Will Italy meet the ambitious European target for R&D expenditure? Natura non facit saltus

The paper addresses the issue as to whether the Italian scientific and technological system can achieve the Barcelona targets set by the heads of state and government (3% of GDP devoted to R&D by the year 2010) and by the R&D guidelines set by the Italian Ministry for Education, University, and Research (MIUR) (to achieve the level of 1.75% of the ratio in the year 2006).The projections built in the paper show that such objectives are well beyond Italy's potential, and that according to a “natura non facit saltus” (NNFS) projection, the country will be able to raise the ratio from 1.04% in 2002 to 1.55% in 2010. Such projection rests on rather optimistic assumptions: higher priority attached to R&D in the government budget, an increasing propensity of firms to invest their value added in R&D, and an increase of researchers' salaries.The difficulty to achieve the objectives set at the European and national level is due to the low starting point, to the lack of additional investment from 2001 to 2003, and to structural factors such as insufficient supply of qualified human resources, the small size of high-tech industry, and constraints imposed by the reduction of public spending.The attainment of R&D policy objectives is more and more dependent on education, industrial, and public budget policies; the issue of a thorough review of the governance of the whole S&T system at the national level is therefore raised. The situation is accentuated by the fact that decisions taken at national level are conditioned by the European union (through the Framework Program, the regulations regarding state aid to firms, etc.) by multinational enterprises, which operate on a global scale, and by regions in the framework of their autonomy.     

Quality risk aversion, conjectures, and new product diffusion

In this paper, we provide a generalization of the standard models of the diffusion of a new product. Consumers are heterogeneous and risk averse, and the firm is uncertain about the demand curve: both learn from past observations. The attitude towards risk has important effects with regard to the diffusion pattern. In our model, downward-biased signals to consumers can prevent the success of the product, even if its objective quality is high: a “lock-in” result. We show, in addition, that the standard logistic pattern can be derived from the model. Finally, we discuss the steady states of the learning dynamics, with regard to the multiplicity and the local stability of equilibria, and to their welfare properties.     

Model building and estimation strategies for implementing the Balanced Scorecard in Health sector

Over last two decades, the concept of the Balanced Scorecard has had broad application by the health sector internationally, including Hospitals systems and national healthcare systems or organizations. However, the lack of literature on causal-effect relationships between different types of dimensions and indicators poses difficulty in conceptualising and implementing a quality evaluation system based on Balanced Scorecard. Methodologically, the most natural context for Balanced Scorecard conceptualization and estimation deals with Structural Equation Models with latent variables. Partial Least Squares Path Modelling has found increased applications, thanks to its ability to handle complex models. However, the lack of a global optimization criterion makes it difficult to evaluate this procedure. The aim of this article is to propose a methodological conceptualization of the Balanced Scorecard in a new context, as the Health sector, using a suitable statistical approach to estimate causal relationships among specified latent dimensions, together with a model building strategy, a necessary step when expert knowledge is too weak to build a robust and well suited model. Specifically, within the Structural Equation Models framework a two-step model building strategy is presented; the first step build the measurement models based on a clustering (around latent variables) technique and the second step build the structural model based on partial correlations and a procedure that selects the best model in terms of predictive power, measured by the mean of the R ² for the endogenous latent variables. Finally, an application based on administrative archives of Lombardy region (Italy) illustrates the presented methodology.     

AN ANNUAL CHAIN INDEX OF ITALY'S "REAL" PRODUCT, 1861–1989

The construction and application of a Paasche annual chain index to the Italian GDP figures between 1861 and 1989 is the subject of this article. Comparison with product at constant prices shows, except for war periods, a revaluation of GDP in favour of the annual chain index of around 0.10 percent. The most striking differences are concentrated into periods of marked changes in relative prices, and the new index modifies the temporal pattern of the growth trend by accentuating, without altering the periodization, cyclical fluctuations.     

Social Interactions, Local Spillovers and Unemployment

I analyse a model that explicitly incorporates local interactions and allows agents to exchange information about job openings within their social networks. Agents are more likely to be employed if their social contacts are also employed. The model generates a stationary distribution of unemployment that exhibits positive spatial correlations. I estimate the model via an indirect inference procedure, using Census Tract data for Chicago. I find a significantly positive amount of social interactions across neighbouring tracts. The local spillovers are stronger for areas with less educated workers and higher fractions of minorities. Furthermore, they are shaped by ethnic dividing lines and neighbourhood boundaries.     

Indirect estimation of large conditionally heteroskedastic factor models,with an application to the Dow 30 stocks

We derive indirect estimators of conditionally heteroskedastic factor models in which the volatilities of common and idiosyncratic factors depend on their past unobserved values by calibrating the score of a Kalman-filter approximation with inequality constraints on the auxiliary model parameters. We also propose alternative indirect estimators for large-scale models, and explain how to apply our procedures to many other dynamic latent variable models. We analyse the small sample behaviour of our indirect estimators and several likelihood-based procedures through an extensive Monte Carlo experiment with empirically realistic designs. Finally, we apply our procedures to weekly returns on the Dow 30 stocks.     

HUMAN CAPITAL MEASUREMENT: A SURVEY

Abstract After a short history of the concept of human capital (henceforth HC) in economic thought ( Section 1 ), this study presents the two main methods for estimating the value of the stock of HC – the retrospective and prospective one – with a review of the models proposed ( Section 2 ). These methods are linked both to the theory of HC investment as a rational choice ( Section 3 ), the literature analysing the contribution of HC investment to economic growth and the HC estimating method through educational attainment ( Section 4 ). The more recent literature on HC as a latent variable is also assessed ( Section 5 ) and a new method of estimation where HC is seen both as an unknown function of formative indicators and as a ‘latent effect’ underlying earned income is proposed ( Section 6 ). Section 7 concludes.