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.     

Partner‐diversity effects on alliance termination in the early stage of green alliance formation: Empirical evidence from carbon‐emission reduction projects in Latin America

Partner diversity is a key influencer in interorganizational alliances, and several empirical studies have shown that its outcomes are contingent on alliance‐specific factors. We extend this research as well as the growing literature on green alliances, in which partner diversity is uniquely high. Specifically, we examine partner‐diversity effects on alliance termination in the early stage of green alliance formation. We hypothesize that in this context, size disparity increases termination likelihood, whereas organizational variety and cultural separation have the opposite effect. To test our hypotheses, we use a sample of 366 alliance projects located in Latin America and submitted to the Kyoto Protocol's Clean Development Mechanism for evaluation, validation, and registration from 2004 to 2014. Our findings contribute to several research streams and provide practical guidance for successful formation of alliances focused on environmental protection.     

Do institutional investors drive corporate transparency regarding business contribution to the sustainable development goals?

Institutional investors show increasing interest in how companies align their corporate social responsibility strategies with the sustainable development goals (SDGs) proposed by the United Nations (UN). The information disclosed in this regard is essential to know and monitor business contribution to the 2030 Agenda. In this paper, we analyze the influence that institutional investors have on the adoption of the disclosure strategy established by UN and the Global Reporting Initiative (GRI)—GRI‐SDG Compass. The results obtained for a sample of 989 international companies, which prepare their sustainability reports following the GRI guidelines, show that ownership by foreign investors, pension funds, and “other” investors boosts the relevance of the information disclosed in relation to the 2030 Agenda. On the contrary, government, financial institutions, and cross holdings have no impact on the information systems developed.     

Long‐term economic consequences of corporate environmental responsibility: Evidence from heavily polluting listed companies in China

In this study, we aim to investigate the long‐term economic consequences of corporate environmental responsibility (CER) by companies from the perspective of earnings persistence and investors' response. Based on firm‐level data of 1,010 heavily polluting listed companies in China, the empirical results are as follows. First, the CER of China's heavily polluting listed companies has significantly improved their earnings persistence, that is, earnings quality. Second, the positive long‐term economic effect of CER has been achieved through two paths: improving companies' operational efficiency and reducing their credit costs. Third, CER increases investors' response to heavily polluting companies' accounting earnings. Moreover, state‐owned listed companies achieve more significant positive long‐term economic effects from CER than others. The results suggest that heavily polluting companies should correctly identify the long‐term value of CER rather than pay excessive attention to the impact of CER on their current costs and benefits.     

Towards an integrated model for the transference of environmental responsibility

This research explores how environmental responsibility transfers to others. Although environmental responsibility is one of the most debated topics in environmental management literature, a framework describing how a teacher's environmental responsibility shapes the student's environmental responsibility is still lacking. Adopting emotional contagion theory as a lens, the paper addresses this issue via the mediation of emotional contagion and the moderation of a teacher's environmental concern. Based on a survey of teachers and students (N = 283) conducted in Pakistan, our findings provide evidence that a teacher's environmental responsibility can better transfer to students via emotional contagion. Additionally, our data establish that the transference of environmental responsibility from teachers to students via emotional contagion is more effective when the teacher has a high level of environmental concern than when he or she has a low level of environmental concern. Finally, the authors discuss the implications for policy in detail.