More Information: http://www.international.ucla.edu/lai/event/13239#.WuoL1C7waUl Household surveys in Mexico include only limited information on race and ethnicity. The identification of racial and ethnic characteristics beyond membership to indigenous populations has been historically […]
"Meeting the Challenge of Homelessness"
Ending homelessness and serving the needs of our most vulnerable individuals and families is possible, but it requires sustained effort. Culhane will kick off the week by reviewing the national situation, including progress and continued hurdles. He will also describe unique challenges for cities like LA where many homeless are unsheltered.
"The Promise of Integrated Data Systems for Social Science Research"
Abstract: Integrated Data Systems (IDS) linking administrative, public agency data hold great promise for rapid and low-cost implementation and evaluation of homeless initiatives. Culhane will review the legal, ethical, scientific and economic challenges of interagency data sharing, as well as systematic efforts including policy reform and inter-agency collaboration to overcome these challenges. Finally, he will review important new IDS initiatives in LA County and California.
Organizers: Patrick Heuveline and Geoff Robinson May 30th, 2018 4240 Public Affairs Building Senior seminar will screen film titled “Angkar”. This is a joint effort between Prof. Patrick Heuveline, Sociology […]
"The Role of Parental Wealth & Income in Financing Children's College Attendance & Its Consequences"
Abstract: This paper examines the influence of parental wealth and income on their children's college attendance and parents' financial support for it and whether the latter affects the subsequent levels of indebtedness of parents and their children. We use data from the PSID, especially data in the 2013 Rosters and Transfers Module on the incidence and amounts of parents' financial support for their children's college. To instrument for the potential endogeneity of parental housing wealth and income on these decisions, we use changes in parents' local housing and labor market conditions. We find that increases in both parents' housing wealth and income increase the likelihood of their children attending college through the effect of parents' financial support. This parental financing of college leads to parents carrying more debt, but their children having no greater student loan debt after graduation. We also find that parental financing of their child's college education significantly increases the probability that the child actually graduates from college.
"Early Estimates of the Effects of Public School Shootings in California on California Public Schools"
Abstract: I employ data from California public schools covering years 1991 to 2017 with data on public school shootings in the state of California over the same period to study the effects of school shootings on schools. This project aims to understand how dropout, enrolment, and achievement measures respond to school shootings. A secondary objective includes discerning whether fatal and non-fatal shootings have differential effects on schools and student outcomes. I will present early results, and I welcome helpful comments and criticism.
The Trouble with Pink and Blue, Gender expression, stigma, and health among U.S. children and adolescents
Abstract: Dr. Gordon will offer a conceptual model for understanding gender expression and health and illustrate this model with examples from recent research on gender nonconformity, school-based violence and bullying, and selected health outcomes in samples of U.S. high school students and young adults.
2018-2019 CCPR Welcome and Introductions
Please come join us to learn all about the California Center for Population Research!
Professors Jennie Brand, Patrick Heuveline and Hiram Beltran-Sanchez will be presenting.
This will be the kick-off event for the start of the upcoming 2018-2019 CCPR Seminar Series.
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
FP3.0: Increasing the uptake of Long-Acting Reversible Contraceptives (LARCs) among adolescent females and young women in Cameroon
In sub-Saharan Africa, 25% of teenagers have started childbearing (ICF, 2015). While young women describe many of these births as planned and intentional, women under the age of 20 also have the greatest percentage of mistimed/unintended pregnancies compared to all other age groups. For example, in Cameroon, more than 30% of the births to this group were unwanted or wanted later (DHS 2011). Low age at first birth has a significant impact on the spacing of births and timing of future pregnancies. It may also reduce accumulation of human capital for both the mother and the child.
Co-sponsored with the UCLA Luskin Senior Fellows Speaker Series
Social Science in the Age of Genomics
The cost of genetic information has been dropping at a rate faster than that of Moore's law in microcomputing. As a result, the science of genetic prediction has improved by leaps and bounds in recent years, and with it has emerged a novel field: sociogenomics. Sociogenomics seeks to integrate genetic and environmental information to obtain a more robust, complete picture of the causes of human behavior. This talk will highlight some recent examples of sociogenomic research, touching upon issues such as adolescent peer effects, racial discrimination, assortative mating, and fertility patterns. The talk will conclude by discussing the social and policy implications of genetic prediction.
Co-sponsored with the Public Policy and Applied Social Science Seminar Series
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 […]
Making Sense of Sensitivity: Extending Omitted Variable Bias
We extend the omitted variable bias framework with a suite of tools for sensitivity analysis in regression models that: (i) does not require assumptions about the treatment assignment nor the nature of confounders; (ii) naturally handles multiple confounders, possibly acting non-linearly; (iii) exploits expert knowledge to bound sensitivity parameters; and, (iv) can be easily computed using only standard regression results. In particular, we introduce two novel sensitivity measures suited for routine reporting. The robustness value describes the minimum strength of association unobserved confounding would need to have, both with the treatment and the outcome, to change the research conclusions. The partial R2 of the treatment with the outcome shows how strongly confounders explaining all the residual outcome variation would have to be associated with the treatment to eliminate the estimated effect. Next, we offer graphical tools for elaborating on problematic confounders, examining the sensitivity of point estimates, t-values, as well as “extreme scenarios”. Finally, we describe problems with a common “benchmarking” practice and introduce a novel procedure to instead formally bound the strength of confounders based on comparison to observed covariates. We apply these methods to a running example that estimates the effect of exposure to violence on attitudes toward peace.
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
Immigration Restrictions as Active Labor Market Policy: Evidence from the Mexican Bracero Exclusion
An important class of active labor market policy has received little impact evaluation: immigration barriers intended to raise wages and employment by shrinking labor supply. Theories of endogenous technical advance raise the possibility of limited or even perverse impact. We study a natural policy experiment: the exclusion of almost half a million Mexican bracero farm workers from the United States to improve farm labor market conditions. With novel labor market data we measure state-level exposure to exclusion and model the absent changes in technology or crop mix. We fail to reject zero labor market impact, inconsistent with this model.
Co-sponsored with the Public Policy and Applied Social Sciences Seminar
Abortion utilization in Ohio’s changing legislative context
Changes in Ohio, most notably legislation and policy changes since 2011, likely have impacted women’s access to abortion. Many abortion clinics in Ohio have closed in the past seven years, and several others are currently engaged in litigation and are at risk of closure. Clinic closures influence the distance that women travel when seeking abortion. Coupled with the impact of an Ohio law that mandates a 24-hour waiting period after a woman’s initial abortion consultation, loss of a nearby clinic may put abortion out of reach for many women. Other legislation limits where abortions can and cannot be performed and to what gestational stage abortions are performed. This presentation will provide preliminary findings about population-level shifts in abortion utilization, with special attention to change over time, geographic variation, and groups of women who may be underserved.
Co-sponsored with The Bixby Center
Internal vs. external validity in studies with incomplete populations
Researchers working with administrative data rarely have access to the entire universe of units they need to estimate effects and make statistical inferences. Examples are varied and come from different disciplines. In social program evaluation, it is common to have data on all households who received the program, but only partial information on the universe of households who applied or could have applied for the program. In studies of voter turnout, information on the total number of citizens who voted is usually complete, but data on the total number of voting-eligible citizens is unavailable at low levels of aggregation. In criminology, information on arrests by race is available, but the overall population that could have potentially been arrested is typically unavailable. And in studies of drug overdose deaths, we lack complete information about the full population of drug users.
In all these cases, a reasonable strategy is to study treatment effects and descriptive statistics using the information that is available. This strategy may lack the generality of a full-population study, but may nonetheless yield valuable information for the included units if it has sufficient internal validity. However, the distinction between internal and external validity is complex when the subpopulation of units for which information is available is not defined according to a reproducible criterion and/or when this subpopulation itself is defined by the treatment of interest. When this happens, a useful approach is to consider the full range of conclusions that would be obtained under different possible scenarios regarding the missing information. I discuss a general strategy based on partial identification ideas that may be helpful to assess sensitivity of the partial-population study under weak (non-parametric) assumptions, when information about the outcome variable is known with certainty for a subset of the units. I discuss extensions such as the inclusion of covariates in the estimation model and different strategies for statistical inference.
Co-sponsored with the Political Science Department, Statistics Department and the Center for Social Statistics
Matching Methods for Causal Inference with Time-Series Cross-Section Data
Matching methods aim to improve the validity of causal inference in observational studies by reducing model dependence and offering intuitive diagnostics. While they have become a part of standard tool kit for empirical researchers across disciplines, matching methods are rarely used when analyzing time-series cross-section (TSCS) data, which consist of a relatively large number of repeated measurements on the same units.
We develop a methodological framework that enables the application of matching methods to TSCS data. In the proposed approach, we first match each treated observation with control observations from other units in the same time period that have an identical treatment history up to the pre-specified number of lags. We use standard matching and weighting methods to further refine this matched set so that the treated observation has outcome and covariate histories similar to those of its matched control observations. Assessing the quality of matches is done by examining covariate balance. After the refinement, we estimate both short-term and long-term average treatment effects using the difference-in-differences estimator, accounting for a time trend. We also show that the proposed matching estimator can be written as a weighted linear regression estimator with unit and time fixed effects, providing model-based standard errors. We illustrate the proposed methodology by estimating the causal effects of democracy on economic growth, as well as the impact of inter-state war on inheritance tax. The open-source software is available for implementing the proposed matching methods.
Co-sponsored with the Political Science Department, Statistics Department and the Center for Social Statistics
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
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
