The 2020 U.S. Census will, for the first time since 1950, ask about residents’ citizenship status.The effect of doing so on census completion across different racial/ethnic groups is, however, unknown. Leveraging a survey experiment (n = 9,035 respondents) we are the first to assess the causal effect of this question change. We find that asking about citizenship status significantly increases the percent of questions skipped, with particularly strong effects among Hispanics, and makes respondents less likely to report having members of their household who are of Hispanic ethnicity. Although our study is not designed to address household size effects, when we extrapolate to the general population, our results imply that asking about citizenship will reduce the number of Hispanics reported in the 2010 Census by approximately 4.2 million, or around 8.4 percent of the 2010 Hispanic population.

Introduction: The major stressors associated with the COVID‐19 pandemic provide an opportunity to understand the extent to which protective factors against depression may exhibit gender‐specificity.

Method: This study examined responses from multiple waves of a 50 states non‐ probability internet survey conducted between May 2020 and January 2021. Participants completed the PHQ‐9 as a measure of depression, as well as items characterizing social supports. We used logistic regression models with population reweighting to examine association between absence of even mild depressive symptoms and sociodemographic features and social supports, with interaction terms and stratification used to investigate sex‐specificity.

Results: Among 73,917 survey respondents, 31,199 (42.2%) reported absence of mild or greater depression—11,011/23,682 males (46.5%) and 20,188/50,235 (40.2%) females. In a regression model, features associated with greater likelihood of depression‐resistance included at least weekly attendance of religious services (odds ratio [OR]: 1.10, 95% confidence interval [CI]: 1.04–1.16) and greater trust in others (OR: 1.04 for a 2‐unit increase, 95% CI: 1.02–1.06), along with level of social support measured as number of social ties available who could provide care (OR: 1.05, 95% CI: 1.02–1.07), talk to them (OR: 1.10, 95% CI: 1.07–1.12), and help with employment (OR: 1.06, 95% CI: 1.04–1.08). The first two features showed significant interaction with gender (p < .0001), with markedly greater protective effects among women.

Conclusion: Aspects of social support are associated with diminished risk of major depressive symptoms, with greater effects of religious service attendance and trust in others observed among women than men.

This study presents the results of a quasi-experiment (N= 307) conducted over the course of 10 weeks in Spring of 2016 to assess the effectiveness of a game platform designed to facilitate political engagement, attention, efficacy, knowledge, and participation among college students. Results indicate positive effects of gameplay on several key dimensions of political engagement, including voter registration, virtual political participation (following a candidate on Twitter, liking a candidate on Facebook, and watching debates), and consumption of public affairs information (including National Public Radio, non-NPR political talk radio, and online news aggregator sites). Additionally, gameplay provided significantly greater benefits to students with the lowest rates of political knowledge at baseline. Overall, participants reported high rates of game satisfaction, with 79% of participants reporting being very to somewhat pleased if they were asked to play the game again. These results are discussed in terms of the implications for civics education, pedagogy, and political engagement among young people.

Social and medical scientists are often concerned that the external validity of experimental results may be compromised because of heterogeneous treatment effects. If a treatment has different effects on those who would choose to take it and those who would not, the average treatment effect estimated in a standard randomized controlled trial (RCT) may give a misleading picture of its impact outside of the study sample. Patient preference trials (PPTs), where participants’ preferences over treatment options are incorporated in the study design, provide a possible solution. In this paper, we provide a systematic analysis of PPTs based on the potential outcomes framework of causal inference. We propose a general design for PPTs with multi-valued treatments, where participants state their pre- ferred treatments and are then randomized into either a standard RCT or a self-selection condition. We derive nonparametric sharp bounds on the average causal effects among each choice-based sub- population of participants under the proposed design. We also propose a sensitivity analysis for the violation of the key ignorability assumption sufficient for identifying the target causal quantity. The proposed design and methodology are illustrated with an original study of partisan news media and its behavioral impact.