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.     

The trade-offs between economic efficiency and job creation in olive grove smallholdings

Cooperation among farmers is an effective alternative to reduce production costs in smallholdings of traditional olive groves and increase their low profitability. However, an increase in efficiency reduces working hours and the labour required for the management of farms. This fact can lead to negative effects in rural areas, especially considering the importance of the social aspect of the crop. Therefore, to contribute to the global discussion on the transition towards more sustainable farming, it is important to know, ex ante, what level of employment is generated by olive groves and what could be lost under more competitive and efficient scenarios. The purpose of the work is to quantify the expected impact on employment by the implementation of cooperative management methods in order to generate true and accurate information that can be used in the design of future policies that aim to increase the economic profitability of small olive farms. The results make clear that this way of managing farms, by increasing the efficiency of agricultural work, has a significant negative impact on employment. However, this management formula does encourage the professionalisation of the sector by generating quality employment, in addition to contributing to the preservation of small farms and the socio-cultural and territorial benefits they generate.     

Brexit: Tracking and disentangling the sentiment towards leaving the EU

On 23 June, 2016, the UK held a referendum to decide whether to stay in the European Union or leave. The uncertainty surrounding the outcome of this referendum had major consequences for public policy, investment decisions, and currency markets. We discuss some of the subtleties involved in smoothing and disentangling poll data in light of the problem of tracking the dynamics of the intention to Brexit, and propose a multivariate singular spectrum analysis method that produces trendlines on the unit simplex. The trendline yield via multivariate singular spectrum analysis is shown to resemble that of local polynomial smoothing, and singular spectrum analysis presents the nice feature of disentangling the dynamics directly into components that can be interpreted as changes in public opinion or sampling error. The merits and disadvantages of some different approaches for obtaining smooth trendlines on the unit simplex are contrasted, in terms of both local polynomial smoothing and multivariate singular spectrum analysis.     

Crime prediction by data-driven Green’s function method

We develop an algorithm that forecasts cascading events, by employing a Green’s function scheme on the basis of the self-exciting point process model. This method is applied to open data of 10 types of crimes happened in Chicago. It shows a good prediction accuracy superior to or comparable to the standard methods which are the expectation–maximization method and prospective hotspot maps method. We find a cascade influence of the crimes that has a long-time, logarithmic tail; this result is consistent with an earlier study on burglaries. This long-tail feature cannot be reproduced by the other standard methods. In addition, a merit of the Green’s function method is the low computational cost in the case of high density of events and/or large amount of the training data.     

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.     

Managers’ exploration activities and individual unlearning: the mediating role of learning orientation and reflection

Abstract

The main goal of this study was to investigate the effects of managers’ exploration activities on learning orientation, reflection, and unlearning of team members. A questionnaire survey was conducted among 115 employees in 23 teams from a Japanese pharmaceutical company. The results of multi-level analyses suggest that managers’ exploration activities had a direct influence on team members’ learning orientations, which subsequently had a positive influence on their unlearning, with and without the mediation of reflection. These findings suggest that a subordinate’s unlearning is driven by managers’ activities through motivational and cognitive processes. The theoretical and practical implications of the findings are discussed.     

Workplace civility training: understanding drivers of motivation to learn

Abstract

Training is recommended as an important human resource management (HRM) practice to prevent mistreatment and enhance civility, but little is known about what influences the effectiveness of civility training. The central aim of this study was to address how workgroup conditions influence employees' attitudes about civility training and motivation to learn, which previous research shows is a predictor of training outcomes. Predictors were posited to include psychological and workgroup climate for civility, and personal and ambient mistreatment experiences. These predictors were hypothesized to drive positive (training discrepancy) and negative (training skepticism) pre‐training attitudes, which in turn were expected to influence motivation to learn. Results suggest the influence of climate for civility and mistreatment experiences on motivation to learn is largely indirect via pre‐training attitudes. Training skepticism and training discrepancy have conflicting influences on motivation to learn. Findings provide an empirical basis for HRM professionals to maximize employee motivation to learn in their own civility interventions.     

科协系统开展代表履职培训的实践与思考

中国科协全国代表大会代表是中国科协联系凝聚广大科技工作者的重要纽带，通过培训提高代表履职能力和履职实效，是充分发挥代表作用、更好地服务科技工作者的一项基础性、长期性工作，已经成为科协系统联络服务代表工作的一项重要内容。本文系统总结了中国科协和地方科协开展的代表履职培训工作的基本情况，分析了培训特点、培训方式和存在的不足，在此基础上，提出科协系统加强代表履职培训的对策建议。     

高校辅导员和大学生互建信任关系的重要性与实现途径

辅导员是高校学生管理工作中必不可少的中坚力量。辅导员与学生的关系是高校中最为突出和直接的人际关系,二者建立互相信任的关系尤为重要。论文分析了高校辅导员与大学生相互缺乏信任的表象,阐述其危害,进一步明确了互建信任关系的重要性,并从四个方面讲述了建立信任关系的实现途径。