Integrating Operational and Organizational Aspects in Interdependent Infrastructure Network Recovery

Managing risk in infrastructure systems implies dealing with interdependent physical networks and their relationships with the natural and societal contexts. Computational tools are often used to support operational decisions aimed at improving resilience, whereas economics‐related tools tend to be used to address broader societal and policy issues in infrastructure management. We propose an optimization‐based framework for infrastructure resilience analysis that incorporates organizational and socioeconomic aspects into operational problems, allowing to understand relationships between decisions at the policy level (e.g., regulation) and the technical level (e.g., optimal infrastructure restoration). We focus on three issues that arise when integrating such levels. First, optimal restoration strategies driven by financial and operational factors evolve differently compared to those driven by socioeconomic and humanitarian factors. Second, regulatory aspects have a significant impact on recovery dynamics (e.g., effective recovery is most challenging in societies with weak institutions and regulation, where individual interests may compromise societal well‐being). And third, the decision space (i.e., available actions) in postdisaster phases is strongly determined by predisaster decisions (e.g., resource allocation). The proposed optimization framework addresses these issues by using: (1) parametric analyses to test the influence of operational and socioeconomic factors on optimization outcomes, (2) regulatory constraints to model and assess the cost and benefit (for a variety of actors) of enforcing specific policy‐related conditions for the recovery process, and (3) sensitivity analyses to capture the effect of predisaster decisions on recovery. We illustrate our methodology with an example regarding the recovery of interdependent water, power, and gas networks in Shelby County, TN (USA), with exposure to natural hazards.     

On Optimality of Bayesian Wavelet Estimators

Abstract.  We investigate the asymptotic optimality of several Bayesian wavelet estimators, namely, posterior mean, posterior median and Bayes Factor, where the prior imposed on wavelet coefficients is a mixture of a mass function at zero and a Gaussian density. We show that in terms of the mean squared error, for the properly chosen hyperparameters of the prior, all the three resulting Bayesian wavelet estimators achieve optimal minimax rates within any prescribed Besov space     for p  ≥ 2. For 1 ≤  p  < 2, the Bayes Factor is still optimal for (2 s +2)/(2 s +1) ≤  p  < 2 and always outperforms the posterior mean and the posterior median that can achieve only the best possible rates for linear estimators in this case.     

Have Labor Market Reforms at the Turn of the Millennium Changed the Job and Employment Durations of new Entrants?

Many European countries during the 1990s and early 2000s tried to raise labor market flexibility by relaxing their employment protection legislation. This paper investigates whether the reforms have influenced job and employment stability for labor market entrants in Germany and Italy. Duration models are estimated using comparable administrative data for the two countries. The results provide, to some extent, evidence of a decrease in job stability. However, this is not accompanied by a compensating increase in employment stability during the first 3 years after labor market entry.     

Serious case reviews: The lived experience of Black children

Despite the many high‐profile Black child deaths in England, race as a factor remains a largely underexplored factor of serious case reviews (SCRs). Evidence from analysis of SCRs indicates that race receives limited attention, or is virtually absent. Given that the main function of SCRs is to provide opportunities for learning lessons to improve practice, the way in which issues of race and culture may influence child protection processes for Black children is therefore of critical importance. In this article, we employ content analysis to examine the extent that race and cultural factors are considered in SCRs involving Black children. It is argued that race is often an important factor influencing Black children's experiences of abuse and neglect, as well as their encounters in the child protection system. This article therefore poses two key questions: (a) What questions are asked about race, ethnicity, and culture in SCRs concerning Black children? (b) How did the SCRs extract lessons to be learnt for improving practice to safeguard Black children? By extending the analysis of race and ethnicity in SCRs, this article furthers our understandings of the needs of Black children in the child protection system.     

Happy People Have Children: Choice and Self-Selection into Parenthood

There is mixed evidence in the existing literature on whether children are associated with greater subjective well-being, with the correlation depending on which countries and populations are considered. We here provide a systematic analysis of this question based on three different datasets: two cross-national and one national panel. We show that the association between children and subjective well-being is positive only in developed countries, and for those who become parents after the age of 30 and who have higher income. We also provide evidence of a positive selection into parenthood, whereby happier individuals are more likely to have children.