International Trade and Inter-Industry Wage Structure in Swedish Manufacturing - Evidence from matched employer-employee data

This study examines the inter-industry wage structure in Swedish manufacturing by using matched employer-employee data for the period 1996 to 2000. First, we use detailed individual and job characteristics to estimate industry-specific and time-varying wage premiums. Second, we investigate the impact of international trade on wage premiums, after controlling for effects of domestic competition and technical progress. Our results indicate that industries that face intensive import competition from low-income countries have lower wage premiums. Surprisingly, the wage premiums are not related to export intensities. Furthermore, technical progress, measured by investment in R&D activity, appears to enhance inter-industry wage premiums.Inter-industry wage structure; International trade; Matched employer- employee data

A Quadratically Regularized Functional Canonical Correlation Analysis for Identifying the Global Structure of Pleiotropy with NGS Data

Investigating the pleiotropic effects of genetic variants can increase statistical power, provide important information to achieve deep understanding of the complex genetic structures of disease, and offer powerful tools for designing effective treatments with fewer side effects. However, the current multiple phenotype association analysis paradigm lacks breadth (number of phenotypes and genetic variants jointly analyzed at the same time) and depth (hierarchical structure of phenotype and genotypes). A key issue for high dimensional pleiotropic analysis is to effectively extract informative internal representation and features from high dimensional genotype and phenotype data. To explore multiple levels of representations of genetic variants, learn their internal patterns involved in the disease development, and overcome critical barriers in advancing the development of novel statistical methods and computational algorithms for genetic pleiotropic analysis, we proposed a new framework referred to as a quadratically regularized functional CCA (QRFCCA) for association analysis which combines three approaches: (1) quadratically regularized matrix factorization, (2) functional data analysis and (3) canonical correlation analysis (CCA). Large-scale simulations show that the QRFCCA has a much higher power than that of the nine competing statistics while retaining the appropriate type 1 errors. To further evaluate performance, the QRFCCA and nine other statistics are applied to the whole genome sequencing dataset from the TwinsUK study. We identify a total of 79 genes with rare variants and 67 genes with common variants significantly associated with the 46 traits using QRFCCA. The results show that the QRFCCA substantially outperforms the nine other statistics.Comment: 64 pages including 12 figure

Electroluminescent ceramics excited by low electrical field

Strong green-light emission occurs in Eu:SrAl₂O₄ ceramics and Eu:SrAl₂O₄ /poly(vinylidene fluoride-trifluoroethylene) (P(VDF-TrFE)) composites when excited by a lower dc or ac voltage. That emission is caused by strong electric-mechanic-optic interaction. The composite shows stronger luminescent emission intensity in comparison to similar ceramics because of an enhanced piezoelectric effect from P(VDF-TrFE)—a typical piezoelectric polymer