The Impact of The Government’s COVID-19 Restriction Policies On Human Mobility: Evidence From The United States

Purpose – In responding to COVID-19, governments around the world have imposed various restrictions with different levels of success. One important aspect of pandemic control is the willingness of individuals to stay home when possible. The purpose of this paper is to study the impact of government restrictions on human mobility in the United StatesMethodology/approach – Structural equation modelling is used to explore the issue. First, we use path regression analysis and factor analysis to identify the main factors that influence mobility. Second, we use total effect decomposition to investigate the deeper relationship between government restrictions and human mobility.Finding – Two important findings are revealed First, the economic environment is the fundamental and direct factor affecting human mobility. There is a significant negative relationship between economic environment and human mobility, meaning that where economic conditions are bad mobility is greater. Second, government restrictions and the scale of the pandemic do not directly affect human mobility. Government restriction indirectly influences human mobility through economic environment as a mediating variable. Therefore, the economic environment has a significant mediating effect.Originality/value – Existing literature lacks research on the mediating effect between government restrictions and human mobility. This paper provides new empirical evidence for the research topic by studying the mediating effect between government restrictions and human mobility. This provides policymakers with a more detailed picture of the processes through which policies operate.

Anomaly resolution via decomposition

In this paper, we apply decomposition to orbifolds with quantum symmetries to resolve anomalies. Briefly, it has been argued by, e.g. Wang–Wen–Witten, Tachikawa that an anomalous orbifold can sometimes be resolved by enlarging the orbifold group so that the pullback of the anomaly to the larger orbifold group is trivial. For this procedure to resolve the anomaly, one must specify a set of phases in the larger orbifold, whose form is implicit in the extension construction. There are multiple choices of consistent phases, which give rise to physically distinct resolutions. We apply decomposition, and find that theories with enlarged orbifold groups are equivalent to (disjoint unions of copies of) orbifolds by nonanomalous subgroups of the original orbifold group. In effect, decomposition implies that enlarging the orbifold group is equivalent to making it smaller. We provide a general conjecture for such descriptions, which we check in a number of examples.

University Applicants from Refugee Backgrounds and the Intention to Drop Out from Pre‐Study Programs: A Mixed‐Methods Study

The mixed‐methods project WeGe investigates key factors for refugees’ integration into pre‐study programs and conditions for successful transitions to higher education institutions (HEIs). In this article, we first examine the dropout intentions of international students and refugee students participating in formal pre‐study programs at German HEIs to disclose both barriers and resources. We use insights from migration research to extend theoretical student dropout models and analyse novel data from a quantitative survey with international and refugee students in pre‐study programs. Our findings show that refugee students intend to drop out from pre‐study programs more often than other international students. This difference disappears when other characteristics are controlled for. Effect decomposition shows that financial problems and perceived exclusion are driving dropout intentions of refugee students, whereas German language use in everyday life and a strong connection to the prospective field of study function as a resource and reduce the dropout risk. Depending on the reference group, deficits or resources of refugee students become apparent. This result suggests that refugees should be addressed as a student group in their own right. As a second step, we analyse qualitative expert interviews to reconstruct the staff’s perspectives on barriers and resources of refugee students to analyse how the driving factors of dropout intentions are represented in their knowledge. In particular, we show if and how this knowledge is used to address refugees and to develop inclusive educational concepts within pre‐study programs.

Exact parametric causal mediation analysis for a binary outcome with a binary mediator

With reference to causal mediation analysis, a parametric expression for natural direct and indirect effects is derived for the setting of a binary outcome with a binary mediator, both modelled via a logistic regression. The proposed effect decomposition operates on the odds ratio scale and does not require the outcome to be rare. It generalizes the existing ones, allowing for interactions between both the exposure and the mediator and the confounding covariates. The derived parametric formulae are flexible, in that they readily adapt to the two different natural effect decompositions defined in the mediation literature. In parallel with results derived under the rare outcome assumption, they also outline the relationship between the causal effects and the correspondent pathway-specific logistic regression parameters, isolating the controlled direct effect in the natural direct effect expressions. Formulae for standard errors, obtained via the delta method, are also given. An empirical application to data coming from a microfinance experiment performed in Bosnia and Herzegovina is illustrated.

Sensitivity Analysis for Pretreatment Confounding With Multiple Mediators

The causal mediation literature has developed techniques to assess the sensitivity of an inference to pretreatment confounding, but these techniques are limited to the case of a single mediator. In this article, we extend sensitivity analysis to possible violations of pretreatment confounding in the case of multiple mediators. In particular, we develop sensitivity analyses under three alternative approaches to effect decomposition: (1) jointly considered mediators, (2) identifiable direct and indirect paths, and (3) interventional analogues effects. With reasonable assumptions, each approach reduces to a single procedure to assess sensitivity in the presence of simultaneous pre- and posttreatment confounding. We demonstrate our sensitivity analysis techniques with a framing experiment that examines whether anxiety mediates respondents’ attitudes toward immigration in response to an information prompt.

Longitudinal Mediation Analysis Using Natural Effect Models

Mediation analysis is concerned with the decomposition of the total effect of an exposure on an outcome into the indirect effect, through a given mediator, and the remaining direct effect. This is ideally done using longitudinal measurements of the mediator, which capture the mediator process more finely. However, longitudinal measurements pose challenges for mediation analysis, because the mediators and outcomes measured at a given time point can act as confounders for the association between mediators and outcomes at a later time point; these confounders are themselves affected by the prior exposure and outcome. Such posttreatment confounding cannot be dealt with using standard methods (e.g., generalized estimating equations). Analysis is further complicated by the need for so-called cross-world counterfactuals to decompose the total effect. This work addresses these challenges. In particular, we introduce so-called natural effect models, which parameterize the direct and indirect effect of a baseline exposure with respect to a longitudinal mediator and outcome. These can be viewed as a generalization of marginal structural mean models to enable effect decomposition. We introduce inverse probability weighting techniques for fitting these models, adjusting for (measured) time-varying confounding of the mediator-outcome association. Application of this methodology uses data from the Millennium Cohort Study, a longitudinal study of children born in the United Kingdom between September 2000 and January 2002.