Mandatory disclosure,greenhouse gas emissions and the cost of equity capital: UK evidence of a U-shaped relationship

This paper examines the effects of disclosing greenhouse gas (GHG) information mandatorily on the cost of equity capital (COC) using a longitudinal unbalanced panel database of the United Kingdom's FTSE 350 firms for the period 2011–2016. We use a nonlinear panel quantile regression (PQR) model to examine the relationship between GHG disclosure (GHGD) and COC in the United Kingdom. This technique was supplemented by conducting a two-step generalised method of moment (GMM) estimation to address any concerns related to the potential existence of endogeneity problems. Our findings suggest that high-level GHGD appeared to be negatively associated with COC up to a certain level, which is known as the turning point; then, any increase in GHGD is likely to increase the COC. This means that the nonlinear association between GHGD and COC is evidenced in our study and takes a U shape. Likewise, our findings are associative of a moderating effect of the 2013 carbon disclosure regulation (CDR) on the GHGD–COC nexus. We argue that mandatory GHGD and GHG risk are linked so that those companies that are associated with higher GHG risk have a tendency to be better disclosers. Consequently, we urge regulators to design GHGD regulations in a way that mirrors corporate environmental risk and leads to a lower COC in order to align the interests of corporations with those of the society at large.     

资本外逃的经济效应与应对政策选择研究

资本外逃是一个复杂的经济问题,是由多种原因造成的,并会给一国经济发展带来不可估量的风险和损失。基于此,论文梳理了资本外逃的研究现状,提出了资本外逃所产生的经济效应,在此基础上,提出了资本外逃的政策选择,为一国在治理资本外逃时提供了政策选择和借鉴。     

The role of innovation in venture capital: empirical evidence from European Union and EFTA countries

This article investigates the effects of innovation attempts on the venture capital and investment activity in the cases of the selected European Union plus European Free Trade Agreement countries using annual panel data and by controlling for real income growth and business sophistication. Our findings suggest that innovation has positively significant effects on venture capital in the cases without opt-out countries (United Kingdom and Denmark); however, these effects become negative in the cases with opt-out countries. Policy implications are provided in the conclusion section of this study.     

Social capital and well-being in the Italian provinces

Recent years witnessed a growing interest in the concept of well-being and quality of life, as alternative to the Gross Domestic Product (GDP). The determinants of well-being, especially at the regional and provincial levels, is understudied in a macro perspective, as opposed to a micro perspective (individuals, survey data) that has been the dominant approach until recently. In this paper, we estimate an empirical model for the Italian NUTS-3 provinces to evaluate the role of social capital on well-being by using aggregated (provincial) data. Our findings suggest that social capital, social security programs, income, and grant-making activities by Bank Foundations, even though not uniformly distributed across Italy, positively affect well-being, thus contributing to explain the persistent dualism that characterizes the Italian economy.     

Token entrepreneurs: a review of gender,capital, and context in technology entrepreneurship

ABSTRACT

This article reviews the literature on gender and entrepreneurship in technology to explore individual and contextual factors maintaining the token status of women in this field. It examines how the intersection of gender and context influences participation rates in entrepreneurship, and suggests that the deeply embedded cultural and cognitive associations that frame both technology and entrepreneurship as masculine concepts create barriers for women when these contexts overlap. It offers a framework for research and practice that aids in the analysis of complex multi-level barriers that control access to the forms of capital necessary for initial and continued participation in technology entrepreneurship. Given calls for women to participate more fully in high-growth technology ventures, it highlights the need for research to incorporate broader analytical perspectives that simultaneously examine both the barriers faced by women in these contexts and the factors that systemically sustain them.     

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.     

Does studying abroad enhance employability?

Over 200,000 European students study abroad under the Erasmus programme. It appears that studying abroad may impart skills which are helpful for professional careers after graduation. Approximately, 54% of European students report that participation in the Erasmus exchange programme helped them to obtain a first job; however, interestingly, those from Central and Eastern European countries report it much more often—in 73% of cases. The aim of this paper is to find out whether studying abroad enhances the employability of higher education graduates in Poland. Using the propensity score matching method and data from a nationwide tracer survey of Polish graduates, we find that Polish students who completed at least one semester of their studies abroad do not benefit in terms of a higher employment rate after graduation.     

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.