"Why does Costa Rica outperform the United States in life expectancy?  A tale of two inequality gradients"

Abstract: Costa Rica is among the few low or middle income countries with high quality adult vital statistics mortality data. We link these mortality records with census data to create a Costa Rican National Longitudinal Mortality Study, and compare adult mortality patterns to those in the United States. We find that mortality in the U.S. is 18% higher than in Costa Rica among adult men and 10% higher among middle-aged women, despite the several times higher income and health expenditures of the U.S. The U.S.’s underperformance is strongly linked to its much steeper socioeconomic (SES) gradients in health. Although the highest SES quartile in the U.S. has better mortality than the highest quartile in Costa Rica, U.S. mortality in its lowest quartile is markedly worse than in Costa Rica’s lowest quartile. Further examination of cause-specific mortality and risk factors suggest that these patterns are strongly related to behaviors leading to lung cancer and heart disease.

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"Kernel balancing: a weighting approach for causal inference and sample adjustment"

Abstract: When making causal inferences from observational data under the assumption of no unobserved confounders, matching and weighting estimators are used to adjust the joint distribution of observed covariates for treated and control units to be the same. Similarly, investigators often have data from an observed sample, which they wish to adjust to make more similar to a target sample or known population. However, existing weighting and matching approaches for both problems have important limitations: matches are generally not exact, and standard weighting approaches ensure that the observed sample is similar to the target sample/population only on a finite set of pre-specified moments. I introduce kernel balancing, first in the context of causal inference and then as a solution to the general sample-adjustment problem. The method works by taking a high-dimensional expansion of the observed covariates, and choosing weights on the control group (or observed sample) such that it has equal means to the treated group (or target sample) on this high-order expansion of the covariates. By using kernels, it is possible to choose an expansion such that all continuous functions of the covariates are linear in that expansion. This proves very desirable, as the weighting then ensures that any unspecified but plausibly important continuous function of the covariates (such as a ratio of two variables) will automatically have the same means for the two groups as well. I provide empirical examples, and show that this method also implies that a particular estimator of the entire multivariate density of covariates is the same for the two samples at every observed location in the covariate space. An R package implementing the procedure is available from the author.

"Immigrant Generation and Adolescent Childbearing in the United States"

Abstract: Despite recent declines, the teen birth rate in the United States is still the highest among high-income countries. Immigrant youth can be expected to increasingly shape US trends in adolescent childbearing as their share of the youth population continues to grow. About one in four US children has foreign-born parents currently, up from 6% in 1960; this share is projected to rise to one-third by 2050. In this study, I use data from the National Longitudinal Study of Adolescent Health (Add Health) to examine how the risk of early childbearing varies by immigrant generation; to what extent generational variation reflects discrepancies in the timing of sexual onset (versus post onset factors); and what family, neighborhood, and individual-level social factors underlie generational differences. I will also describe a new data collection project called the mDiary Study of Adolescent Relationships, which pairs a year-long diary study with an ongoing birth cohort study to increase understanding of the content and quality of teen partnerships over time, and of the childhood precursors and health and developmental consequences of teen relationship behavior.

"Beyond LATE: Economic Choices and the Identification of Multiple Treatment Effects "

Abstract: “Monotonicity” refers to a condition in choice models with instrumental variables in which a local variation of an instrument shifts all agents toward or against a choice. This paper presents a useful framework to investigate the role of monotonicity in the identification of causal effects in multiple choice models with categorical instrumental variables.  I first examine a new monotonicity condition that applies to unordered choice models with multiple treatments.  Like its analogous property in the binary choice model, I show that unordered monotonicity imply and is implied by additive separability in observables and unobservables in choice equations.  I show that unordered monotonicity may arise from preference properties of choice behavior. I then exemplify the use of preference properties to identify causal effects in choice models where monotonicity does not hold. I show that identification and equivalence results flow from simple properties of binary matrices.

"The Shifting Structure of Intragenerational Inequality"

Abstract: Traditional stratification and inequality research often treats individuals as single points of observation in the stratification system. This paper extends current scholarship on economic inequality by invoking the life course perspective to study the intragenerational pattern of wage inequality, focusing particularly on how its structure has changed across cohorts. Using over 40 years of national representative data from CPS and PSID, I found that inequality increases over the life course for all cohorts born between 1941 and 1970. Further, cross-cohort comparison reveals that the amount of intragenerational growth of inequality has increased from earlier to later cohorts, suggesting that the labor market plays a more important role in generating inequality in recent years. Microlevel decomposition analysis suggests that the relative importance of the underlying mechanisms for intragenerational inequality has also shifted across cohorts, with a growing amount of intragenerational growth of inequality attributable to education-based cumulative advantage and residual inequality.