The Trans-Pacific Labor Seminar is part of a conference series that brings together Japanese and U.S. economics scholars. The idea is to foster trans-pacific exchange and collaboration, and usually half of the participants are from Japan and half are U.S. based. The conference is co-sponsored by the International Institute, the Terasaki Center for Japanese Studies, […]
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
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
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 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 […]
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
Instructor: Michael Tzen Title: Practical Survey Analysis Location: November 15, 2018, 2:00-3:00 PM 4240 Public Affairs Building CCPR Seminar Room Content: We'll walk thru key steps of a data analysis involving a complex survey design. Please RSVP below https://goo.gl/forms/evIP7G8PN0UBG7x72 slides rscript
Organizers: Manisha Shah and Daniel Posner November 26, 2018 4240 Public Affairs Building EASST invites East African researchers to apply for a 4-month fellowship at UC Berkeley to build skills in rigorous social science research and impact evaluation–these are the fellows who won this fellowship. Each scholar will present on the following topics; Apollo Maima: […]
Information session on data available at the Census Research Data Center (RDC) at UCLA and how to access it: Data availability of five types of confidential government data available in the RDC Business Data (Economic Census, Annual Survey of Manufactures, Longitudinal Foreign Trade Transactions Database, Commodity Flow Survey, Business Research and Development and Innovation Survey) […]
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
"Replication and Reproducibility in Social Sciences and Statistics: Context, Concerns, and Concrete Measures"
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
Instructor: Michael Tzen Title: Merging Entities: Deterministic, Approximate, & Probabilistic Location: January 31, 2019, 2:00-3:00 PM 4240 Public Affairs Building CCPR Seminar Room Content: Combining information from different groups is a fundamental procedure in the data analysis pipeline. Using NBA and NCAA data, we will walk through deterministic, approximate, and probabilistic methods to merge entities […]
Organizer: Paavo Monkkonen February 8, 2019 4240 Public Affairs Building The Luskin Latin American Cities Initiative ( https://ciudades.luskin.ucla.edu/ ) is hosting a workshop on urban planning this Friday, February 8th from 10:00am to 2:00pm. The main objective of the workshop is to compare the roles of Federal and State entities in local planning efforts both […]
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
Workshop: Tips for Success in Publishing in Peer Review Journals: An Editor's Perspective Presentation by Prof. Gilbert Gee Prof. Gee Dr. Gee is currently the Editor of the Journal of Health and Social Behavior. He has also been a guest editor for Child Development, Asian American and Pacific Islander Nexus Journal, and the Asian American Journal of Psychology.
