TESTING FOR EMPLOYEE DISCRIMINATION USING MATCHED EMPLOYER-EMPLOYEE DATA: THEORY AND EVIDENCE

We use recent matched employer-employee data to directly investigate if white workers have a taste for racial discrimination in Britain. Based on a new structural model with individual and firm heterogeneity, we develop and test two predictions. Firstly, white employees with a taste for discrimination should report lower levels of job satisfaction the larger the proportion of ethnic minorities at their workplace. Secondly, white employees would have to be compensated by higher wages if required to work alongside ethnic minority co-workers. Both hypotheses are clearly supported for white males in our data, after comprehensively controlling for individual, job, and workplace characteristics. However, the evidence is weaker for females. The white male wage premium for working amongst only ethnic minority co-workers, as compared to working only with whites, is about 12%. Importantly, it appears that neither of these effects operates via realised racial prejudice at the workplace or white employees’ feelings concerning their job security.Employee Discrimination, Compensating Differentials, StructuralEstimation, Wages, Job Satisfaction

Testing for Employee Discrimination in Britain using Matched Employer-Employee Data.

We use recent matched employer-employee data to directly test if white workers have a taste for racial discrimination in Britain. We formally introduce individual and firm heterogeneity into the discrimination model used by Becker (1957, 1971) which we extend to generate predictions consistent with an employee taste for discrimination. We argue firstly that white employees with a taste for discrimination should report lower levels of job satisfaction the larger the proportion of ethnic minorities at their workplace. Secondly, white employees would have to be compensated by higher wages if required to work alongside ethnic minority co-workers. Both hypotheses are clearly supported for white males in our data, after comprehensively controlling for individual, job, and workplace characteristics. The white male wage premium for working amongst only ethnic minority co-workers, as compared to working only with whites, is about 12%. Importantly, it appears that neither of these effects operates via realised racial prejudice at the workplace or white employees' feelings concerning their job security.Matched employer-employee data, discrimination, job satisfaction, compensating wage differentials

Testing for Employee Discrimination using Matched Employer-Employee Data: Theory and Evidence

We use recent matched employer-employee data to directly investigate if white workers have a taste for racial discrimination in Britain. Based on a new structural model with individual and firm heterogeneity, we develop and test two predictions. Firstly, white employees with a taste for discrimination should report lower levels of job satisfaction the larger the proportion of ethnic minorities at their workplace. Secondly, white employees would have to be compensated by higher wages if required to work alongside ethnic minority co-workers. Both hypotheses are clearly supported for white males in our data, after comprehensively controlling for individual, job, and workplace characteristics. However, the evidence is weaker for females. The white male wage premium for working amongst only ethnic minority co-workers, as compared to working only with whites, is about 12%. Importantly, it appears that neither of these effects operates via realised racial prejudice at the workplace or white employees' feelings concerning their job security.Employee Discrimination, Compensating Differentials, Structural Estimation, Wages, Job Satisfaction

A Dynamic Data-Driven Simulation Approach for Preventing Service Level Agreement Violations in Cloud Federation

The new possibility of accessing an infinite pool of computational resources at a drastically reduced price has made cloud computing popular. With the increase in its adoption and unpredictability of workload, cloud providers are faced with the problem of meeting their service level agreement (SLA) claims as demonstrated by large vendors such as Amazon and Google. Therefore, users of cloud resources are embracing the more promising cloud federation model to ensure service guarantees. Here, users have the option of selecting between multiple cloud providers and subsequently switching to a more reliable one in the event of a provider’s inability to meet its SLA. In this paper, we propose a novel dynamic data-driven architecture capable of realising resource provision in a cloud federation with minimal SLA violations. We exemplify the approach with the aid of case studies to demonstrate its feasibility. Keywords

Spin Waves in Disordered III-V Diluted Magnetic Semiconductors

We propose a new scheme for numerically computing collective-mode spectra for large-size systems, using a reformulation of the Random Phase Approximation. In this study, we apply this method to investigate the spectrum and nature of the spin-waves of a (III,Mn)V Diluted Magnetic Semiconductor. We use an impurity band picture to describe the interaction of the charge carriers with the local Mn spins. The spin-wave spectrum is shown to depend sensitively on the positional disorder of the Mn atoms inside the host semiconductor. Both localized and extended spin-wave modes are found. Unusual spin and charge transport is implied.Comment: 14 pages, including 11 figure

dReDBox: Materializing a full-stack rack-scale system prototype of a next-generation disaggregated datacenter

Current datacenters are based on server machines, whose mainboard and hardware components form the baseline, monolithic building block that the rest of the system software, middleware and application stack are built upon. This leads to the following limitations: (a) resource proportionality of a multi-tray system is bounded by the basic building block (mainboard), (b) resource allocation to processes or virtual machines (VMs) is bounded by the available resources within the boundary of the mainboard, leading to spare resource fragmentation and inefficiencies, and (c) upgrades must be applied to each and every server even when only a specific component needs to be upgraded. The dRedBox project (Disaggregated Recursive Datacentre-in-a-Box) addresses the above limitations, and proposes the next generation, low-power, across form-factor datacenters, departing from the paradigm of the mainboard-as-a-unit and enabling the creation of function-block-as-a-unit. Hardware-level disaggregation and software-defined wiring of resources is supported by a full-fledged Type-1 hypervisor that can execute commodity virtual machines, which communicate over a low-latency and high-throughput software-defined optical network. To evaluate its novel approach, dRedBox will demonstrate application execution in the domains of network functions virtualization, infrastructure analytics, and real-time video surveillance.This work has been supported in part by EU H2020 ICTproject dRedBox, contract #687632.Peer ReviewedPostprint (author's final draft

Fate of the Aortic Arch Following Surgery on Aortic Root and Ascending Aorta in Bicuspid Aortic Valve.

BACKGROUND: Recent guidelines support more aggressive surgery for aneurysms of the ascending aorta and root in patients with bicuspid aortic valve. However, the fate of the arch after surgery of the root and ascending aorta is unknown. We set out to assess outcomes following root and ascending aortic surgery and subsequent growth of the arch. METHODS: Between 2005 and 2016, 536 consecutive patients underwent surgery for aneurysm of the root and ascending aorta. 168 had bicuspid aortic valve. Patients with dissection were excluded. Arch diameter was measured before and after surgery, at six months and then annually. RESULTS: Of 168 patients, 127 (75.6%) had aortic root replacement and 41 (24.4%) had ascending replacement. Mean age was 57±12.8 years, 82.7% were males and five operations were performed during pregnancy. There was one (0.6%) hospital death. One (0.6%) patient had a stroke and one (0.6%) had re-sternotomy for bleeding. Median ICU and hospital stays were 1 and 6 days respectively. Follow-up was complete for 94% at a median of 5.9 years (1-139 months). Aortic arch diameter was 2.9 cm preoperatively and 3.0 cm at follow-up. There was 97% freedom from reoperation and none of the patients required surgery on the arch. CONCLUSIONS: Prophylactic arch replacement during aortic root and ascending aortic surgery in patients with bicuspid aortic valve is not supported. Our data does not support long term surveillance of the rest of the aorta in this population

From {Solution Synthesis} to {Student Attempt Synthesis} for Block-Based Visual Programming Tasks

Block-based visual programming environments are increasingly used to introduce computing concepts to beginners. Given that programming tasks are open-ended and conceptual, novice students often struggle when learning in these environments. AI-driven programming tutors hold great promise in automatically assisting struggling students, and need several components to realize this potential. We investigate the crucial component of student modeling, in particular, the ability to automatically infer students' misconceptions for predicting (synthesizing) their behavior. We introduce a novel benchmark, StudentSyn, centered around the following challenge: For a given student, synthesize the student's attempt on a new target task after observing the student's attempt on a fixed reference task. This challenge is akin to that of program synthesis; however, instead of synthesizing a {solution} (i.e., program an expert would write), the goal here is to synthesize a {student attempt} (i.e., program that a given student would write). We first show that human experts (TutorSS) can achieve high performance on the benchmark, whereas simple baselines perform poorly. Then, we develop two neuro/symbolic techniques (NeurSS and SymSS) in a quest to close this gap with TutorSS