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Stephenson et al find "alarmingly high rates" of intimate partner violence among male couples

Social Science One making available data that "may rival the total amount that currently exists in the social sciences"

Stafford's findings on gender gap in children's allowances suggest entrenched nature of wage gap

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Student volunteers needed for IAPHS Annual Meeting in Washington, DC, Oct 3-5. Register July 23.

West et al. examine HS seniors' nonmedical use of prescription stimulants to boost study

Seefeldt promoted to associate professor of social work, associate professor of public policy

Martha Bailey elected to the Board of Officers of the Society of Labor Economists

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Graph of treatment effects

Analyzing response variability to common treatment

1/27/2014 feature story

Yu Xie, Jeffrey Smith, and Daniel Almirall develop statistical methods to estimate heterogeneous treatment effects and a set of tools to help analyze these effects in demographic research.

More Information.

Yu Xie
Daniel Almirall

Project Information:

Heterogeneous Treatment Effects in Demographic Research

One essential feature common to all demographic phenomena is variability across units of analysis. Individuals differ greatly not only in attributes and outcomes of interest to social and behavioral scientists, but also in how they respond to a common treatment, intervention, or stimulation. We call the second type of variability 'heterogeneous treatment effects.' The proposed research assembles an interdisciplinary team from sociology, economics, statistics, and demography to investigate the consequences of and methodological approaches to heterogeneous treatment effects. The proposed research has five specific aims: 1. It will demonstrate, with combined observational and experimental data, heterogeneous treatment effects in demographic research. 2. It will develop statistical methods for estimating heterogeneous treatment effects using the instrumental variable approach with quasi-experimental data. 3. It will further demonstrate the usefulness of estimating heterogeneous treatment effects in observational data with propensity score methods, especially with applications to studies of the impact of family-level shocks on children?s psychosocial skills. 4. It will demonstrate, through micro-level simulations, that in the presence of heterogeneous treatment effects, the use of standard statistical methods may give rise to treatment effect estimates with compositional biases. 5. It will develop a set of diagnostic and analytical tools that will help researchers and practitioners to analyze heterogeneous treatment effects in demographic research.

Yu Xie, Jeffrey A. Smith, Hongwei Xu

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