Capitalization of land development rights in rural China: A choice experiment on individuals’ preferences in peri-urban Shanghai

The capitalization of land development rights is acknowledged as the key to improving compensations for rural landholders and ameliorating land-related social conflicts in peri-urban China. Investigations into reasonable as well as feasible compensation standards and the capitalization mechanism are yet to be done. This study uses a survey-based choice experiment to empirically estimate the value of land development rights in peri-urban Shanghai for three forms of rural land conversion – acquisition, consolidation and (informal) sale. The results show that heterogeneity across individuals’ preferences translates into different utilities from land development, which affects individual’s willingness to participate in this process. This effect is found to be the strongest for land sales on the informal market. If, however, land conversion is managed by local governments and rural collectives, the expected utilities of individuals have no significant effect on the amount of land development. The results suggest that capitalizing land development rights under consideration of land attributes and individuals’ preferences would lift compensation standard to a level that strikes a balance among competing land-related interests. In addition, it would promote China’s land development by bypassing the institutional constraints imposed by the current land tenure system.     

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.     

Towards a fairer manager performance measure: a DEA application in the retail industry

Abstract

Retailers need to determine the performance of their individual stores and, beyond the stores, the managers in charge of running them. The aim of this paper is to develop performance standards that can be applied to fairly distribute rewards to managers regarding their performances by taking into account store neighbourhood characteristics. In order to propose a fairer measure of store performance, the authors introduce and explore the concepts of organizational justice and store efficiency. They use real data from a French supermarket chain and a geomarketing approach. The two-step Data Envelopment Analysis (DEA) model results are compared with a retailer’s ranking. The retailer tends to favour points of sale having a more important sales area, with more employees and operating in a more densely populated area with a higher buying power. The ranking stemming from the DEA model links store performance to other geo-demographic variables.     

“80”后知识型员工组织承诺行为一致性研究——基于贝叶斯估计模型和因子分析法的实证分析

随着"80后"这一代人走向社会成为企业人力资源的主力军,关于"80后"人员的组织行为研究成为热点。本文数据来自对企业组织中"80后"知识型员工的组织行为的问卷调查,采用因子分析、Bayes估计、Pearson、Spearman相关系数方法对组织承诺和工作绩效的关系进行分析,结果表明:(1)情感承诺与关系绩效和任务绩效存在强的正相关关系;(2)持续承诺对任务绩效有负影响与关系绩效存在弱的正相关关系;(3)规范承诺与关系绩效和任务绩效存在较强的正相关关系。     

农村土地的确权流转问题——基于马克思资源配置理论的分析

为了确保农村土地的正常流转,要求建立明晰的产权结构,因而也就产生了土地确权的要求。本文旨在通过描述目前农村土地确权流转的背景及现状,运用马克思的资源配置理论中的土地产权理论和级差地租理论对土地确权流转过程进行理论分析,并且针对现实中存在的问题,相应的从健全农地流转市场、加大金融保险支持力度、抑制"非粮化"、确保农民主体地位等方面提出了对策建议。