Board of Directors and CSR in Banking: The Moderating Role of Bank Regulation and Investor Protection Strength

This paper brings together research on boards of directors as the backbone of corporate governance and corporate social responsibility (CSR) practices in the banking industry. The underlying idea is that some characteristics of bank boards, in particular independence and gender diversity, may impact the CSR commitments of banks. By making use of a sample of 159 banks in nine countries during the period 2004–2010, our empirical evidence suggests that banks with more independent directors and more female members on their boards incline toward socially responsible behaviour. Our results also suggest that institutional factors play a significant role in these effects. They show that in greater regulatory and stronger investor protection environments, board independence and gender diversity have more influence on the social behaviour of banks.     

Debt contracts in the presence of performance manipulation

Empirical evidence suggests that firms often manipulate reported numbers to avoid debt covenant violations. We study how a firm’s ability to manipulate reports affects the terms of its debt contracts and the resulting investment and manipulation decisions that the firm implements. Our model generates novel empirical predictions regarding the use and the level of debt covenant, the interest rate, the efficiency of investment decisions, and the likelihood of covenant violations. For example, the model predicts that the optimal debt contract for firms with relatively strong (weak) corporate governance (i.e., cost of manipulation) induces overinvestment (underinvestment). Moreover, for firms with strong (weak) corporate governance, an increase in corporate governance quality leads to tighter (looser) covenant, more (less) frequent covenant violations and lower (higher) interest rate. Our model highlights that the interest rate, which is a common proxy for the cost of debt, neither accounts for the distortion of investment efficiency nor the expected manipulation costs arising under debt financing. We propose a measure of cost of debt capital that accounts for these effects.     

The raw material basis of global value chains: allocating environmental responsibility based on value generation

A new approach to allocate environmental responsibility, the ‘value added-based responsibility’ allocation, is presented in this article. This metric allocates total environmental pressures occurring along an international supply chain to the participating sectors and countries according to the share of value added they generate within that specific supply chain. We show that – due to their position in global value chains – certain sectors (e.g. services) and countries (e.g. Germany) receive significantly greater responsibility compared to other allocation approaches. This adds a new perspective to the discussions concerning a fair distribution of mitigation costs among nations, companies and consumers.     

Carbon taxes and the double dividend hypothesis in a recursive-dynamic CGE model for Spain

A carbon tax is potentially a policy that can reduce CO₂ emissions and mitigate climate risks, at lowest economy-wide costs. We develop a dynamic CGE model for Spain to assess the economic and environmental effects of a carbon tax, and test the double dividend (DD) hypothesis. We simulate the impact of three carbon taxes: €10, €20 and €30 per ton of CO₂. For each tax, four ‘revenue recycling’ scenarios are examined: a reduction of taxes on capital, on labor, on value-added tax, and a scenario in which revenues are not recycled. We find a DD for taxes of €10/ton and lower, within five to seven years of implementation. We estimate an annual CO₂ emissions reduction of around 10% with this tax. Under some circumstances, the DD can be achieved for a tax of €20/ton. In any case, recycling revenues to cut pre-existing taxes reduces costs of imposing carbon taxes.     

Creation of models for calculation of coefficients of terrain passability

Quality & Quantity - The content of this article is a paper about a complex mathematical model of terrain passability which respects geographical and meteorological conditions in terrain and...     

Using clustering ensemble to identify banking business models

The business models of banks are often seen as the result of a variety of simultaneously determined managerial choices, such as those regarding the types of activities, funding sources, level of diversification, and size. Moreover, owing to the fuzziness of data and the possibility that some banks may combine features of different business models, the use of hard clustering methods has often led to poorly identified business models. In this paper we propose a framework to deal with these challenges based on an ensemble of three unsupervised clustering methods to identify banking business models: fuzzy c‐means (which allows us to handle fuzzy clustering), self‐organizing maps (which yield intuitive visual representations of the clusters), and partitioning around medoids (which circumvents the presence of data outliers). We set up our analysis in the context of the European banking sector, which has seen its regulators increasingly focused on examining the business models of supervised entities in the aftermath of the twin financial crises. In our empirical application, we find evidence of four distinct banking business models and further distinguish between banks with a clearly defined business model (core banks) and others (non‐core banks), as well as banks with a stable business model over time (persistent banks) and others (non‐persistent banks). Our proposed framework performs well under several robustness checks related with the sample, clustering methods, and variables used.     

Inequality and Income Distribution in Global Value Chains

Global value chains (GVCs), led by transnational corporations (TNCs), have reshaped the world division of labor over the past two decades. GVCs are pervasive in low technology manufacturing, such as textile and apparel, as well as in more advanced industries like automobiles, electronics, and machines. This hierarchical division of labor generates wild competition at the lower value-added stages of production, where low wages and low profit margins prevail for workers and contract manufacturers in developing countries. At the top of the hierarchy another kind of competition prevails, centered on the ability to monitor and control intellectual property rights related to innovation, finance, and marketing. We argue that GVCs have had crucial effects on income inequality and the appropriation of rents in modern capitalism.     

Visual displays: analytical study and applications to graphs and real data

Principal axis methods such as principal component analysis (PCA) and correspondence analysis (CA) are useful for identifying structures in data through interesting planar graphic displays. However, some kinds of data sets can be dealt alternatively with PCA or CA. This paper focuses on methods, such as PCA and CA, and on visual displays. Our aim is to illustrate the implications for a potential user of selecting either method, and its advantages and disadvantages, from an applied point of view. This is a matter covered broadly in textbooks and elsewhere considering theoretical arguments. Our purpose is to contribute to the comparison between these methods, over the same data set, in order to illustrate them for the practitioner. In the first part of this paper we present a novel analytical study of a binary matrix associated with a non-oriented axis-symmetric graph and show that CA outperforms standardized PCA for the reconstitution and visualization of such kind of graphs. In the second part we present a case using real data dealing with the distribution of employees in different economic sectors for the countries of the European Union, analyzed by means of standardized PCA and two-way CA, in order to see the differences between the two methods in practice.