Calibrated predictions for multivariate competing risks models

Prediction models for time-to-event data play a prominent role in assessing the individual risk of a disease, such as cancer. Accurate disease prediction models provide an efficient tool for identifying individuals at high risk, and provide the groundwork for estimating the population burden and cost of disease and for developing patient care guidelines. We focus on risk prediction of a disease in which family history is an important risk factor that reflects inherited genetic susceptibility, shared environment, and common behavior patterns. In this work family history is accommodated using frailty models, with the main novel feature being allowing for competing risks, such as other diseases or mortality. We show through a simulation study that naively treating competing risks as independent right censoring events results in non-calibrated predictions, with the expected number of events overestimated. Discrimination performance is not affected by ignoring competing risks. Our proposed prediction methodologies correctly account for competing events, are very well calibrated, and easy to implement.     

Wage Dispersion and Equilibrium Search Models: Some Evidence from Italy

This paper provides a structural estimation of an equilibrium search model with on‐the‐job search and heterogeneity in firms' productivities using a sample of Italian male workers. Results indicate that arrival rates of offers for workers are higher when unemployed than when employed and firms exploit their monopsony power when setting wages. As a result, workers earn far less than their marginal product. The model is then used to study regional labour market differentials in Italy. Wide variation in frictional transition parameters across areas helps to explain persistent unemployment and wage differentials.     

The Virtual Image Technology,Media, and Construction of the Visual Reality

We are experiencing a passage from the mostly representational function of images (the traditional media of photography, cinematography, television) to constructed images made with new technologies, especially computer graphics and virtual reality. We are left with images as formal abstractions, that make us think of a mediation of cognitive/ experiential models of reality, rather than a simple representative reproduction. With virtual reality the image presents itself more and more as reality, stressing its constructional statute. The image is no longer a map that orients the mental ways, but a real territory to explore.     

RETHINKING THE EFFECT OF IMMIGRATION ON WAGES

This paper calculates the effects of immigration on the wages of native US workers of various skill levels in two steps. In the first step we use labor demand functions to estimate the elasticity of substitution across different groups of workers. Second, we use the underlying production structure and the estimated elasticities to calculate the total wage effects of immigration in the long run. We emphasize that a production function framework is needed to combine own‐group effects with cross‐group effects in order to obtain the total wage effects for each native group. In order to obtain a parsimonious representation of elasticities that can be estimated with available data, we adopt alternative nested‐CES models and let the data select the preferred specification. New to this paper is the estimate of the substitutability between natives and immigrants of similar education and experience levels. In the data‐preferred model, there is a small but significant degree of imperfect substitutability between natives and immigrants which, when combined with the other estimated elasticities, implies that in the period from 1990 to 2006 immigration had a small effect on the wages of native workers with no high school degree (between 0.6% and +1.7%). It also had a small positive effect on average native wages (+0.6%) and a substantial negative effect (−6.7%) on wages of previous immigrants in the long run.     

IMMIGRATION,JOBS, AND EMPLOYMENT PROTECTION: EVIDENCE FROM EUROPE BEFORE AND DURING THE GREAT RECESSION

In this paper we analyze the impact of immigrants on the type and quantity of native jobs. We use data on 15 Western European countries during the 1996–2010 period. We find that immigrants, by taking manual‐routine type of occupations pushed natives towards more “complex” (abstract and communication) jobs. This job upgrade was associated to a 0.7% increase in native wages for a doubling of the immigrants' share. These results are robust to the use of an IV strategy based on past settlement of immigrants across European countries. The job upgrade slowed but did not come to a halt during the Great Recession. We also document the labor market flows behind it: the complexity of jobs offered to new native hires was higher relative to the complexity of lost jobs. Finally, we find evidence that such reallocation was larger in countries with more flexible labor laws.     

DO TRANSFER TAXES REDUCE INTERGENERATIONAL TRANSFERS?

We estimate the effect of taxes on intergenerational transfers by exploiting a sequence of Italian reforms culminating with the abolishment of transfer taxes. We use the Surveys of Household Income and Wealth from 1993 to 2006, which have data on real estate transfers, and information on potential donors and recipients. Difference‐in‐differences estimates indicate that the abolition of transfer taxes increases the probability of high‐wealth donors making a transfer by two percentage points and increases the area transferred by 9.3 square meters relative to poorer donors.     

On Bayesian learning from Bernoulli observations

We provide a reason for Bayesian updating, in the Bernoulli case, even when it is assumed that observations are independent and identically distributed with a fixed but unknown parameter _θ0${\theta }_0$. The motivation relies on the use of loss functions and asymptotics. Such a justification is important due to the recent interest and focus on Bayesian consistency which indeed assumes that the observations are independent and identically distributed rather than being conditionally independent with joint distribution depending on the choice of prior.     

Agent‐Based Recovery Model for Seismic Resilience Evaluation of Electrified Communities

In this article, an agent‐based framework to quantify the seismic resilience of an electric power supply system (EPSS) and the community it serves is presented. Within the framework, the loss and restoration of the EPSS power generation and delivery capacity and of the power demand from the served community are used to assess the electric power deficit during the damage absorption and recovery processes. Damage to the components of the EPSS and of the community‐built environment is evaluated using the seismic fragility functions. The restoration of the community electric power demand is evaluated using the seismic recovery functions. However, the postearthquake EPSS recovery process is modeled using an agent‐based model with two agents, the EPSS Operator and the Community Administrator. The resilience of the EPSS–community system is quantified using direct, EPSS‐related, societal, and community‐related indicators. Parametric studies are carried out to quantify the influence of different seismic hazard scenarios, agent characteristics, and power dispatch strategies on the EPSS–community seismic resilience. The use of the agent‐based modeling framework enabled a rational formulation of the postearthquake recovery phase and highlighted the interaction between the EPSS and the community in the recovery process not quantified in resilience models developed to date. Furthermore, it shows that the resilience of different community sectors can be enhanced by different power dispatch strategies. The proposed agent‐based EPSS–community system resilience quantification framework can be used to develop better community and infrastructure system risk governance policies.     

An Optimization‐Based Framework for the Identification of Vulnerabilities in Electric Power Grids Exposed to Natural Hazards

This article proposes a novel mathematical optimization framework for the identification of the vulnerabilities of electric power infrastructure systems (which is a paramount example of critical infrastructure) due to natural hazards. In this framework, the potential impacts of a specific natural hazard on an infrastructure are first evaluated in terms of failure and recovery probabilities of system components. Then, these are fed into a bi‐level attacker–defender interdiction model to determine the critical components whose failures lead to the largest system functionality loss. The proposed framework bridges the gap between the difficulties of accurately predicting the hazard information in classical probability‐based analyses and the over conservatism of the pure attacker–defender interdiction models. Mathematically, the proposed model configures a bi‐level max‐min mixed integer linear programming (MILP) that is challenging to solve. For its solution, the problem is casted into an equivalent one‐level MILP that can be solved by efficient global solvers. The approach is applied to a case study concerning the vulnerability identification of the georeferenced RTS24 test system under simulated wind storms. The numerical results demonstrate the effectiveness of the proposed framework for identifying critical locations under multiple hazard events and, thus, for providing a useful tool to help decisionmakers in making more‐informed prehazard preparation decisions.