Risk attitudes, job mobility and subsequent wage growth during the early career

Job change is a decision under uncertainty: It is associated with costs whereas the decision is made without full knowledge about future benefits. In order to investigate the relationship between willingness to take risks and job mobility, we first extend a model for on-the-job search with nonwage job characteristics by including heterogeneity in risk attitudes. Second, we empirically test the model's implications showing that individuals who are more risk-averse choose to change their jobs less often than more risk-tolerant individuals. This difference in the job changing behaviour leads to only moderate differences wage growth during early career: Risk-averse individuals tend to have on average higher wage gains from each job change and have obtained higher overall wage growth at the end of the early career phase

Risk attitudes, job mobility and subsequent wage growth during the early career

In this paper, we investigate the relationship between individuals' willingness to take risk and job mobility during the early career. Job change is a risky decision since it involves substantial costs without entirely foreseeing the benefits at the time the decision is made. We incorporate risk preferences as an additional parameter influencing the individual job change behaviour in an on-the-job search model accounting for nonwage job characteristics. Empirically, we show that more risk-averse individuals voluntarily change their jobs less often compared to more risk-tolerant individuals. In addition, since risk-averse individuals demand higher compensation for the risk associated with uncertain nonwage job characteristics, we find that their job changes are associated with higher wage gains. However, more risk-averse individuals do not obtain higher overall wage growth as a result of the early career compared to more risk-tolerant individuals

The migration and attrition behaviour among Hungarian radiographers

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Studies on the disbonding initiation of interfacial cracks.

With the continuing trend of decreasing feature sizes in flip-chip assemblies, the reliability tolerance to interfacial flaws is also decreasing. Small-scale disbonds will become more of a concern, pointing to the need for a better understanding of the initiation stage of interfacial delamination. With most accepted adhesion metric methodologies tailored to predict failure under the prior existence of a disbond, the study of the initiation phenomenon is open to development and standardization of new testing procedures. Traditional fracture mechanics approaches are not suitable, as the mathematics assume failure to originate at a disbond or crack tip. Disbond initiation is believed to first occur at free edges and corners, which act as high stress concentration sites and exhibit singular stresses similar to a crack tip, though less severe in intensity. As such, a 'fracture mechanics-like' approach may be employed which defines a material parameter--a critical stress intensity factor (K{sub c})--that can be used to predict when initiation of a disbond at an interface will occur. The factors affecting the adhesion of underfill/polyimide interfaces relevant to flip-chip assemblies were investigated in this study. The study consisted of two distinct parts: a comparison of the initiation and propagation phenomena and a comparison of the relationship between sub-critical and critical initiation of interfacial failure. The initiation of underfill interfacial failure was studied by characterizing failure at a free-edge with a critical stress intensity factor. In comparison with the interfacial fracture toughness testing, it was shown that a good correlation exists between the initiation and propagation of interfacial failures. Such a correlation justifies the continuing use of fracture mechanics to predict the reliability of flip-chip packages. The second aspect of the research involved fatigue testing of tensile butt joint specimens to determine lifetimes at sub-critical load levels. The results display an interfacial strength ranking similar to that observed during monotonic testing. The fatigue results indicate that monotonic fracture mechanics testing may be an adequate screening tool to help predict cyclic underfill failure; however lifetime data is required to predict reliability

Identifying healthy individuals with Alzheimer’s disease neuroimaging phenotypes in the UK Biobank

BackgroundIdentifying prediagnostic neurodegenerative disease is a critical issue in neurodegenerative disease research, and Alzheimer's disease (AD) in particular, to identify populations suitable for preventive and early disease-modifying trials. Evidence from genetic and other studies suggests the neurodegeneration of Alzheimer's disease measured by brain atrophy starts many years before diagnosis, but it is unclear whether these changes can be used to reliably detect prediagnostic sporadic disease.MethodsWe trained a Bayesian machine learning neural network model to generate a neuroimaging phenotype and AD score representing the probability of AD using structural MRI data in the Alzheimer's Disease Neuroimaging Initiative (ADNI) Cohort (cut-off 0.5, AUC 0.92, PPV 0.90, NPV 0.93). We go on to validate the model in an independent real-world dataset of the National Alzheimer's Coordinating Centre (AUC 0.74, PPV 0.65, NPV 0.80) and demonstrate the correlation of the AD-score with cognitive scores in those with an AD-score above 0.5. We then apply the model to a healthy population in the UK Biobank study to identify a cohort at risk for Alzheimer's disease.ResultsWe show that the cohort with a neuroimaging Alzheimer's phenotype has a cognitive profile in keeping with Alzheimer's disease, with strong evidence for poorer fluid intelligence, and some evidence of poorer numeric memory, reaction time, working memory, and prospective memory. We found some evidence in the AD-score positive cohort for modifiable risk factors of hypertension and smoking.ConclusionsThis approach demonstrates the feasibility of using AI methods to identify a potentially prediagnostic population at high risk for developing sporadic Alzheimer's disease