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...     

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.     

Inference of the Russian drug community from one of the largest social networks in the Russian Federation

The criminal nature of narcotics complicates the direct assessment of a drug community, while having a good understanding of the type of people drawn or currently using drugs is vital for finding effective intervening strategies. Especially for the Russian Federation this is of immediate concern given the dramatic increase it has seen in drug abuse since the fall of the Soviet Union in the early nineties. Using unique data from the Russian social network ‘LiveJournal’ with over 39 million registered users worldwide, we were able for the first time to identify the on-line drug community by context sensitive text mining of the users’ blogs using a dictionary of known drug-related official and ‘slang’ terminology. By comparing the interests of the users that most actively spread information on narcotics over the network with the interests of the individuals outside the on-line drug community, we found that the ‘average’ drug user in the Russian Federation is generally mostly interested in topics such as Russian rock, non-traditional medicine, UFOs, Buddhism, yoga and the occult. We identify three distinct scale-free sub-networks of users which can be uniquely classified as being either ‘infectious’, ‘susceptible’ or ‘immune’.     

ASSESSING THE EFFECT OF PUBLIC SUBSIDIES ON FIRM R&D INVESTMENT: A SURVEY

This survey examines the empirical literature on the relationship between public R&D subsidies and private R&D investment over the past five decades. The survey reveals a considerable heterogeneity of empirical results that cannot be explained fully by methodological issues. We aim to provide further explanations of the possible causes of that heterogeneity. In particular, we emphasise a set of issues that, in our view, are critical to understanding the potential effect of public R&D subsidies on private R&D spending. Special attention is paid to the dynamic aspects and composition of firm R&D, the constraints faced by the firm (such as financial constraints), and the amount and source of public subsidies. None of these issues have been investigated in depth. We formulate a set of research assumptions to guide future empirical research in this field.     

Multi-product capacitated facility location problem with general production and building costs

This research introduces the multi-product capacitated facility location problem with general production and building costs (MP-CFLPGC). The MP-CFLPGC extends previous problems found in the literature by including multiple products and general production and building cost functions that allow the modeling of different behaviors like economies of scale and congestion. The MP-CFLPGC is formulated as a mixed integer linear program (MILP). To evaluate the performance of the proposed formulation we analyze the results of a commercial optimizer on a set of 288 randomly generated test instances that resemble the Colombian cement industry supply chain. After one hour the optimizer achieved an optimality gap of 1.0 % or less in 55 % out of the 288 test instances. On average the optimality gap was 3.57 %. Additionally, we propose a randomized mathematical-programming-based heuristic for the test instances where the MILP formulation presents significantly high optimality gaps.