Why Do Countries Matter so Much in Corporate Social Performance?

Why do levels of corporate social performance (CSP) differ so much across countries? We answer this question in an examination of CSP ratings of more than 2,600 companies from 36 countries. We find that firm characteristics explain very little of the variations in CSP ratings. In contrast, variations in country factors such as stages of economic development, culture, and institutions account for a significant proportion of variations in CSP ratings across countries. In particular, we find that CSP ratings are high in countries with high income-per-capita, strong civil liberties and political rights, and cultures oriented toward harmony and autonomy. Furthermore, we find that home country factors explain a smaller portion of the overall variations in CSP for multinationals and cross-listed firms than for non-multinationals and pure domestic firms, respectively

Neutrino and anti-neutrino transport in accretion disks

We numerically solve the one dimensional Boltzmann equation of the neutrino and anti-neutrino transport in accretion disks and obtain the fully energy dependent and direction dependent neutrino and anti-neutrino emitting spectra, under condition that the distribution of the mass density,temperature and chemical components are given. Then, we apply the resulting neutrino and anti-neutrino emitting spectra to calculate the corresponding annihilation rate of neutrino pairs above the neutrino dominated accretion disk and find that the released energy resulting from the annihilation of neutrino pairs can not provide sufficient energy for the most energetic short gamma ray bursts whose isotropic luminosity can be as high as $10^{52}$ ergs/s unless the high temperature zone where the temperature is beyond 10 MeV can stretch over 200 km in the disk. We also compare the resulting luminosity of neutrinos and anti-neutrinos with the results from the two commonly used approximate treatment of the neutrino and anti-neutrino luminosity: the Fermi-Dirac black body limit and a simplified model of neutrino transport, i.e., the gray body model, and find that both of them overestimate the neutrino/anti-neutrino luminosity and their annihilation rate greatly. Additionally, as did in Sawyer (2003), we also check the validity of the two stream approximation, and find that it is a good approximation to high accuracy.Comment: Phys. Rev. D in press, 15 preprint papers, 5 figure

The Impact of Nonfarm Activities on Agricultural Productivity in Rural China

Although evidence abounds that the development of rural non-farm activities have increased rural household income and contributed to rural development, the underlying structure and mechanism of the linkage between agricultural productivity and non-farm activities is poorly understood. Using a unique panel dataset of Chinese villages, this article examines the mechanism by which non-farm activities influence agricultural productivity. I find that Chinese villages’ non-farm revenue has a significant positive effect on agricultural land productivity. Although non-farm activities do withdraw labor out of agriculture and therefore dampen land productivity, that negative effect is negligible in comparison with the land productivity improvement brought by nonfarm revenue-financed infrastructure capital investment.Rural non-farm activities, labor migration, agricultural productivity, infrastructure capital., Agricultural and Food Policy, Productivity Analysis, O13, Q18,

Simultaneous Feature Learning and Hash Coding with Deep Neural Networks

Similarity-preserving hashing is a widely-used method for nearest neighbour search in large-scale image retrieval tasks. For most existing hashing methods, an image is first encoded as a vector of hand-engineering visual features, followed by another separate projection or quantization step that generates binary codes. However, such visual feature vectors may not be optimally compatible with the coding process, thus producing sub-optimal hashing codes. In this paper, we propose a deep architecture for supervised hashing, in which images are mapped into binary codes via carefully designed deep neural networks. The pipeline of the proposed deep architecture consists of three building blocks: 1) a sub-network with a stack of convolution layers to produce the effective intermediate image features; 2) a divide-and-encode module to divide the intermediate image features into multiple branches, each encoded into one hash bit; and 3) a triplet ranking loss designed to characterize that one image is more similar to the second image than to the third one. Extensive evaluations on several benchmark image datasets show that the proposed simultaneous feature learning and hash coding pipeline brings substantial improvements over other state-of-the-art supervised or unsupervised hashing methods.Comment: This paper has been accepted to IEEE International Conference on Pattern Recognition and Computer Vision (CVPR), 201