创新要素投入产出效率随机变动测度与结果分析

创新要素配置在投入产出两个环节中生成大量不完全技术信息，投入产出效率随机变动更加明显。从投入产出数据集合中提炼共同因子，与不可观测变量构成预测器，建立因子增广向量自回归模型(FAVAR)，分析随机变动方差构成，测度投入产出效率随机变动程度。随机变动效应包括水平效应、稳定性效应和规模效应。投入产出效率自回归扰动项表示随机变动程度，将其细分为共同因子，计算预测器方差及其与投入产出效率的协方差。基于669家上市公司月度、季度、半年度和年度技术研发数据，比较分析投入产出效率随机变动程度及形成原因，引入脉冲响应法分析变量方差构成与变动特征，提出相关政策建议。     

Managing organizational entry in China: the roles of newcomer–supervisor exchange,incumbent support,and perceived insider status

The socialization literature has long recognized the important role of perceived insider status in facilitating newcomer adjustment, but knowledge on how to cultivate this desirable psychological state is limited. This study examines newcomer–supervisor exchange as an antecedent of perceived insider status of newcomers, which in turn facilitates their social adjustment and task mastery during organizational entry. We also explore incumbent support as a boundary condition for these effects. We conducted a survey study on 320 new employees and 92 supervisors in China. This study revealed that newcomer–supervisor exchange was related positively to newcomers’ perceived insider status, and perceived insider status mediated the positive relationships of newcomer–supervisor exchange with social adjustment and task mastery. In addition, incumbent support moderated the positive relationship between newcomer–supervisor exchange and perceived insider status, as well as the positive mediated relationships of newcomer–supervisor exchange with social adjustment and task mastery through perceived insider status. Specifically, these relationships were weaker when incumbent support was higher. Theoretical and practical implications were discussed.     

The Politics of Inequality as Organised Spectacle: Why the Swiss Do Not Want to Tax the Rich

In 2015, Swiss voters had the opportunity to impose a tax on the super rich in a popular vote and thereby fund a redistributive policy. However, a large majority voted against its seemingly obvious self-interest and rejected the tax. We propose an explanation for this puzzling outcome, bridging the usually separate behavioralist and institutionalist perspectives on the politics of inequality. We start from the observation that political economy tends to neglect processes of preference formation. Theorising preferences as socially constructed, we show that interest groups played a major role in shaping the outcome of the vote. Business frames were multiplied through allied parties and the media and had a major impact on individual voting behaviour. In addition, we demonstrate that interest groups representing business interests derive the content of their communication from business’s structurally privileged position in the capitalist economy. Specifically, creating uncertainty about possible perverse effects of government policies on jobs and growth is a powerful tool to undermine popular support. Frames based on this structural power ultimately explain why the Swiss refrained from ‘soaking the rich.’     

Testing the asymmetric effects of exchange rate pass‐through in BRICS countries: Does the state of the economy matter?

This paper investigates not only the question of whether there is exchange rate pass‐through (ERPT) but also the extent to which the pass‐through is asymmetric or state‐dependent in the BRICS countries. Using monthly data from 1999:M1 to 2019:M12 and non‐linear smooth transition vector autoregressive (STVAR) model, our results provide evidence of period‐specific ERPT between the upper and lower regime periods, governed by the selected transition variables. The results further suggest that the pass‐through of exchange rate is higher when the economy is experiencing large appreciations and expansions as well as large depreciations and recessions. Theimplication for these findings is that ERPT is strongly affected by the state of the economy.     

A Statistical Model to Investigate the Reproducibility Rate Based on Replication Experiments

The reproducibility crisis, that is, the fact that many scientific results are difficult to replicate, pointing to their unreliability or falsehood, is a hot topic in the recent scientific literature, and statistical methodologies, testing procedures and p‐values, in particular, are at the centre of the debate. Assessment of the extent of the problem–the reproducibility rate or the false discovery rate–and the role of contributing factors are still an open problem. Replication experiments, that is, systematic replications of existing results, may offer relevant information on these issues. We propose a statistical model to deal with such information, in particular to estimate the reproducibility rate and the effect of some study characteristics on its reliability. We analyse data from a recent replication experiment in psychology finding a reproducibility rate broadly coherent with other assessments from the same experiment. Our results also confirm the expected role of some contributing factor (unexpectedness of the result and room for bias) while they suggest that the similarity between original study and the replica is not so relevant, thus mitigating some criticism directed to replication experiments.     

The Impact of Public Expenditure and International Trade on Agricultural Productivity in China

The “industry nurturing agriculture” reforms and World Trade Organization accession led to dramatic growth in public expenditure and international trade in China’s agricultural sector. This article aims to estimate the effects of public expenditure and trade on agricultural productivity in China for 2004–2015. A semi-parametric production function with shape constraints is introduced to derive more accurate productivity before the productivity determinants are analyzed with an emphasis on public expenditure and trade. The empirical result shows that public expenditure and exports can effectively improve agricultural productivity, while imports have no significant effects. Policy implications are discussed in the context of supply-side reforms.     

Deep learning for modeling the collection rate for third-party buyers

This study evaluates a wide range of machine learning techniques such as deep learning, boosting, and support vector regression to predict the collection rate of more than 65,000 defaulted consumer credits from the telecommunications sector that were bought by a German third-party company. Weighted performance measures were defined based on the value of exposure at default for comparing collection rate models. The approach proposed in this paper is useful for a third-party company in managing the risk of a portfolio of defaulted credit that it purchases. The main finding is that one of the machine learning models we investigate, the deep learning model, performs significantly better out-of-sample than all other methods that can be used by an acquirer of defaulted credits based on weighted-performance measures. By using unweighted performance measures, deep learning and boosting perform similarly. Moreover, we find that using a training set with a larger proportion of the dataset does not improve prediction accuracy significantly when deep learning is used. The general conclusion is that deep learning is a potentially performance-enhancing tool for credit risk management.     

Health screening strategies for international air travelers during an epidemic or pandemic

Air travelers can carry an infectious disease's pathogenic microorganism in their bodies and spread the disease from one country to another in a few days. To delay the spread, health screening stations may be set up at airport terminals to screen travelers. This research tested three different health screening strategies, each with a different combination of screening stations at trip origins, destinations and connecting airports. Discrete event simulations were performed, based on the 2014 to 2016 Ebola virus epidemic, with special focus on travelers from the West African countries traveling to the United States, including travelers who transferred flights at airports in European Union member states. The effectiveness of the screening strategies was analyzed in terms of correct detection, missed detection and false alarm rate. The results showed that exit screening at trip origins brought big improvements in the performance measurements compared to no screening. However, additional screening at the destinations and connecting airports contributed marginal benefits.