Sustainable efficiency drivers in Eurasian airports: Fuzzy NDEA approach based on Shannon's entropy

This research explores the physical infrastructure and flight consolidation efficiency drivers of Eurasian airports regarding their infrastructure and movement productivity levels. A novel Fuzzy Double-Frontier Network DEA (FDFNDEA) model is proposed to investigate the relationship between desirable (freight and passenger turnovers) and undesirable (pollutant emission levels due to aircraft movements) outputs against the respective infrastructure usage, fuel consumed, and movements performed at each of the 23 Eurasian airports from 2000 to 2018. This balance between desirable and undesirable outputs emerges spatially and temporally due to the evolution of the airport system's productive resources at each one of the Eurasian countries over the period observed. Shannon's entropy is used as the cornerstone to quantify the input and output vagueness of this evolution in Triangular Fuzzy Numbers (TFN), thus allowing the accurate building of alternative optimistic and pessimistic double-frontier efficiency. Differently from previous research, Shannon's entropy is the key for measuring input and output vagueness levels in light of the maximal entropy principle. This principle states that the distribution that best represents the current state of knowledge is the one with largest entropy. Maximal entropy yields bias-free decision-making in the sense that the input/output distributional profiles for Eurasian airports contain the maximal possible heterogeneity, working as a robust or best/worst-case scenario against eventual unconsidered assumptions. Hence, optimistic and pessimistic Malmquist Productivity Indexes (MPI) for overall and each stage productivity results are subsequently regressed against contextual variables related to airport characteristics and regional socio-demographic and economic indicators of each Eurasian country using bootstrapped Cauchy regressions. The findings revealed the spatial heterogeneity of productivity factors and airport performance across Eurasia. Results also demonstrated the negative impact of income inequality and the positive impact of private participation on technological progression in the Eurasian airport industry.     

Machine Learning and Expert Judgement: Analyzing Emerging Topics in Accounting and Finance Research in the Asia–Pacific

In this paper, we focus on the question to what extent machine learning (ML) tools can be used to support systematic literature reviews. We apply a ML approach for topic detection to analyze emerging topics in the literature—our context is accounting and finance research in the Asia–Pacific region. To evaluate the robustness of the approach, we compare findings from the automated ML approach with the results from a manual analysis of the literature. The automated approach uses a keyword algorithm detection mechanism whereby the manual analysis uses common techniques for qualitative data analysis, that is, triangulation between researchers (expert judgement). From our paper, we conclude that both methods have strengths and weaknesses. The automated analysis works well for large corpora of text and provides a very standardized and non-biased way of analyzing the literature. However, the human researcher is potentially better equipped to evaluate current issues and future trends in the literature. Overall, the best results might be achieved when a variety of tools are used together.     

Determinants of China's bilateral trade balance in global value chains

This paper identifies the determinants of China's bilateral trade balance using a new measure based on international input–output data, the so-called ‘trade in value-added’ (TiVA), which can prevent double counting in the estimation of bilateral trade balance. Our results show that using a measure based on gross exports, rather than TiVA, causes relatively large overestimation of the impact of the RMB exchange rate on China's bilateral trade balance. This overestimation is mainly because that the increasing production of exports may require increasing intermediate imports as a consequence of international fragmentation of production in global value chains. In addition, our results also show that the impact of FDI inflows on China's bilateral trade balances depends on the position and role of China and its trading partners in GVCs.     

中国农村特色扶贫开发道路的制度分析：基于产权的视角

研究目标：借鉴马克思及西方产权理论主要思想，研究揭示中国农村特色扶贫开发道路的制度选择及经验。研究方法：基于产权理论对经济事实进行逻辑推演，并采用1978~2012年中国农村统计数据进行检验。研究发现：中国农村特色扶贫开发道路主要体现在农村资源（土地和人力）的产权制度改革上，由此形成的资源产权激励效应和正向扩散效应，正是中国农村人口发生大规模减贫的最重要原因及主要经验，并构成了中国特色社会主义制度的一部分。研究创新：本文基于产权理论的研究为减贫提供一个新的微观制度视角解释。研究价值：总结提炼中国农村特色扶贫开发道路的一般性理论经验，对今后减贫事业推进具有借鉴意义。     

Rationality of weather predictions and insurance purchases: testing the gambler’s and hot hand fallacies

Deviations from the rational behaviour assumed in many economic models have been found in a variety of settings. Two such deviations, the gambler’s and hot hand fallacies have been found in lab settings, as well as in consequential real-world decisions. Previous economic experiments have shown that the behaviour of professionals can differ from that of the general population. In this paper, we use data from two experiments conducted with a particular group of professionals who make yearly high-stakes decisions in the face of uncertain weather and market conditions: agricultural producers. In the experiments, participants were asked to make predictions about the coming year’s weather and market conditions and make decisions in a familiar decision context. Results indicate evidence of the gambler’s fallacy, such that participants were less likely to predict a good outcome if the previous outcome(s) were good. We also observe that participants were more likely to gamble if a previous gamble was successful, but find no impact on two successful gambles. These combined results indicate that even professionals with many years of experience can exhibit behaviours that deviate from those assumed by classical models.     

Neighbors and friends: How do European political parties respond to globalization?

This paper explores the relationship between globalization and party positions accounting for potential differences between left- and right-wing parties. The analysis is based on a panel model of 36 political parties in 18 Western European countries between 1970 and 2015. We find that right-wing parties move leftward in response to globalization, while left-wing parties do not alter their position. Additionally, we find that ideological party positions are affected by foreign parties’ positions of the same ideological bloc. These findings indicate that there is a convergence towards the left of the political spectrum due to right-wing party moderation.     

Estimating effects of cooperative membership on farmers’ safe production behaviors: Evidence from pig sector in China

This research studies the determinants of pig farmers’ participation in farmers’ cooperatives and the effects of farmers’ cooperatives on pig farmers’ behaviors in adopting safe production practices using data from a household survey of 540 cooperative farmers and 270 non-cooperative farmers from four main pig production provinces in China. The propensity score matching (PSM) method was adopted to deal with possible self-selection bias associated with farmers’ participation in farmers’ cooperatives due to observables, which is further supplemented by a sensitivity analysis to assess the degree to which the PSM results are robust to the presence of unobservables. The PSM results show that the cooperative membership has significant and positive influence on farmers’ propensity to adopt safe production practices and the effects are heterogeneous across a number of key cooperative, farm and household attributes. Specifically, the membership effects tend to be bigger for cooperatives led by Investor-owned firms (IOFs) and farms of small production scale. And the effects tend to be greater for households (1) of medium and high level of education, (2) of less than 10 years of pig production experience, (3) of no off-farm job experience, and (4) that are specialized in pig production. The sensitivity analysis further increases our confidence in the results for the feed use and the breed use, however, the results for vaccination, drug use and waste disposable are more sensitive to the influence of unobservables, therefore should be interpreted with caution.     

Some issues in blockchain for accounting and the supply chain,with an application of distributed databases to virtual organizations

This paper reviews some recent blockchain‐based applications for information capture, distribution and preservation. As part of that review, this paper examines two key concerns with current blockchain designs for accounting and supply chain transactions: data independence and multiple semantic models for the same information distribution problem. Blockchain applications typically integrate database, application and presentation tiers all in the same ledger. This results in a general inability to query information in the ledger and other concerns. Further, since most applications appear to be private blockchain applications, there is a concern of agents needing to accommodate multiple blockchains depending on who their trading partners are and what they request. Finally, this paper uses a distributed database to design a ‘blockchain‐like’ system for virtual organizations.     

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.