Sequential order as an extraneous factor in editorial decision

Academic journal editors reject a significant portion of first submissions without sending them out for peer review. This decision, desk rejection, is made to reduce the workload on associate editors and referees, to give the submitting author a head start on revision or pursuit of an alternative venue, as well as to achieve quicker turnaround time for the journal. Desk rejection is a judgement based on the manuscript's perceived quality, impact and fit with the journal's scope. Could extraneous factors, which are unrelated to the content of a manuscript, affect the editorial decision? This paper examines whether the sequential order in which manuscripts are submitted to a large academic journal affects the editorial decision. Becoming the first submission on the editor's list of manuscripts to review increases the probability of a desk rejection by up to 7% without any effect on the likelihood of a rejection after peer review

Estimating the effects of oil price shockson the Kazakh economy

This paper explores the role of oil for the Kazakh economy. In order to assess thedegree of volatility the oil price features, it, firstly, discusses the literature on oil price behaviour. Secondly, it analyzes the effect of oil price declines on key macroeconomicvariables such as real GDP, inflation and real exchange rates using vectorautoregressive (VAR) models. In this respect, the paper deviates from a large number of papers on oil price effects as it considers a transition rather than a developed economy and an oil exporting rather than an oil importing country. The key findings to emerge from this paper are, first, that the price of oil is influenced by a large number of factors, which results in a considerable degree of volatility. Secondly, all variables considered in theVAR model exhibit a strong negative significant reaction on oil price declines, and, thirdly, a standard linear VAR model is appropriate for capturing the Kazakh oil-macro relationship.Oil price, VAR-Models, oil exporting economy.

International knowledge flows and the administrative barriers to mobility

Face-to-face contact, even temporary one, helps researchers form personal ties and transfer tacit knowledge. The ability of researchers to colocate, including attendance at international conferences, workshops and seminars, is affected by the administrative barriers to international mobility. This paper uses a gravity-style empirical framework to examine the link between international knowledge flows and immigration policies. The results suggest that the paper walls erected by such policies reduce not just the mobility of individuals, but also the diffusion of knowledge. A moderately restrictive mobility barrier reduces incoming and outgoing knowledge flows by about 0.8-1.3% per year. The effect of knowledge-exporting country’s policy persists for nearly 10 years. There is also a short-term asymmetry: diffusion of recent knowledge is affected more by the immigration policy of a knowledgeexporter rather than a knowledge-importer

Far-field subwavelength acoustic imaging by deep learning

Seeing and recognizing an object whose size is much smaller than the illumination wavelength is a challenging task for an observer placed in the far field, due to the diffraction limit. Recent advances in near and far field microscopy have offered several ways to overcome this limitation; however, they often use invasive markers and require intricate equipment with complicated image post-processing. On the other hand, a simple marker-free solution for high-resolution imaging may be found by exploiting resonant metamaterial lenses that can convert the subwavelength image information contained in the near-field of the object to propagating field components that can reach the far field. Unfortunately, resonant metalenses are inevitably sensitive to absorption losses, which has so far largely hindered their practical applications. Here, we solve this vexing problem and show that this limitation can be turned into an advantage when metalenses are combined with deep learning techniques. We demonstrate that combining deep learning with lossy metalenses allows recognizing and imaging largely subwavelength features directly from the far field. Our acoustic learning experiment shows that, despite being thirty times smaller than the wavelength of sound, the fine details of images can be successfully reconstructed and recognized in the far field, which is crucially enabled by the presence of absorption. We envision applications in acoustic image analysis, feature detection, object classification, or as a novel noninvasive acoustic sensing tool in biomedical applications

International stocks and flows of students and researchers reconstructed from ORCID biographies

This document describes a dataset of estimated bilateral flows and stocks of students and researchers (including some other types of high-skilled workers) for more than 200 countries (and territories) since 1990. The data is derived by analysing education and employment histories of more than 650 thousand individuals registered with ORCID. Comparison with independent data sources supports technical validity and representativeness of this data. The dataset provides new measures of the geography of a subset of high-skilled labour and opens opportunities for exploring hypotheses related to migration and agglomeration, impact of immigration policy, scientific production and development, academic mobility, and brain drain

Exploring the world of Economics through RePEc data

This document describes the data available through RePEc and related services: CitEc, CollEc, EDIRC, IDEAS, Genealogy and EconPapers. The document is purely descriptive, and is intended as a guide to some of the data available through RePEc on authors, institutions, collaborations, and networks

International stocks and flows of students and researchers reconstructed from ORCID biographies

This document describes a dataset of estimated bilateral flows and stocks of students and researchers (including some other types of high-skilled workers) for more than 200 countries (and territories) since 1990. The data is derived by analysing education and employment histories of more than 650 thousand individuals registered with ORCID. Comparison with independent data sources supports technical validity and representativeness of this data. The dataset provides new measures of the geography of a subset of high-skilled labour and opens opportunities for exploring hypotheses related to migration and agglomeration, impact of immigration policy, scientific production and development, academic mobility, and brain drain