General Purpose Technologies and local knowledge accumulation — A study on MNC subunits and local innovation centers

This research looks into the innovative activities of subunits of large non-U.S firms in the U.S from 1969 to 1995, suggesting that the innovations in the subunits of multinational corporations are closely linked to their specializations in General Purpose Technologies (GPTs). GPTs enable recombining knowledge from different domains on an international scale, and that the specialization of innovations in GPT fields within a subunit is positively associated with the innovative capacity at large, along with the degree of technological diversification of that specific subunit. As firms expand geographically to tap into local innovation centers to develop new growth alternatives, specialization of GPTs in these firms actually facilitates further geographical dispersion of innovative activities across locations. Lastly, additional specializations of innovations in GPT fields within the host innovation centers does help in attracting innovation activities of firms from a wider range of industries.     

中国农药施用与农业经济增长脱钩状态：时空特征与影响因素

[目的]明确农药施用与农业经济增长关联状态及其影响因素,为实现"控药减害增收"的目标提供借鉴。[方法]基于2005—2015年数据利用脱钩理论和对数平均分解指数法分解进行分析。[结果]中国农业经济增长伴生大量农药施用,仅少数省域表现出农药施用和农业经济增长扩张绝对脱钩的理想状态,东北西北部分省域农业经济增长则以更大幅度的农药投入增长为代价。中西部地区劳动力规模和技术进步效应是推动农药施用与农业经济增长脱钩的因素;东部地区种植规模变化、劳动力规模和技术进步因素均是推动农药施用和农业经济增长脱钩的因素;东北地区仅劳动力规模效应成为推动脱钩的力量。[结论]推动农药施用与农业经济增长脱钩需要强化农业科技的创新与普及,完善农业生产过程中的技术支撑,推动农业病虫害监测预警,加大对环境友好型低污染农药和肥料的推广度;构建农户施药的多重约束机制,完善农业病虫灾害保险减少农户对生产过程的风险感知,发挥农业合作社对农户施药行为的规制作用,完善产品分级认证和安全准入制度;通过农地合理流转和农业机械化等方式集约利用农业生产资源;因地制宜推动农药施用与农业经济增长脱钩,结合区域资源优势和农业发展阶段有针对性地设计对策措施。     

Measuring Farm and Market Level Economic Impacts of Improved Maize Production Technologies in Ethiopia: Evidence from Panel Data

While it is often recognised that agricultural technology adoption decisions are intertwined and best characterised by multivariate models, typical approaches to examining adoption and impacts of agricultural technology have focused on single technology adoption choice and ignored interdependence among technologies. We examine farm‐ and market‐level impacts of multiple technology adoption choices using comprehensive household survey data collected in 2010/11 and 2012/13 in Ethiopia. Economic surplus analysis combined with panel data switching endogenous regression models are used to compute the supply shift parameter (K‐shift parameter), while at the same time controlling for the endogeneity inherent in agricultural technology adoption among farmers. We find that our improved technology set choices have significant impacts on farm‐level maize yield and maize production costs, where the greatest effect appears to be generated when various technologies are combined. The change in maize yield and production costs results in an average 26.4% cost reduction per kilogram of maize output (the K‐shift parameter). This increases the producer and consumer surpluses by US$ 140 and US$ 105 million per annum, respectively. These changes in economic surplus help to reduce the number of poor people by an estimated 788 thousand per year. We conclude that deliberate extension efforts and other policies that encourage integration of technologies are important for maize technologies to yield their full potential at both farm and market levels.     

Spatial movement patterns among intra-destinations using social network analysis

To explore popularly visited tourist locations, travel movement patterns, and movement points, this study collected samples of 321 Chinese tourists and 337 Japanese tourists who were visiting major tourist destinations in Seoul and its vicinity in South Korea. Results of analyzing movement patterns showed that Japanese tourists tend to be clustered around popular attractions, whereas Chinese tourists tend to spread over a larger area of attractions. Some specific shopping and amusement attractions were the locations most popularly visited by both groups. The start points and end points in the two groups’ itineraries were dissimilar overall, even though their patterns were similar in regard to major preferred tourist attractions. Thus, the findings of this study have the potential to contribute to understanding spatial mobility in a tourism destination through tracking tourists’ movement patterns.     

Predicting credit card delinquencies: An application of deep neural networks

The objective of this paper is twofold. First, it develops a prediction system to help the credit card issuer model the credit card delinquency risk. Second, it seeks to explore the potential of deep learning (also called a deep neural network), an emerging artificial intelligence technology, in the credit risk domain. With real-life credit card data linked to 711,397 credit card holders from a large bank in Brazil, this study develops a deep neural network to evaluate the risk of credit card delinquency based on the client's personal characteristics and the spending behaviours. Compared with machine-learning algorithms of logistic regression, naive Bayes, traditional artificial neural networks, and decision trees, deep neural networks have a better overall predictive performance with the highest F scores and area under the receiver operating characteristic curve. The successful application of deep learning implies that artificial intelligence has great potential to support and automate credit risk assessment for financial institutions and credit bureaus.     

Big data driven cycle time parallel prediction for production planning in wafer manufacturing

Cycle time forecasting (CTF) is one of the most crucial issues for production planning to keep high delivery reliability in semiconductor wafer fabrication systems (SWFS). This paper proposes a novel data-intensive cycle time (CT) prediction system with parallel computing to rapidly forecast the CT of wafer lots with large datasets. First, a density peak based radial basis function network (DP-RBFN) is designed to forecast the CT with the diverse and agglomerative CT data. Second, the network learning method based on a clustering technique is proposed to determine the density peak. Third, a parallel computing approach for network training is proposed in order to speed up the training process with large scaled CT data. Finally, an experiment with respect to SWFS is presented, which demonstrates that the proposed CTF system can not only speed up the training process of the model but also outperform the radial basis function network, the back-propagation-network and multivariate regression methodology based CTF methods in terms of the mean absolute deviation and standard deviation.     

Demystifying residents' walking behaviors: Active transport in South East Queensland,Australia

This study investigates the correlates of a resident's walking behaviors and aims to shed light on mechanisms through which walking may be encouraged. The results of this investigation paint a complex and nuanced picture of the residents’ walking behaviors in South East Queensland, Australia. The results suggest that sociodemographic characteristics separately may contribute greatly to whether or not one engages in walking behaviors. Further, cumulatively these differences might be greater for some groups of residents compared to others. One of the most prominent findings of the study is that the purpose, the characteristics of the origin, and the characteristics of the destination of a trip tended to be similar in the heterogeneity they exhibit over the distribution of time spent walking. For example, pick something up, undertake work, or engage in personal business, move to or from a workplace, shop, or social place are activities that tend to be associated with walking as a main mode of transport and a higher number of walking episodes. However, these trips tended to be short.     

Innovation in the bioeconomy – dynamics of biorefinery innovation networks

The bioeconomy has become a central concept in many strategies for future economic development, emphasising an increasing need for collaboration across industries and sectors for innovation. This paper unpacks aspects of collaboration in the bioeconomy by looking at the development of innovation networks for biorefinery technologies from 2004 to 2014 based on innovation project data from Swedish public funding agencies using a stochastic actor-oriented model for network analysis. The analysis shows that although the network grew significantly during the time period, indicating an increasing interest in biorefinery technology innovation, inter-sectoral collaboration is not favoured over intra-sectoral collaboration. As is known from previous work on social networks trust-building is a key driver for collaboration, as actors tend to form collaborations with previous partners or indirectly connected partners, creating clustered networks.     

普惠金融与县域资金外流：减贫还是致贫*——基于中国592个国家级贫困县的研究

本文利用全国592个国家级贫困县的数据，采用空间计量模型实证分析了普惠金融对县域资金外流的影响，并验证了贫困县资金外流是否会产生致贫效应。本文研究发现：如果普惠金融只注重解决贫困地区对金融机构的接触性排斥，会进一步加剧资金外流，对减贫产生负向影响，即存在显著的致贫效应。这种致贫效应具有明显的空间外溢性，邻近县域的贫困状况在很大程度上会彼此“传染”，并具有空间衰减特征的地理边界。因而需要客观认识普惠金融的本质，有针对性地选择恰当有效的实施载体，解决好对信贷产品等关键金融服务的使用性排斥问题。