Why Is Unemployment Duration a Sorting Criterion in Hiring?

Recent evidence from large-scale field experiments has shown that employers use job candidates’ unemployment duration as a sorting criterion. In the present study, we investigate the mechanisms underlying this pattern. To this end, we conduct a lab experiment in which participants make hiring decisions concerning fictitious job candidates with diverging unemployment durations. In addition, these participants rate the job candidates on statements central to four theoretical mechanisms often related to the scarring effect of unemployment: general signalling theory, (perceived) skill loss, queuing theory, and rational herding. We use the resulting data to estimate a multiple mediation model, in which the effect of the duration of unemployment on hiring intentions is mediated by the four theories. The lower hiring chances of the long-term unemployed turn out to be dominantly driven by the perception of longer unemployment spells as a signal of lower motivation

Conditioning Facies Simulations with Connectivity Data

When characterizing and simulating underground reservoirs for flow simulations, one of the key characteristics that needs to be reproduced accurately is its connectivity. More precisely, field observations frequently allow the identification of specific points in space that are connected. For example, in hydrogeology, tracer tests are frequently conducted that show which springs are connected to which sink-hole. Similarly well tests often allow connectivity information in a petroleum reservoir to be provided. To account for this type of information, we propose a new algorithm to condition stochastic simulations of lithofacies to connectivity information. The algorithm is based on the multiple-point philosophy but does not imply necessarily the use of multiple-point simulation. However, the challenge lies in generating realizations, for example of a binary medium, such that the connectivity information is honored as well as any prior structural information (e.g. as modeled through a training image). The algorithm consists of using a training image to build a set of replicates of connected paths that are consistent with the prior model. This is done by scanning the training image to find point locations that satisfy the constraints. Any path (a string of connected cells) between these points is therefore consistent with the prior model. For each simulation, one sample from this set of connected paths is sampled to generate hard conditioning data prior to running the simulation algorithm. The paper presents in detail the algorithm and some examples of two-dimensional and three-dimensional applications with multiple-point simulation

Conditions for Passenger Aircraft Minimum Fuel Consumption, Direct Operating Costs and Environmental Impact

Purpose - Find optimal flight and design parameters for three objectives: minimum fuel consumption, Direct Operating Costs (DOC), and environmental impact of a passenger jet aircraft. --- Approach - Combining multiple models (this includes aerodynamics, specific fuel consumption, DOC, and equivalent CO2 mass) into one generic model. In this combined model, each objective's importance is determined by a weighting factor. Additionally, the possibility of further optimizing this model by altering an aircraft's wing loading is analyzed. --- Research limitations - Most models use estimating equations based on first principles and statistical data. --- Practical implications - The optimal cruise altitude and speed for a specific objective can be approximated for any passenger jet aircraft. --- Social implications - By using a simple approach, the discussion of optimizing aircraft opens up to a level where everyone can participate. --- Value - To find a general answer on how to optimize aviation, operational and design-wise, by using a simple approach