A Quantitative Clustering Approach to Ultrametricity in Spin Glasses

We discuss the problem of ultrametricity in mean field spin glasses by means of a hierarchical clustering algorithm. We complement the clustering approach with quantitative testing: we discuss both in some detail. We show that the elimination of the (in this context accidental) spin flip symmetry plays a crucial role in the analysis, since the symmetry hides the real nature of the data. We are able to use in the analysis disorder averaged quantities. We are able to exhibit a number of features of the low $T$ phase of the mean field theory, and to claim that the full hierarchical structure can be observed without ambiguities only on very large lattice volumes, not currently accessible by numerical simulations.Comment: 15 pages with color figure

The CAP 2013 reform of direct payments: redistributive effects and impacts on farm income concentration in Italy

Abstract The Common Agricultural Policy has traditionally provided support to farm incomes via direct payments under the Single Payment Scheme. This article analyzes whether the reform of the SPS will decrease the concentration of direct payments and evaluates the effect of the new direct payment scheme on the redistribution of farm incomes. The decomposition of the Gini coefficient allows analysis of the evolution of farm income and direct payment distributions in Italy from 2014 to 2020, and empirical results based on the Italian Farm Accountancy Data Network show that the CAP 2013 reform is expected to decrease the concentration of direct payments. However, the reform is also expected to limit the reduction in farm income inequality due to the adoption of a partial convergence model (the so-called "tunnel" model) instead of a total convergence model as well as, more generally, the increasing share of farm income that is dependent on increased market exposure, leading to higher risks of price volatility and increasing pressure on income

What Drives Marketing and Organizational Innovation in the Food Industry? A Comparison between Italy and Germany

In the food industry low amounts of capital are invested in innovation and R&D and companies are mainly engaged in developing product and process innovations in order to keep up with continuously changing consumer preferences. Notwithstanding, marketing and organizational innovation are becoming pivotal for food companies in order to specifically meet these preferences, and develop new business practices which allow them to implement successful external relationships aimed at a greater and successful innovation activity.In this regard, the present paper aims to shed lights on the determinants of both types of non-technological innovations in two of the largest EU food and drink producers by turnover and value added: Italy and Germany. To this purpose, an econometric analysis is run using microdata of the Community Innovation Survey (CIS) carried out in 2012.Results highlight that, apart from some significant differences concerning the role of knowledge sources, training activities represent a relevant driver for both marketing and organizational innovations in both countries

Drivers, effects and peculiarities of innovation activities in the food industry: a comparison across EU Member States using CIS data

Innovation is a clear target of the Europe 2020 growth strategy. It has been widely postulated that cooperation is especially important for innovation in the food industry because it has traditionally been regarded as a “low tech” sector. This paper analyses how different forms of cooperation affect innovation activities in the EU’s food industry. In particular, the study addresses the question of how cooperation between companies and key chain agents influences innovative activity. To do so, we analysed data at the country level drawn from the Community Innovation Survey (CIS). The aggregated data allowed us to investigate national system-level processes that must be considered the outcomes of micro-level decisions and policies. A random effect linear model is formulated and estimated to analyse the panel data obtained from five CIS waves. The model indicates that cooperation with universities positively affects innovative activity and, surprisingly, that government financial support has not been an effective instrument to foster innovation by food companies