Measuring industrial upgrading: applying factor analysis in a global value chain framework

ABSTRACT

A key question for promoting international competition is how to improve the position of countries and industries in global value chains (GVCs). The first step is to properly measure industrial upgrading in GVCs. This is not a trivial issue because upgrading has not been defined unambiguously. Several authors have used different (and sometimes related) measures, all of which indicate certain aspects of upgrading. Rather than trying to find the single, ultimate measure of upgrading, we propose a different approach. We examine the multidimensionality of industrial upgrading, using eight indicators in factor analysis. Four of the eight indicators adopt the GVC perspective and include, for example, the growth of the share in value-added exports. We provide three quantitative dimensions of industrial upgrading: process upgrading, product upgrading, and skill upgrading. With these dimensions, we compare and analyze the upgrading of different countries and industries using the World Input–Output Database.     

江苏省农田保护经济补偿政策绩效评价与空间差异研究

［目的］分析评价江苏省现有农田保护经济补偿政策绩效与空间差异对建立完善长效的农业生态补偿机制和推动我国农业生态环境的可持续发展具有重要意义。［方法］以江苏省为研究区域，运用熵权-TOPSIS法，选取2010—2017年全省及苏北、苏中、苏南三个区域内11个指标，计算江苏省及三大区域农田保护经济补偿政策综合绩效值与经济、生态和社会效益单项绩效值，进行比较分析。［结果］（1）江苏省农田保护经济补偿政策在2010—2017年8年综合绩效值由0189波动增长到0841，效果初现且具有较强推广价值。（2）经济效益绩效值以年均257%的增长率由0增长到1，效果最为显著，社会效益次之，生态效益自2013年后为绩效值中最低值，与前两项最大差距在05左右，反映出环境改善、农田质量提高效应远低于农户创收、社会公平效应，政策拟合度不高。（3）江苏省农田保护经济补偿政策效果存在空间差异，综合来看政策效果表现为苏南>苏中>苏北，苏南凭借经济优势在2016年和2017年的综合绩效值上比苏中苏北高出01、在经济与社会绩效值以002的差距领先且生态绩效值没有下降拐点，具有长效可持续性。［结论］现阶段农田保护经济补偿政策效果依赖地方政府经济实力，尚未形成良性长效机制，需从制度体系、环境经济手段、差别化补偿措施、农业补贴结构和宣传等5方面着手，推进农田保护经济补偿政策更好地服务生态环境。     

国有林场改革成效评价——职工获得感的统计分析

利用国有林场职工监测数据,基于物质维度和精神维度,从改革后职工自身的收益情况、职工对林场的整体评价、职工对林场管理方式的评价等方面,采用比较分析法和主成分分析法对国有林场改革职工获得感进行分析。研究结果表明:国有林场改革后职工获得感有所提升,改革进程与职工获得感呈强正相关性,改革促进了林场的可持续发展。但林场整体基础设施环境一般,场部相对较好,管护站点则较差。同时,在领导班子建设方面应注重自主、公平,仍需提高领导水平。     

基于MBP算法和深度学习的人脸识别

为了解决在深度学习提取人脸图像特征时,易忽略其局部结构特征和缺乏对其旋转不变性学习的问题,提出了一种基于单演局部二值模式(MBP)与深度学习相结合的高效率人脸识别方法。首先,用多尺度单演滤波器对图像进行滤波,得到幅值和方向信息;其次,用LBP算法和象限比特的方法进行编码,分块计算组合其直方图特征;然后,将提取的单演特征作为深度信念网络(DBN)的输入,逐层训练优化网络参数,得到优异的网络模型;最后,将训练好的DBN网络在ORL人脸数据库上进行人脸识别实验,进行识别率计算,其识别率为98.75%。所提出的方法使用无监督的贪婪算法,隐藏层设定为2层,使用反向传播算法优化网络。相较于已知的人脸识别方法,MBP+DBN算法对光照、表情和部分遮挡变化具有较好的鲁棒性,在人脸识别中识别率较高,具有一定的优势,为图像特征提供了一种新的识别方法。     

HOW DO RATING AGENCIES’ DECISIONS IMPACT STOCK MARKETS? A META‐ANALYSIS

The purpose of this study is to examine how credit rating agencies’ decisions impact the stock market using a systematic and quantitative review of existing empirical studies. Specifically, we employ a meta‐regression analysis (MRA) to investigate the extent and nature of the effect of rating agencies’ decisions on the stock market. We survey 62 studies published between 1978 and 2015. Our first finding is that the cumulative average abnormal returns calculated from this empirical literature are affected by publication bias. After controlling for publication bias, the main findings of our meta‐analysis indicate that negative rating decisions cause statistically significant negative abnormal returns. This evidence suggests an informational effect. Our results also indicate that positive rating decisions do not have a significant effect. Finally, the MRA results reveal the importance of several factors related to primary study design, as well as to the nature of the data.     

A spatial autoregressive stochastic frontier model for panel data incorporating a model of technical inefficiency

By integrating Battese and Coelli’s (1995) model and the spatial autoregressive model (SAR), a spatial autoregressive stochastic frontier model for panel data is developed. The main feature of this frontier model is a spatial lag term of explained variables and the joint structure of a production possibility frontier with a model of technical inefficiency. The model addresses both spatial dependence and heteroskedastic technical inefficiency. This study applies maximum likelihood methods considering the endogenous spatial lag term. The proposed model nests several existing models. Further, an empirical analysis using data on the Japanese manufacturing industry is conducted and the existing models are tested against the proposed model, which is found to be statistically supported. The findings suggest that estimates in the existing spatial and non-spatial models may exhibit bias because of lack of determinants of technical inefficiency, as well as a spatial lag. This bias also affects the technical efficiency score and its ranking.     

Co-movement in Stock Prices in Emerging Economies: The Case of the Caricom Region

The Grand Anse Declaration of 1989 recognised the need for financial integration within the emerging economies that comprise the CARICOM region, as a way of furthering the wider process of economic integration and, indeed, economic development in that region. Using co-movement as a measure of financial integration, this paper investigates the co-movement in stock prices among the Barbados, the Jamaica and the Trinidad and Tobago Stock Exchanges, the three major exchanges within the CARICOM region. It also examines how integrated these exchanges are with the New York Stock Exchange. The GARCH-Copula methodology and, to a lesser extent, estimated correlation coefficients, are used to attain this objective. There appears to be co-movement in stock prices and returns within the CARICOM stock markets and significant dependence structures between the returns of the three CARICOM stock markets. However, there is considerably less evidence of integration between the CARICOM markets and the New York Stock Exchange.     

Secrets to design an effective message on Facebook: an application to a touristic destination based on big data analysis

The objective of this research is to identify which are the key variables for designing a message in a social network that can be used by an advertiser to generate Positive/Negative Engagement. The message’s design variables have been classified into four main groups: (i) Message Tools (presence of text, images, video, labels, applications, interactive games and events calendar or others), (ii) Appropriate Message Structure (length and intelligibility), (iii) Informative Cues (links to the brand, orientation towards the product or the brand, topics relevant to the audience, and a remuneration’s promise) and (iv) Persuasive and Emotional Cues (emotional signals, valence, endorsement and influencer mentions). The focus has been a tourist destination: Brand Spain that is advertised through Facebook. A content analysis was carried out and regression analysis with optimal scaling was used on 180 Spain brand’s publications; 57,626 audience reactions to such publications; and 1361 audience comments on the Brand Spain Official Fan Page. According to our results, from the four blocks of predictive variables, only two of them are useful to predict Positive/Negative Engagement: (i) the use of Message Tools (videos), and (ii) the use of informative cues (relevant topics, links on posts, and post’s orientation towards product).     

A multivariate distance nonlinear causality test based on partial distance correlation: a machine learning application to energy futures

This paper proposes a multivariate distance nonlinear causality test (MDNC) using the partial distance correlation in a time series framework. Partial distance correlation as an extension of the Brownian distance correlation calculates the distance correlation between random vectors X and Y controlling for a random vector Z. Our test can detect nonlinear lagged relationships between time series, and when integrated with machine learning methods it can improve the forecasting power. We apply our method as a feature selection procedure and combine it with the support vector machine and random forests algorithms to study the forecast of the main energy financial time series (oil, coal, and natural gas futures). It shows substantial improvement in forecasting the fuel energy time series in comparison to the classical Granger causality method in time series.     

Assessing qualitative similarities between financial reporting frameworks using visualization and rules: COREP vs. pillar 3

Financial institutions are struggling with larger volume, more specific and greater frequency of regulatory reporting after the global financial crisis in 2008, especially those that need to report to multiple jurisdictions. To help to improve reporting efficiency, this paper aims to assess the existence of similarities between templates related to credit and counter party credit risk of COREP and Pillar 3 regulatory reporting frameworks by applying Correspondence Analysis and Association Rules Mining. Our results suggest a high degree of overlap between these reporting frameworks, more prominently the three business functions as Front office, Finance and Risk. These patterns can be used as guidance for financial institutions to reshape their reporting architecture.