Do special issues offer something special? An overview of research trends in Telecommunications Policy special issues

This study explores the value of special issues by analyzing the characteristics of special issues of Telecommunications Policy (TP). Specifically, this study examines the status of special issue publications, the characteristics of their articles compared with those of general issues, and the factors affecting article citations. For the analysis, all information on TP articles published from 1976 to 2018 was collected from the Scopus database. A comparative analysis as well as a negative binomial regression were conducted on papers published after 1994, when the first special issue was published. The results show that special issues are distinguished from general issues by various characteristics, such as multinational author collaboration, number of references, keywords, and funding. While general issue papers are more frequently cited in academia, special issue papers show better performance in terms of online captures and social media sharing. Moreover, the smaller number of variables affects the frequency of citations in the case of special issues, revealing that content and format are more influential than author factors.     

Finding Closest Targets in Non-Oriented DEA Models: The Case of Convex and Non-Convex Technologies

This paper draws attention for the fact that traditional Data Envelopment Analysis (DEA) models do not provide the closest possible targets (or peers) to inefficient units, and presents a procedure to obtain such targets.It focuses on non-oriented efficiency measures (which assume that production units are able to control, and thus change, inputs and outputs simultaneously) both measured in relation to a Free Disposal Hull (FDH) technology and in relation to a convex technology. The approaches developed for finding close targets are applied to a sample of Portuguese bank branches.     

Bancarization and violent attacks from guerrilla and other illegal groups in Colombia

Income shocks affect violence through the opportunity cost and rapacity effects. Existing studies focus on the impact of transitory shocks, especially commodity price innovations. This paper builds on this literature and studies the causal effect of permanent income shocks on armed conflict in Colombia. Using a rich dataset reporting all guerrilla and other armed groups' attacks by municipality between 2009 and 2014 and information on the provision of banking services, it shows that increasing bancarization leads to reductions in violence. These results have important implications for public policy in countries with a long history of violence. They suggest that promoting financial inclusion is useful for reducing conflict.     

Predicting bank insolvencies using machine learning techniques

Proactively monitoring and assessing the economic health of financial institutions has always been the cornerstone of supervisory authorities. In this work, we employ a series of modeling techniques to predict bank insolvencies on a sample of US-based financial institutions. Our empirical results indicate that the method of Random Forests (RF) has a superior out-of-sample and out-of-time predictive performance, with Neural Networks also performing almost equally well as RF in out-of-time samples. These conclusions are drawn not only by comparison with broadly used bank failure models, such as Logistic, but also by comparison with other advanced machine learning techniques. Furthermore, our results illustrate that in the CAMELS evaluation framework, metrics related to earnings and capital constitute the factors with higher marginal contribution to the prediction of bank failures. Finally, we assess the generalization of our model by providing a case study to a sample of major European banks.     

The effects of handling outliers on the performance of bankruptcy prediction models

Ratio type financial indicators are the most popular explanatory variables in bankruptcy prediction models. These measures often exhibit heavily skewed distribution because of the presence of outliers. In the absence of clear definition of outliers, ad hoc approaches can be found in the literature for identifying and handling extreme values. However, it is not clear how these different approaches can affect the predictive power of models. There seems to be consensus in the literature on the necessity of handling outliers, at the same time, it is not clear how to define extreme values to be handled in order to maximize the predictive power of models. There are two possible ways to reduce the bias originating from outliers: omission and winsorization. Since the first approach has been examined previously in the literature, we turn our attention to the latter. We applied the most popular classification methodologies in this field: discriminant analysis, logistic regression, decision trees (CHAID and CART) and neural networks (multilayer perceptron). We assessed the predictive power of models in the framework of tenfold stratified crossvalidation and area under the ROC curve. We analyzed the effect of winsorization at 1, 3 and 5% and at 2 and 3 standard deviations, furthermore we discretized the range of each variable by the CHAID method and used the ordinal measures so obtained instead of the original financial ratios. We found that this latter data preprocessing approach is the most effective in the case of our dataset. In order to check the robustness of our results, we carried out the same empirical research on the publicly available Polish bankruptcy dataset from the UCI Machine Learning Repository. We obtained very similar results on both datasets, which indicates that the CHAID-based categorization of financial ratios is an effective way of handling outliers with respect to the predictive performance of bankruptcy prediction models.     

基于台风危害的厦门地区园林树木受损 原因研究

基于2016年9月16日1614号台风对厦门绿地的严 重影响，通过为期2个月对台风现场实地调研以及相关部门抢 险的数据统计，第一时间了解此次台风中厦门重要园林树种的 受害状况，分析树木在台风中的受损原因。台风灾害发生后给 厦门市的园林树木以及景观带来巨大的损害，因此对于厦门地 区园林树木受损原因的研究非常有必要。研究了受损原因后， 才可以在灾前以及灾后对重要的园林树木种进行栽培养护管 理，增加厦门绿化的抗灾能力以及灾后的及时恢复工作。     

Collaboration and innovation: An empirical study of Indian technological enterprises

In 2010, the Indian government declared 2010–2020 to be the ‘Decade of Innovation’ and established the State Innovation Councils and Sectoral Innovation Councils to encourage and facilitate innovation by technological firms. In this paper, we study the relationship between collaboration and innovation in a cross‐section of Indian firms, paying particular attention to the impact of the innovation councils. Our results suggest that domestic collaborations have an important impact on firm innovation, as do the innovation councils, but that the impact of the councils is less for firms that collaborate.     

Comparative randomized inverse sampling

According to previous literature, we define randomized inverse sampling for comparing two treatments with respect to a binary response as the sampling that stops when a total fixed number of successes, irrespective of the treatments, are observed. We have obtained elsewhere the asymptotic distributions for the counting variables involved and have shown them to be equivalent to the corresponding asymptotic distributions for multinomial sampling. In this paper, we start deriving the same basic results using different techniques, and we then show how they give rise to genuinely novel procedures when translated into finite sample approximations. As the main example, a novel confidence interval for the logarithm of the odds ratio of two success probabilities can be constructed in the case of comparative randomized inverse sampling. Some advantages over the standard multinomial sampling in terms of coverage probabilities are visible when no adjustment for cells with zero counts is applied; otherwise, the two sampling schemes appear to be fairly equivalent. This is a reassurance that under certain circumstances, inverse sampling can be safely chosen over more traditional sampling schemes.