Deep Uncertainties in Sea-Level Rise and Storm Surge Projections: Implications for Coastal Flood Risk Management

Sea levels are rising in many areas around the world, posing risks to coastal communities and infrastructures. Strategies for managing these flood risks present decision challenges that require a combination of geophysical, economic, and infrastructure models. Previous studies have broken important new ground on the considerable tensions between the costs of upgrading infrastructure and the damages that could result from extreme flood events. However, many risk-based adaptation strategies remain silent on certain potentially important uncertainties, as well as the tradeoffs between competing objectives. Here, we implement and improve on a classic decision-analytical model (Van Dantzig 1956) to: (i) capture tradeoffs across conflicting stakeholder objectives, (ii) demonstrate the consequences of structural uncertainties in the sea-level rise and storm surge models, and (iii) identify the parametric uncertainties that most strongly influence each objective using global sensitivity analysis. We find that the flood adaptation model produces potentially myopic solutions when formulated using traditional mean-centric decision theory. Moving from a single-objective problem formulation to one with multiobjective tradeoffs dramatically expands the decision space, and highlights the need for compromise solutions to address stakeholder preferences. We find deep structural uncertainties that have large effects on the model outcome, with the storm surge parameters accounting for the greatest impacts. Global sensitivity analysis effectively identifies important parameter interactions that local methods overlook, and that could have critical implications for flood adaptation strategies.     

Toward General Principles for Resilience Engineering

Maintaining the performance of infrastructure-dependent systems in the face of surprises and unknowable risks is a grand challenge. Addressing this issue requires a better understanding of enabling conditions or principles that promote system resilience in a universal way. In this study, a set of such principles is interpreted as a group of interrelated conditions or organizational qualities that, taken together, engender system resilience. The field of resilience engineering identifies basic system or organizational qualities (e.g., abilities for learning) that are associated with enhanced general resilience and has packaged them into a set of principles that should be fostered. However, supporting conditions that give rise to such first-order system qualities remain elusive in the field. An integrative understanding of how such conditions co-occur and fit together to bring about resilience, therefore, has been less clear. This article contributes to addressing this gap by identifying a potentially more comprehensive set of principles for building general resilience in infrastructure-dependent systems. In approaching this aim, we organize scattered notions from across the literature. To reflect the partly self-organizing nature of infrastructure-dependent systems, we compare and synthesize two lines of research on resilience: resilience engineering and social-ecological system resilience. Although some of the principles discussed within the two fields overlap, there are some nuanced differences. By comparing and synthesizing the knowledge developed in them, we recommend an updated set of resilience-enhancing principles for infrastructure-dependent systems. In addition to proposing an expanded list of principles, we illustrate how these principles can co-occur and their interdependencies.     

DESPERATE TIMES CALL FOR DESPERATE MEASURES: GOVERNMENT SPENDING MULTIPLIERS IN HARD TIMES

We investigate state-dependent effects of fiscal multipliers and allow for endogenous sample splitting to determine whether the U.S. economy is in a slack state. When the endogenized slack state is estimated as the period of the unemployment rate higher than about 12%, the estimated cumulative multipliers are significantly larger during slack periods than nonslack periods and are above unity. We also examine the possibility of time-varying regimes of slackness and find that our empirical results are robust under a more flexible framework. Our estimation results point out the importance of the heterogenous effects of fiscal policy and shed light on the prospect of fiscal policy in response to economic shocks from the current COVID-19 pandemic. (JEL C32, E62, H20, H62)     

Pressure to Attend Therapy,Dyadic Adjustment,and Adverse Childhood Experiences: Direct and Indirect Effects on the Therapeutic Alliance in Couples Therapy

In this study we examine the role that pressure to attend therapy, dyadic adjustment, and adverse childhood experiences (ACEs) play in developing the therapeutic alliance. A total of 351 couples received treatment as usual at three family therapy training clinics. Participants rated predictor variables at intake and alliance at the fourth session. Results of a path analysis indicate that each partner's dyadic adjustment is directly associated with the quality of her or his own alliance. In addition, when male partners report more ACEs and pressure to attend treatment, their own alliance scores decrease. Additionally, when one partner reports feeling pressure to attend therapy, the other partner's alliance decreases. Finally, for males, there is an indirect effect of dyadic adjustment on alliance through pressure to attend therapy. These results suggest that clinicians should routinely assess relationship adjustment, how pressured each partner is feeling to attend treatment, and ACEs; as these may impact alliance quality.     

An Introduction and Illustration of Bayesian Modeling in Couple and Family Therapy Research

Bayesian modeling is becoming increasing popular as a method for data analyses in the social sciences and can move couple, marriage, and family therapy (C/MFT) research forward. Bayesian modeling helps researchers better understand the uncertainty of findings and incorporate previous research into analyses. Other benefits of Bayesian modeling are the straightforward interpretation of findings, high-quality inferences even with small samples (in combination with an informative prior), and the ability to work with complex data structures (observations nested in relationships and time points) which are common in C/MFT research. This article introduces the benefits of Bayesian modeling and provides an example of an Actor–Partner Interdependence Model using R. Information on how to conduct the same analyses using Stata and MPlus is provided in the Supplemental Information.     

The intergenerational transmission of income mobility between mothers and their young adult daughters: The mediating role of their daughters' educational attainment

This study examines whether the daughters' educational attainment mediates the intergenerational transmission of economic mobility between mothers and their young adult daughters. To create mother–daughter dyads, two data sets were combined: The National Longitudinal Survey of Youth 1979 (NLSY79) and the National Longitudinal Survey of Youth 79 for Children and Young Adults (NLSY79 CY). A total of 2,456 dyads were included for analysis. We used a mediation model to explore the relationship between mothers' income and their young adult daughters' income. Mothers' income was associated with their young adult daughters' educational attainment and income. The mediation model indicated partial mediation of the relationship between mothers' income and their young adult daughters' income via their young adult daughters' educational attainment. Addressing issues of income inequality among mothers may serve as a buffer against the low upward mobility across generations for their young adult daughters raised by low-income mothers. It is imperative to provide programs and financial assistance for mothers to bolster their income and thereby their daughters' educational attainment and income in young adulthood and therefore improve economic mobility from mothers to daughters.     

Multiracial Identity Integration: Perceptions of Conflict and Distance among Multiracial Individuals

This article examines how multiracial individuals negotiate their different and sometimes conflicting racial identities. Drawing from previous work on bicultural identity integration (see   Benet-Martínez & Haritatos, 2005 ), we proposed a new construct, multiracial identity integration (MII), to measure individual differences in perceptions of compatibility between multiple racial identities. We found that MII is composed of two independent subscales: racial distance that describes whether different racial identities are perceived as disparate, and racial conflict that describes whether different racial identities are perceived as in conflict. We also found that recalling positive multiracial experiences increased MII, while recalling negative multiracial experiences decreased MII. These findings have implications for understanding the psychological well-being of multiracial individuals, and the development of social policy and programs catered to this population.     

Relationships between Economics,Welfare, Social Network Factors,and Net Migration in the United States

By using economics, welfare and social network factors as frames of reference, this study aims to explore the relationship between these three factors and net migration to various US states. Adopting related variables collected from official aggregate data, this study first utilizes Logit Regression analysis to draw out seven variables that best explain net migration to the various states, then employs these variables in LISREL analyses to build a model explaining the factors influencing net migration to the various US states. Concretely, this research obtained the following findings: (1) the seven variables ‐‐ the average rate of net migrants of 2002–2005, Medicaid, federal aid, employment rate, non‐poverty population rate, and SSI subsidy ‐‐ all significantly affected (p < 0.01 or p < 0.05) net migration in 2006; (2) the main influences on net migration for the various states are, from highest to lowest, social network, economic, and welfare factors. More specifically, a better explanation is that, through the social network factor, economic and welfare factors exert an increased influence on the net number of migrants; and (3) as for the influence of social network factors on the number of net migrants, the social network factor for the previous year was found to best explain domestic migration flows, while the social network factor for the previous three‐to‐four years best explained international migration flows.