study (HRS) family, the Brazilian Longitudinal Study of Aging. The speakers will describe the overall study design and the main topics covered, highlight […]

  • Workshop: Getting your Computational Tools for Research

    CCPR Seminar Room 4240 Public Affairs Building, Los Angeles, CA, United States

    Instructor: Michael Tzen Title: Getting your Computational Tools for Research Location: October 25, 2018, 2:00-3:00 PM 4240 Public Affairs Building CCPR Seminar Room Content: We'll get you started on Github, Rstudio, Stata, and accessing Hoffman2 (UCLA's high performance computing cluster). Please RSVP below https://goo.gl/forms/W6hkM3bnOjfnYGJw2 slides

  • Erin Hartman, University of California Los Angeles

    CCPR Seminar Room 4240 Public Affairs Building, Los Angeles, CA, United States

    Covariate Selection for Generalizing Experimental Results

    Researchers are often interested in generalizing the average treatment effect (ATE) estimated in a randomized experiment to non-experimental target populations. Researchers can estimate the population ATE without bias if they adjust for a set of variables affecting both selection into the experiment and treatment heterogeneity.Although this separating set has simple mathematical representation, it is often unclear how to select this set in applied contexts. In this paper, we propose a data-driven method to estimate a separating set. Our approach has two advantages. First, our algorithm relies only on the experimental data. As long as researchers can collect a rich set of covariates on experimental samples, the proposed method can inform which variables they should adjust for. Second, we can incorporate researcher-specific data constraints. When researchers know certain variables are unmeasurable in the target population, our method can select a separating set subject to such constraints, if one is feasible. We validate our proposed method using simulations, including naturalistic simulations based on real-world data.

    Co-Sponsored with The Center for Social Statistics

  • UCLA CCPR