Measuring Corporate Environmental Performance: A Methodology for Sustainable Development

Assessing corporate environmental performance (CEP) that is both comprehensive and consistent with sustainable development both for society and companies, while at the same time taking heed of the facts and interests of each stakeholder, is not a simple feat. Due to the multidimensional character of the sustainability concept, several questions must be considered in the evaluation process: (i) the qualitative nature of indicators and the complexity of developing a synthetic index; (ii) the difficulty of choosing properly statistical techniques for aggregation and (iii) the difficulty of introducing stakeholders’ preferences in the assessment models. This paper is an attempt to address this challenge by developing a framework for the assessment of CEP, based on the application of a fuzzy multi‐criterion decision‐making (MCDM) method. To achieve positive scores in a CEP assessment, an organization should be strategically committed and engage in environmental management and governance structures that are translated into good results in terms of both engagement and operational performance. Unless such premises are explicitly incorporated into the assessment criteria, the results would reveal that the CEP measurement could not be brought into a line with an earnest ambition of achieving true sustainable development. Copyright © 2015 John Wiley & Sons, Ltd and ERP Environment     

FROM INSTITUTIONS TO FINANCIAL DEVELOPMENT AND GROWTH: WHAT ARE THE LINKS?

This paper presents an integrated overview of the literature linking institutions, financial development, and economic growth. From the large body of research on institutional development, the paper first selects those contributions that make it possible to study the role of institutional arrangements in ameliorating/worsening the information frictions and transaction costs that characterize the development of financial markets. The paper then investigates the theoretical mechanisms by which these specific frictions affect economic growth and presents the stock of empirical evidence quantifying the impact of institutions on growth through financial development.     

A new approach to the identification of regional clusters: hierarchical clustering on principal components

This study focuses on the identification of regional business clusters as a primary step in the design and implementation of cluster-based development strategies. A methodology that has not been used previously to identify clusters is applied to data on inter-industry linkages from the input–output table of a region in northern Spain. The first advantage of this approach, hierarchical clustering on principal components (HCPC), over the use of factorial analysis alone, is that it involves the application of objective clustering techniques to the principal components analysis results, which leads to a better cluster solution. A second advantage is derived from using a mixed algorithm for the clustering process – a combination of the Ward’s classification method with the K-means algorithm – which improves the robustness of the final results.     

Ugo Broggi: a precursor in mathematical economics

The concern for the existence of solution to the Walras - Cassel model is usually dated at the beginning of the 1930s, and one decade later the proof of existence of utility function. Ugo Broggi, however, posed both issues in 1923 and 1919, respectively, and even hinted at modern ways of solving them. He was an outstanding mathematician, a former disciple of David Hilbert and collaborator with the Giornale degli Economisti. Broggi's achievements are also linked to a critical reading of Osorio's treatise on Paretian economics.     

The role of venture capitalist to enhance the growth of Spanish and Italian university spin-offs

The establishment of spin-offs to commercialise university knowledge/technology is a potential mechanism to promote economic and innovative development. Nevertheless, University Spin-Offs (USOs) are usually resource-constrained, especially in obtaining funding, limiting their growth. Venture Capital (VC) investors play an important role in the financing and the improvement of their managerial skills, which are critical for firm growth. This paper aims to explore both the effect of VC partners on the USOs’ growth and the cross-national differences in the role played by them. To study both issues, we empirically analysed 516 Spanish and 904 Italian USOs created by 50 Spanish and 57 Italian universities, respectively, and observed them between 2005 and 2013. The results showed different effects in the Spanish and Italian cases. While in Spain the presence of VC partners positively affects the USOs’ growth, in Italy there is not a significant effect. This evidence calls for systematic policies by public administrations and universities to foster USO growth.     

Computing DSGE models with recursive preferences and stochastic volatility

This paper compares different solution methods for computing the equilibrium of dynamic stochastic general equilibrium (DSGE) models with recursive preferences such as those in Epstein and Zin, 1989, Epstein and Zin, 1991 and stochastic volatility. Models with these two features have recently become popular, but we know little about the best ways to implement them numerically. To fill this gap, we solve the stochastic neoclassical growth model with recursive preferences and stochastic volatility using four different approaches: second- and third-order perturbation, Chebyshev polynomials, and value function iteration. We document the performance of the methods in terms of computing time, implementation complexity, and accuracy. Our main finding is that perturbations are competitive in terms of accuracy with Chebyshev polynomials and value function iteration while being several orders of magnitude faster to run. Therefore, we conclude that perturbation methods are an attractive approach for computing this class of problems.     

Double dividend,dynamic Laffer effects and public abatement

In a very stylized endogenous growth economy with pollution and public abatement activities and without any production externality, we show that the government may exploit dynamic Laffer effects to achieve a double dividend through an environmental tax reform, while fulfilling its commitment to provide an exogenously specified sequence of expenditures in the form of lump-sum transfers to consumers.     

Determinants of bank’s interest margin in the aftermath of the crisis: the effect of interest rates and the yield curve slope

Empirical Economics - This paper analyses the determinants of net interest margin, focusing on the impact of interest rates and the slope of the yield curve, using a broad panel of data from 32...     

Spatial Regression Analysis vs. Kriging Methods for Spatial Estimation

Due to the rapid development of Geographic Information Systems (GIS) in recent years, spatial data analysis has received considerable attention and played an important role in social science. Although many standard statistical techniques are attractive in traditional data analysis, they cannot be implemented uncritically for spatial data. Generally, most of the studies in spatial data analysis can be divided into two branches: the model-driven approach and the data-driven approach. The main aim of this paper is the comparison of both approaches. To carry out such a task, crime rate data in Columbus (Ohio), coming from a well-known database, have been used. The main aim of this paper is to illustrate how spatial effects can be viewed as spatial econometric models, which assess the limitations of standard techniques in a spatial context, suggesting alternative methods to deal with this problem. An application to the crime rate in Columbus (Ohio) has been carried out.

A Stochastic Model with Penalized Coefficients for Spatial Price Comparisons: An Application to Regional Price Indexes in Italy

This paper focuses on a new strand of research that uses stochastic approach for making spatial price comparisons. We propose a novel method to account for the presence of spatial dependencies in consumer prices and consequently in price indexes by imposing penalization conditions on the estimation of traditional CPD models leading to the spatially-penalized country-product-dummy (SP-CPD) model. The paper proposes an appropriate estimation strategy, which enables us to simultaneously estimate all the parameters in the model, including the smoothing parameter of the penalization term instead of determining it externally. In order to estimate spatial price indexes for areas lacking in price data, we suggest applying the kriging methodology to the price indexes obtained from the SP-CPD model. This new approach is applied to official Italian CPI data for constructing regional spatial price indexes for 2014. The results show that price levels are higher in the Northern-Central regions than in the South.