Assessing the Impact of Alternative Water Pricing Schemes on Income Distribution

The reform of water pricing policies may represent an effective instrument for enhancing the efficient use of water resource. However, policy makers fear that a change in the pricing methods may cause income loss for some farmers, and that this income inequality may generate public discontent and policy inertia. The aim of this paper was to compare some pricing methods in order to measure their effects on income distribution. The analysis focuses on the income distribution among different types of farms, and the income distribution between different social groups (landowners, capitalists and workers) in the short term. A linear programing model based on expected utility theory is used to take into account the effect of commodity prices and rainfall variability, which are among the most relevant factors affecting farmers’ income. According to the findings, water pricing schemes do not affect the income distribution among farm types, although a significant impact emerges on the distribution among social groups, and in particular on the wages of temporary workers.     

MUTUAL AND SOCIAL EFFICIENCY OF ITALIAN CO‐OPERATIVE BANKS: AN EMPIRICAL ANALYSIS

In this study we analyze the concept of efficiency in co‐operative banks (CBs) from the points of view of the mutuality and sustainability of business. According to the extant literature, CBs are often less efficient than traditional banks, but the reason for this apparently higher inefficiency is strictly correlated to their statutory commitments. The purpose of this paper is to verify, through an empirical survey of 33 Tuscan ‘Banche di Credito Cooperativo’ (BCC), if a particular type of Italian CB is less or more competitive compared to non‐co‐operative ones by using and adapting two financial indicators: the financial value added and the cost‐income ratio. Our findings show that by implementing appropriate corrections to the value‐added indicator and to the cost‐income ratio, BCC appear efficient and mission‐oriented, with a significantly reduced performance gap in comparison to non‐co‐operative credit institutions.     

When counterfeits raise the appeal of luxury brands

Counterfeiting is a widespread practice throughout the world. The conventional wisdom is that it affects branded goods negatively. In this paper, however, we suggest that counterfeiting may actually benefit certain luxury brands. By means of two studies, we show how the market presence of luxury counterfeit items can increase consumers’ willingness to pay for original brands. In Study 1, we show that the presence of luxury counterfeits can increase consumers’ willingness to pay for well-known original brands, but not for lesser-known ones. Brand awareness plays a moderating role in the positive relationship between counterfeiting and willingness to pay (WTP). In Study 2, we address the psychological mechanisms that explain this increased willingness to pay. The results show that consumers’ (a) pleasure at being envied, (b) pleasure in distinguishing themselves, and (c) perception of the quality of the original goods fully mediate the relation between the presence of counterfeit in the market and consumers’ WTP for originals. We subsequently discuss the theoretical and managerial implications of the two study results.     

<Emphasis Type="Italic">Notes and Comments:</Emphasis> Profitability in a multiple strategy market

The link between martingales and arbitrage is well-known in financial theory: arbitrage is not available if and only if there exists an equivalent measure such that the discounted prices are martingales with respect to this measure (MME). As a consequence, under MME, any previsible (non-anticipative) strategy cannot have secure (without risk) profit. Moreover, a careful reading of a bootstrap proof of the first fundamental theorem of asset pricing (see Schachermeier (1992)) underlines the fact that, if there is no possibility of arbitrage during any unit interval, then no arbitrage is allowed with any finite temporal horizon strategy. Mathematics Subject Classification (2000): Primary: 60G48; Secondary: 60G40, 60G07 Journal of Economic Literature Classification: C50, C72, D84     

Quantifying knowledge exchange in R&amp;D networks: a data-driven model

We propose a model that reflects two important processes in R&D activities of firms, the formation of R&D alliances and the exchange of knowledge as a result of these collaborations. In a data-driven approach, we analyze two large-scale data sets, extracting unique information about 7500 R&D alliances and 5200 patent portfolios of firms. These data are used to calibrate the model parameters for network formation and knowledge exchange. We obtain probabilities for incumbent and newcomer firms to link to other incumbents or newcomers able to reproduce the topology of the empirical R&D network. The position of firms in a knowledge space is obtained from their patents using two different classification schemes, IPC in eight dimensions and ISI-OST-INPI in 35 dimensions. Our dynamics of knowledge exchange assumes that collaborating firms approach each other in knowledge space at a rate μ for an alliance duration τ. Both parameters are obtained in two different ways, by comparing knowledge distances from simulations and empirics and by analyzing the collaboration efficiency \(\mathcal {\hat {C}}_{n}\). This is a new measure that takes in account the effort of firms to maintain concurrent alliances, and is evaluated via extensive computer simulations. We find that R&D alliances have a duration of around two years and that the subsequent knowledge exchange occurs at a very low rate. Hence, a firm’s position in the knowledge space is rather a determinant than a consequence of its R&D alliances. From our data-driven approach we also find model configurations that can be both realistic and optimized with respect to the collaboration efficiency \(\mathcal {\hat {C}}_{n}\). Effective policies, as suggested by our model, would incentivize shorter R&D alliances and higher knowledge exchange rates.     

Nationalism,cognitive ability,and interpersonal relations

Interpersonal relations are shaped by the judgements associated with the social categories that individuals perceive in their social contacts. I develop a model of how those judgments form based on a theory of symbolic values. The model depicts the interaction between two values, one associated with an inherited ethnic trait (“nationality”) and one with an endogenous achievement trait (“income”). Individuals with lower cognitive ability are predicted to invest more value on nationalism and to have hostile relations with immigrants. Multiple equilibria are possible, and better schooling may eliminate equilibria with xenophobia. Econometric findings based on data from three large surveys corroborate the predictions derived from the theoretical model.     

Bayesian Value-at-Risk with product partition models

In this paper we propose a novel Bayesian methodology for Value-at-Risk computation based on parametric Product Partition Models. Value-at-Risk is a standard tool for measuring and controlling the market risk of an asset or portfolio, and is also required for regulatory purposes. Its popularity is partly due to the fact that it is an easily understood measure of risk. The use of Product Partition Models allows us to remain in a Normal setting even in the presence of outlying points, and to obtain a closed-form expression for Value-at-Risk computation. We present and compare two different scenarios: a product partition structure on the vector of means and a product partition structure on the vector of variances. We apply our methodology to an Italian stock market data set from Mib30. The numerical results clearly show that Product Partition Models can be successfully exploited in order to quantify market risk exposure. The obtained Value-at-Risk estimates are in full agreement with Maximum Likelihood approaches, but our methodology provides richer information about the clustering structure of the data and the presence of outlying points.     

Multiplicative noise,fast convolution and pricing

In this work we detail the application of a fast convolution algorithm to compute high-dimensional integrals in the context of multiplicative noise stochastic processes. The algorithm provides a numerical solution to the problem of characterizing conditional probability density functions at arbitrary times, and we apply it successfully to quadratic and piecewise linear diffusion processes. The ability to reproduce statistical features of financial return time series, such as thickness of the tails and scaling properties, makes these processes appealing for option pricing. Since exact analytical results are lacking, we exploit the fast convolution as a numerical method alternative to Monte Carlo simulation both in the objective and risk-neutral settings. In numerical sections we document how fast convolution outperforms Monte Carlo both in speed and efficiency terms.